Advances in Human Biology: Combining Genetics and Molecular

Review ArticleAdvances in Human Biology Combining Geneticsand Molecular Biophysics to Pave the Way for PersonalizedDiagnostics and Medicine

Emil Alexov

Computational Biophysics and Bioinformatics Department of Physics Clemson University Clemson SC 29634 USA

Correspondence should be addressed to Emil Alexov ealexovclemsonedu

Received 29 January 2014 Revised 23 April 2014 Accepted 17 June 2014 Published 7 July 2014

Academic Editor Yinan Wei

Copyright copy 2014 Emil Alexov This is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Advances in several biology-oriented initiatives such as genome sequencing and structural genomics along with the progress madethrough traditional biological and biochemical research have opened up a unique opportunity to better understand the moleculareffects of human diseases Human DNA can vary significantly from person to person and determines an individualrsquos physicalcharacteristics and their susceptibility to diseases Armed with an individualrsquos DNA sequence researchers and physicians can checkfor defects known to be associated with certain diseases by utilizing various databases However for unclassified DNA mutationsor in order to reveal molecular mechanism behind the effects the mutations have to be mapped onto the corresponding networksandmacromolecular structures and then analyzed to reveal their effect on the wild type properties of biological processes involvedPredicting the effect of DNA mutations on individualrsquos health is typically referred to as personalized or companion diagnosticsFurthermore once the molecular mechanism of the mutations is revealed the patient should be given drugs which are the mostappropriate for the individual genome referred to as pharmacogenomics Altogether the shift in focus in medicine towards moregenomic-oriented practices is the foundation of personalized medicine The progress made in these rapidly developing fields isoutlined

1 Introduction

The human body is a delicate self-regulating machine whichcan respond to its surroundings and internal needs Such self-regulation involves various processes ranging from processeson atomic and molecular level to processes occurring inorgans and tissues Despite such tremendous complexitysomehow all humans broadly speaking are quite similarHowever slight differences in DNA can lead to a multitudeof other physical differences Some of these differences areharmless such as eye and hair color [1] race [2] and skincolor [3 4] while other differencesmay be disease-associated(see special J Mol Biol issue [5]) The differences amongindividuals and their susceptibility to diseases are not onlydue to the single nucleoside polymorphisms (SNPs) butalso due to the fact that different individuals have differentcopy numbers variations (CNVs) for various genes [6ndash9]As pointed out by Haraksingh and Snyder [6] the CNVs

are perhaps even more important for the humans than theSNPs a statement supported by other researchers [10ndash13] Inthe end from the viewpoint of personalized diagnostics andmedicine the most important task is to differentiate betweendisease-causing and harmless DNA differences At the sametime from the viewpoint of Biology and Biophysics onewants to reveal the molecular mechanisms arising from allof the DNA differences in order to understand the biologicalprocesses taking place in human body Figure 1 schematicallyillustrates harmless and disease-causing DNA differenceswhere different individuals carrying differentDNAmutationsare different in either their physical appearance (tall andshort) or a predisposition to diseases (healthy and sick)

This paper outlines the progress made in human genomesequencing anddetection ofDNAdifferences as the necessaryfirst step for personalized diagnostics It is followed byreviewing the advances made in methods for discriminatingdisease-causing and harmless mutations Simply predicting

Hindawi Publishing CorporationAdvances in BiologyVolume 2014 Article ID 471836 16 pageshttpdxdoiorg1011552014471836

2 Advances in Biology

I amshort

I amtall I am

sickI amhealthy

Figure 1 Illustration of human harmless variation (short and tallman) and human variation-causing disease (healthy and sick man)

that a given DNA defect is disease-causing is not enough foran effective personalized treatment and thus the paper pro-ceeds to review the approaches and techniques for predictingthe molecular mechanism of disease-causing mutations forthe needs of personalized diagnostics pharmacogenomicsand personalized medicine

2 Progress Made in Genome Sequencing andDatabase Development

Personalized diagnostics and medicine cannot be developedwithout access to the genomic data of the patient whichin turn requires inexpensive and fast methods for indi-vidual genome sequencing [14 15] The progress made indeveloping methods and techniques for detecting geneticvariations was recently outlined in several works [16ndash18]These techniques are rapidly evolving and various companiespromised or already achieved the goal of being able tosequence an entire genome for a price tag of $1000 withina day [19 20] However despite the success of whole-genomesequencing it is now understood that the analysis of genomicvariations with respect to disease susceptibility is a muchmore complicated process and requires significant efforts[21] If the sequencing is attempting to target a particulardiseasemdashin the simplest case a monogenic diseasemdashthen theanalysis of the variations within a particular gene is typicallya quite doable task However if the question is broaderand if one wants to investigate the whole genome of thecurrently healthy individual with the goal of predicting futuredisease-causing defects the problem becomes enormouslycomplicated One ends up with thousands or millions ofvariants spread over many different genes and noncodingregions of the genome Identifying which of these variantsmight be associatedwith disease predisposition is not a trivialtask To assist in solving this difficult challenge the 1000genomes project was foundedThis project is aimed at reveal-ing human variations within the entire genome by using thewhole-genome sequencing of the DNA of 1000 volunteersfrom different backgrounds [22 23] This is intended toprovide information on the most frequently observed DNA

variations and the data is now available on the internet [24]One can assume that genetic variations identified within the1000 genomes project are not necessarily disease-causingsince the volunteers are healthy individuals However itshould also be noted that some diseases have very late onsetsandmay not bemanifested at this stage in the individualrsquos life

The interpretation of the individualrsquos genomic data hasanother difficult component CNVs Although important andfrequently associated with diseases CNVs cannot be easilyused to reveal the molecular effect of a disease One canspeculate that a larger number of copies of a given genewill automatically result in a greater expression level of thisparticular protein and that this is the cause of the diseaseHowever this mechanism will not be discussed in thispaper because understanding the effect requires a detailedknowledge of the biological reactions associated with thetarget protein and how the change in the concentration levelof individualmacromolecules will affect the cellular function

The progress in this fast growing field prompted develop-ment of databases on various levels such as disease-orienteddatabases to databases storing all of the known human DNAvariations (Table 1)The creation of such databases serves twovery important purposes providing benchmarks to test insilico predictions and providing template cases or patternsfor the detection of disease-causing mutation(s) Perhapsthe most popular disease-oriented database is McKusickrsquosOnline Mendelian Inheritance in Man (OMIM) database[25 26] which is amanually curated database of human genesand genetic disorders including genetic phenotypes [26]Since its establishment in early 1960s many researchers havecontributed to various aspects of the OMIM database suchas developing extra features resulting in OMIM derivativesas PhenOMIM (for phenotypic comparison) [27] OMiR (toreveal associations between OMIM diseases and microR-NAs) [28] CSI-OMIM (assisting clinical synopsis search inOMIM) [29] CGMIM (for text-mining of cancer genes)[30] and many other applications On the other end of thespectrum is the dbSNP [31 32] database at National Centerfor Biotechnology Information (NCBI) As of December2013 it contains more than 140 million single nucleotidepolymorphisms (SNPs) and the rate of new submissions isconstantly increasing In terms of distinguishing betweendisease-causing and harmlessmutations typically one wouldcreate a pseudo-harmless database by taking out all of theentries from dbSNP that are listed in OMIM [33] Theremaining SNPs can be considered neutral or harmlessalthough exceptions to this rule will always be found Itshould be noted that many other databases exist as wellsome that focus on a particular disease [34 35] others thatfocus on nonsynonymous SNPs [36 37] or regulatory SNPs[38] or one that focuses on a particular family of genes[39 40]

Although the goal of this paper is not to provide com-prehensive review of all existing human variation databasesthe HapMap [41] project and database cannot be omittedThe goal of the HapMap project is to develop a map ofthe common patterns of human DNA sequence variation Itis intended to be used to provide information about genesand patterns causing natural differences among individuals

Advances in Biology 3

Table1To

olsa

ndmetho

dsforp

redictingeffectsof

mutations

Purpose

Nam

eURL

Descriptio

nandadvantagesdisa

dvantages

Databases

OMIM

httpwwwncbinlm

nihgovomim

Manually

curatedfre

elyavailable

dbSN

Phttpwwwncbinlm

nihgovSNP

FreelyavailableInclu

dessho

rtvaria

tions

innu

cleotides

equences

from

awider

ange

oforganism

s1000

Genom

esA

DeepCa

talogof

Hum

anGenetic

Varia

tion

httpwww1000geno

mesorg

Freelyavailableprovides

toolsfor

vario

ussearches

aswith

inpo

pulationsallelefre

quency

andlin

kage

disequ

ilibrium

structure

HGMD

httpwwwhgmdorg

Requ

ireslicensebut

isthem

ostcom

prehensiv

edatabase

ofhu

man

mutations

andprovides

visualizationtoolsa

ndsearch

engines

Networking

Cytoscape

httpwwwcytoscapeo

rg

Anop

ensource

softw

arep

latform

forv

isualizing

molecular

interactionnetworks

andbiologicalpathways

andintegratingthesen

etworks

with

anno

tatio

nsgene

expressio

nprofi

lesandotherstatedata

NetworkPo

rtal

httpnetworkssy

stemsbiologynet

Provides

analysisandvisualizationtoolsfor

selected

gene

regu

latory

networks

toaidresearchersin

biologicaldiscoveryandhypo

thesisdevelopm

ent

Network2Ca

nvas

httpwwwmaayanlabnetN

2CU1fP4

qJWAkI

Allo

wsfor

generatio

nof

drug-drugsim

ilarityand

functio

nalassociatio

nof

gene

canvases

3Dstructurem

odeling

Swiss

Mod

elhttpsw

issmod

elexpasyorg

Automated

server

allowing3D

structure

ofap

rotein

tobe

predictedinpu

tting

thes

equenceo

fthe

target

I-TASSER

httpzhanglabcc

mbmedumicheduI-T

ASSER

Fu

llyautomated

3Dstructurep

redictorespecially

useful

ford

ifficulttargets(lowsequ

ence

similarityto

know

nstr

uctures)

