PROTEIN MODELLING Presented - PowerPoint Presentation

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PROTEIN MODELLING

Presented by Sadhana SSlide2

definition

Protein structure prediction/protein modelling is the prediction of the three-dimensional structure of protein from its amino acid sequence i.e., the prediction of its folding & its secondary, tertiary, & quaternary

structure from its primary structureSlide3

Why to predict protein structure?

Owing to significant efforts in genome sequencing over nearly three decades, gene sequences from many organism have been deduced. Over 100 million nucleotide sequences from over 300 thousand different organisms have been deposited in the major DNA databases, DDBJ/ EMBL/

GenBank

totaling almost 200 billion nucleotide bases.

Over 5 million of these nucleotide sequences have been translated into amino acid sequences and deposited in the

UniProtKB

database. Slide4
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However, the protein sequences themselves are usually insufficient for determining protein function as the biological function of proteins is intrinsically linked to three dimensional protein structure.

The most accurate structural characterization of proteins is provided by X-ray crystallography and NMR spectroscopy. Owing to the technical difficulties and labor intensiveness of these methods, the number of protein structures solved by experimental methods lags far behind the accumulation of protein sequencesSlide6

Many proteins are simply too large for NMR analysis and cannot be crystallized for X-ray diffraction

. Protein modeling(computational methods) is the only way to obtain structural information if experimental techniques fail

.

The ultimate goal of protein modeling is to predict a structure from its sequence with an accuracy that is comparable to the best results achieved experimentally.Slide7

Can we predict structure from sequence?Slide8

Computational Methods

The three major approaches for three-dimensional (3D) structure predictions areAb initio methods

Threading methods

Comparative modelling

/ homology modellingSlide9

What is Homology

Modelling? It is the prediction of the three-dimensional structure of a given protein sequence (target) based on an alignment to one or more known protein structures (templates).

If similarity between the target sequence and the template sequence is detected, structural similarity can be assumed.Slide10

Homology modeling, also known as Comparative modeling

of protein is the technique which allows to construct an unknown atomic-resolution model of the "target" protein from:1. Its amino acid sequence and

2.An experimental 3Dstructure of a related homologous protein (the "template").

Homology

ModellingSlide11

Basis for homology modelling?

Structure of a protein is uniquely determined by its amino acid sequenceStructure is much more conserved than sequence during evolution.

Proteins sharing high sequence similarity should have similar protein fold.

Higher the similarity, higher is the confidence in the modeled structure.Slide12

Homology modeling is a multistep process that can be summarized in seven steps:

1. Template recognition & initial alignment2. Alignment corrections 3. Backbone generation4. Loop modeling5. Side-chain modeling

6. Model optimization7. Model validationSlide13

TEMPLATE

RECOGNITIONAchieved by searching the PDB of known protein structures using the target sequence as the query.

Templates can be found using the target sequence as a query for searching using FASTA or BLAST, & PSI-BLAST or PDB-BLAST

Select the best template(min.30%) from a library of known protein structures derived from the PDB.Slide14

ALIGNMENT

Purpose – to propose the homologies between the sites in two or more sequences

Insertions & deletions are placed

Types

Pairwise alignment

Multiple alignmentSlide15
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Correct alignment is necessary to create the most probable 3D structure of the target.

If sequences aligns incorrectly, it will result in false positive or negative results.Important steps to consider:gap penalties

Scoring alignments

Alignment algorithmsSlide17

Alignments are scored (substitution score) in order to define similarity between 2 amino acid residues in the sequences

A substitutions score is calculated for each aligned pair of letters.Alignment algorithms- DPA, BLAST & FASTA

Alignment CorrectionsSlide18
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example

Structure of alignment 1 and 2 with the template Slide20

Alignment Outcome

The (true) alignment indicates the evolutionary process giving rise to the different sequences starting from the same ancestor sequence and then changing through mutations (insertions, deletions, and substitutions)Slide21

One simply copies the coordinates of those template residues that show up in the alignment with the model sequence

If two aligned residues differ- only backbone coordinates(N, C-alpha, C & O) are copiedIt they are same- side chain is also included

BACKBONE GENERATIONSlide22

Backbone Generation

For SCRs - copy coordinates from known structures

.

For variable regions (VR) - copy from known structure, if the residue types are similar; otherwise, use databases for

loop

sequences.Slide23

Knowledge based- PDB is searched

Energy based- energy function is used to judge the quality of loopMolecular modeling/dynamic programs are used

Loop ModellingSlide24

Loop ModellingSlide25

1. Use of rotamer libraries (backbone dependent)

2. Molecular mechanics optimization

- Dead-end elimination (heuristic)

- Monte Carlo (heuristic)

- Branch & Bound (exact)

Side Chain ModellingSlide26

Model refinement/optimization

Idealization of bond geometryRemoval of unfavorable non-bonded contacts

Performed by energy minimization with force fields such as CHARMM, AMBER, or GROMOS

Major errors are removedSlide27

Evaluation/validation of the model

Internal evaluationSelf-consistency checksAssessment of stereochemistry of the model

PROCHECK & WHATCHECK

External evaluation

Tests whether a correct template was used

PROSA & VERIFY3DSlide28
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Applications

Designing mutants to test hypotheses about the function of a protein.Identifying active & binding sites.

Predicting antigenic epitopes.

Simulating protein-protein docking.

Confirming a remote structural relationship.Slide30

Web servers

Swiss- model server (http://www.expasy.ch/swissmod/)

CPHModels (

http://www.cbs.dtu.dk/servi

ces/CPH

models/

)

SDSC1 (

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

)

FAMS (

http://www.physchem.pharm.kitasato-u.ac.jp/FAMS/fams.html

)

ModWeb

(

http://www.guitar.rockefeller.edu/modweb

)Slide31
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References

Zhumur Ghosh &

Bibekanand

mallik

. bioinformatics-

P

rinciples & applications. Oxford university press

S C

Rastogi

,

N.Mendiratta

, & P

Rastogi

. Bioinformatics- methods & applications. Eastern economy edition. Prentice hall of India. New Delhi

Philip.E.Bourne

&

Helge

Wiessig

. Structural Bioinformatics. John Wiley & Sons.

NewYork

C A

Orengo

, D T Jones & J M Thornton. Bioinformatics- gene, proteins, & computers. BIOS . Scientific PublishersSlide33