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Computational Protein Folding Ming Li Canada Research Chair in Bioinformatics Cheriton School of Computer Science University of Waterloo.

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Presentation on theme: "Computational Protein Folding Ming Li Canada Research Chair in Bioinformatics Cheriton School of Computer Science University of Waterloo."— Presentation transcript:

1 Computational Protein Folding Ming Li Canada Research Chair in Bioinformatics Cheriton School of Computer Science University of Waterloo

2 AT TA C C C C G G G G G T T T A A A A T C AT mRNA Protein transcription translation Human: 3 billion bases, 30k genes. E. coli: 5 million bases, 4k genes (A,C,G,U) (20 amino acids) Codon: three nucleotides encode an amino acid. 64 codons 20 amino acids, some w/more codes cDNA reverse transcription

3 Coding proteins

4 They are built from 20 amino acids and fold in space into functional shapes

5 Several polypeptide chains can form more complex structures:

6 Why should you care? Through 3 billion years of evolution, nature has created an enormous number of protein structures for different biological functions. Understanding these structures is key to proteomics. Fast computation of protein structures is one of the most important unsolved problems in science today. Much more important than, for example, the P≠NP conjecture. We now have a real chance to solve it.

7 Proteins – the life story Proteins are building blocks of life. In a cell, 70% is water and 15%-20% are proteins. Examples: hormones – regulate metabolism structures – hair, wool, muscle,… antibodies – immune response enzymes – chemical reactions Sickle-cell anemia: hemoglobin protein is made of 4 chains, 2 alphas and 2 betas. Single mutation from Glu to Val happens at residue 6 of the beta chain. This is recessive. Homozygotes die but Heterozygotes have resistance to malaria, hence it had some evolutionary advantage in Africa. 1 in 12 African Americans are carriers.

8 What happened in sickle-cell anemia Mutating to Valine. Hydrophobic patch on the surface. Mutating to Valine. Hydrophobic patch on the surface. Codon: GTT GTA,GTC, GTG Hemoglobin Glu: Glutamic acid, E, Codon: GAA,GAG

9 Amino acids There are 500 amino acids in nature. Only 20 (22) are used in proteins. The first amino acid was discovered from asparagus, hence called Asparagine, in All 20 amino acids in proteins are discovered by Traces of glycin, alanine etc were found in a meteorite in Australia in That brings the conjecture that life began from extraterrestrial origin.

10 20 Amino acids Polar amino acids Serine Threonine Tyrosine Histidine Cysteine Asparagine Glutamine Tryptophan Hydrophobic amino acids Alanine Valine Phenylalanine Proline Methionine Isoleucine Leucine Charged Amino Acids Aspartic acid Glutamic acid Lysine Arginine Simplest Amino Acid Glycine Polar: one positive and one negative charged ends, e.g. H 2 O is polar, oil is non-polar. Neutral Non-polar

11 The Φ and Ψ angles The angle at N-C α is Φ angle The angle at C α -C’ is Ψ angle No side chain is involved (which is at C α ) These angles determine the backbone structure. CαCα

12 Homologous proteins have similar structure and functions Being homologous means that they have evolved from a common ancestral gene. Hence at least in the past they had the same structure and function. Caution: old genes can be recruited for new functions. Example: a structural protein in eye lens is homologous to an ancient glycolytic enzyme.

13 Conserving core regions Homologous proteins usually have conserved core regions. When we model one protein after a similar protein with known structure, the main problem becomes modeling loop regions. Modeling loops can also depend on database to some degree. Side chains: only a few side-chain conformations frequently occur – they are called rotamers, there is a such a database.

14 There are not too many candidates! There are only about 1000 topologically different domain structures. There is no reason whatsoever that we cannot compute their structures accurately.

15 Protein data bank As of Oct 10, 2006 there are structures. But there are only about 1000 unique folds. And its growth is very slow. Each year, over 90% structures deposited into PDB have similar folds in PDB already.

