Structural Bioinfo

8/6/2019 Structural Bioinfo

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STRUCTURAL BIOINFORMATICS( Toward A High-Resolution Understanding of Biology )


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Objectives of Lecture

Structural Bioinformatics What is 3D Structure Prediction Significance of 3D Structure Prediction Central Dogma Fundamentals of Protein StructureProtein Data bank (PDB) To be aware of a number of Structure Prediction methods: Homology Modeling Fold Recognition/ThreadingAb initio Protein Folding ApproachesApplications of Structural BioinformaticsAnalog-Based design Structure-Based design


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Structural BioinformaticsStructural Bioinformatics

Structural Bioinformatics is a subset of Bioinformatics

concerned with the use of biological structures-Protein, DNA, RNA, Ligands and complexes thereofto further our understanding of biological systems.


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What is protein structure prediction?

A prediction of the (relative) spatial position of each

atom in the tertiary structure generated from

knowledge only of the primary structure

(sequence).


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Significance of Protein Structure

Prediction In evolutionary related proteins structure is much better

preserved than sequence.

3D protein structure offers much more information then justthe amino acid sequence. By comparison with known structures we can infer probable

biological functions of new proteins By mapping the residue conservations on to the structure we

can infer active sites and possibly the molecular function


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We can also identify regions involved in protein-proteininteractions.

We can reconstruct (at least partially) the structure of protein complexes identified by other experimental methods.

We can build homology models.


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The central dogma

DNA ------- RNA ---------- Protein{A,C,T,G} {A,C,G,U} {A,D,..Y}Guanine, Cytosine TU

Thymine, Adenine


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Fundamentals of Protein Structure


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Terminology

Primary Structure-- The sequence of amino acidresidues in the proteins.

--MESSTHEDRKVLDL


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Amino acids and the peptidebond

C first side chain carbon (except for glycine).

C atoms


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Secondary Structure

A first level description of 3D structure. The peptide backbone of DNA has areas of positive charge

and negative charge These areas can interact with one another to form hydrogenbonds

The result of these hydrogen bonds are two types ofstructures:

alpha helices beta pleated sheets


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Secondary Structure I: TheE-

Helix


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Several beta-strands assembleinto abeta-sheet (a tertiarystructural element)

Secondary Structure II: The -

Strand

(About 3.4)


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Antiparallel -Sheets


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Parallel -Sheets


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Mixed -Sheets


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Tertiary Structure: The Global Three

Dimensional Structure

Secondary structure elements pack together to form astructural core

Tertiary structure results from the folding of alpha helicesand beta pleated sheets

Factors influencing tertiary structure include: Hydrophobic/hydrophilic interactions Hydrogen bonding Disulfide linkages Folding by chaperone proteins


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Tertiary Structure: Different Representations


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(Richardson-style)Ribbon Diagrams

are tracesoftheprotein

backbone

emphasizing the 3-D arrangementofa-helices and b-strands.

This arrangement is called

the proteinfold or the proteinfolding topology.


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This is much rather likewhat other molecules see when theyencounter a protein!

This is a representation ofthe molecularsurface (Van der Waals

surface) of a hemagglutinin domain withbound sialic acid.

Tertiary Structure: Different Representations


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Supersecondary Structures: Between

Secondaryand Tertiary Structure

For example:- alpha- -above

- -hairpin - left


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Quaternary Structure

Association of Multiple Polypeptide Chains. Quaternary structure results from the interaction of

independent polypeptide chains

Factors influencing quaternary structure include: Hydrophobic/hydrophilic interactions Hydrogen bonding The shape and charge distribution on associating

polypeptides


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Side Chain Properties

Hydrophobic amino acids stay inside of a protein.

Hydrophilic ones tend to stay in the exterior of aprotein.

Oppositely charged amino acids can form salt

bridge.

Polar amino acids can participate hydrogen bonding.


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Domain, Motif, Fold

Domain: a discrete portion of a protein assumed to foldindependently of the rest of the protein and possessing its

own function. Most proteins have multiple domains.The overall shape of a domain is called a fold. There are onlya few thousand possible folds.Super-secondary structure, motif

Frequently occurring structure patterns among multipleproteins, which are not necessarily have similar folds.


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Determination of protein

structures

X-ray Crystallography

NMR (Nuclear Magnetic Resonance)

EM (Electron microscopy)


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A repository for 3-D biological macromolecular structure. Established in 1971 at Brookhaven National Lab (7structures) It includes proteins, nucleic acids and viruses. Obtained by X-Ray crystallography (80%) or NMRspectroscopy (16%). Submitted by biologists and biochemists from around theworld.

