Protein structure prediction

1. Protein structure prediction Homology-based methods

2. Reading Required reading for this weekDavid Baker and Andrej Sali, “Protein Structure Prediction and Structural Genomics” Science 2001 Pevsner, Ch. 11 (Protein Structure; note addition to syllabus) Additional sources for this lecture: Park et al, “Sequence comparisons using multiple sequences detect three times as many remote homologues as pairwise methods.” JMB 1998 Baxevanis and Ouellette, Bioinformatics (previous course text:) Chapter 8: Predictive methods using protein sequences (Ofran and Rost) 198-219Chapter 9: Protein structure prediction and analysis (Wishart) 224-247Chapter 12: Creation and analysis of protein multiple sequence alignments (Barton) Much of the text in the slides that follow are drawn either verbatim or paraphrased from these texts.

3. Next lecture: PHYRE

4. Topics Covered • Secondary structure prediction methods • 3D fold prediction • Ab initio protein structure prediction • Homology-based methods of fold recognition • Comparative model construction (aka homology model construction) • Community evaluation of protein structure prediction • Critical Assessment of protein Fold Prediction (CASP) http://predictioncenter.org/ • Structural Genomics Initiative

5. Primary, Secondary, Tertiary and Quaternary Structure

6. Hierarchical descriptions of proteins(follows the folding process) • Primary structure: the amino acid sequence • Secondary structure: “regular local structure of linear segments of polypeptide chains” (Creighton) • Helix (~35% of residues): subtypes: ,  and 310 • Beta sheet (~25% of residues) • Both types predicted by Linus Pauling (Corey and Pauling, 1953;  helix first described by Pauling in 1951) • Other less common structures: • Beta turns • 3/10 helices • Ω loops • Remaining unclassifiable regions sometimes termed “random coil” or “unstructured regions” • Tertiary structure: “Overall topology of the folded polypeptide chain” (Creighton) • Mediated by hydrophobic interactions between distant parts of protein • Quaternary structure: “Aggregation of the separate polypeptide chains of a protein” (Creighton) Baxevanis & Ouellette (Ch. 9, p.224, Wishart)

7. Information required for folding is (mostly) contained in the primary sequence • Early on, proteins were shown to fold into their native structures in isolation • This led to the belief that structure is determined by sequence alone (Anfinsen, 1973) • Over the last decade, a significant number of proteins have been shown to not fold properly in the test tube (e.g., requiring the assistance of chaperonins) • Nevertheless, the native 3D structure is assumed to be in some energetic minimum • This led to the development of ab initio folding methods Baxevanis & Ouellette (Ch. 9, Wishart)

8. Folding pathways • Evidence that local structure segments form first, and then pack against each other to form 3D fold • Exploited in protein fold prediction, Rosetta method • Simons, Bonneau, Ruczinski & Baker (1999). Ab initio Protein Structure Prediction of CASP III Targets Using ROSETTA. Proteins • Semi-stable structural intermediates on folding pathway to lowest-energy conformation • Prof. Susan Marqusee, Berkeley Baxevanis & Ouellette (Ch. 9, Wishart)

9. Desulfovibrio vulgaris Anacystis nidulans Condrus crispus Anabaena 7120 Principles of Protein Structure GFCHIKAYTRLIMVG… evolution folding Fold Recognition Comparative Modeling Ab initio prediction Andras Fiser, Albert Einstein College of Medicine

10. TARGET TEMPLATE ASILPKRLFGNCEQTSDEGLKIERTPLVPHISAQNVCLKIDDVPERLIPERASFQWMNDK ASILPKRLFGNCEQTSDEGLKIERTPLVPHISAQNVCLKIDDVPERLIPE MSVIPKRLYGNCEQTSEEAIRIEDSPIV---TADLVCLKIDEIPERLVGE Steps in Comparative Protein Structure Modeling START Template Search Target – Template Alignment Model Building Model Evaluation No OK? Yes END Andras Fiser, Albert Einstein College of Medicine

11. Why Protein Structure Prediction? Y 2005 Sequences 2,300,000 Structures 29,000 We know the experimental 3D structure for ~1% of the protein sequences Andras Fiser, Albert Einstein College of Medicine

12. Ab initio prediction Comparative Modeling Applicable to any sequence Not very accurate (>4 Ang RMSD), Attempted for proteins of <100 residues Accuracy and applicability are limited by our understanding of the protein folding problem Applicableto those sequences only that share recognizable similarity to a template structure Fairly accurate ( <3 Ang RMSD), typically comparable to a low resolution X-ray experiment. Not limited by size Accuracy and applicability are rather limited by the number of known folds Protein structure modeling Andras Fiser, Albert Einstein College of Medicine

