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Summary of Protein Structure Prediction Approaches

This summary provides an overview of the four main approaches to protein structure prediction, including structural genomics, fold recognition, structural annotation of Drosophila, and fold recognition methods. It also discusses the methodologies and practical aspects of fold recognition.

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Summary of Protein Structure Prediction Approaches

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  1. Structure Prediction II

  2. Summary of the four main approaches to structure prediction. Note that there are overlaps between nearly all categories. Structure prediction

  3. Structural genomics • ab initio not very good • homology based methods OK • limited structure universe • $$$ + robots = structures of current “white space” • skip difficult proteins/structures

  4. Fold recognition (FR) • aligning sequences to known structures (the ``fold library'') • like homology modelling except you're lucky if you can find a parent! • used when `standard' sequence methods fail • generally uses structural information • very rough models can be made

  5. Structural annotation of Drosophila • 21% annotated to structure (PSI-BLAST) • should be possible to annotate 20% more to remote homologues of known structure

  6. Fold Recognition Methods • 3D-1D profiles (Bowie, 1991) • Threader (Jones, 1992) • Predicted Secondary structures (Fischer, 1996; Rost, 1996) • PSI-BLAST (Altschul, 1997) • HMMs (Karplus, 1997) • Profile-Profile based (Rychlewski, 2000; von Ohsen 2001) • Combinations (Fischer, 2000, Kelley, 2000) • Many more....

  7. Basic principles of FR • structure conserved more than sequence • structural constraints on sequence • locally - i.e. sec. str. preferences, Gly/Pro in turns • globally - hydrophobic core, residue contacts • sequence-structure alignment must make sense in 3D • no gaps in core secondary structures • no missing strands from sheets

  8. FR methodologies

  9. 3D profiles

  10. 3D-profiles definitions

  11. 3d-profiles • The original sequence-structure approach. Doesn't really work, but historically interesting... • Bowie JU, Lüthy R, Eisenberg D. A method to identify protein sequences that fold into a known three-dimensional structure. Science. 1991 Jul 12;253(5016):164-70

  12. Threading or “Fold recognition” VIFVLWGNAARQKCNLLFQTKHQHAVLACPH

  13. Pair potentials Outline: thread the sequence onto a known backbone structure, optimise the alignment so that the intramolecular side-chain interactions are most favourable. With the double-dynamic programming alignment algorithm of THREADER and the Bryant method (refs. below) it's possible to ignore sequence information from the known structure. Other so-called `frozen approximation' pair-potential methods use it. The THREADER paper (short on details, unfortunately): Jones DT, Taylor WR, Thornton JM. A new approach to protein fold recognition. Nature. 1992 Jul 2;358(6381):86-9. Another successful method using a different alignment algorithm: Panchenko A, Marchler-Bauer A, Bryant SH. Threading with explicit models for evolutionary conservation of structure and sequence. Proteins. 1999;Suppl 3:133-40. http://www.ncbi.nlm.nih.gov/Structure/RESEARCH/casp3/pap.html

  14. GenThreader Original THREADER takes way too long. GenThreader takes short-cuts: • fast sequence-sequence alignment against fold library • build 3D-model from alignment • evaluate pair potentials in model • evaluate solvent potentials in model • train a neural network to make decision based on: alignment score, sequence lengths, pair-potential score, solvation score Jones DT. GenThreader: an efficient and reliable protein fold recognition method for genomic sequences. J Mol Biol. 1999 Apr 9;287(4):797-815 Jones DT, Tress M, Bryson K, Hadley C. Successful recognition of protein folds using threading methods biased by sequence similarity and predicted secondary structure. Proteins. 1999;37(S3):104-111.

  15. Profiles and secondary structure matching • ca. 1994 secondary structure prediction accuracy was respectable • simple FR methods matching sequence and secondary structure did quite well • in CASP2&3, careful use of PSI-BLAST was competitive with FR methods

  16. 3D-PSSM methodology Design criteria: should run fast, use multiple seqs., predicted sec. str and other structural information • Kelley LA, MacCallum RM, Sternberg MJ. Enhanced genome annotation using structural profiles in the program 3D-PSSM. J Mol Biol. 2000 Jun 2;299(2):499-520.

  17. 3D-PSSM benchmarking • Probe-template pairs: same SCOP superfamily, PSI-BLAST

  18. Practical aspects of FR • Pre-processing of sequences • ignore non-globular regions: transmembrane regions, coiled coil, low complexity regions, signal peptides • identify domain boundaries & repeats - run separately • make best possible multiple sequence alig./sec. str. pred. • consensus between methods • can you trust confidence values? • compare function of query (if known) with templates (SAWTED) • check models make sense in 3D • is function conserved between query and template • hand edit alignments?

  19. How good is FR? • LiveBench and CASP measure performance. • E-values work reasonably well. • In the real world, you might get a few percent more ``hits'' with FR compared to PSI-BLAST. • Individual researcher vs. genome-wide analysis • Structure information not necessary?

  20. Manual vs Automatic fold recognition at CASP4 (2000) • Manual experts much better. • Most automatic servers use one method and set of parameters • Manual experts use several methods/parameters. • Automatic methods returns "the best prediction" • Manual experts look down the list. • Automatic methods do not examine the final model.