Mod

eller

httpsalilaborgm

odeller

Stand-alon

e3Dstr

ucture

predictorCan

beinsta

lled

locally

andused

forlarge-scalepredictio

ns

Proteinsta

bility

FoldX

httpfoldxcrges

Fastassessmento

fthe

changeso

fthe

unfoldingfre

eenergy

caused

bymutations

usingem

piric

alform

ula

I-Mutant

httpgp

cr2biocom

pun

iboit7E

emidioI-

MutantI-M

utanth

tmVe

ryfastandaccuratepredictio

nof

thec

hanges

ofthe

foldingfre

eenergycaused

bymutationsTh

einp

utcan

bejustthes

equenceo

fthe

protein

ERIS

httpdo

khlabun

cedu

toolseris

UsesM

edusaforce

field

tomakep

redictions

ofthe

change

ofthefolding

freee

nergycaused

bymutations

Proteininteractions

BeAtMuSiC

httpbabyloneulbac

bebeatmusic

Evaluatesc

hangeinbind

ingaffi

nitybetweenproteins

caused

bysin

gle-sitem

utations

intheirsequence

ICM

httpwwwmolsoftcomguim

utation-protein-bind

inghtml

Com

putesthe

change

inbind

ingfre

eenergyof

aproteincomplex

upon

mutationof

asingler

esidue

Requ

ireslicense

Robetta

httprobetta

bakerlaborgalascansub

mitjsp

Provides

estim

ationforthe

bind

ingfre

eenergychanges

caused

bymutations

toAlanine


Table1Con

tinued

Purpose

Nam

eURL

Descriptio

nandadvantagesdisa

dvantages

Subcellularlocalizationand

pHdepend

ence

WoL

FPS

ORT

httpwolfpsortorg

Predictsthes

ubcellu

larlocalizationof

proteins

based

ontheira

minoacid

sequ

encesTh

epredictions

are

basedon

both

know

nsortingsig

nalm

otifs

andam

ino

acid

content

PredSL

httpaiasbiolu

oagrPredSL

Stand-alon

ecod

eutilizingneuralnetworksM

arkov

chainsand

HMMsfor

thep

redictionof

thes

ubcellu

lar

localizationof

proteins

UniLo

cTh

isisaw

ebserver

usingPS

I-Blasttoinferh

omolog

ybetweenqu

erys

equencea

ndalreadya

nnotated

proteins

Macromolecular

functio

n

SNAP

httpsw

wwrostlaborgservicessnap

SNAPisan

euraln

etwork-basedmetho

dthatuses

insilico

deriv

edproteininform

ation(egsecond

ary

structureconservatio

nsolventaccessib

ilityetc)in

ordertomakep

redictions

regardingfunctio

nalityof

mutated

proteins

MutationA

ssessor

httpmutationassessoro

rg

Thes

erverp

redictsthe

functio

nalimpactof

aminoacid

substitutions

inproteins

SIFT

httpsift

jcviorg

Thep

redictionisbasedon

thed

egreeo

fcon

servationof

aminoacid

resid

uesinsequ

ence

alignm

entsderiv

edfro

mclo

selyrelated

sequ

ences


[42ndash45] as well as the predisposition to diseases [46 47]responses to drugs [48] and cell phenotype [49]

3 Progress Made in Developing Methods forRevealing the Molecular Mechanisms ofDisease-Causing Missense Mutations

The progress made in developing approaches to reveal themolecular mechanism of disease-causing mutations is out-lined in several reviews [50ndash52] Here we briefly summarizethe major approaches and developments focusing on thosewhich allow not only for classification of mutations asdisease-causing or harmless but also for providing informa-tion on what the dominant molecular mechanism behindthe mutation is (Table 1) The focus of this paper is utilizingstructural information to deliver predictions however inprinciple one canmake reasonably specific predictions aboutthe effect of mutations on the protein interaction networkusing sequence information only Because of this the dis-cussion below begins with a networking analysis and otherassociated approaches and then outlines the progress madein the structural space and finally it demonstrates how thestructural information can be used to reveal the details of theeffects of a mutation

31 Progress Made in Networking Every macromoleculeparticipates in various interactions resulting in a complexnetwork in the cell Understanding the effects of mutationsrequires evaluating the corresponding effect on the entirenetwork as discussed recently [53] Such an analysis is crucialfor understanding complex diseases that is diseases causedby mutations in several genes The observation that the samedisease can be caused by different mutations in differentgenes leads to the conclusion that the phenotype is causedby multiple modifications at the molecular level perhapsby disrupting the same network components Because ofthis complex diseases are frequently referred to as diseasesof pathways [53 54] Understanding the effect of geneticdifferences on the corresponding networks requires generat-ing the network representation and mapping the differencesonto it Typically this is done by generating a graph onwhich the genes are placed at the nodes (vertices) and theinteractions are represented as the links (edges) betweenthe nodes Perhaps the most widely used resource for thevisualization of such networks is Cytoscape [55ndash58] althoughmany alternative solutions do exist [59ndash62] The main chal-lenge is to identify or predict which genetic mutations affectwhich interaction in other words how to best map themutations onto the edges of the graph In some limitedcases associating a particular mutation with a particularinteraction can be done by extracting data from the literatureanalyzing the 3D structure of the corresponding complexperforming docking and then analyzing the structure of thedocked complex or predicting residues that participate inthe interaction (correlated mutation sites) [63 64] This isstill one of the main bottlenecks for large-scale modelingEven if the genetic defects can be successfully associated withthe edges of the network and assuming that these mutations

simply remove the corresponding edge (a very simplifiedassumption sincemore frequently themutations weaken [3365] or strengthen [66] molecular interactions not completelyabolishing them) the next question is to predict the effectof edge removal on the disease phenotype Only if all thesequestions are properly addressed can a prediction be made asto what the molecular mechanism of given disease (utilizingnetworking approach) is and in turn be able to point outwhichmolecular interactions are affected and how this affectsthe cellular function

Another challenge is that human interactome is far fromcomplete and there are many missing interactions whichhave not been discovered yet [67 68] In addition there arealsomany interactions detected by high-throughputmethodswhich may not be real physical interactions taking placein the cell [69 70] Combined with dynamic nature ofinteractome [71 72] it is clear that significant work needsto be done to better understand how mutations affect thenetwork and in turn how the changes in the interactomelocal or global are associated with the wild type function ofthe cell In particular it is important to take into account theredundancy in the human interactome to prioritize plausiblegenes involved in a disease [73]

32 Progress Made in Structural Genomic Consortiums and3D Structure Predictions Structural genomic consortiumsare intended to promote development of methods tools andapproaches to deliver the 3D structures of novel proteins[74ndash77] Depending on the overall goal the focus variesfrom determining the 3D structure of proteins found inthe human genome proteins of medical importance orproteins from other genomes In the process of selectingtargetswhose structures are to be experimentally determinedeither by the means of X-ray crystallography or by NMRresearchers frequently pick up genes which represent largeclass of proteins with no 3D structure available [76 78] Suchan approach is intended to result in an equally populatedconformational space and to provide homologous 3D struc-tures for a maximum number of protein sequences Withthe ever-growing Protein Data Bank (PDB) [79 80] whichas of December 2013 has 96596 experimentally determinedmacromolecular structures (including proteins RNA andDNA) the investigations focusing on a particular gene(protein) are frequently able to find either the 3D structureof the wild type protein or the structure of a close homologin the PDB with an unfortunate lack of membrane andscaffolding proteins If the 3D structure of the target proteinis not available one should build a model using the mostappropriate homolog(s)

There are many different approaches for 3D structurepredictions varying from homology-based to first-principle-based approaches [81ndash87] While all these methods havestrengths and weaknesses from point of view of deliveringhigh quality 3D models including models for large proteinsthe homology-based approaches are far superior to the restAs summarized by Moult there is a significant improvementin methods utilizing template-based approaches which canbe seen comparing the results of tenth Critical Assessment of


Structure Prediction (CASP) experiments [88] The resulting3D models of individual macromolecules especially if basedon highly homologous template(s) are of a higher quality thatallows formeaningful structural analysis [89 90] and even forcarrying out various energy calculations [91 92]

At the same time since practically every macromoleculeis involved in various interactions including interactions withother macromolecules [93 94] it is equally important toreveal the interacting partners and the structure of the cor-responding protein complexes Several databases summarizeand provide details about such interactions [95ndash98] includ-ing the changes to the binding affinity caused by mutations[99] While a significant amount of thermodynamics dataexists very few structures of macromolecular complexes areavailable (as compared with monomeric macromolecules)and therefore the structures have to be predicted in mostcases [100ndash103] The 3D structures are typically modeled viaeither homology-based methods [104ndash108] or docking [109ndash112] The performance of these approaches is tested in thecommunity-wide experiment on the Critical Assessment ofPredicted Interactions (CAPRI) [113] and it was concludedthat the performance of docking and scoring methods hasremained quite robust but challenges still exist [113ndash116]Either way one needs either experimentally determined 3Dstructure or a high quality model of the correspondingmacromolecular complex in order to carry out structuralanalysis and evaluate the various energy components [33 65]

