1. Protein Folding and Protein Threading
Some slides from
Tolga Can, CENG 465: Introduction to Bioinformatics, Middle East Technical University, Turkey
Kristen Huber, EC 697S: Topics in Computational Biology, University of Massachusetts

2. Protein threading Structure is better conserved than sequence
Structure can adopt a
wide range of mutations.
Physical forces favor
certain structures.
Number of folds is limited.
Currently ~700
Total: 1,000 ~10, TIM barrel
Tolga Can, METU, CENG 465

3. Protein Threading
Basic premise
Statistics from Protein Data Bank (~35,000 structures)
The number of unique structural (domain) folds in nature
is fairly small (possibly a few thousand)
90% of new structures submitted to PDB in the past
three years have similar structural folds in PDB
Tolga Can, METU, CENG 465

4. Concept of Threading
Thread (align or place) a query protein sequence onto a template structure in “optimal” way
Good alignment gives approximate backbone structure
Query sequence
MTYKLILNGKTKGETTTEAVDAATAEKVFQYANDNGVDGEWTYTE
Template set
Tolga Can, METU, CENG 465

5. Protein Threading – energy function
MTYKLILNGKTKGETTTEAVDAATAEKVFQYANDNGVDGEWTYTE
how preferable to put two particular residues nearby: E_p
how well a residue fits a structural environment: E_s
alignment gap penalty: E_g
total energy: E_p + E_s + E_g
find a sequence-structure alignment to minimize the energy function
Tolga Can, METU, CENG 465

6. Prediction of Protein Structures
Examples – a few good examples
actual
predicted
actual
predicted
actual
predicted
actual
predicted
Tolga Can, METU, CENG 465

7. Prediction of Protein Structures
Not so good example
Tolga Can, METU, CENG 465

8. CASP/CAFASP CASP: Critical Assessment of Structure Prediction
CAFASP: Critical Assessment of Fully Automated Structure Prediction
CASP
Predictor
CAFASP
Predictor
Won’t get tired
High-throughput
Tolga Can, METU, CENG 465

9. Protein Threading
Kristen Huber, UMass, EC 697S

10. Protein Threading
Kristen Huber, UMass, EC 697S

11. Protein Threading (RAPTOR)
Jinbo Xu, Ying Xu, Dongsup Kim, Ming Li.
RAPTOR: Optimal Protein Threading by Linear Programming
Journal of Bioinformatics and Computational Biology, April 2003
Given a query sequence S = (s1, s2, s3, …sn) and a template (library) sequence T = (t1, t2, t3, …tm), pair up elements from S and T, by possibly inserting gaps, while minimizing an energy function
Assumptions
the template is a sequence of cores (conserved segments – α-helix or β-sheet) connected by loops
gaps are allowed only within the loops
only interactions between residues in the cores
are considered;
interaction between residues is assumed to exist
if they are within 7 Ǻ and at least 4 positions away

12. Protein Threading (RAPTOR)
Steps of the RAPTOR algorithm:
build a contact map for the template structure
find all possible alignments for each core within the query sequence
build a contact map for the query sequence and template structure
define energy function and carry out minimization
Em – mutation score
Es – environment fitness score
Ep – pairwise interaction score
Eg – gap penalty score
Ess – secondary structure compatibility
Wx – weights (determined experimentally)

13. Protein Threading (RAPTOR)
Step 1: Build a contact map for the template structure
contact map indicates interactions between cores, i.e.if any two residues within the cores interact
Xu et al., JBCB, 2003

14. Protein Threading (RAPTOR)
Step 3: Build a contact map for the query and template
Xu et al., JBCB, 2003

15. Protein Threading (RAPTOR)
Step 4: define energy function and carry out minimization
Xu et al., JBCB, 2003