  21. CAFASP-CONSENSUS • Use the meta server to collect predictions from web-servers. • For each prediction list the SCOP fold. • "Trivial", "Easy" and "Hard" predictions. • Trivial: Significant scores • Easy: Many servers give the same result. • Hard: No consistency • Select one prediction. • Make predictions publicly available. • Results in CASP4: • Ranked as number 7 in Fold recognition in CASP4 • Better than all single servers

  22. Pcons (Lundström et al, 2001) • Pcons Methodology: • Collect predictions • Record the scores for each model. • Calculate the number of neighbors • Results • Trivial: Significant predictions • Easy: Many servers give the same result • Hard: No consistent fold. • Predict the "Quality" of all models

  23. Pcons performance

  24. Dependency on Pcons Inputs

  25. Pcons performance • Results: • More correct predictions • Significantly higher specificity • Model similarity most important • Reasons for improvements: • Model similarity not used before. • Generation of several models for each target-template pair. • Better scoring function • Different methods better for different targets.

  26. Methods to predict "quality" of models. • Surface area based methods • 3D-1D profiles • Pairwise distances • THREADER • Prosa • Atom contacts • errat • Chemistry • ProCheck • WhatCheck ???????????

  27. Methods to test predictors of "quality" • Threading • Gapped threading/Fold Recognition • Decoys • Folding Simulations

  28. Uses full atom models with these parameters: Atom-Atom contacts (13 types) Residue- Residue contacts (6 types) Surface area (4 categories) SecStr-Q3 compared to psipred difference in C between model template fatness of model fraction modeled ProQ (Wallner & Elofsson, 2003) • Neural networks that predicts quality of proteins • Trained on LiveBench models • Predicts "quality" with • Cc=0.76 • Z-score 2.7 • Z-native 5.1

  29. Development of ProQ • Trained on 11108 LB2 models • Quality measured with MaxSub and LGscore • 1 894 correct models • 8 270 incorrect models • All atom models built by MODELLER • MaxSub and LGscore predicted • Testing different input parameters

  30. ProQ specificity

  31. ProQ: Conclusions • Comparable performance to other methods when detective native structure • Better when detecting correct models. • Can be combined with Pcons into Pmodeller • Available from http://www.sbc.su.se/~bjorn/ProQ/

  32. ProQ: News • C-version (Cc=0.65/0.55) • Local version to predict local Quality. • Version focused on better methods under development • Multiple Sequence Alignment version under development

  33. Pmodeller (Wallner et al, 2003) • Pmodeller=Pcons+MODELLER+ProQ

  34. Pmodeller performance

  35. Tertiary structure prediction • ``true'' ab initio • three main approaches • simulation: fold up polypeptide from extended form • screening: make many structures, select ``best'' • fragment assembly: ``ROSETTA'' hybrid local prediction, simulation & screening • None of these really work ``off the shelf'', although ROSETTA has been applied to all Pfam families of unknown structure.

  36. Rosetta outline • Best new fold (ab initio) method in CASP3&4 • Start with extended chain • Monte Carlo fragment assembly • Repeat MC many times (and for homol seqs) • Filter models • Cluster models • Pick large clusters

  37. Fragment assembly • Don't try to predict every detail of the backbone/sidechains • Use fragments of known structure • In ROSETTA 3 or 9 residues long • Selected by PSI-BLAST search against PDB sequences with a generous E-value threshold • Other people have used fragment assembly too

  38. Monte Carlo optimisation Initial configuration (random or extended) Make a randomised MOVE on configuration Measure change in quality of structure (DE) IF better () ACCEPT MOVE ELSIF rand ACCEPT MOVE ELSE REJECT MOVE GO TO 2. (reduce T if you like)

  39. Rosetta MC Energy Function • Compactness (radius of gyration) • Hydrophobic burial • Polar side chain contacts (statistical pairwise potential) • Hydrogen bonding between beta-strands • Hard-sphere repulsion (VdW)

  40. Rosetta: Filtering the models • Between 6,000 and 150,000 models generated • Contact Order • Generated models are biased towards simple structures • Filter models to give correct contact order distribution for domains of that size/composition • Sheet filter • Add side chains, calculate atomic physical potential (to eliminate poorly packed structures)

  41. Rosetta: clustering the models • Compare models to each other with RMSD • Models can come from different family members • Cutoff varied to give 80-100 members in largest cluster • The largest clusters are assumed to contain the best structures (attractors in folding space...?)

  42. Recent improvements to Rosetta • Additional refinement step from best clusters using all atom refinements • Make small dihedral changes • Rebuild sidechains • Minimize (in dihedral space) • Evaluate energy • Go To 1 • 5 out 16 small proteins < 1.5 Å

  43. Methods to assign structures • Fold Recognition • 3D-PSSM • HMMs • Superfamily • SCOP/CATH • PFAM ?

  44. Fraction residues assigned

  45. Analysis • Some domains very common • Some very rare (only one copy) • Some duplicated very often • WHY ?

  46. Evolution of power law behavior • Duplications • Larger families are more likely to duplicate • Can reproduce most of what is seen • 50% still remains unclassified • Many orphan domains • What are the origin of these

  47. Most frequent domains • E.coli (Eubacteria) • P-loop hydrolase • Rossman • Meth. Thermo. (Archeae) • P-loop • Ferrodoxins • Sacch. Cerv. (Unicellular eukaryot) • P-loop hydrolase • Protein Kinase • C. Elegans • Membrane all alpha • P-loop • Immunoglobulin

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