The above considerations are with respect to the wildtype macromolecules which from genetics perspective typ-ically are referred to as dominant allele It is quite unlikelyto expect that the 3D structures of the minor alleles orrareunique mutant macromolecules and the correspondingcomplexes will be experimentally determined independentlyInstead the mutant structures are built from the wild typestructures by either side chain replacement [117ndash121] orinsertiondeletion of a structural segment [122ndash124] andfurther structural relaxation [33 65 125ndash128]

33 Progress Made in Understanding the Details of Disease-Causing Mechanisms Utilizing Structural InformationRevealing the effect(s) of genetics differences on the wildtype cellular function can be done either experimentallyor in silico It is quite unlikely that experimental approachwill be applied for each individual case due to the factthat experiments are time-consuming and may require asignificant investment Due to this in silico approaches mustbe utilized Since the goal is to reveal the details of the effectnot just the effect itself one needs structural informationTo reiterate once more it should be clarified that forexample a prediction that a given mutation destabilizes thecorresponding protein which can be done without structuralinformation is not sufficient for understanding the detailsof the effect Instead one has to be able to predict whatthe structural changes caused by the mutation(s) are andhow these changes can be reduced or eliminated by smallmolecule stabilizers Below we review the progress madein several major directions such as predicting the effect on(Section 331) protein integrity [50] (Section 332) protein

interactions [129] and (Section 333) protein subcellularlocalization and pH-dependent properties We purposelyfocus on these directions because in principle these effectscan be fixed with external stimuli such as small moleculesInterested readers should be referred to several other reviewpapers exploring different effects [5 51 52] In the end it isimportant to recognize that the most successful predictionsare expected to be done addressing the effects above andsimultaneously taking into account the specificity of thefunction of the corresponding target However frequentlythe precise function or the details are unknown and haveto be predicted The necessity of revealing macromolecularfunction in terms of understanding the disease mechanismand the progress made in this direction are discussed inSection 334

331 The Effect on Protein Integrity The effect on proteinintegrity is typically assessed via predicting the changes of thefolding free energy conformational dynamics and hydrogenbond networks [50] With this in mind one of the mainobstacles in predicting if a givenmutation is deleterious is theambiguity of how large the deviation from native propertyof a given protein should be in order to be disease-causingFor example some proteins are very stable having a largefolding free energy and small changes caused by mutation(s)may not be deleterious At the other end of the spectrum areintrinsically unstable proteins with a folding free energy ofa few kcalmol for them almost any change in the foldingfree energy is expected to be deleterious In order to avoidthis particular problem with respect to protein folding freeenergy an approach was developed to mutate all nativeresidues to the rest of ninety amino acids and to constructthe mutability landscape to guide the selection of deleteriousmutations [130] Such an approach allows the decision tobe made based on the energy landscape of each particularprotein Another investigation introduced quantities such asldquotolerancerdquo and ldquomutabilityrdquo for mutation sites to indicateif the site itself can tolerate substitutions and also to detectif these substitutions are amino acid specific [131] Variousapproaches exist to predict the changes of protein stabilitydue tomutations [132ndash137]The performance of such selectedmethods including resources which do not utilize structuralinformation was reviewed in recent reports and it wasindicated that the ability of the methods to deliver accuratepredictions is quite limited [138] and better tools are required[139]

The above considerations focus mostly on protein foldingfree energy changes caused by mutations however of equalimportance are the effects of the mutations on macromolec-ular dynamics and the details of hydrogen bonding especiallyin the neighborhood of the active site Alteration of thehydrogen bond network within the active site or other struc-tural regions important for the biological reaction is typicallyalways deleterious [126 128 140 141] Changes in macro-molecular dynamics especially for proteins whose functionrequires conformational changes can cause diseases [66142ndash144] These changes in the hydrogen bond pattern andconformational flexibility are typically predicted via standard


molecular dynamics or energy minimization simulationsProvided that the mutations do not cause drastic structuralalterations the existing molecular dynamics packages arequite successful in revealing these changes [50]

332 The Effect on Protein Interactions Essential compo-nents of cellular machinery are protein-protein interactionsAny missense mutations especially those at the proteinbinding sites can affect the affinity and interaction ratesas discussed in a recent review [129] Currently there areseveral structure-based approaches to predict the changes ofthe binding free energy due to missense mutations [132 145ndash150] These methods utilize the experimentally delivered 3Dstructure of the corresponding protein-protein complex Ifthe structure of the complex is not available the alternative isto dock the monomeric proteins to predict the 3D structureof the complex and then to evaluate the effect of themutationon the binding affinity The performance of such approachesto predict structural changes and changes in the bindingaffinity caused by mutations is reviewed in recent article [151]and it is concluded that significant improvement is needed toimprove the performance

Despite the fact that the existingmethods are not particu-larly accurate to predict the exact changes of the binding freeenergy due to mutation as can be seen from benchmarkingtests against various databases of experimental data points[95 97 99] the predictions still can be used to evaluatethe trend of the changes without being too concernedabout the magnitude of the changes [33 65 66 131] Inaddition the structures of the corresponding complexeseither experimentally available or modeled in silico can beused for structural analysis to predict the effect of mutations[152 153] With this in mind of particular interest is theinferred biomolecular interaction server (IBIS) at NIHNCBI[154 155] Thus one can use structural information to makea reasonable prediction about whether the mutation will betolerated or not that is if themutation will have drastic effecton the proteinrsquos wild type interactions

333 The Effect on Subcellular Localization and pH Depen-dence Macromolecules carry out their function by sens-ing various environments and particularly in the cellare localized in different subcellular compartments or aretrafficked across different compartments Each subcellularcompartment as well as different body organs has a specificcharacteristic pH as compiled in several reports [156ndash160]Macromolecules must be delivered to the correct compart-ment in order to function properly and any mutation thatchanges the signal peptide will have a deleterious effect onthe function [161ndash163] In addition any mutation that altersthe pH-dependent properties either the pH dependence ofprotein stability [156 157] or the protein-protein interactions[156 157 160 164 165] (including the changes of protonationstates [166 167]) may be deleterious Such an analysis is noteasy to do since the decision about the effect must be takeninto account alongwith the subcellular or organ characteristicpH where the wild type protein is supposed to functionwhich is information that is not typically available

If the characteristic pH is known and the structures ofthe corresponding macromolecules and their complexes areavailable then there are many in silico tools to predict theeffect of mutations on the pH dependence of folding andinteractions as recently reviewed [168] Some of them predictthe conformational changes and the changes of hydrogenbond patterns as well providing additional information to beanalyzed The performance of the existing methods of pKacalculations is increasing the accuracy to much higher levelsby reducing the overall error to less than 1 kcalmol [169]this range is frequently sufficient for analyzing the effect ofmutations

In terms of predicting the effect of mutations on theproperties of the signaling peptide one can assess the effectusing various databases and servers of signaling peptides[170ndash172] Although considerations must be made about theaccessibility of the signaling peptide from the water phase inmost cases just the sequence information is needed to makethe prediction

334 The Macromolecular Function and Effects of MutationsIn the above paragraph the macromolecular function wasfrequently mentioned and it was repeatedly said that theeffects of mutations should be evaluated in terms of theireffect on macromolecular function However there are stillmacromolecules in the human genome which are not anno-tated [173] even for those whose 3D structures were exper-imentally determined via Structural Genomics Initiativesso termed orphan proteins [174 175] It is infeasible thatthese functionalities will be experimentally studied and theseproteins and RNAs should be annotated computationally[173 176ndash179] Having in mind the importance of developingin silico tools for functional annotation recently the firstlarge-scale community-based critical assessment of proteinfunction annotation (CAFA) experiment has begun [180]The results from the first round are quite encouraging interms of the fact that standard sequence-based approachessuch as Blast are capable of detecting sequence similarity andthus of inferring function [181] but it was indicated that thereis a need for improvement of currently available approaches[180] The main challenges include the definition of proteinfunction and evaluation of predictions to be independent ofthe dataset [181]

In conclusion of this section it should be clarifiedthat indeed the currently available methods for structureanalysis and predictions energy calculations hydrogen bondnetwork modeling assessment of conformational dynam-ics and functional annotations are not perfect and needimprovement Still if applied together to study any particularmacromolecule and its associated mutations it typicallydelivers meaningful results as indicated by comparing withthe experimental data of the relevant case studies [66 92 126ndash128 131 182 183]

4 Personalized Diagnostics

Armed with the abovementioned tools the ultimate goal isto be able to detect disease-causing DNA defects even before


Individualrsquos genome

Identificationof DNA

differences

$1000 genome

HapMap projectIdentification ofdisease-causing

DNA defects

Revealingmolecular

mechanism

Personalizeddiagnostics

Structuralfunctionalgenomics

modeling

Pharmacogenomics Preventive caremethodsmethods

Personalizedmedicine

In silico methods

In silico structural

In silicoIn silico

and so forth

1000 genomes project

OMIM dbSNP

Figure 2 Flowchart representing the basic components behind personalized genome-oriented medicine

the disease is clinically manifested [184 185] however it isequally important to pinpoint the disease-causing effect [6692 127 128 183] (Figure 2) The last case of investigations isessential for building a library of DNAdefects associatedwithparticular diseases that is database of genotypes causing par-ticular disease [186] The increasing number and size of suchdatabases is essential for fast and precise diagnostics since theonly information required is the individualrsquos genome Oncethe individual genome is mapped onto the database of thediseasesrsquo genotypes the prediction of the disease predisposalcan be done instantly Perhaps the best approach is to collectDNA samples from all individuals especially individuals intheir early life make such a screening routine and monitorthe individualrsquos health throughout their life

