Molecular Modeling 9. Protein structure prediction, part 2: Homology modeling, fold recognition & threading

Transcription

1 Molecular Modeling 9 Protein structure prediction, part 2: Homology modeling, fold recognition & threading

2 The project... Remember: You are smarter than the program. Inspecting the model: Are amino acids that are conserved in the binding site in the same place. That is reasonable. Are the key residues that you think will interact with your ligand placed to do so? To do the docking you need a: PDB file of the protein (the model) A file specifying the binding site, e.g. D3.32 (an ASP in the site). A smiles file for the ligand, e.g. Cc1nc[nH]c1CCN CHEMBL275443

3 Example: Dopamine D3 receptor

4 Lecture on monday... Think about questions that you would like to have answered on Tuesday: Which areas are most difficult? Do you want more specific reading instructions for some parts? On Tuesday I will give a summary of what you should know. Deadline for preliminary report!!! Hand one in...

5 The exercise yesterday... RMSD/Rank measures how well docking can (predict experimental geometry)/(identify ligand among decoys, enrichment)

6 Last lecture & This lecture Previous lecture: Molecular docking: Sampling & Scoring Lead optimization This lecture: Back to protein structure prediction Homology/comparative modeling (part II:more) Fold recognition & threading

7 The problem: Sequence to Structure

8 Why do we want to predict protein structure? Experimental effort to determine protein structure is very large and costly. Some are impossible to solve with current methods. The gap between the size of the protein sequence data and protein structure data is large and increasing. compare

9 Methods for protein structure predicton Two basic methods: 1. Homology modeling Uses evolutionary similarities, functional similarities 2. Fold recognition or threading Uses structural knowledge, fold similarity : for harder targets Today we will look at homology modeling again, how that can be improved, and to discuss point 2.

10 Steps in comparative/homology modeling Find suitable template(s) Build alignment between target and template(s) Build model(s) Replace side chains Resolve conflicts in the structure Model loops (regions without an alignment) Evaluate and select model(s)

11 Homology/Comparative modeling START TARGET SEQUENCE TEMPLATE STRUCTURE Identify related structures (templates)...acghtkildikgidywiahkalcteklftkcelsqnlydidgy... Align target sequence to template structures Build a model for the target sequence using information from template structures Evaluate the model ALIGNMENT TARGET...SCDKLLDDELDDDIACAKKILAIKGID... TEMPLATE...SCDKFLDDDITDDIMCAKKILDIKGID... TARGET MODEL NO model OK? YES QUALITY PROFILE QUALITY protein model X END RESIDUE POSITION

12 Three homology modeling approaches 1. Rigid body assembly SWISS-MODEL 2. Segment based Segmod 3. Distance restraints Modeller

13 1. Rigid body assembly Simplest approach: Transfer the backbone conformation of the template to the unknown protein. Example: SWISS-MODEL: Copy backbone Copy side chains - if they are the same Build loops Build side chains

14 2. Segment-based approach Start with a set of Calpha coordinates as an inital framework for the target protein. This framework is based on a homologous protein identified from a sequence alignment. Break down the target sequence into fragments. The initial framework is used to guide to find matching segments in a database based on known protein structures. The fragments are fitted into the growing target structure. Make many alternatives by adding segments in random order. Make an average of all models and do a short minimization. That s your final model.

15 2. Segment-based approach Inital framework Build backbone Add fragments in random order Make average of different models and minimize

16 protein model X RESIDUE POSITION 3.Distance restraints: Modeller Generate many constraints: Homology derived constraints Distances and angles between aligned positions should be similar Stereochemical constraints Bond lengths, bond angles, dihedral angles, non-bonded atom-atom contacts Model derived by optimizing constraints. Models can further be evaluated using Ramachandran plot and/or DOPE score (statistical pair-potential for protein structures) START Identify related structures (templates) Align target sequence to template structures Build a model for the target sequence using information from template structures Evaluate the model NO model OK? YES END TARGET SEQUENCE...ACGHTKILDIKGIDYWIAHKALCTEKLFTKCELSQNLYDIDGY... QUALITY ALIGNMENT TEMPLATE STRUCTURE TARGET...SCDKLLDDELDDDIACAKKILAIKGID... TEMPLATE...SCDKFLDDDITDDIMCAKKILDIKGID... QUALITY PROFILE TARGET MODEL

17 Model evaluation (when you don t know the answer) Check of stereochemistry bond lengths & angles, peptide bond planarity, side-chain ring planarity, chirality, torsion angles, clashes Check of spatial features hydrophobic core, solvent accessibility, distribution of charged groups, atomatom-distances, atomic volumes, mainchain hydrogen bonding What do you know about the protein?

18 Benchmarking of the three approaches All are not equal: A benchmark of different homology modeling programs BJÖRN WALLNER AND ARNE ELOFSSON Stockholm Bioinformatics Center, Albanova University Center, Stockholm University, Stockholm, Sweden (RECEIVED November 22, 2004; FINAL REVISION February 18, 2005; ACCEPTED February 18, 2005) Distance restraints MODELLER Classical mechanics SWISS-MODEL Segment based SegMod/ENCAD

19 Which method is best? RMSD

20 Which method is best? Backbone phi/psi Caused by minimization step

21 Which method is best? Side chains

22 Benchmark: improvements The model is rarely closer to the target than the template was...

23 Example of improvements Template - red Model - green Target - blue Shows the scale of improvements

24 Where can you find/get homology models? Manual - do it yourself! Great if you know what you re doing Automated modeling servers Good tool for easy generation of models. Swiss-Model: Pre-calculated models Never up to date and often bad. Modeller:

25 Further improvement of homology models? Many of you have been trying several templates for your GPCR targets. Perhaps the models are good in different parts - multiple templates? But wait, why not include all at the same time?