16 Why do proteins fold? The folded structure of a protein is actually thermodynamically less favorable because it reduces the disorder or entropy of the protein. So, why do proteins fold? One of the most important factors driving the folding of a protein is the interaction of polar and nonpolar side chains with the environment. Nonpolar (water hating) side chains tend to push themselves to the inside of a protein while polar (water loving) side chains tend to place themselves to the outside of the molecule. In addition, other noncovalent interactions including electrostatic and van der Waals will enable the protein once folded to be slightly more stable than not. When oil, a nonpolar, hydrophobic molecule, is placed into water, they push each other away. Since proteins have nonpolar side chains their reaction in a watery environment is similar to that of oil in water. The nonpolar side chains are pushed to the interior of the protein allowing them to avoid water molecule and giving the protein a globular shape. There is, however, a substantial difference in how the polar side chains react to the water. The polar side chains place themselves to the outside of the protein molecule which allows for their interact with water molecules by forming hydrogen bonds. The folding of the protein increases entropy by placing the nonpolar molecules to the inside, which in turn, compensates for the decrease in entropy as hydrogen bonds form with the polar side chains and water molecules.

17 Marginal Stability The marginal stability between native and denatured states is biologically important Control quantities of some proteins Timing Must be able to degrade and create proteins easily. Fast turnover means marginal stability Some enzymes need structural flexibility.

18 How does nature fold proteins? X-ray studies show each sequence has a unique fold, although having many sub-states with minor structural differences. How did they all get to there? Each protein did random search? This is impossible, time- wise, the problem is NP-hard. In real life, they fold within 0.1 to 1000 seconds, in vivo or in vitro. They do parallel search, and once one found it, it starts cascade effect (like the prion protein)? Project: show this is also not possible. More likely: there is a fast kinetic folding pathway. The obstacles on such pathway becomes key issues (such as formation of wrong disulfide bonds etc) Finding the folding path experimentally is difficult since the intermediates have very short lifetime.

19 Folding steps and molten globules Step 1: within a few milliseconds, local secondary structures form, also some native like alpha helix and beta strand positions. This is called molten globule. Not unique. Step 2: lasts up to 1 second, native elements and tertiary structures begin to develop, possibly sub-domains, although not docked perhaps. Step 3: single native form is reached, forming native interactions, including hydrophobic packing in the interior & fixing surface loops.

20 Burying hydrophobic side chains The last step is the biggest mystery. There is a very little change in free energy by forming the internal hydrophobic bonds for alpha and beta structures since the in unfolded state, equally stable hydrogen bonds can also be formed to water molecules!! Thus secondary structure formation cannot be thermodynamic driving force of protein folding. On the other hand, there is a large free energy change by bringing hydrophobic side chains out of contact with water and into contact with each other in the interior of a globular entity. Thus a likely scenario: Hydrophobic side chains partially buried very early Thus it vastly reduces the number of possible conformations that need to be searched because only those that are sterically accessible within this shape can be sampled. Furthermore, when side chains are buried, their polar backbone –NH and –CO groups are also buried in a hydrophobic environment, hence unable to form hydrogen bonds to water – hence they bond to each other – so you get alpha and beta structures.

21 The α helix Hydrogen bond Height: 5.4A per turn. Each residue gives1.5A rise 5.4A The arrow indicates direction from N to C terminal Note: natural α helices are right-handed

22 Water molecule, H 2 O

23 Hydrogen bond (you know ionic bond and covalent bond from high school) Water (H 2 O) Ammonia (NH 3 ) –– ++ O H H ++ –– N H H H A hydrogen bond results from the attraction between the partial positive charge on the hydrogen atom of water and the partial negative charge on the nitrogen atom of ammonia. ++ ++ ++

24 Walking on water

25 Antiparallel β strands Side chains in purple Hydrogen bonds, note their unevenness

26 Core question Looking at the protein sequences of globular proteins, one finds that hydrophobic side chains are usually scattered along the entire sequence, seemingly randomly. In the native state of folded protein, ½ of these side chains are buried, and the rest are scattered on the surface of the protein, surrounded by hydrophilic side chains. The buried hydrophobic side chains are not clustered in the sequence. Central Question: what causes these residues to be selectively buried during the early and rapid formation of the molten globule?