While database of disease-causing genotypes is anextremely important health issue there will always be newgenotypes which cannot be detected by such an approachbefore the clinical manifestation of the disease occurs Toassociate a new genotype with a particular disease and revealthe molecular mechanism behind it will require applying theapproaches described above Perhaps in some limited casesthemolecularmechanism and the disease association of thesenew disease-causing mutations will be revealed by the meansof experimental techniques or in model organisms and thenthey will be added to the appropriate genotype databaseHowever in the vast majority of the cases the molecularmechanism will have to be revealed in silico Essentially oneshould be able to address the following hypothetical scenarioand provide a diagnosis for a particular individual givenan individualrsquos genome the goal is to identify all the poten-tially disease-causing mutations by comparing them to thedatabases of disease-causing genotypes Then the rest of the

individualrsquos DNA differences (with respect to the ldquostandardrdquohuman DNA) must be analyzed in silico and disease-causingmutations must be identified among the DNA differencescausing natural differences in human population Howeverthe completion of such a task is not trivial because notonly the distinction between disease-causing and harmlessmutations is difficult but also more importantly the linkagebetween predicted disease-causing mutations and the diseaseis extremely challenging especially with complex diseasesStill developing biomarkers to personalize cancer treatmentby identifying cancer-associated genes that can differentiateone type of cancer from another will enable the use of highlytailored therapies [187] The problem is slightly less compli-cated formonogenic diseases since the disease is known to becaused by themalfunction of a particular gene (protein) and ifthe given mutation in this protein is predicted to be disease-causing then most probably it is associated with the samemonogenic disease However notable exceptions do exist asfor example missense mutations occurring in MECP2 geneand causing either Rett syndrome [188 189] Huntingtonrsquosdisease [190] or other disorders [191]

5 Pharmacogenomics

With ever-increasing amount of clinical data it is nowwidelyunderstood that different races [192] ethnicities [193 194]genders [195 196] age [197 198] groups and so forth responddifferently to various medications (Figure 2) A drug which isquite efficient for the treatment of a particular disease for agroup of people sharing the same or a similar genotype maynot work well for another group of people belonging to adifferent genotypeThismay result from different phenotypes


of the disease among these groups of people but even if thephenotype is the same amongst the group members still theefficacy of the drug may depend on the differences in thegenotypes A prominent example of differing drug responsesis human cytochrome P450 [199] One of the isoforms ofhuman cytochrome P450 CYP2D6 is primarily responsiblefor metabolizing hydrocodone to hydromorphone a typicaldrug treatment after surgery [200] However it was foundthat a variant of CYP2D6 theCYP2D617 common inAfricanAmericans does not metabolize hydrocodone efficiently[201] Having prior knowledge of such cases and even moreimportantly being able to predict the drug efficiency basedon the patientrsquos genome is crucial for successful treatmentIf such information is readily available then the prescriptioncan be personalized by prescribing different dosages depend-ing on the patientrsquos genotype Even further frequently thereare several drugs designed to treat certain diseases and theselection of the best drug for the treatment should be basedon the patientrsquos genotype as well Currently the data is veryscarce [202ndash204] and much work must be done in order tomake pharmacogenomics a more common practice

6 PersonalizedPrecise Medicine

The culmination of the usefulness of the individualrsquos genomicdata resides in personalized medicine [205] The basic con-cepts of personalizedmedicine or sometimes called precisionmedicine are outlined in a recent article [206] Essentially itis a combination or a joint venture of personalized diagnos-tics pharmacogenomics and personalized preventive care[207ndash209] (Figure 2) Since personalized diagnostics andpharmacogenomics were already discussed above the mainfocus here is the personalized preventive care Ignoringethical issues associated with providing individuals withpredictions about their long term health [210] an early pre-ventive treatment for plausible disease would have enormouseffect on society and the individuals themselves Perhapspreventive care can be divided into several categories (a)preventive care for conditional diseases (b) preventive carefor development diseases and (c) preventive care for anindividualrsquos lifetime

The most easily addressable preventive care is the carefor individuals who may develop a disease which dependson certain (environmental) conditions Obviously avoidingthese conditions will dramatically decrease the disease riskFor example Chronic Beryllium disease is a disorder foundin some individuals being exposed to Beryllium [211] inaddition to having a particular genotype If every individualapplying for a job inBeryllium rich environment is genotypedand individuals possessing the risk genotype are notified ofthis risk and potential dangers then this would be the bestpreventive care for people susceptible to Chronic Berylliumdisease Other examples are the cases of individuals predis-posed to lung or skin cancers [212 213] These individualsshould avoid smoking or exposure to intense ultravioletlight respectively The list of examples can be extended tomany other cases but the message is that clear identificationof individuals predisposed to diseases whose development

depends on certain conditions would greatly decrease theirreliance on medical treatment later on in life In addition inmental disorders the susceptibility profile of each individualdepends on the psychosocial environment and this should betaken into account in delivering the prognosis [214]

Developmental diseases are typically quite severe andeven if the patient survives the effects are often perma-nent Another important distinction between developmentaldiseases and other diseases is that once they are clinicallymanifested it is typically too late for treatment Due tothe severity of these diseases predicting an individualrsquosgenetic predispositions must be done at a very early stagein their development and the appropriate treatment must beadministered [215]

Finally there are many diseases and conditions whichrequire a lifetime of care [216] It is desirable that suchcases are detected before the patient becomes sick Howeverthe preventive care in such a case when the disease is stillnot manifested will require quite different (from current)thinking from both the patient and the primary physician[217] It may require decisions which will be difficult to justifywithout presence of the disease and in some cases may resultin the wrong treatment The straightforward solution is toavoid radical interventions but to subject these high riskpatients to constant monitoring and frequent examinations

7 Concluding Remarks

This paper attempts to outline the current development tak-ing place in several rapidly evolving disciplines personalizeddiagnostics pharmacogenomics and personalized medicineand also how structural and conventional biology and insilico biophysics are embedded in these efforts It is quitelikely that individual genotyping will become a standardtest similar to currently used blood test and the decisionsabout individualrsquos health will be based on the correspondinggenotype The decisions about their health for either person-alized preventive care or personalized treatment will be stillindividualized but not to the extent that each person willreceive an individualized drug rather both the preventivecare and drug prescription will be grouped into categoriesdepending on common genotypes and phenotypes Withthis in mind structural and functional genomics along withbetter computational approaches will play crucial roles in thedevelopment of these methods

However many challenges still exist in fully utilizinggenomic data to guide personalized medicine and pharma-cogenomics [218] Recent completion of the 1000 genomespilot project [219] revealed that most individuals carry 250 to300 loss-of-function variants in annotated genes and 50 to 100variants previously implicated in inherited disorders [220] Inaddition to this observation it is known that the severity of adisease depends onmany factors and for individual carryingthe same disease-causing mutation(s) the manifestation canbe quite different At the same time it was pointed outthat disease-associated variants differ radically from variantsobserved in the 1000 genomes project dataset [221] providinga hope that despite the natural complexity the genetic


information will be used to provide better diagnostics andtreatment

It should be pointed out that it is clear that personal-ized medicine and pharmacogenomics will never be totallyldquopersonalrdquo The time and the effort to bring scientific dis-covery to the clinic including the time for clinical trialsare prohibitively large and cannot be done on an individualbasis Instead the causes of the diseases should be generalizedinto classes and specific ldquoindividualizedrdquo treatment should beoffered depending on individualrsquos DNA defect falling into aspecific class for which particular treatment does exist

Conflict of Interests

The author declares that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

The work was supported by an institutional grant fromClemson University the office of the Provost

References

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[2] T J Hoffmann Y ZhanMN Kvale et al ldquoDesign and coverageof high throughput genotyping arrays optimized for individualsof East Asian African American and Latino raceethnicityusing imputation and a novel hybrid SNP selection algorithmrdquoGenomics vol 98 no 6 pp 422ndash430 2011

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[4] S Anno T Abe and T Yamamoto ldquoInteractions betweenSNP alleles at multiple loci contribute to skin color differencesbetween caucasoid andmongoloid subjectsrdquo International Jour-nal of Biological Sciences vol 4 no 2 pp 81ndash86 2008

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[6] R R Haraksingh and M P Snyder ldquoImpacts of variation inthe human genome on gene regulationrdquo Journal of MolecularBiology vol 425 no 21 pp 3970ndash3977 2013

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[120] Z Xiang P J Steinbach M P Jacobson R A Friesner andB Honig ldquoPrediction of side-chain conformations on proteinsurfacesrdquo Proteins Structure Function and Genetics vol 66 no4 pp 814ndash823 2007

[121] Z Xiang and B Honig ldquoExtending the accuracy limits ofprediction for side-chain conformationsrdquo Journal of MolecularBiology vol 311 no 2 pp 421ndash430 2001

[122] S Liang C Zhang and Y Zhou ldquoLEAP highly accurateprediction of protein loop conformations by integrating coarse-grained sampling and optimized energy scores with all-atomrefinement of backbone and side chainsrdquo Journal of Computa-tional Chemistry vol 35 no 4 pp 335ndash341 2014

[123] K Zhu and T Day ldquoAb initio structure prediction of theantibody hypervariable H3 looprdquo Proteins Structure Functionand Bioinformatics vol 81 no 6 pp 1081ndash1089 2013

[124] S Zhao K Zhu J Li and R A Friesner ldquoProgress insuper long loop predictionrdquo Proteins Structure Function andBioinformatics vol 79 no 10 pp 2920ndash2935 2011

[125] N M Glykos and M Kokkinidis ldquoMeaningful refinementof polyalanine models using rigid-body simulated annealingapplication to the structure determination of the A31P RopmutantrdquoActa Crystallographica Section D Biological Crystallog-raphy vol 55 no 7 pp 1301ndash1308 1999