13. Structural Genomics Definition: The aim of structural genomics is to put every protein sequence within a “modeling distance” of a known protein structure. Size of the problem: There are a few thousand domain fold families. There are ~20,000 sequence families (30% sequence id). Solution: Determine protein structures for as many different families as possible. Model the rest of the family members using comparative modeling Andras Fiser, Albert Einstein College of Medicine

14. Structural Genomics Characterize most protein sequences (red) based on related known structures (green). The number of “families” is much smaller than the number of proteins Andras Fiser, Albert Einstein College of Medicine

16. The utility of a comparative model depends on its accuracy Accuracy is closely linked to sequence similarity David Baker and Andrej Sali, Protein Structure Prediction and Structural Genomics, Science 2001

17. Ca RMSD Å (% EQV) 2 (50) 1 (80) 0 (100) Anacystis nidulans Anabaena7120 COMPARATIVE MODELING Condruscrispus Desulfovibrio vulgaris Clostridium mp. 20 50 100 % SEQUENCE IDENTITY Comparative Protein Structure Modeling Flavodoxin family KIGIFFSTSTGNTTEVA… Andras Fiser, Albert Einstein College of Medicine

18. TARGET TEMPLATE ASILPKRLFGNCEQTSDEGLKIERTPLVPHISAQNVCLKIDDVPERLIPERASFQWMNDK ASILPKRLFGNCEQTSDEGLKIERTPLVPHISAQNVCLKIDDVPERLIPE MSVIPKRLYGNCEQTSEEAIRIEDSPIV---TADLVCLKIDEIPERLVGE Steps in Comparative Protein Structure Modeling START Template Search Target – Template Alignment Model Building Model Evaluation No OK? Yes END Andras Fiser, Albert Einstein College of Medicine

19. Incorrect template Misalignment Distortion in correctly aligned regions Region without a template Side chain packing Typical Errors in Comparative Models MODEL X-RAY TEMPLATE Andras Fiser, Albert Einstein College of Medicine

20. Template identification START Template Search • Fast but less sensitive: e.g. BLAST • Better: Intermediate sequence search • Even better: Profile/HMM and iterative search methods (e.g. PSI-BLAST) • Searching against libraries of HMMs and profiles for solved structures • Profile-profile alignment (e.g., Hhalign, PHYRE) • Including 2ary structure prediction • Structure-based threading Target – Template Alignment Model Building Model Evaluation No OK? Yes END

21. Target-template alignment START Template Search • Note that the methods for identifying candidate templates normally produce an alignment • but these alignments are unlikely to be optimal • The alignment method used must be tuned to the level of evolutionary divergence between the target and template • Manual refinement/editing of the alignment is often used to improve the comparative model Target – Template Alignment Model Building Model Evaluation No OK? Yes END

22. 1BK8 Antimicrobial Protein 1 (Ah-Amp1) Common horse chestnut Proteins can diverge structurally and functionally from a common ancestor 1AGT Agitoxin 2 Egyptian Scorpion (K+ channel inhibitor) 1MYN Drosomycin, Antifungal protein Fruit Fly 1CN2 Toxin 2 Mexican scorpion (Na+ channel inhibitor) 1AYJ Antifungal protein 1 (RS-AFP1) Radish

23. Structural alignment example ID EC Function 1E9Y 3.5.1.5 Urease 1J79 3.5.2.3 Dihydroorotase Identity 9.8% Equivalent Residues 40%

24. Sequence and structural divergence are related “The relation between the divergence of sequence and structure in proteins”, Chothia and Lesk. EMBO Journal 1986

25. Surface residues mutate more readily “The relation between the divergence of sequence and structure in proteins”, Chothia and Lesk. EMBO Journal 1986

26. Sequence and structural divergence are correlated with alignment errors Accuracy of sequence alignment relative to structural alignment Left three columns show results of structural alignment %ID: Structure pairs have been placed into bins based on sequence identity given the structural alignment #pair: number of pairs in each bin %Superpos: percent positions that are within ~3Angstroms RMSD (between backbone C-alpha carbons) Right three columns give Cline Shift scores for pairwise sequence alignments relative to the structural alignment. The best CS score possible is 1; negative scores indicate incorrect over-alignment with very few (or no) correctly aligned residue pairs.

