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Protein Tertiary Structure Prediction. Structural Bioinformatics. The Different levels of Protein Structure. Primary: amino acid linear sequence. Secondary:  -helices, β -sheets and loops. Tertiary : the 3D shape of the fully folded polypeptide chain. PDB: Protein Data Bank.

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Protein Tertiary Structure Prediction

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Protein Tertiary Structure Prediction

Structural Bioinformatics

The Different levels of Protein Structure

Primary: amino acid linear sequence.

Secondary: -helices, β-sheets and loops.

Tertiary: the 3D shape of the fully folded

polypeptide chain

PDB: Protein Data Bank

  • DataBase of molecular structures :

    Protein, Nucleic Acids (DNA and RNA),

  • Structures solved by

    X-ray crystallography


    Electron microscopy

RCSB PDB – Protein Data Bank

How can we view the protein structure ?

  • Download the coordinates of the structure from the PDB

  • Launch a 3D viewer program

    For example we will use the program Pymol

    The program can be downloaded freely from

    the Pymol homepage

  • 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


Predicting 3D Structure

Outstanding difficult problem

Based on sequence homology

  • Comparative modeling (homology)

    Based on structural homology

  • Fold recognition (threading)

Based on Sequence homology

Comparative Modeling

Similar sequences suggests similar structure

Sequence and Structure alignments of two Retinol Binding Protein

Structure Alignments

There are many different algorithms for structural Alignment.

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

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.


Based on Sequence homology

Comparative Modeling

Similar sequence suggests similar structure

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

Based on Sequence homology

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



Alignment length (L)

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

Based on Sequence homology

Comparative Modeling Methods

MODELLER (Sali –Rockefeller/UCSF)

SCWRL (Dunbrack- UCSF )


Based on Sequence homology

Comparative Modeling

Modeling of a sequence based on known structures

Consist of four major steps :

  • 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

Based on Structure homology

Fold Recognition

Based on Secondary Structure

Protein Folds: sequential and spatial arrangement of secondary structures



Similar folds usually mean similar function




The same fold can have multiple functions


12 functions

31 functions

TIM barrel

Based on Structure homology

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

Based on Structure homology

Basic steps in Fold Recognition :

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

Query sequence


Goal: find to what folding template the sequence fits best

There are different ways toevaluate sequence-structure fit

Potential fold

Based on Secondary Structure homology

There are different ways toevaluate sequence-structure fit

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



-10 ... -123 ... 20.5


Based on Secondary Structure homology

Programs for fold recognition

  • TOPITS (Rost 1995)

  • GenTHREADER (Jones 1999)


  • 3D-PSSM

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?FOLDITSolve Puzzles for Science

A computer game to fold proteins

What’s Next

Predicting function from structure

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

ATP binding domain of protein MJ0577

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

Wanted !

Automated methodsto predict function from the protein structures resulting from the structural genomic project.

As a result of the Structure Genomic

initiative many structures of proteins

with unknown function will be solved

Approaches for predicting function from structure

ConSurf - Mapping the evolution conservation on the protein structure

Approaches for predicting function from structure

PFPlus – Identifying positive electrostatic patches on the protein structure

Approaches for predicting function from structure

SHARP2 – Identifying positive electrostatic patches on the protein structure

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

Basic Steps

1. Building a Model

  • 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 Steps

2. Predicting the function of a new protein

  • Calculate the properties for a new protein

    And represent them in a vector

  • Predict whether the tested protein belongs to the family


Y14 – A protein sequence translated from an ORF

(Open Reading Frame)

Obtained from the Drosophila complete Genome




Support Vector Machine (SVM)

To find a hyperplane that maximally

separates the RNA-binding from non-RNA binding

into two classes

RNA binding

=[x1, x2, x3…]






Non-NA binding

=[y1, y2,y3…]

Input space

Feature space




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