[126] Z Zhang S Teng L Wang C E Schwartz and E AlexovldquoComputational analysis ofmissensemutations causing Snyder-Robinson syndromerdquoHumanMutation vol 31 no 9 pp 1043ndash1049 2010

[127] N Dolzhanskaya M A Gonzalez F Sperziani et al ldquoA novelpLeu(381)Phe mutation in presenilin 1 is associated with veryearly onset and unusually fast progressing dementia as well aslysosomal inclusions typically seen in Kufs diseaserdquo Journal ofAlzheimerrsquos Disease vol 39 no 1 pp 23ndash27 2013

[128] L Boccuto K Aoki H Flanagan-Steet et al ldquoA mutation ina ganglioside biosynthetic enzyme ST3GAL5 results in salt


amp pepper syndrome a neurocutaneous disorder with alteredglycolipid and glycoprotein glycosylationrdquo Human MolecularGenetics vol 23 no 2 pp 418ndash433 2014

[129] C M Yates and M J E Sternberg ldquoThe effects of non-synonymous single nucleotide polymorphisms (nsSNPs) onprotein-protein interactionsrdquo Journal of Molecular Biology vol425 pp 3949ndash3963 2013

[130] M Hecht Y Bromberg and B Rost ldquoNews from the proteinmutability landscaperdquo Journal ofMolecular Biology vol 425 no21 pp 3937ndash3948 2013

[131] Z Zhang J Norris C Schwartz and E Alexov ldquoIn silicoand in vitro investigations of the mutability of disease-causingmissense mutation sites in spermine synthaserdquo PLoS ONE vol6 no 5 Article ID e20373 2011

[132] L Wickstrom E Gallicchio and R M Levy ldquoThe linearinteraction energymethod for the prediction of protein stabilitychanges upon mutationrdquo Proteins Structure Function andBioinformatics vol 80 no 1 pp 111ndash125 2012

[133] Y Li and J Fang ldquoPROTS-RF a robust model for predictingmutation-induced protein stability changesrdquo PLoS ONE vol 7no 10 Article ID e47247 2012

[134] E H Kellogg A Leaver-Fay and D Baker ldquoRole of confor-mational sampling in computing mutation-induced changes inprotein structure and stabilityrdquoProteins Structure Function andBioinformatics vol 79 no 3 pp 830ndash838 2011

[135] Y Dehouck J M Kwasigroch D Gilis and M RoomanldquoPoPMuSiC 21 a web server for the estimation of proteinstability changes uponmutation and sequence optimalityrdquoBMCBioinformatics vol 12 article 151 2011

[136] C M Frenz ldquoNeural network-based prediction of mutation-induced protein stability changes in staphylococcal nuclease at20 residue positionsrdquo Proteins Structure Function andGeneticsvol 59 no 2 pp 147ndash151 2005

[137] E Capriotti P Fariselli and R Casadio ldquoI-Mutant20 predict-ing stability changes upon mutation from the protein sequenceor structurerdquo Nucleic Acids Research vol 33 no 2 pp W306ndashW310 2005

[138] G Thiltgen and R A Goldstein ldquoAssessing predictorsof changes in protein stability upon mutation using self-consistencyrdquo PLoS ONE vol 7 no 10 Article ID e460842012

[139] S Khan and M Vihinen ldquoPerformance of protein stabilitypredictorsrdquo Human Mutation vol 31 no 6 pp 675ndash684 2010

[140] K Schurmann M Anton I Ivanov C Richter H Kuhnand M Walther ldquoMolecular basis for the reduced catalyticactivity of the naturally occurring T560m mutant of human1215-lipoxygenase that has been implicated in coronary arterydiseaserdquo Journal of Biological Chemistry vol 286 no 27 pp23920ndash23927 2011

[141] S Wang W Zhao H Liu H Gong and Y Yan ldquoIncreasing120573B1-crystallin sensitivity to proteolysis caused by the congenitalcataract-microcornea syndromemutation S129RrdquoBiochimica etBiophysica Acta vol 1832 no 2 pp 302ndash311 2013

[142] S Witham K Takano C Schwartz and E Alexov ldquoA missensemutation in CLIC2 associated with intellectual disability ispredicted by in silico modeling to affect protein stability anddynamicsrdquo Proteins Structure Function and Bioinformatics vol79 no 8 pp 2444ndash2454 2011

[143] H Tsukamoto and D L Farrens ldquoA constitutively activatingmutation alters the dynamics and energetics of a key conforma-tional change in a ligand-free G protein-coupled receptorrdquoTheJournal of Biological Chemistry vol 288 pp 28207ndash28216 2013

[144] J Y Lee and D S Kim ldquoDramatic effect of single-basemutation on the conformational dynamics of human telomericG-quadruplexrdquo Nucleic Acids Research vol 37 no 11 pp 3625ndash3634 2009

[145] R Guerois J E Nielsen and L Serrano ldquoPredicting changes inthe stability of proteins and protein complexes a study of morethan 1000 mutationsrdquo Journal of Molecular Biology vol 320 no2 pp 369ndash387 2002

[146] Y Dehouck J M Kwasigroch M Rooman and D GilisldquoBeAtMuSiC prediction of changes in protein-protein bindingaffinity onmutationsrdquoNucleic Acids Research vol 41 ppW333ndashW339 2013

[147] A Benedix C M Becker B L de Groot A Caflisch and RA Bockmann ldquoPredicting free energy changes using structuralensemblesrdquo Nature Methods vol 6 no 1 pp 3ndash4 2009

[148] T Kortemme and D Baker ldquoA simple physical model for bind-ing energy hot spots in protein-protein complexesrdquo Proceedingsof the National Academy of Sciences of the United States ofAmerica vol 99 no 22 pp 14116ndash14121 2002

[149] G Rastelli A Del Rio G Degliesposti and M Sgobba ldquoFastand accurate predictions of binding free energies using MM-PBSA and MM-GBSArdquo Journal of Computational Chemistryvol 31 no 4 pp 797ndash810 2010

[150] V Z Spassov and L Yan ldquopH-selective mutagenesis of protein-protein interfaces in silico design of therapeutic antibodieswith prolonged half-liferdquo Proteins Structure Function andBioinformatics vol 81 no 4 pp 704ndash714 2013

[151] R Moretti S J Fleishman R Agius M Torchala and P ABates ldquoCommunity-wide evaluation of methods for predictingthe effect of mutations on protein-protein interactionsrdquo Pro-teins vol 81 pp 1980ndash1987 2013

[152] A David R Razali M N Wass and M J E SternbergldquoProtein-protein interaction sites are hot spots for disease-associated nonsynonymous SNPsrdquo Human Mutation vol 33no 2 pp 359ndash363 2012

[153] Y Zhang M Motamed J Seemann M S Brown and J LGoldstein ldquoPoint mutation in luminal Loop 7 of scap proteinblocks interaction with Loop 1 and abolishes movement toGolgirdquoThe Journal of Biological Chemistry vol 288 no 20 pp14059ndash14067 2013

[154] B A Shoemaker D Zhang M Tyagi et al ldquoIBIS (InferredBiomolecular Interaction Server) reports predicts and inte-grates multiple types of conserved interactions for proteinsrdquoNucleic Acids Research vol 40 no 1 pp D834ndashD840 2012

[155] EW Sayers T Barrett D A Benson et al ldquoDatabase resourcesof the National Center for Biotechnology Informationrdquo NucleicAcids Research vol 40 no 1 pp D13ndashD25 2012

[156] K Talley and E Alexov ldquoOn the pH-optimum of activityand stability of proteinsrdquo Proteins Structure Function andBioinformatics vol 78 no 12 pp 2699ndash2706 2010

[157] E Alexov ldquoNumerical calculations of the pH of maximalprotein stability the effect of the sequence composition andthree-dimensional structurerdquoEuropean Journal of Biochemistryvol 271 no 1 pp 173ndash185 2004

[158] P Chan and J Warwicker ldquoEvidence for the adaptation ofprotein pH-dependence to subcellular pHrdquo BMC Biology vol7 article 69 2009

[159] P Chan J Lovric and J Warwicker ldquoSubcellular pH andpredicted pH-dependent features of proteinsrdquo Proteomics vol6 no 12 pp 3494ndash3501 2006


[160] A V Onufriev and E Alexov ldquoProtonation and pK changes inprotein-ligand bindingrdquo Quarterly Reviews of Biophysics vol46 no 2 pp 181ndash209 2013

[161] M Kimura J Machida S Yamaguchi A Shibata and TTatematsu ldquoNovel nonsense mutation in MSX1 in familialnonsyndromic oligodontia subcellular localization and role ofhomeodomainMH4rdquo European Journal of Oral Sciences vol122 no 1 pp 15ndash20 2014

[162] Y Erzurumlu F AydinKose O Gozen D Gozuacik E A Tothand P Ballar ldquoA unique IBMPFD-related P97VCP mutationwith differential binding pattern and subcellular localizationrdquoInternational Journal of Biochemistry and Cell Biology vol 45no 4 pp 773ndash782 2013

[163] Y Hosaka H Hanawa T Washizuka et al ldquoFunction subcel-lular localization and assembly of a novel mutation of KCNJ2in Andersenrsquos syndromerdquo Journal of Molecular and CellularCardiology vol 35 no 4 pp 409ndash415 2003

[164] P J Kundrotas and E Alexov ldquoElectrostatic properties ofprotein-protein complexesrdquo Biophysical Journal vol 91 no 5pp 1724ndash1736 2006

[165] R C Mitra Z Zhang and E Alexov ldquoIn silico modeling ofpH-optimum of protein-protein bindingrdquo Proteins StructureFunction and Bioinformatics vol 79 no 3 pp 925ndash936 2011

