2. Structural bioinformatics
Predicting Protein structure

3. What is Structural Bioinformatics?
is the branch of bioinformatics which is related to the analysis and prediction of the three-dimensional structure of biological macromolecules such as proteins, RNA, and DNA.
It deals with generalizations about macromolecular 3D structure such as comparisons of overall folds and local motifs, principles of molecular folding, evolution, and binding interactions, and structure/function relationships,

4. Structural bioinformatics vs. bioinformatics
DNA mapping
DNA and protein sequence
Development of algorithms for data mining
Determine of 3D structures in biomolecules
Analysis and comparison of biomolecular structures
Prediction of biomolecular structure.

5. Experimental techniques for structure determination
X-ray Crystallography
Nuclear Magnetic Resonance spectroscopy (NMR)

6. Prediction

7. Question?
Why is structure prediction is hard..?

8. Structure Prediction Approaches
Homology (Comparative) Modeling
Based on sequence similarity with a protein for
which a structure has been solved.
Threading (Fold Recognition)
Requires a structure similar to a known structure
Ab-initio fold prediction
Not based on similarity to a sequence\structure

9. Ab-initio fold prediction
Given only the sequence, try to predict the structure based on physico-chemical properties (energy, hydrophobicity etc.)

10. Fold Recognition (Threading)
Given a sequence and a library of folds, thread the sequence through each fold. Take the one with the highest score.

11. Homology Modeling – Basic Idea
A protein structure is defined by its amino acid sequence.
Closely related sequences adopt highly similar structures, distantly related sequences may still fold into similar structures.
Three-dimensional structure of
proteins from the same family is
more conserved than their
primary sequences.
שני חלבונים מפולד ה-TIM BARREL פונקציה זהה, אחוז זהות בינוני וניתן לראות שהמבנה דומה מאוד
אפשר אפילו לחזק את האמירה:
Therefore, if similarity between two proteins is detectable at the sequence level, structural similarity can usually be assumed.
Moreover, proteins that share low or even nondetectable sequence similarity often will have similar structures.
Triophospate ismoerases
44.7% sequence identity
0.95 RMSD

12. General Scheme Searching for structures related to the query sequence
Selecting templates
Aligning query sequence with template structures
Building a model for the query using information from the template structures
Evaluating the model
Fiser A et al. Methods in Enzymology 374: (2004)

13. General Scheme

14. 1. Searching For Structures

15. How to select the right template?
Close subfamily - phylogenetic tree
“Environment” similarity

16. More than one template Two ways to combine multiple templates:
Global model – alignment with different domain of the target with little overlap between them
Local model – alignment with the same part of the target
In general, it is frequently beneficial to include in the modeling process all the templates that differ substantially from each other, if they share approximately the same overall similarity to the target sequence.

17. 3. Aligning
All comparative modeling programs depend on a target-template alignment.
When the sequence similarity between the template and target proteins is high, simple pairwise alignments are usually fine (e.g. Needleman- Wunsch global alignment).
But some times blast is required.

18. Sequence alignment algorithms
Examples: the two most used in homology modeling are:
BLAST: General strategy is to optimize the maximal segment pair (MSP) score - BLAST computes similarity, not alignment
FastA (local alignment): searches for both full and partial sequence matches, i.e., local similarity obtained; more sensitive than BLAST, but slower; many gaps may represent a problem

19. Building the model

20. 5. Model Evaluation
The accuracy of the model depends on its sequence identity with the template:
Internal evaluation – self consistency checks
External evaluation – relies on information that was not used in the model calculation

21. Summary

22. Thank you 
Any Questions ?