1. PROTEIN MODELLING
Presented by
Sadhana S

2. 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 structure

3. 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.

5. 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 sequences

6. 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.

7. Can we predict structure from sequence?

8. Computational Methods
The three major approaches for three- dimensional (3D) structure predictions are
Ab initio methods
Threading methods
Comparative modelling / homology modelling

9. 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.

10. Homology Modelling
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").

11. Basis for homology modelling?
Structure of a protein is uniquely determined by its amino acid sequence
Structure 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.

12. 1. Template recognition & initial alignment 2. Alignment corrections
Homology modeling is a multistep process that can be summarized in seven steps:
1. Template recognition & initial alignment
2. Alignment corrections
3. Backbone generation
4. Loop modeling
5. Side-chain modeling
6. Model optimization
7. Model validation

13. TEMPLATE RECOGNITION
Achieved 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.

14. ALIGNMENT
Purpose – to propose the homologies between the sites in two or more sequences
Insertions & deletions are placed
Types
Pairwise alignment
Multiple alignment

16. gap penalties Scoring alignments Alignment algorithms
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 algorithms

17. Alignment Corrections
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

19. example
Structure of alignment 1 and 2 with the template

20. 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)

21. It they are same- side chain is also included
BACKBONE GENERATION
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 copied
It they are same- side chain is also included

22. 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.

23. Loop Modelling Knowledge based- PDB is searched
Energy based- energy function is used to judge the quality of loop
Molecular modeling/dynamic programs are used

24. Loop Modelling

25. Side Chain Modelling
1. Use of rotamer libraries (backbone dependent) 2. Molecular mechanics optimization - Dead-end elimination (heuristic) - Monte Carlo (heuristic) - Branch & Bound (exact)

26. Model refinement/optimization
Idealization of bond geometry
Removal of unfavorable non-bonded contacts
Performed by energy minimization with force fields such as CHARMM, AMBER, or GROMOS
Major errors are removed

27. Evaluation/validation of the model
Internal evaluation
Self-consistency checks
Assessment of stereochemistry of the model
PROCHECK & WHATCHECK
External evaluation
Tests whether a correct template was used
PROSA & VERIFY3D

29. 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.

30. Web servers Swiss- model server (http://www.expasy.ch/swissmod/)
CPHModels (
SDSC1 (
FAMS ( u.ac.jp/FAMS/fams.html)
ModWeb (

32. References
Zhumur Ghosh & Bibekanand mallik. bioinformatics- Principles & 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 Publishers