27. Sequence alignment accuracy decreases as sequence divergence increases

28. Constructing a comparative model START Template Search • Rigid Body Assembly (COMPOSER) • Segment Matching (SEGMOD, 3DPSSM) • Satisfaction of Spatial Restraints (MODELLER) • Integrated (NEST) loop modeling, side chain modeling Target – Template Alignment Model Building Model Evaluation No OK? Yes END Andras Fiser, Albert Einstein College of Medicine

29. Comparative model evaluation START • Stereochemistry (PROCHECK, WHATCHECK) • Environment (Profiles3D, Verify3d) • Statistical potentials based methods (PROSAII) Is the model reliable? A model is reliable when it is based on a correct template and on an approximately correct alignment. Template Search Target – Template Alignment Model Building Model Evaluation No OK? Yes END Andras Fiser, Albert Einstein College of Medicine

30. Secondary Structure Prediction

31. Why is secondary structure prediction important? • Secondary structure diverges less rapidly than primary sequence • Knowledge or prediction of 2ary structure improves detection and alignment of remote homologs • 3d-pssm, PHYRE, SAM T02 (fold prediction servers) Baxevanis & Ouellette (Ch. 9, Wishart)

32. Basic types of secondary structure • Helices ( and others) •  is most common; 3.6 residues/turn • Side chains project outward • Structure is stabilized between hydrogen bonds between the carbonyl (CO) group of one amino acid and the amino (NH) group of the amino acid that is 4 positions C-terminal to it • -Strands (two or more strands interact to form a -sheet) • Other (sometimes called loop, coil, or non-regular) • Most secondary structure prediction methods classify residues to one of three states Baxevanis & Ouellette (Ch. 9, Wishart)

33. Focusing on single residues • Early structure prediction methods focused on the structural characteristics of individual residues • This enabled the larger problem to be decomposed into smaller easier-to-solve problems (enabling the combination of solutions to sub-problems to form a global solution) • This also enabled methods to focus on detecting transmembrane regions, solvent-accessible residues, and other important features of molecules Baxevanis & Ouellette (Ch. 9, Wishart)

34. Secondary structure prediction accuracy is boosted by using homologs • Labeling residues in a sequence as -helix, -sheet or turn/coil (3-state prediction). • Accuracy of prediction enhanced by ~6% when multiple sequence alignments are used vs the use of a single sequence (Cuff & Barton, 1999) • Best methods for 2ary structure prediction -- PSIPRED (Jones 1999) and JNET (Cuff & Barton, unpublished) • Make use of homologs obtained using PSI-BLAST • Have ~>76% accuracy for 3-state prediction • Provide confidence values for each position Baxevanis & Ouellette (Ch. 12, Barton)

35. Amino acid patterns indicative of -strand structures • Short runs of conserved hydrophobic • Buried -strand • An i, i+2, i+4 pattern of conserved hydrophobic residues suggests a surface -strand. • Conserved residues sharing the same physicochemical properties are likely to form one face of a strand. Baxevanis & Ouellette (Ch. 12, Barton)

36. Amino acid patterns indicative of -helical structures • Conservation patterns of i, i+3, i+4, i+7 and variations (e.g., i, i+4, i+7) suggests an alpha helix • Amphiphilic/amphipathic conservation patterns (alternating hydrophobic and polar residues) following an i, i+3, i+4, i+7 pattern (and variations, e.g., i, i+4, i+7) are likely to represent surface helices Baxevanis & Ouellette (Ch. 12, Barton)

37. Identifying loop regions • Insertions and deletions are not well tolerated in the hydrophobic core. • Regions of an MSA that include many gap characters are likely to indicate surface loops. • Also look for small polar residues such as S • Glycine and proline residues can be found in any secondary structure. • However, conserved glycine/proline residues are strongly suggestive of loops. Baxevanis & Ouellette (Ch. 12, Barton)

38. Amino acid preferences for different secondary structures(and identifying loops/turns) http://www.chembio.uoguelph.ca/educmat/phy456/456lec01.htm

39. Early schemes used observed preferences • Various schemes give the amino acids numerical weights or rankings for their preferences, and several computer programs can predict the secondary structure from the given sequence. • Preferences are weak, but provide some signal • The simplest such scheme of Chou and Fasman, Ann. Rev Biochem. (1978), examined the statistical distribution of amino acids in alpha helix, beta sheet and turns or loops, using a set of known protein structures from the protein databank. • A novel sequence can then be scanned, and the tendency of each portion of the sequence to form secondary structure is assessed. http://www.chembio.uoguelph.ca/educmat/phy456/456lec01.htm