27 Folding pathways U i ‘s --- unfolded states, many of them. M i ’s --- molten globule states, i can be 1. Has most secondary structures, but less compact. Converging to F. During this relatively slower process it passes a high energy transition state T. These facts have been verified by NMR, hydrogen exchange, spectroscopy, and thermo-chemistry.

28 Web Lab Protein Structure Determination Wet Lab: X-ray crystallography NMR The wet lab technologies not only are slow and expansive, but also they simply fail for: Protein design Alternative splicing Insoluble proteins Not to mention millions of proteins they can do but will never finish.

29 Computational Approaches

30 RAPTOR: Protein Threading by Linear Programming Make a structure prediction through finding an optimal placement (threading) of a protein sequence onto each known structure (structural template) “placement” quality is measured by some statistics-based energy function best overall “placement” among all templates may give a structure prediction target sequence MTYKLILNGKTKGETTTEAVDAATAEKVFQYANDNGVDGEWTYTE template library

31 Threading

32 Threading Example

33 Introduction to Linear Program Optimize (Maximize or Minimize) a linear objective function e.g. 2x+3y+4z The variables satisfy some linear constraints. e.g. 1. x+y-z ≥ x+y+3z=3 integer program (IP) =linear program (LP) + integral variables LP can be solved within polynomial time --- Interior point method. Simplex method also runs fast. Polynomial time for IP is not likely. It is NP-hard, But: IP can be relaxed to LP, solve the non-integral version Branch-and-bound or branch-and-cut (may cost exponential time)

34 Why Integer Programming? Treat pairwise potentials rigorously critical for fold-level targets Existing exact algorithms for pairwise potentials High memory requirement, or Expensive computational time Inflexibility, messy formulation Exploit correlations between various kinds of item scores in the energy function 99% real data generate integral solutions directly, no branch-and-bound needed.

35 Previous approaches for threading Heuristic Algorithms Interaction-Frozen Algorithm (A. Godzik et al.) Monte Carlo Sampling (T. Madej et al.) Double dynamic programming (D. Jones et al.) Recursive dynamic programming (R. Thiele et al.) Exact Exponential Time Algorithms Branch-and-bound (R.H. Lathrop et al.) Exploit the relationship among various scoring parameters, fast self-threading Divide-and-conquer (Y. Xu et al.) Exploit the topological structure of template contact graphs

36 Formulating Protein Threading by LP Protein Threading Needs: 1.Construction of Template Library 2.Design of Energy Function 3.Sequence-Structure Alignment 4.Template Selection and Model Construction

37 Threading Energy Function how well a residue fits a structural environment: E s (Fitness score) how preferable to put two particular residues nearby: E p (Pairwise potential) alignment gap penalty: E g (gap score) E= E p + E s + E m + E g + E ss Minimize E to find a sequence-structure alignment sequence similarity between query and template proteins: E m (Mutation score) Consistency with the secondary structures: E ss

38 A sample of detail: The objective function is min E = E p + E s + E m + E g + E ss Let x i,j indicate amino acid a i in the query sequence is aligned to position j in the template structure. I.e. x i,j = 1 if a i is aligned to position j, otherwise x i,j =0. Then if that position j is exposed to water, and a i is hydrophobic, then we give a negative weight a i,j in the environmental energy: E s =  a i,j x i,j Some contraints would be x i,j = {0,1}. Or in the LP relaxation: 0 ≤ x i,j ≤ 1.  j=1..n x i,j =1

39 Contact Graph 1.Each residue as a vertex 2.One edge between two residues if their spatial distance is within a given cutoff. 3.Cores are the most conserved segments in the template: alpha-helix, beta- sheet template

40 Simplified Contact Graph

41 Contact Graph and Alignment Diagram

42

43 Variables x(i,l) denotes core i is aligned to sequence position l y(i,l,j,k) denotes that core i is aligned to position l and core j is aligned to position k at the same time. D[i] = set of positions core i can be aligned to. R[i,j,k] = set of positions core j can be aligned to given core i is aligned to k.