26 Multiple templates - using more than one template Not always one template is based Modeller can use multiple templates Average model Often the best is better Experts can make this work Is it possible to make it work automatically?

27 Multiple templates Advantage to use more than one template Modeller can use multiple templates However, the best single template is often better. But there is no simple way of finding the best model! Manually it is possible to do this

28 Some models get better The combined model alternates template to get the best result Local sequence identity affects result.

29 Some models get worse... template 1 template 2 Multiple template: Completely lost!

30 Multiple templates Models get better and worse with multiple templates Better Worse Templates

31 What can be homology modeled? What do we do in the twilight zone?

32 The fold universe Why are there so few protein folds? Chotia: 1000 folds for the molecular biologist Why do most sequences fit a small number of folds?

33 Typical folds 20% of folds account for 80% of proteins Mostly true for RNA too Compare with DNA: Only a single fold Homologous sequences Functional convergence onto folds Physical restrictions

34 Folding patterns Simple permutations of helices/sheets Stable local patterns (lots of h-bonds) Hydrophobic patterns Contiguous sheets

35 Fold recognition: databases Two databases: SCOP / CATH Hierarchical classification of structures Class - / Architecture Folds / Topology Superfamily / Homology Family SCOP manual, CATH semi-manual

36 CATH

37 CATH

38 CATH

39 CATH

40 CATH

41 SCOP

42 SCOP

43 SCOP

44 SCOP

45 SCOP

46 SCOP

47 Methods for protein structure prediction Two basic methods: 1. Homology modeling A clear evolutionary relationship between target and protein of known structure, which can be detected from sequence. 2. Fold recognition or threading If the structure of the target is related to a protein of known structure, but it can not be identified from sequence. The fold can often tell us something about function.

48 Fold recognition/threading FR/Threading is used if you want to predict a general structure for the protein. You are not necessarily interested in the atomic details. Question: Based on an amino acid sequence (of unknown structure) and a set of template (protein) structures, which template represents the target best? Suggestion: Thread the amino acid sequence through every protein structure and see which one is best. Used when standard sequence methods fail. Gives a rough model. Structure is more conserved than sequence.

49 Fold recognition/threading Fold 1 Query sequence Fold 2 compatibility scores Fold 3 Fold N

50 PDB new fold growth is stabilizing! If the fold has not been observed before, threading won t find it! So, it s good that there appears to be a limited number of folds, maybe around Fold recognition relies on that proteins with very different sequences can adopt the same fold.

51 Homology vs. fold detection % seq. ID Approach Fold Detection Homology Modelling Target Sequence Any Sequence?? >= 30-50% ID with template Model Quality Fold Level Atomic Level - The best method of determining 3D structure is to base the model you make on a known structure. - If your sequence is sufficiently similar (>30-50% identity) you could generate an all atom model by homology modeling. Close to 50% of all new sequences can be homology modeled. - In the twilight zone (<20-30%), use fold recognition methods!

52 Basic principles of fold recognition 1.Construction of template library 2. Sequence-structure alignment - threading (compare: sampling in docking) 3.Design of scoring function (compare: scoring in docking) 4.Template selection and model construction Only step one is easy...

53 1. Template library A representative set of protein structures extracted from the PDB database. It satisfies the following conditions: 1.The resolution of each representative structure should be good; 2.A good X-ray structure has higher priority than a NMR structure; 3.The sequence identity between any two representatives should be no more than 30%, in order to save computing time. Examples: CATH: SCOP:

54 2. Sequence-structure alignments Sampling all observed protein folds is of course much easier than sampling all possible conformations (infinite). However, sampling the different alignments efficiently is still extremely challenging - in particular since gaps will be allowed: The score of placing a residue in a position depends on how other residues are aligned. Approximations: Frozen approximation: Assumes all other residues are identical to the templates. High penalties for gaps in secondary structure.

55 3. Scoring function Local and global measures locally - i.e. secondary structure preferences, Gly/Pro in turns. Specific amino acids more probable in secondary structure elements. globally - hydrophobic core, residue contacts, charged residues exposed sequence-structure alignment must make sense in 3D. Few gaps in core secondary structures

56 3. Scoring: Knowledge-based potentials Use knowledge-based potential: Construct a database of all 20x20 or (20*19)/2 amino acid pairs. Use one interaction site per amino acid, not atomistic. Predict a given sequence using the pairwise potentials, Pab A solvation potential reflecting the degree of burial is also included. Frequency of X-Y distance b X-Y distance a

57 3. Scoring Select the template structure with the best score - a first rough model of the protein.

58 Fold recognition methods: Many other.. Structure-based 3D-1D profiles (Bowie, 1991) Threader (Jones, 1992) Prediction and sequence based Predicted Secondary structures (Fischer, 1996; Rost, 1996) Evolutionary information PSI-BLAST (Altschul, 1997) HMMs (Karplus, 1997) Profile-Profile based (Rychlewski, 2000; von Öhsen 2001) Combinations (Fischer, 2000, Kelley, 2000) Consensus methods