[166] M Petukh S Stefl and E Alexov ldquoThe role of protonationstates in ligand-receptor recognition and bindingrdquo CurrentPharmaceutical Design vol 19 no 23 pp 4182ndash4190 2013

[167] B Aguilar R Anandakrishnan J Z Ruscio and A V OnufrievldquoStatistics and physical origins of pK and ionization statechanges upon protein-ligand bindingrdquo Biophysical Journal vol98 no 5 pp 872ndash880 2010

[168] E Alexov E L Mehler N Baker et al ldquoProgress in the predic-tion of pKa values in proteinsrdquo Proteins Structure Function andBioinformatics vol 79 no 12 pp 3260ndash3275 2011

[169] T Carstensen D Farrell Y Huang N A Baker and J ENielsen ldquoOn the development of protein pKa calculationalgorithmsrdquo Proteins Structure Function and Bioinformaticsvol 79 no 12 pp 3287ndash3298 2011

[170] O Emanuelsson S Brunak G von Heijne and H NielsenldquoLocating proteins in the cell using TargetP SignalP and relatedtoolsrdquo Nature Protocols vol 2 no 4 pp 953ndash971 2007

[171] AHoglund P Donnes T BlumH Adolph andO KohlbacherldquoMultiLoc prediction of protein subcellular localization usingN-terminal targeting sequences sequence motifs and aminoacid compositionrdquo Bioinformatics vol 22 no 10 pp 1158ndash11652006

[172] P Horton K Park T Obayashi et al ldquoWoLF PSORT proteinlocalization predictorrdquoNucleic Acids Research vol 35 no 2 ppW585ndashW587 2007

[173] K J Won X Zhang T Wang et al ldquoComparative annotationof functional regions in the human genome using epigenomicdatardquoNucleic Acids Research vol 41 no 8 pp 4423ndash4432 2013

[174] A B Munkacsi A F Porto and S L Sturley ldquoNiemann-Pick type C disease proteins orphan transporters or membranerheostatsrdquo Future Lipidology vol 2 no 3 pp 357ndash367 2007

[175] D Avram A Fields K Pretty On Top D J Nevrivy J EIshmael and M Leid ldquoIsolation of a novel family of C

2H2zinc

finger proteins implicated in transcriptional repression medi-ated by chicken ovalbumin upstream promoter transcriptionfactor (COUP-TF) orphan nuclear receptorsrdquo The Journal ofBiological Chemistry vol 275 no 14 pp 10315ndash10322 2000

[176] J Harrow A Frankish J M Gonzalez et al ldquoGENCODE thereference human genome annotation for the ENCODE projectrdquoGenome Research vol 22 no 9 pp 1760ndash1774 2012

[177] H Chen Y Tian W Shu X Bo and S Wang ldquoComprehensiveidentification and annotation of cell type-specific and ubiqui-tous CTCF-binding sites in the human genomerdquoPLoSONE vol7 Article ID e41374 2012

[178] H Jia M Osak G K Bogu L W Stanton R Johnson andL Lipovich ldquoGenome-wide computational identification andmanual annotation of human long noncoding RNA genesrdquoRNA vol 16 no 8 pp 1478ndash1487 2010

[179] R Guigo P Flicek J F Abril et al ldquoEGASP the humanENCODE Genome Annotation Assessment Projectrdquo GenomeBiology vol 7 supplement 1 article S2 31 pages 2006

[180] P Radivojac W T Clark T R Oron et al ldquoA large-scale eval-uation of computational protein function predictionrdquo NatureMethods vol 10 pp 221ndash227 2013

[181] J Gillis and P Pavlidis ldquoCharacterizing the state of the art inthe computational assignment of gene function Lessons fromthe first critical assessment of functional annotation (CAFA)rdquoBMC Bioinformatics vol 14 no 3 article S15 2013

[182] Z Zhang Y Zheng M Petukh A Pegg Y Ikeguchi andE Alexov ldquoEnhancing human spermine synthase activity byengineered mutationsrdquo PLoS Computational Biology vol 9 no2 Article ID e1002924 2013

[183] Z Zhang J Norris V Kalscheuer et al ldquoA Y328C missensemutation in spermine synthase causes a mild form of snyder-robinson syndromerdquoHumanMolecular Genetics vol 22 no 18pp 3789ndash3797 2013

[184] D H Spencer K L Bubb and M V Olson ldquoDetectingdisease-causing mutations in the human genome by haplotypematchingrdquo American Journal of Human Genetics vol 79 no 5pp 958ndash964 2006

[185] B B Fitterer N A Antonishyn P L Hall and D C Lehotay ldquoApolymerase chain reaction-based genotyping assay for detectinga novel sandhoff disease-causing mutationrdquoGenetic Testing andMolecular Biomarkers vol 16 no 5 pp 401ndash405 2012

[186] A J P Smith J Palmen W Putt P J Talmud S E Humphriesand F Drenos ldquoApplication of statistical and functionalmethodologies for the investigation of genetic determinants ofcoronary heart disease biomarkers lipoprotein lipase genotypeand plasma triglycerides as an exemplarrdquo Human MolecularGenetics vol 19 no 20 Article ID ddq308 pp 3936ndash3947 2010

[187] S D Ramsey D Veenstra S R Tunis L Garrison J J Crowleyand L H Baker ldquoHow comparative effectiveness research canhelp advance ldquopersonalized medicinerdquo in cancer treatmentrdquoHealth Affairs vol 30 no 12 pp 2259ndash2268 2011

[188] C A Chapleau J Lane J Larimore W Li L Pozzo-Miller andA K Percy ldquoRecent progress in Rett syndrome and MECP2dysfunction assessment of potential treatment optionsrdquo FutureNeurology vol 8 no 1 pp 21ndash28 2013

[189] A Banerjee E Romero-Lorenzo and M Sur ldquoMeCP2 makingsense of missense in Rett syndromerdquo Cell Research vol 23 pp1244ndash1246 2013

[190] K N McFarland M N Huizenga S B Darnell et al ldquoMeCP2a novel Huntingtin interactorrdquo Human Molecular Genetics vol23 no 4 pp 1036ndash1044 2014

[191] B Suter D Treadwell-Deering H Y Zoghbi D G Glaze and JL Neul ldquoBrief report MECP2 mutations in people without rettsyndromerdquo Journal of Autism and Developmental Disorders vol44 no 3 pp 703ndash711 2014


[192] R Bowser ldquoRace as a proxy for drug response the dangers andchallenges of ethnic drugsrdquo De Paul Law Review vol 53 no 3pp 1111ndash1126 2004

[193] S L Chan C Suo S C Lee B C Goh K S Chia and Y YTeo ldquoTranslational aspects of genetic factors in the prediction ofdrug response variability a case study of warfarin pharmacoge-nomics in a multi-ethnic cohort fromAsiardquo PharmacogenomicsJournal vol 12 no 4 pp 312ndash318 2012

[194] D E Johnson K Park and D A Smith ldquoEthnic variation indrug response Implications for the development and regulationof drugsrdquo Current Opinion in Drug Discovery and Developmentvol 11 no 1 pp 29ndash31 2008

[195] J M Gorman ldquoGender differences in depression and responseto psychotropic medicationrdquo Gender Medicine vol 3 no 2 pp93ndash109 2006

[196] S Bano S Akhter and M I Afridi ldquoGender based responseto fluoxetine hydrochloride medication in endogenous depres-sionrdquo Journal of the College of Physicians and Surgeons Pakistanvol 14 no 3 pp 161ndash165 2004

[197] A R Ferrari R Guerrini G Gatti M G Alessandrı PBonanni and E Perucca ldquoInfluence of dosage age and co-medication on plasma topiramate concentrations in childrenand adults with severe epilepsy and preliminary observationson correlations with clinical responserdquoTherapeutic Drug Moni-toring vol 25 no 6 pp 700ndash708 2003

[198] T Q Tran C Z Grimes D Lai C L Troisi and L Y HwangldquoEffect of age and frequency of injections on immune responseto hepatitis B vaccination in drug usersrdquo Vaccine vol 30 no 2pp 342ndash349 2012

[199] V Y Martiny and M A Miteva ldquoAdvances in molecularmodeling of human cytochrome P450 polymorphismrdquo Journalof Molecular Biology vol 425 pp 3978ndash3992 2013

[200] M E Stauble A W Moore and L J Langman ldquoHydrocodonein postoperative personalized pain management pro-drug ordrugrdquo Clinica Chimica Acta vol 429 pp 26ndash29 2014

[201] K Handa I Nakagome N Yamaotsu H Gouda and S HironoldquoIn silico study on the inhibitory interaction of drugs withwild-type CYP2D61 and the natural variant CYP2D617rdquo DrugMetabolism and Pharmacokinetics vol 29 no 1 pp 52ndash60 2014

[202] B Moy D Tu J L Pater et al ldquoClinical outcomes of ethnicminority women in MA17 a trial of letrozole after 5 years oftamoxifen in postmenopausal women with early stage breastcancerrdquo Annals of Oncology vol 17 no 11 pp 1637ndash1643 2006

[203] M Zhan J A Flaws L Gallicchio K Tkaczuk LM Lewis andR Royak-Schaler ldquoProfiles of tamoxifen-related side effects byrace and smoking status in women with breast cancerrdquo CancerDetection and Prevention vol 31 no 5 pp 384ndash390 2007

[204] A N Tucker K A Tkaczuk L M Lewis D Tomic C KLim and J A Flaws ldquoPolymorphisms in cytochrome P4503A5(CYP3A5) may be associated with race and tumor characteris-tics but not metabolism and side effects of tamoxifen in breastcancer patientsrdquo Cancer Letters vol 217 no 1 pp 61ndash72 2005