Other sites:MMDB (EBI): msd.ebi.ac.ukNCBI: www.ncbi.nlm.nih.gov/Structure/

Protein Data bank (PDB)


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Growth ofProtein Data Bank (PDB): The Motivation

The number of unique folds in nature is fairly small(possibly a few thousands)

90% of new structures submitted to PDB in the past three

years have similar structural folds in PDB

New fold

Old fold


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Protein Structure Prediction

Methods

Comparative Modeling Method:

Homology Modeling Method

Threading Method

Ab initio folding Method


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Experimental

Sequence

Database

Searching

Abinitiomethod

Structure

Homolog?

NO

YES

Homology

ModelingProtein Threading

Protein structure prediction flowchart

HomologyModeling


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Homology Modeling

Predicts the three-dimensional structure of a given proteinsequence (TARGET) based on an alignment to one or moreknown protein structures (TEMPLATES)

If similarity between the TARGET sequence and theTEMPLATE sequence is detected, structural similarity can beassumed.

In general, 30% sequence identity is required for generating useful models.


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7 Steps In Homology Modeling


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Step 1: ID HomologuesinPDB

PRTEINSEQENCEPRTEINSEQUENC

EPRTEINSEQNCEQWERYTRASDFHG

TREWQIYPASDFGHKLMCNASQERWWPRETWQLKHGFDSADAMNCVCNQWER

GFDHSDASFWERQWK

Query Sequence PDB


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Step 1: ID HomologuesinPDB

PRTE

INSEQE

NCE

PRTE

INSEQ

UE

NCEPRTEINSEQNCEQWERYTRASDFHG

TREWQIYPASDFGHKLMCNASQERWW

PRETWQLKHGFDSADAMNCVCNQWER

GFDHSDASFWERQWK




PRETWQLKHGFDSADAMNCVCNQWERGFDHSDASFWERQWK



TREWQIYPASDFG


EPRTEINSEQQWEWEWQWEWEQWEW

EWQRYEYEWQWNCEQWERYTRASDF

HG

TREWQIYPASDWERWEREWRFDSFG



TREWQIYPASDFGPRTEINSEQENCEPRTEINSEQ

UE

NCE

PRTE

INSEQ

NCEQWE

RYTRASDFHGTREWQIYPASDFG

TREWQIYPASDFGPRTEINSEQENCEPRTEINSEQUENCEPRTEINSEQNCEQWERYTRASDFHGTREWQ











TREWQIYPASDFG



TREWQIYPASDFGPRTEINSEQENC

Hit#1

Hit#2

Query sequencePDB


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Step 2: Align Sequences

G E N E T I C S

G 10 0 0 0 0 0 0 0

E 0 10 0 10 0 0 0 0

N 0 0 10 0 0 0 0 0

E 0 0 0 10 0 0 0 0

S 0 0 0 0 0 0 0 10

I 0 0 0 0 0 10 0 0

S 0 0 0 0 0 0 0 10

G E N E T I C S

G 10 0 0 0 0 0 0 0

E 0 10 0 10 0 0 0 0

N 0 0 10 0 0 0 0 0

E 0 0 0 10 0 0 0 0

S 0 0 0 0 0 0 0 10

I 0 0 0 0 0 10 0 0

S 0 0 0 0 0 0 0 10

G E N E T I C SGE

NESI

S

60 40 30 20 20 0 10 040

302020100

50

302020100

30

402020100

30

203020100

20202020100

0

0100200

10101010100

00010010

DynamicProgramming


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Alignment

Key step in Homology Modeling.

Global (Needleman-Wunsch) alignment is absolutely

required.

Small error in alignment can lead to big error instructural model.

Multiple alignments are usually betterthan pairwise

alignments.

Alignment is prepared by superimposing all template

structures.


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Two zonesofsequencealignment


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Step 3: Find SCRs

Query

Hit #1

Hit #2

ACDEFGHIKLMNPQRST--FGHQWERT-----TYREWYEG

ASDEYAHLRILDPQRSTVAYAYE--KSFAPPGSFKWEYEA

MCDEYAHIRLMNPERSTVAGGHQWERT----GSFKEWYAA

SCR#1 SCR#2


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Structurally Conserved regions (SCRs)

Corresponds to the most stable structures orregions (usually interior) of protein.

Corresponds to sequence regions with lowestlevel of gapping, highest level of sequenceconservation.

