Protein Tertiary Structure Prediction

Structural Bioinformatics

Primary: amino acid linear sequence.

Secondary: -helices, β-sheets and loops.

Tertiary: the 3D shape of the fully folded polypeptide chain

The Different levels of Protein Structure

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PDB: Protein Data Bank

• DataBase of molecular structures :

Protein, Nucleic Acids (DNA and RNA),

• Structures solved by

X-ray crystallography

NMR

Electron microscopy

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RCSB PDB – Protein Data Bank

http://www.rcsb.org/pdb/

How can we view the protein structure ?

• Download the coordinates of the structure from the PDB http://www.rcsb.org/pdb/

• Launch a 3D viewer program For example we will use the program Pymol The program can be downloaded freely from the Pymol homepage http://pymol.sourceforge.net/

• Upload the coordinates to the viewer

Pymol example• Launch Pymol• Open file “1aqb” (PDB coordinate file)• Display sequence• Hide everything• Show main chain / hide main chain• Show cartoon • Color by ss• Color red• Color green, resi 1:40

Help http://pymol.sourceforge.net/newman/user/toc.html

Predicting 3D Structure

– Comparative modeling (homology)

Based on structural homology

– Fold recognition (threading)

Outstanding difficult problem

Based on sequence homology

Comparative ModelingSimilar sequences suggests similar structure

Sequence and Structure alignments of two Retinol Binding Protein

Structure Alignments

The outputs of a structural alignment are a superposition of the atomic coordinates and a minimal Root Mean Square Distance (RMSD) between the structures. The RMSD of two aligned structures indicates their divergence from one another.

Low values of RMSD mean similar structures

There are many different algorithms for structural Alignment.

Dali (Distance mAtrix aLIgnment)

DALI offers pairwise alignments of protein structures. The algorithm uses the three-dimensional coordinates of each protein to calculate distance matrices comparing residues.

See Holm L and Sander C (1993) J. Mol. Biol. 233:123-138.

SALIGN http://salilab.org/DBALI/?page=tools

Comparative Modeling

Builds a protein structure model based on its alignment to one or more related protein structures in the database

Similar sequence suggests similar structure

Comparative Modeling• Accuracy of the comparative model is

related to the sequence identity on which it is based

>50% sequence identity = high accuracy

30%-50% sequence identity= 90% modeled

<30% sequence identity =low accuracy (many errors)

Homology Threshold for Different Alignment Lengths

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Alignment length (L)

Homology Threshold (t)

A sequence alignment between two proteins is considered to imply structural homology if the sequence identity is equal to or above the homology threshold t in a sequence region of a given length L.

The threshold values t(L) are derived from PDB

Comparative Modeling

• Similarity particularly high in core– Alpha helices and beta sheets preserved– Even near-identical sequences vary in loops

Comparative Modeling Methods

MODELLER (Sali –Rockefeller/UCSF)

SCWRL (Dunbrack- UCSF )

SWISS-MODEL http://swissmodel.expasy.org//SWISS-MODEL.html

Comparative ModelingModeling of a sequence based on known structuresConsist of four major steps :1. Finding a known structure(s) related to the sequence

to be modeled (template), using sequence comparison methods such as PSI-BLAST

2. Aligning sequence with the templates

3. Building a model

4. Assessing the model

Fold Recognition

Hemoglobin TIM

Protein Folds: sequential and spatial arrangement of secondary structures

Similar folds usually mean similar function

Homeodomain Transcriptionfactors

The same fold can have multiple functions

Rossmann

TIM barrel

12 functions

31 functions

Fold Recognition

• Methods of protein fold recognition attempt to detect similarities between protein 3D structure that have no significant sequence similarity.

• Search for folds that are compatible with a particular sequence.

• "the turn the protein folding problem on it's head” rather than predicting how a sequence will fold, they predict how well a fold will fit a sequence

Basic steps in Fold Recognition :

Compare sequence against a Library of all known Protein Folds (finite number)

Query sequenceQuery sequence

MTYGFRIPLNCERWGHKLSTVILKRP...

