Lecture 5Protein Modeling

June 4, 2008

Protein Structure Prediction

3D Protein Structure

ALA

C

LEU

C

PRO

C

VAL

C

ARG

C

? ? ?

backbone

sidechain

The Protein Folding Problem

we know that the function of a protein is determined in large part by its 3D shape (fold, conformation)

can we predict the 3D shape of a protein given only its amino-acid sequence?

Motivation Want to identify the function of genes we find,

and what different mutations/alleles do One gene = one protein (sort of)

Function of protein = function of gene

Function can be determined in many ways Gene expression, knockouts, etc But these take time, and are prone to mistakes

Goal: If we can structure every protein, learning their functions isnt too far away

Thornton et al 2000 (Nature)

ai.stanford.edu/~serafim/CS262_2006/Slides

Protein Architecture proteins are polymers consisting of amino acids linked by

peptide bonds each amino acid consists of

a central carbon atom an amino group a carboxyl group a side chain

differences in side chains distinguish different amino acids

2NHCOOH

3D Protein Structure

backbonebackbonesidechainbackbonesidechainC-alpha

Peptide Bondsaminogroup

carboxylgroup

sidechain

carbon (common reference point for coordinates of a structure)

Amino Acid Side Chains side chains vary in: shape, size, charge, polarity

Levels of Description protein structure is often described at four different scales

primary structure secondary structure tertiary structure quaternary structure

Levels of Description

Secondary Structure secondary structure refers to certain common repeating

structures it is a local description of structure two common secondary structures

helices strands/sheets

a third category, called coil or loop, refers to everything else

Helices

carbon

hydrogenbond

individualamino acid

Sheets

Ribbon Diagram Showing Secondary Structures

Levels of Description

What Determines Conformation? in general, the amino-acid sequence of a protein determines

the 3D shape of a protein [Anfinsen et al., 1950s] but some exceptions

all proteins can be denatured some proteins are inherently disordered (i.e. lack a regular

structure) some proteins get folding help from chaperones there are various mechanisms through which the

conformation of a protein can be changed in vivo post-translational modifications such as

phosphorylation prions etc.

What Determines Conformation?

what physical properties of the protein determine its fold? rigidity of the protein backbone interactions among amino acids, including

electrostatic interactions van der Waals forces volume constraints hydrogen, disulfide bonds

interactions of amino acids with water

Determining Protein Structures protein structures can be determined

experimentally (in many cases) by x-ray crystallography nuclear magnetic resonance (NMR)

Myoglobin

From www.inst.bnl.gov/GasDetectorLab/x-rays/SRI94.htm

Myoglobin

S.E.V. Phillips. "Structure and refinement of oxymyoglobin at 1.6 resolution.", J. Mol. Biol. 1980, 142, 531.

X-ray Crystallography

proteincrystal collection

plate

x-raybeam

diffractionpattern

electrondensity map

(3D picture)

Electron Density Map Interpretation

GIVEN: 3D Electron Density Map

Electron Density Map Interpretation

FIND: All-atom Protein Model

NMR

Nuclear Magnetic Resonance Spectroscopy Cannot handle large proteins like X-ray Exploits the chemical environment to return

distances between atoms Can use knowledge of restraints to identify

positions of atoms that produce peaks

Protein structure determination in solution by NMR spectroscopy Wuthrich K. J Biol Chem. 1990 December 25;265(36):22059-62

Experimental Methods

Very expensive and time-consuming Computational methods can help with time

Many proteins still cannot be done in this manner

More motivation

there is a large sequence-structure gap300K protein sequences in SwissProt

database50K protein structures in PDB database

key question: can we predict structures by computational means instead?