[205] P C Ng S S Murray S Levy and J C Venter ldquoAn agenda forpersonalized medicinerdquoNature vol 461 no 7265 pp 724ndash7262009

[206] Y Bromberg ldquoBuilding a genome analysis pipeline to predictdisease risk and prevent diseaserdquo Journal of Molecular Biologyvol 425 no 21 pp 3993ndash4005 2013

[207] J D Momper and J A Wagner ldquoTherapeutic drug monitoringas a component of personalized medicine applications in pedi-atric drug developmentrdquoClinical Pharmacology ampTherapeuticsvol 95 pp 138ndash140 2014

[208] S J Bielinski J E Olson J Pathak R M Weinshilboum andL Wang ldquoPreemptive genotyping for personalized medicinedesign of the right drug right dose right time-using genomicdata to individualize treatment protocolrdquoMayo Clinic Proceed-ings vol 89 pp 25ndash33 2014

[209] W Burke S Brown Trinidad and N A Press ldquoEssentialelements of personalized medicinerdquo Urologic Oncology vol 32no 2 pp 193ndash197 2014

[210] F R Vogenberg C I Barash and M Pursel ldquoPersonalizedmedicine part 2 ethical legal and regulatory issuesrdquoPharmacyandTherapeutics vol 35 pp 624ndash642 2010

[211] L S Welch K Ringen J Dement et al ldquoBeryllium diseaseamong construction trade workers at department of energynuclear sitesrdquo American Journal of Industrial Medicine vol 56no 10 pp 1125ndash1136 2013

[212] A Kricker B K Armstrong A J McMichael S Madronichand F de Gruijl ldquoSkin cancer and ultravioletrdquo Nature vol 368no 6472 p 594 1994

[213] E R Park J M Streck I F Gareen et al ldquoA qualitative studyof lung cancer risk perceptions and smoking beliefs amongnational lung screening trial participantsrdquo Nicotine amp TobaccoResearch vol 16 pp 166ndash173 2014

[214] B S McEwen and L Getz ldquoLifetime experiences the brain andpersonalized medicine an integrative perspectiverdquoMetabolismvol 62 supplement 1 pp S20ndashS26 2013

[215] K A Mussatto R G Hoffmann G M Hoffman J S Tweddelland L Bear ldquoRisk and prevalence of developmental delay inyoung children with congenital heart diseaserdquo Pediatrics vol133 pp e570ndashe577 2014

[216] A RMiller ldquoLifetime care for patients with autismrdquoCMAJ vol182 no 10 pp 1079ndash1080 2010

[217] J van der Leeuw P M Ridker Y van der Graaf and FL Visseren ldquoPersonalized cardiovascular disease preventionby applying individualized prediction of treatment effectsrdquoEuropean Heart Journal vol 35 no 13 pp 837ndash843 2014

[218] E Faulkner L Annemans L Garrison et al ldquoChallenges inthe development and reimbursement of personalizedmedicine-payer and manufacturer perspectives and implications forhealth economics and outcomes research a report of the ISPORpersonalized medicine special interest grouprdquo Value in Healthvol 15 no 8 pp 1162ndash1171 2012

[219] L Clarke X Zheng-Bradley R Smith et al ldquoThe 1000 genomesproject data management and community accessrdquo NatureMethods vol 9 no 5 pp 459ndash462 2012

[220] G R Abecasis D Altshuler A Auton L D Brooks and R MDurbin ldquoA map of human genome variation from population-scale sequencingrdquo Nature vol 467 pp 1061ndash1073 2010

[221] T A de Beer R A Laskowski S L Parks et al ldquoAminoacid changes in disease-associated variants differ radically fromvariants observed in the 1000 genomes project datasetrdquo PLOSComputational Biology vol 9 no 12 Article ID e1003382 2013

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of


Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology


Molecular Biology International

GenomicsInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


BioinformaticsAdvances in

Marine BiologyJournal of



Signal TransductionJournal of


BioMed Research International

Evolutionary BiologyInternational Journal of



Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Genetics Research International


Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational



Enzyme Research



Microbiology


I amshort

I amtall I am

sickI amhealthy

Figure 1 Illustration of human harmless variation (short and tallman) and human variation-causing disease (healthy and sick man)

that a given DNA defect is disease-causing is not enough foran effective personalized treatment and thus the paper pro-ceeds to review the approaches and techniques for predictingthe molecular mechanism of disease-causing mutations forthe needs of personalized diagnostics pharmacogenomicsand personalized medicine

2 Progress Made in Genome Sequencing andDatabase Development

Personalized diagnostics and medicine cannot be developedwithout access to the genomic data of the patient whichin turn requires inexpensive and fast methods for indi-vidual genome sequencing [14 15] The progress made indeveloping methods and techniques for detecting geneticvariations was recently outlined in several works [16ndash18]These techniques are rapidly evolving and various companiespromised or already achieved the goal of being able tosequence an entire genome for a price tag of $1000 withina day [19 20] However despite the success of whole-genomesequencing it is now understood that the analysis of genomicvariations with respect to disease susceptibility is a muchmore complicated process and requires significant efforts[21] If the sequencing is attempting to target a particulardiseasemdashin the simplest case a monogenic diseasemdashthen theanalysis of the variations within a particular gene is typicallya quite doable task However if the question is broaderand if one wants to investigate the whole genome of thecurrently healthy individual with the goal of predicting futuredisease-causing defects the problem becomes enormouslycomplicated One ends up with thousands or millions ofvariants spread over many different genes and noncodingregions of the genome Identifying which of these variantsmight be associatedwith disease predisposition is not a trivialtask To assist in solving this difficult challenge the 1000genomes project was foundedThis project is aimed at reveal-ing human variations within the entire genome by using thewhole-genome sequencing of the DNA of 1000 volunteersfrom different backgrounds [22 23] This is intended toprovide information on the most frequently observed DNA

variations and the data is now available on the internet [24]One can assume that genetic variations identified within the1000 genomes project are not necessarily disease-causingsince the volunteers are healthy individuals However itshould also be noted that some diseases have very late onsetsandmay not bemanifested at this stage in the individualrsquos life

The interpretation of the individualrsquos genomic data hasanother difficult component CNVs Although important andfrequently associated with diseases CNVs cannot be easilyused to reveal the molecular effect of a disease One canspeculate that a larger number of copies of a given genewill automatically result in a greater expression level of thisparticular protein and that this is the cause of the diseaseHowever this mechanism will not be discussed in thispaper because understanding the effect requires a detailedknowledge of the biological reactions associated with thetarget protein and how the change in the concentration levelof individualmacromolecules will affect the cellular function

The progress in this fast growing field prompted develop-ment of databases on various levels such as disease-orienteddatabases to databases storing all of the known human DNAvariations (Table 1)The creation of such databases serves twovery important purposes providing benchmarks to test insilico predictions and providing template cases or patternsfor the detection of disease-causing mutation(s) Perhapsthe most popular disease-oriented database is McKusickrsquosOnline Mendelian Inheritance in Man (OMIM) database[25 26] which is amanually curated database of human genesand genetic disorders including genetic phenotypes [26]Since its establishment in early 1960s many researchers havecontributed to various aspects of the OMIM database suchas developing extra features resulting in OMIM derivativesas PhenOMIM (for phenotypic comparison) [27] OMiR (toreveal associations between OMIM diseases and microR-NAs) [28] CSI-OMIM (assisting clinical synopsis search inOMIM) [29] CGMIM (for text-mining of cancer genes)[30] and many other applications On the other end of thespectrum is the dbSNP [31 32] database at National Centerfor Biotechnology Information (NCBI) As of December2013 it contains more than 140 million single nucleotidepolymorphisms (SNPs) and the rate of new submissions isconstantly increasing In terms of distinguishing betweendisease-causing and harmlessmutations typically one wouldcreate a pseudo-harmless database by taking out all of theentries from dbSNP that are listed in OMIM [33] Theremaining SNPs can be considered neutral or harmlessalthough exceptions to this rule will always be found Itshould be noted that many other databases exist as wellsome that focus on a particular disease [34 35] others thatfocus on nonsynonymous SNPs [36 37] or regulatory SNPs[38] or one that focuses on a particular family of genes[39 40]

Although the goal of this paper is not to provide com-prehensive review of all existing human variation databasesthe HapMap [41] project and database cannot be omittedThe goal of the HapMap project is to develop a map ofthe common patterns of human DNA sequence variation Itis intended to be used to provide information about genesand patterns causing natural differences among individuals


Table1To

olsa

ndmetho

dsforp

redictingeffectsof

mutations

Purpose

Nam

eURL

Descriptio

nandadvantagesdisa

dvantages

Databases

OMIM

httpwwwncbinlm

nihgovomim

Manually

curatedfre

elyavailable

dbSN

Phttpwwwncbinlm

nihgovSNP


dessho

rtvaria

tions

innu

cleotides

equences

from

awider

ange

oforganism

s1000

Genom

esA

DeepCa

talogof

Hum

anGenetic

Varia

tion

httpwww1000geno

mesorg


toolsfor

vario

ussearches

aswith

inpo

pulationsallelefre

quency

andlin

kage

disequ

ilibrium

structure

HGMD

httpwwwhgmdorg

Requ

ireslicensebut

isthem

ostcom

prehensiv

edatabase

ofhu

man

mutations

andprovides

visualizationtoolsa

ndsearch

engines

Networking

Cytoscape

httpwwwcytoscapeo

rg

Anop

ensource

softw

arep

latform

forv

isualizing

molecular

interactionnetworks



etworks

with

anno

tatio

nsgene

expressio

nprofi


NetworkPo

rtal

httpnetworkssy

stemsbiologynet

Provides


selected

gene

regu

latory

networks

toaidresearchersin


thesisdevelopm

ent

Network2Ca

nvas


2CU1fP4

qJWAkI

Allo

wsfor

generatio

nof

drug-drugsim

ilarityand

functio

nalassociatio

nof

gene

canvases

3Dstructurem

odeling

Swiss

Mod

elhttpsw

issmod

elexpasyorg

Automated

server

allowing3D

structure

ofap

rotein

tobe

predictedinpu

tting

thes

equenceo

fthe

target

I-TASSER

httpzhanglabcc

mbmedumicheduI-T

ASSER

Fu

llyautomated

3Dstructurep

redictorespecially

useful

ford


ence

similarityto

know

nstr

uctures)