40. Improving secondary structure prediction • Peer pressure (pressure from the neighbors): A minimum of 4 amino acids out of 6 should show alpha preference, or 3 out of 5 beta preference, or clusters of 2-3 breakers in a sequence of 4 are needed to set the secondary structure in any region, and individual misfits adopt the secondary structure of their neighbours. • Learning secondary structure preferences from expanded data sets: More recent prediction schemes take advantage of larger data sets to examine amino acid preference for different regions in a helix or different positions in a tight turn. • Up-weighting conserved residues: In addition, sequences of homologous proteins may be compared. The rationale is that highly conserved amino acids contribute more to the three dimensional structure than unconserved, and different weightings can be introduced to the statistical analysis. • Improved accuracy: The accuracy of prediction has risen from about 55% using the simple Chou-Fasman method, where the tendency is to overpredict, to almost 80% using current methods. http://www.chembio.uoguelph.ca/educmat/phy456/456lec01.htm

41. Amino acid propensities for different structural environments • Propensities are weak but contribute to prediction accuracy • E.g., Glu (E) occurs in alpha helices only 59% more frequently than random • Helical propensities • Partial charge of helix dipole favors • Acidic Asp (D) and Glu (E) residues at N-terminus of helices • Basic Lys (K), Arg (R ) and His (H) residues at C-terminus • Pro (P) residues are more common at the N-terminal first turn of helix • Asn (N), Asp (D), Ser (S) and Thr (T) residues often occur at first turn of helix (side chain hydrogen bonding to backbone of third residue) Creighton, Proteins

42. The new generation of secondary structure prediction • Based on machine learning concepts • Training set: learn implicit rules, principles and model parameters from labelled data (sequences whose secondary structures are known for each position) • Test set: sequences of unknown structure • Used machine learning method called artificial neural networks(designed to simulate biological neural networks in the brain) • PHDsec (Rost et al 1994, Rost et al 1996) Baxevanis & Ouellette Ch 8 (Ofran and Rost)

43. Neural Network for Protein Structure Prediction

44. Key to success in machine learning algorithms • “The success of machine learning algorithms depends on the careful choice of the biologically based features used for training… and a sufficiently large and accurate training set” • To enhance prediction accuracy on novel data, training data diversity is also critical • Exploit knowledge that local environment is important: to predict 2ary structure of residue ‘i’, consider all residues in a window around i:i-n, … i, … i+n. Baxevanis & Ouellette Ch 8 (Ofran and Rost)

45. Assessing performance evaluations • “Overall, the correct evaluation of performance for prediction methods is an art in itself; only a handful of methods turned out over time to not have been overestimated by their developers.” • Evaluation must be performed on a standard dataset • Training and test data should be rigorously kept separate • Standard deviations of estimates should be provided Baxevanis & Ouellette Ch 8 (Ofran and Rost)

46. Other problems with comparing different methods • Performance reported in literature can take different forms • Accuracy and coverage • Positive (or negative) predictive power • Sensitivity and specificity • Machine learning terms (e.g., Matthews coefficients) • Wilcoxon paired score signed rank tests • Or might be based on different criteria for success • per residue • per secondary structure element • per protein • Others measure performance only in cases where a prediction has high confidence (with a likelihood of a lower FP rate) Baxevanis & Ouellette Ch 8 (Ofran and Rost)

47. How do the methods compare? • Best methods now reach 76% accuracy at 3-state prediction (helix, strand, random coil) • Rost 2001 • See EVA website for detailed comparisons • Metaservers: • Consensus approaches combining weighted predictions from different servers • These almost always outperform individual methods • Shown in both CASP and EVA Baxevanis & Ouellette Ch 8 (Ofran and Rost)

48. Caveats • Even when an experimental structure is available, it is sometimes unclear where one secondary structure element ends and another begins • Low-confidence predictions (and regions of disagreement across servers) can correspond to structurally ambiguous regions • Real-life example: Prion protein (involved in bovine spongiform encephalopathy, Creutzfeld-Jakob disease, etc). • Region assumed to be responsible for aggregation believed to flip from experimentally determined helical structure to (predicted) strand in diseased individuals • All the best secondary structure prediction methods predict this region to be beta (“incorrect”) Baxevanis & Ouellette Ch 8 (Ofran and Rost)

49. Secondary structure prediction programs • PSI-PRED (David Jones; makes use of distant homologs detected using PSI-BLAST - most popular) • JNET (Cuff & Barton) • PHD (Rost & Sander) Baxevanis & Ouellette Ch 8 (Ofran and Rost)

50. PSIPRED