Usually corresponds to secondary structures.


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Step 4: Find SVRs

ACDEFGHIKLMNPQRST--FGHQWERT-----TYREWYEG

ASDEYAHLRILDPQRSTVAYAYE--KSFAPPGSFKWEYEA

MCDEYAHIRLMNPERSTVAGGHQWERT----GSFKEWYAA

HHHHHHHHHHHHHCCCCCCCCCCCCCCCCCCBBBBBBBBB

Query

Hit #1Hit #2

SVR Loop


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Step 5: Side Chain Modeling

Rotamer placement and positioning is done via a

superposition algorithm using rotamers.


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Step 6: Model Optimization

Efficient way of polishing and shining your protein

model

Removes atomic overlaps and unnatural strains in the

structure

Stabilizes or reinforces strong hydrogen bonds, breaksweak ones

Brings protein to lowest energy in about 1-2 minutes

CPU time

Several freeware options to choose XPLOR (Axel Brunger,Yale)

GROMACS (Gronnigen, The Netherlands)

AMBER (Peter Kollman, UCSF)

CHARMM (Martin Karplus, Harvard)

TINKER (Jay Ponder, Wash U))


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Step 7: Model Validation

PROCHECK -http://www.biochem.ucl.ac.uk/~roman/procheck/procheck.html

PROSA II -http://lore.came.sbg.ac.at/People/mo/Prosa/prosa.html

VADAR -http://www.pence.ualberta.ca/ftp/vadar/

DSSP -http://www.embl-heidelberg.de/dssp/


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Homology Modeling On Web

http://www.expasy.ch/swissmod/SW ISS-MODEL.html


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http://www.cmbi.kun.nl:1100/W IWWWI/


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http://cl.sdsc.edu/hm.html


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Raw Sequence

Predicted structure

Use templates to buildthe structure of the homologous

sequence


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MQQPMNYPCP QIFWVDSSAT SSWAPPGSVF PCPSCGPRGP DQRRPPPPPPPVSPLPPPSQPLPLPPLTPL KKKDHNTNLW LPVVFFMVLV ALVGMGLGMY QLFHLQKELA

ELREFTNQSLKVSSFEKQIA NPSTPSEKKE PRSVAHLTGN PHSRSIPLEW EDTYGTALISGVKYKKGGLVINETGLYFVY SKVYFRGQSC NNQPLNHKVY MRNSKYPEDL VLMEEKRLNYCTTGQIWAHSSYLGAVFNLT SADHLYVNIS QLSLINFEES KTFFGLYKL

Use of SwissPDB Viewer to build the structure offollowing sequence


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1TNRADOGB


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After magic fit


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Activate the raw sequence


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The Preliminary Result


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Protein Threading

Makes structure prediction through identification of good sequence-structure fit.

Protein threading can predict only the backbone structure of a protein (side-chainshave to be predicted using other methods)

Predicted Actual


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Ab Initio 3D structure prediction

Aims to predict tertiary structure from basic physico-chemicalproperties.

It is used when Homology Modeling & Threading have failed(no homologies are evident ).

Does not rely on any detection of similarity to sequence ofknown structure.

As yet very unreliable for practical predictions.


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Analog Based Design

The analog based approach mainly uses

Pharmacophoric maps and Quantitative structure

Activity Relationship (QSAR) to identify or modify alead in the absence of a known 3D structure of the

receptor.


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Structure-Based Design

Structure-based approach starts with thestructure of the receptor site, such as the

active site in protein.

Docking comes under this category of design.


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Quantitative Structure Activity relationship(QSAR)

QSAR is an applied series of mathematical models built to predict biologicaland physicochemical behavior of molecules based on their chemicalstructures.

It alleviates the need to determine molecular activity of hundreds of similarcompounds that would take large amounts of resources to determineindividually.

The underlying premise of QSAR is that Biological Activity is correlated to its

physiochemicalparameters.

BA = f (biological + Chemical + Physical)

Biological activity can be any measured such as IC50, orED50.


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QSAR Table

Structure Bioproperty Structural properties

Comp.1 Bio1 P1 P2 P3 P4

Comp.2 Bio2 " " " "

Comp.3 Bio3 " " " "

Comp.4 Bio3 " " " "

BA = k1P1 + k2P2 + k3P3 + ...


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EXTERNAL VALIDATION OF QSARMODELS

Entire dataset

Test setTraining set

Model development (q2) Prediction of thetest set (R2)


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Thank You

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Structural Bioinfo