Mod

eller

httpsalilaborgm

odeller

Stand-alon

e3Dstr

ucture

predictorCan

beinsta

lled

locally

andused


ns

Proteinsta

bility

FoldX

httpfoldxcrges

Fastassessmento

fthe

changeso

fthe

unfoldingfre

eenergy

caused

bymutations

usingem

piric

alform

ula

I-Mutant

httpgp

cr2biocom

pun

iboit7E

emidioI-

MutantI-M

utanth

tmVe


nof

thec

hanges

ofthe

foldingfre

eenergycaused

bymutationsTh

einp

utcan

bejustthes

equenceo

fthe

protein

ERIS

httpdo

khlabun

cedu

toolseris

UsesM

edusaforce

field

tomakep

redictions

ofthe

change

ofthefolding

freee

nergycaused

bymutations

Proteininteractions

BeAtMuSiC

httpbabyloneulbac

bebeatmusic

Evaluatesc

hangeinbind

ingaffi

nitybetweenproteins

caused

bysin

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Com

putesthe

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asingler

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httprobetta


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Provides

estim

ationforthe

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tinued

Purpose

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eURL

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nandadvantagesdisa

dvantages


pHdepend

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WoL

FPS

ORT

httpwolfpsortorg

Predictsthes

ubcellu

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proteins

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ontheira

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encesTh

epredictions

are

basedon

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otifs

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ino

acid

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httpaiasbiolu

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Stand-alon

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arkov

chainsand

HMMsfor

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redictionof

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UniLo

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Macromolecular

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n

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etwork-basedmetho

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ation(egsecond

ary


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redictions

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nalityof

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MutationA

ssessor


rg

Thes

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redictsthe

functio

nalimpactof

aminoacid

substitutions

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httpsift

jcviorg

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redictionisbasedon

thed

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ence

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OMIM dbSNP





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Acknowledgment


References
























































































































































































2H2zinc
























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


Table1To

olsa

ndmetho

dsforp

redictingeffectsof

mutations

Purpose

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eURL

Descriptio

nandadvantagesdisa

dvantages

Databases

OMIM

httpwwwncbinlm

nihgovomim

Manually

curatedfre

elyavailable

dbSN

Phttpwwwncbinlm

nihgovSNP


dessho

rtvaria

tions

innu

cleotides

equences

from

awider

ange

oforganism

s1000

Genom

esA

DeepCa

talogof

Hum

anGenetic

Varia

tion

httpwww1000geno

mesorg


toolsfor

vario

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inpo

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quency

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disequ

ilibrium

structure

HGMD

httpwwwhgmdorg

Requ

ireslicensebut

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prehensiv

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andprovides

visualizationtoolsa

ndsearch

engines

Networking

Cytoscape

httpwwwcytoscapeo

rg

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ensource

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arep

latform

forv

isualizing

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interactionnetworks



etworks

with

anno

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NetworkPo

rtal

httpnetworkssy

stemsbiologynet

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selected

gene

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nvas


2CU1fP4

qJWAkI

Allo

wsfor

generatio

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functio

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3Dstructurem

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Swiss

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Automated

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rotein

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I-TASSER

httpzhanglabcc

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Fu

llyautomated

3Dstructurep

redictorespecially

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know

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uctures)

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httpsalilaborgm

odeller

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e3Dstr

ucture

predictorCan

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lled

locally

andused


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Proteinsta

bility

FoldX

httpfoldxcrges

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fthe

changeso

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eenergy

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bymutations

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piric

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ula

I-Mutant

httpgp

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ERIS

httpdo

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field

tomakep

redictions

ofthe

change

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nergycaused

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BeAtMuSiC

httpbabyloneulbac

bebeatmusic

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hangeinbind

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nitybetweenproteins

caused

bysin

gle-sitem

utations

intheirsequence

ICM



inghtml

Com

putesthe

change

inbind

ingfre

eenergyof

aproteincomplex

upon

mutationof

asingler

esidue

Requ

ireslicense

Robetta

httprobetta


mitjsp

Provides

estim

ationforthe

bind

ingfre

eenergychanges

caused

bymutations

toAlanine


Table1Con

tinued

Purpose

Nam

eURL

Descriptio

nandadvantagesdisa

dvantages


pHdepend

ence

WoL

FPS

ORT

httpwolfpsortorg

Predictsthes

ubcellu

larlocalizationof

proteins

based

ontheira

minoacid

sequ

encesTh

epredictions

are

basedon

both

know

nsortingsig

nalm

otifs

andam

ino

acid

content

PredSL

httpaiasbiolu

oagrPredSL

Stand-alon

ecod


arkov

chainsand

HMMsfor

thep

redictionof

thes

ubcellu

lar

localizationof

proteins

UniLo

cTh

isisaw

ebserver

usingPS

I-Blasttoinferh

omolog

ybetweenqu

erys

equencea

ndalreadya

nnotated

proteins

Macromolecular

functio

n

SNAP

httpsw


SNAPisan

euraln

etwork-basedmetho

dthatuses

insilico

deriv

edproteininform

ation(egsecond

ary


nsolventaccessib

ilityetc)in

ordertomakep

redictions

regardingfunctio

nalityof

mutated

proteins

MutationA

ssessor


rg

Thes

erverp

redictsthe

functio

nalimpactof

aminoacid

substitutions

inproteins

SIFT

httpsift

jcviorg

Thep

redictionisbasedon

thed

egreeo

fcon

servationof

aminoacid

resid

uesinsequ

ence

alignm

entsderiv

edfro

mclo

selyrelated

sequ

ences
































differences

$1000 genome


DNA defects

Revealingmolecular

mechanism



modeling



In silico methods


In silicoIn silico

and so forth


OMIM dbSNP





5 Pharmacogenomics


















Acknowledgment


References
























































































































































































2H2zinc
























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


Table1Con

tinued

Purpose

Nam

eURL

Descriptio

nandadvantagesdisa

dvantages


pHdepend

ence

WoL

FPS

ORT

httpwolfpsortorg

Predictsthes

ubcellu

larlocalizationof

proteins

based

ontheira

minoacid

sequ

encesTh

epredictions

are

basedon

both

know

nsortingsig

nalm

otifs

andam

ino

acid

content

PredSL

httpaiasbiolu

oagrPredSL

Stand-alon

ecod


arkov

chainsand

HMMsfor

thep

redictionof

thes

ubcellu

lar

localizationof

proteins

UniLo

cTh

isisaw

ebserver

usingPS

I-Blasttoinferh

omolog

ybetweenqu

erys

equencea

ndalreadya

nnotated

proteins

Macromolecular

functio

n

SNAP

httpsw


SNAPisan

euraln

etwork-basedmetho

dthatuses

insilico

deriv

edproteininform

ation(egsecond

ary


nsolventaccessib

ilityetc)in

ordertomakep

redictions

regardingfunctio

nalityof

mutated

proteins

MutationA

ssessor


rg

Thes

erverp

redictsthe

functio

nalimpactof

aminoacid

substitutions

inproteins

SIFT

httpsift

jcviorg

Thep

redictionisbasedon

thed

egreeo

fcon

servationof

aminoacid

resid

uesinsequ

ence

alignm

entsderiv

edfro

mclo

selyrelated

sequ

ences
































differences

$1000 genome


DNA defects

Revealingmolecular

mechanism



modeling



In silico methods


In silicoIn silico

and so forth


OMIM dbSNP





5 Pharmacogenomics


















Acknowledgment


References
























































































































































































2H2zinc
























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology
































differences

$1000 genome


DNA defects

Revealingmolecular

mechanism



modeling



In silico methods


In silicoIn silico

and so forth


OMIM dbSNP





5 Pharmacogenomics


















Acknowledgment


References
























































































































































































2H2zinc
























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology























differences

$1000 genome


DNA defects

Revealingmolecular

mechanism



modeling



In silico methods


In silicoIn silico

and so forth


OMIM dbSNP





5 Pharmacogenomics


















Acknowledgment


References
























































































































































































2H2zinc
























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology















differences

$1000 genome


DNA defects

Revealingmolecular

mechanism



modeling



In silico methods


In silicoIn silico

and so forth


OMIM dbSNP





5 Pharmacogenomics


















Acknowledgment


References
























































































































































































2H2zinc
























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology




differences

$1000 genome


DNA defects

Revealingmolecular

mechanism



modeling



In silico methods


In silicoIn silico

and so forth


OMIM dbSNP





5 Pharmacogenomics


















Acknowledgment


References
























































































































































































2H2zinc
























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

















Acknowledgment


References
























































































































































































2H2zinc
























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology






Acknowledgment


References
























































































































































































2H2zinc
























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology




























































































































































2H2zinc
























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology
























































































































2H2zinc
























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology





















































































2H2zinc
























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology



















































2H2zinc
























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


















2H2zinc
























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology







































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology








Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

Documents

Advances in Human Biology: Combining Genetics and Molecular