smm -protein structure prediction 2

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THE ROLE OF BIOINFORMATICSIN PROTEIN STRUCTURE STUDY

Jennie Varghese

09BIF089



Protein structure prediction



Introduction

Protein structure prediction is anotherimportant application of bioinformatics.The amino acid sequence of a protein, the so-called primary structure, can be easilydetermined from the sequence on the genethat codes for it. In the vast majority of cases,

this primary structure uniquely determines astructure in its native environment.

http://en.wikipedia.org/wiki/Amino_acid

http://en.wikipedia.org/wiki/Primary_structure

http://en.wikipedia.org/wiki/Primary_structure

http://en.wikipedia.org/wiki/Amino_acid



Introduction

For lack of better terms, structuralinformation is usually classified as oneof secondary , tertiary andquaternary structure. A viable generalsolution to such predictions remains an openproblem. As of now, most efforts have been

directed towards heuristics that work most of the time.

http://en.wikipedia.org/wiki/Secondary_structure

http://en.wikipedia.org/wiki/Tertiary_structure

http://en.wikipedia.org/wiki/Quaternary_structure

http://en.wikipedia.org/wiki/Quaternary_structure

http://en.wikipedia.org/wiki/Tertiary_structure

http://en.wikipedia.org/wiki/Secondary_structure



Introduction

One of the key ideas in bioinformatics is thenotion of homology. In the genomic branch of bioinformatics, homology is used to predict the

function of a gene: if the sequence of gene A,whose function is known, is homologous to thesequence of gene B, whose function is unknown,one could infer that B may share A's function. In

the structural branch of bioinformatics,homology is used to determine which parts of aprotein are important in structure formation andinteraction with other proteins

http://en.wikipedia.org/wiki/Homology_(biology)

http://en.wikipedia.org/wiki/Homology_(biology)



Introduction

. In a technique called homology modeling,this information is used to predict thestructure of a protein once the structure of ahomologous protein is known. This currentlyremains the only way to predict proteinstructures reliably. techniques for predicting

protein structure include protein threadingand de novo (from scratch) physics-basedmodeling.



A good protein structure

• Minimizes disallowedtorsion angles

• Maximizes number of

hydrogen bonds• Minimizes interstitial

cavities or spaces

• Minimizes number of “bad” contacts

• Minimizes number of buried charges




– Secondary structure

– 3D structure• Modeling by homology (Comparative modeling)

• Fold recognition (Threading)• Ab initio prediction– Rule-based approaches– Lattice models– Simulating the time dependence of folding

• Refinement• Exploring the effect of single amino acid substitutions• Ligand effects on protein structure and dynamics

(induced fit)



Modeling by Homology

(Comparative Modeling) Comparative modeling predicts the three-dimensional structure of a given

protein sequence (target) based primarily on its alignment to one or more proteins

of known structure (templates).

The prediction process consists of

• fold assignment,

• target template alignment,• model building, and

• model evaluation and refinement.

The number of protein sequences that can be modeled and the accuracy of

the predictions are increasing steadily because of the growth in the number of

known protein structures and because of the improvements in the modeling

software.

Further advances are necessary in recognizing weak sequence structure

similarities, aligning sequences with structures, modeling of rigid body shifts,

distortions, loops and side chains, as well as detecting errors in a model.

Despite these problems, it is currently possible to model with useful accuracy

significant parts of approximately one third of all known protein sequences.




(Comparative Modeling)



Fold Recognition (Threading)

Methods of protein fold recognition attempt to detect similaritiesbetween

protein 3D structure that are not accompanied by any significantsequence similarity.

The unifying theme of these appraoches is to try and find folds that are

compatible with a particular sequence. Unlike sequence-onlycomparison,

these methods take advantage of the extra information made availableby

3D structure information.

Rather than predicting how a sequence will fold, they predict how well afold

will fit a sequence.



Fold Recognition (Threading) –

2 Kinds

2D Threading or Prediction Based Methods(PBM) Predict secondary structure (SS) or ASA of query

Evaluate on basis of SS and/or ASA matches

3D Threading or Distance Based Methods (DBM) Create a 3D model of the structure

Evaluate using a distance-based “hydrophobicity” orpseudo-thermodynamic potential



Fold Recognition (Threading)



Fold Recognition

Database of 3D structures and sequences

Protein Data Bank (or non-redundant subset)

Query sequence

Sequence < 25% identity to known structures

Alignment protocol

Dynamic programming

Evaluation protocol Distance-based potential or secondary structure

Ranking protocol



Fold Recognition



Ab Initio Prediction

• Predicting the 3D structure without any “priorknowledge”

• Used when homology modelling or threadinghave failed (no homologues are evident)

• Equivalent to solving the “Protein FoldingProblem”

• Still a research problem



Combining Prediction Procedures



Structure Validation

A structure can (and often does) havemistakes

A poor structure will lead to poor models of mechanism or relationship

Unusual parts of a structure may indicatesomething important (or an error)




Assess experimental fit look at Resolution, R-Factor or RMSD

Assess correctness of overall fold look at disposition of hydrophobic residues

Assess structure quality packing

stereochemistry contacts




Servers WHAT IF

http://swift.cmbi.kun.nl/WIWWWI/

Verify3D http://www.doe-mbi.ucla.edu/Services/Verify_3D/

VADAR http://redpoll.pharmacy.ualberta.ca




Programs PROCHECK

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

VADAR http://www.pence.ca/software/vadar/latest/vadar.html

DSSP http://www.cmbi.kun.nl/gv/dssp



Procheck



Conclusions

Protein structures are now sufficientlyabundant and well defined that they can beclassified using well-developed rules of taxonomy

Distant relationships and common rules of folding can be uncovered through fold

classification & comparison



Conclusions

Structure prediction is still one of the keyareas of active research in bioinformatics andcomputational biology

Significant strides have been made over thepast decade through the use of largerdatabases, machine learning methods and

faster computers

smm -protein structure prediction 2

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