Approaches to Protein Structure Prediction

prediction in 1D secondary structure solvent accessibility (which residues are exposed to

water, which are buried) transmembrane helices (which residues span

membranes) prediction in 2D

inter-residue/strand contacts prediction in 3D

homology modeling fold recognition (e.g. via threading) ab initio prediction (e.g. via molecular dynamics)

Prediction in 1D, 2D and 3D

Figure from B. Rost, Protein Structure in 1D, 2D, and 3D, The Encyclopaedia of Computational Chemistry, 1998

predicted secondary structure and solvent accessibility

known secondary structure (E = beta strand) and solvent accessibility

2D Prediction Approaches use secondary structure predictions

to predict short-range contacts (e.g. hydrogen bonds in helices)

use secondary structure predictions to predict strand alignments

use correlated mutations to predict contacts

Prediction in 3D homology modeling

given: a query sequence Q, a database of protein structuresdo:

find protein P has high sequence similarity to Q return Ps structure as an approximation to Qs structure

fold recognition (threading)given: a query sequence Q, a database of known foldsdo:

find fold F such that Q can be aligned with F in a highly compatible manner

return F as an approximation to Qs structure

Prediction in 3D fragment assembly(Rosetta)

given: a query sequence Q, a database of structure fragmentsdo:

find a set of fragments that Q can be aligned with in a highly compatible manner

return the combined fragments as an approximation

molecular dynamicsgiven: a query sequence Qdo:

use laws of physics to to simulate folding of Q

Homology Modeling

0% 100%30%pairwise sequence identity

homologsprobablyunrelated

remotehomologs

20%

most pairs of proteins with similar structure are remote homologs (< 25% sequence identity)

homology modeling usually doesnt work for remote homologs ; most pairs of proteins with < 25% sequence identity are unrelated

Homology-based Prediction

Raw model

Loop modeling

Side chain placement

Refinement

The SCOP DatabaseStructural Classification Of Proteins

FAMILY: proteins that are >30% similar, or >15% similar and have similar known structure/function

SUPERFAMILY: proteins whose families have some sequence and function/structure similarity suggesting a common evolutionary origin

COMMON FOLD: superfamilies that have same secondary structures in same arrangement, probably resulting by physics and chemistry

ai.stanford.edu/~serafim/CS262_2006/Slides

Examples of Fold Classesai.stanford.edu/~serafim/CS262_2006/Slides

Threading

Ab initio Prediction ROSETTA

1. PSI-BLAST homology search

Discard sequences with >25% homology

2. PHD

For each 3-long and each 9-long sequence fragment, get 25 structure fragments that match well

3 M k Ch i M C l

?? ?

ai.stanford.edu/~serafim/CS262_2006/Slides

ai.stanford.edu/~serafim/CS262_2006/Slides

Ab initio Prediction CASP results

ai.stanford.edu/~serafim/CS262_2006/Slides

Summary of current state of the art

ai.stanford.edu/~serafim/CS262_2006/Slides

Open Ended

Ab Initio is the goal, far from it Sidechain prediction Contact Map prediction Search space reduction Parallelization (GPUs) Surface Accessibility

Other areas

Protein-Protein Interaction Drug Design Protein Engineering Ligand Docking/Inhibition Function Prediction

Lecture 5Protein ModelingJune 4, 20083D Protein StructureThe Protein Folding ProblemMotivationSlide Number 5Slide Number 6Protein Architecture3D Protein StructurePeptide BondsAmino Acid Side ChainsLevels of DescriptionLevels of DescriptionSecondary Structurea Helicesb SheetsRibbon Diagram Showing Secondary StructuresLevels of DescriptionWhat Determines Conformation?What Determines Conformation?Determining Protein StructuresSlide Number 21Slide Number 22X-ray CrystallographyElectron Density Map InterpretationElectron Density Map InterpretationNMRSlide Number 27Experimental MethodsMore motivationApproaches to Protein Structure PredictionPrediction in 1D, 2D and 3D2D Prediction ApproachesPrediction in 3DPrediction in 3DSlide Number 35Homology ModelingHomology-based PredictionThe SCOP DatabaseExamples of Fold ClassesThreadingAb initio Prediction ROSETTA Slide Number 42Ab initio Prediction CASP resultsSummary of current state of the artOpen EndedOther areas