Goal: find to what folding template the sequence fits best

There are different ways to evaluate sequence-structure fit

MAHFPGFGQSLLFGYPVYVFGD...

Potential fold

...

1) ... 56) ... n)

...

-10 ... -123 ... 20.5

There are different ways to evaluate sequence-structure fit

Programs for fold recognition

• TOPITS (Rost 1995)

• GenTHREADER (Jones 1999)

• SAMT02 (UCSC HMM)

• 3D-PSSM http://www.sbg.bio.ic.ac.uk/~3dpssm/

Ab Initio Modeling

• Compute molecular structure from laws of physics and chemistry alone Theoretically Ideal solution

Practically nearly impossible

WHY ?– Exceptionally complex calculations– Biophysics understanding incomplete

Ab Initio Methods

• Rosetta (Bakers lab, Seattle)

• Undertaker (Karplus, UCSC)

CASP - Critical Assessment of Structure Prediction

• Competition among different groups for resolving the 3D structure of proteins that are about to be solved experimentally.

• Current state -– ab-initio - the worst, but greatly improved in the last

years. – Modeling - performs very well when homologous

sequences with known structures exist.– Fold recognition - performs well.

What can you do?FOLDIT

Solve Puzzles for Science

A computer game to fold proteins

http://fold.it/portal/puzzles

What’s Next

Predicting function from structure

Structural Genomics : a large scale structure determination project designed to cover all

representative protein structures

Zarembinski, et al., Proc.Nat.Acad.Sci.USA, 99:15189 (1998)

ATP binding domain of protein MJ0577

As a result of the Structure Genomic initiative many structures of proteins with unknown function will be solved

Wanted !Automated methods to predict function from the protein structures resulting from the structural genomic project.

Approaches for predicting function from structure

ConSurf - Mapping the evolution conservation on the protein structure http://consurf.tau.ac.il/

Approaches for predicting function from structure

PFPlus – Identifying positive electrostatic patches on the protein structure http://pfp.technion.ac.il/

Approaches for predicting function from structure

SHARP2 – Identifying positive electrostatic patches on the protein structure http://www.bioinformatics.sussex.ac.uk/SHARP2

Machine learning approach for predicting function from structure

Find the common properties of a protein family (or any group of proteins of interest)

which are unique to the group and different from all the other proteins.

Generate a model for the group and predict new members of the family which have similar properties.

Knowledge Based Approach

• Generate a dataset of proteins with a common function (DNA binding protein)

• Generate a control dataset • Calculate the different properties which are characteristic

of the protein family you are interested for all the proteins in the data (DNA binding proteins and the non-DNA binding proteins

• Represent each protein in a set by a vector of calculated features and build a statistical model to split the groups

Basic Steps1. Building a Model

• Calculate the properties for a new protein

And represent them in a vector

• Predict whether the tested protein belongs to the family

Basic Steps2. Predicting the function of a new protein

TEST CASEY14 – A protein sequence translated from an ORF (Open Reading Frame)Obtained from the Drosophila complete Genome

>Y14PQRSVGWILFVTSIHEEAQEDEIQEKFCDYGEIKNIHLNLDRRTGFSKGYALVEYETHKQALAAKEALNGAEIMGQTIQVDWCFVKG G

Support Vector Machine (SVM)To find a hyperplane that maximallyseparates the RNA-binding from non-RNA bindinginto two classes

Input space Feature space

Kernelfunction

?

newproteinstructure

RNA binding

Non-NA binding

=[x1, x2, x3…]

=[y1, y2,y3…]

>Y14PQRSVGWILFVTSIHEEAQEDEIQEKFCDYGEIKNIHLNLDRRTGFSKGYALVEYETHKQALAAKEALNGAEIMGQTIQVDWCFVKG G

Y14 DOES NOT BIND RNA