44 Formulation 1 E g, E p E s, E ss, E m Encodes interaction structures: the first makes sure no crosses; the second is quadratic, but can be converted to linear: a=bc is eqivalent to: a≤b, a≤c, a≥b+c-1 Encodes scoring system k< l

45 Formulation used in RAPTOR E g, E p E s, E ss, E n Encodes interaction structures Encodes scoring system

46 Solving the Problem Practically 1. More than 99% threading instances can be solved directly by linear programming, the rest can be solved by branch-and-bound with only several branch nodes 2. Less memory consumption 3. Less computational time 4. Easy to extend to incorporate other constraints

47 CPU Time for CAFASP3 targets

48 Fold Recognition Support Vector Machines (SVM) Approach Features are extracted from the alignments A threading pair is treated as a positive pattern only if they are in at least fold-level similarity 60,000 threading pairs are employed to train SVM model. 5% more targets are recognized by SVM approach than the traditional z-Score

49 Lindahl Benchmark Test 976*975 threading pairs are tested, the results of other servers are taken from Shi et al.’s paper.

50 CASP5, CASP6, CASP7 Held every 2 years. RAPTOR consistently ranked high since CASP5. It was voted by CASP5 attendees as the most novel approach, at 62—100 targets each time. 48 hours allowed for each target. No manual intervention. Evaluated by computer programs.

51 Example, CASP5 Target Category CASP5CMCM/FRFR(H)FR(A)NF/FRNF CAFASP 3 HM easy (family level) HM hard (superfamily level) FR (fold level) # targets Prediction Difficulty CM: Comparative Modelling, HM: Homology Modelling FR: Fold Recogniton, NF: New Fold Hard Easy

52 RAPTOR Sensitivity on CASP5 FR targets ServersSum MaxSub Score# correct 3ds5 robetta pmod 3ds3 pmode RAPTOR shgu dsn orfeus pcons fugu3 orf_c ……… pdbblast0.000 ……… blast0.000 (http://ww.cs.bgu.ac.il/~dfischer/CAFASP3, released on Dec., 2002.)http://ww.cs.bgu.ac.il/~dfischer/CAFASP3 30 FR targets 54 servers

53 CAFASP3 Example Target ID: T0136_1 Target Size:144 Superimposable size within 5Å: 118 RMSD:1.9Å Red: Experimental Structure Blue/green: RAPTOR model

54 CASP6, T0199-2, ACE buffalo rank: 9 th From RAPTOR rank 1 model. TM= MaxSub= Good parts: , Left: predicted structure. Right: experimental structure

55 CASP6, T0203 ACE buffalo rank: 1 st From RAPTOR 2 nd model. TM=0.6041, MaxSub= Good parts: 19-57, 89-94, , , Predicted Experimental RAPTOR first Model ranks 5 th

56 CASP6, T0262-2, ACE buffalo rank: 4 th From Fugue3 6 th model. TM=0.4306, MaxSub= Good parts: Predicted Experimental Fugue’s top model ranks low

57 CASP6, T0242, NF, ACE buffalo rank: 1 From RAPTOR rank 5 model. TM score=0.2784, MaxSub score= However, RAPTOR top model ranks 44 th ! Trivial error? Predicted Experimental

58 CASP6, T0238, NF ACE buffalo rank 1 st From RAPTOR 8 th model TM=0.2748, MaxSub= Good part: High TM score, low MaxSub Raptor top model ranks 4 th Predicted Experimental

59 About RAPTOR Jinbo Xu’s Ph.D. thesis work. The RAPTOR system has benefited significantly from PROSPECT (Ying Xu, Dong Xu, et al). References: J. Xu, M. Li, D. Kim, Y. Xu, Journal of Bioinformatics and Computational Biology, 1:1(2003), J. Xu, M. Li, PROTEINS: Structure, Function, and Genetics, CASP5 special issue.

60 Old Paradigm

61 New RAPTOR, New Paradigm Local Threading/ Large fragments Short Fragment selection Super motif/domain Modeling Global threading/ Old RAPTOR Hydrophobic s.c. Burying information Contact Prediction Assembly by Molecular dynamics Loop / Side Chain Modeling Refinement NMR constraints


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