HPHE 4450: Section 11 - Metabolic Prediction Equations Revisited
Protein Structure Prediction 11/11/05 - Iowa State...
Transcript of Protein Structure Prediction 11/11/05 - Iowa State...
Protein Structure Prediction 11/11/05
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Protein StructurePrediction & Modeling
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Bioinformatics SeminarsNov 11 Fri 12:10 BCB Seminar in E164 Lago
Building Supertrees Using DistancesSteve Willson, Dept of Mathematicshttp://www.bcb.iastate.edu/courses/BCB691-F2005.html
Next week - Baker Center/BCB Seminars: (seminar abstracts available at above link)
Nov 14 Mon 1:10 PM Doug Brutlag, StanfordDiscovering transcription factor binding sites
Nov 15 Tues 1:10 PM Ilya Vakser, Univ Kansas Modeling protein-protein interactions both seminars will be in Howe Hall Auditorium
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Protein Structure & Function:Analysis & Prediction
Mon Protein structure: basics; classification,databases, visualization
Wed Protein structure databases - cont.
Thurs Lab Protein structure databases Visualization software Secondary structure prediction
Fri Protein structure prediction Protein-nucleic acid interactions
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Reading Assignment (for Mon-Fri)
Mount Bioinformatics• Chp 10 Protein classification & structure prediction
http://www.bioinformaticsonline.org/ch/ch10/index.html
• pp. 409-491• Ck Errata: http://www.bioinformaticsonline.org/help/errata2.html
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BCB 544 Additional Reading
Required:• Gene Prediction:• Burge & Karlin 1997 JMB 268:78
Prediction of complete gene structures in human genomic DNA
Optional:
• Structure Prediction:• Schueler-Furman…Baker 2005 Science 310:638
Progress in modeling of protein structures and interactions
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Review last lecture:
Protein Structure:Databases, Classification &
Visualization
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Protein sequence databases
• UniProt (SwissProt, PIR, EBI)http://www.pir.uniprot.org
• NCBI Protein http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=Protein
More on these later: protein function prediction
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Protein sequence & structure: analysis• Diamond STING Millennium - many useful structure
analysis tools, including Protein Dossierhttp://trantor.bioc.columbia.edu/SMS/
• SwissProt (UniProt)protein knowledgebasehttp://us.expasy.org/sprot
• InterPROsequence analysis toolshttp://www.ebi.ac.uk/interpro
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Protein structure databases• PDB Protein Data Bank http://www.rcsb.org/pdb/ (RCSB) - THE protein structure database
• MMDB Molecular Modeling Databasehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=Structure
(NCBI Entrez) - has "added" value
• MSD Molecular Structure Database http://www.ebi.ac.uk/msdEspecially good for interactions, binding sites
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Protein structure classification• SCOP = Structural Classification of Proteins
Levels reflect both evolutionary and structural relationshipshttp://scop.mrc-lmb.cam.ac.uk/scop
• CATH = Classification by Class, Architecture, Topology & Homologyhttp://cathwww.biochem.ucl.ac.uk/latest/
• DALI/FSSP (recently moved to EBI & reorganized)• fully automated structure alignments
• DALI server http://www.ebi.ac.uk/dali/index.html• DALI Database (fold classification)
http://ekhidna.biocenter.helsinki.fi/dali/start
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Protein structure visualization• Molecular Visualization Freeware:
http://www.umass.edu/microbio/rasmol
• MolviZ.Orghttp://www.umass.edu/microbio/chime
• Protein Explorer http://www.umass.edu/microbio/chime/pe/protexpl/frntdoor.htm• RASMOL (& many decendents: Protein Explorer,PyMol, MolMol, etc.)
http://www.umass.edu/microbio/rasmol/index2.htm• CHIME
http://www.umass.edu/microbio/chime/getchime.htm
• Cn3Dhttp://www.biosino.org/mirror/www.ncbi.nlm.nih.gov/Structure/cn3d/
• Deep View = Swiss-PDB Viewerhttp://www.expasy.org/spdbv
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Protein structure visualizationSuperb interactive structure visualization software by Jane & Dave Richardson, Duke University
• KINIMAGEhttp://kinemage.biochem.duke.edu/
• Fantastic research tools for structure analysis & refinement
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RCSB PDB - Beta sitehttp://pdbbeta.rcsb.org/pdb/Welcome.do
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MMDBhttp://www.ncbi.nlm.nih.gov/Structure/MMDB/mmdb.shtml
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Cn3Dhttp://www.ncbi.nlm.nih.gov/Structure/CN3D/cn3d.shtml
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Cn3D : Displaying 2' Structures
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Cn3D: Structural Alignments
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SCOP - Structure Classification http://scop.mrc-lmb.cam.ac.uk/scop
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6 main classes of protein structure1) α Domains
• Bundles of helices connected by loops
2) β Domains• Mainly antiparallel sheets, usually with 2 sheets forming
sandwich
3) α/β Domains• Mainly parallel sheets with intervening helices, also
mixed sheets
4) α+β Domains• Mainly segregated helices and sheets
5) Multidomain (α & β)• Containing domains from more than one class
6) Membrane & cell-surface proteins
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CATH - Structure Classification http://cathwww.biochem.ucl.ac.uk/latest/
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Structural Genomics
~ 30,000 "traditional" genes in human genome(not counting: ???)
~ 3,000 proteins in a typical cell> 2 million sequences in UniProt> 33,000 protein structures in the PDB Experimental determination of protein structure
lags far behind sequence determination!Goal: Determine structures of "all" protein folds in nature, using
combination of experimental structure determination methods(X-ray crystallography, NMR, mass spectrometry) & structureprediction
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Structural Genomics Projects
TargetDB: database of structural genomics targetshttp://targetdb.pdb.org
Protein Structure Prediction?
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Protein Folding
"Major unsolved problem in molecular biology"
In cells: spontaneousassisted by enzymesassisted by chaperones
In vitro: many proteins fold spontaneously & many do not!
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Steps in Protein Folding1- "Collapse"- driving force is burial of hydrophobic aa’s
(fast - msecs)2- Molten globule - helices & sheets form, but "loose"
(slow - secs)3- "Final" native folded state - compaction, some 2'
structures rearranged
Native state? - assumed to be lowest free energy - may be an ensemble of structures
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Protein Dynamics
• Protein in native state is NOT static• Function of many proteins depends on conformational
changes, sometimes large, sometimes small• Globular proteins are inherently "unstable"
(NOT evolved for maximum stability)• Energy difference between native and denatured
state is very small (5-15 kcal/mol)(this is equivalent to 1 or 2 H-bonds!)
• Folding involves changes in both entropy & enthalpy
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Protein Structure Prediction
• Structure is largely determined by sequence BUT:
• Similar sequences can assume different structures• Dissimilar sequences can assume similar structures• Many proteins are multi-functional• Protein folding:
• determination of folding pathways• prediction of tertiary structure
still largely unsolved problems
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New today:
Protein Structure Prediction
Secondary structure(text focuses on this - I won't)
Tertiary structure(let's do this instead!)
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Deciphering the Protein Folding Code
• Protein Structure Prediction or "Protein Folding" Problem
given the amino acid sequenceof a protein, predict its3-dimensional structure (fold)
• "Inverse Folding" Problemgiven a protein fold, identifyevery amino acid sequencethat can adopt its3-dimensional structure
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Protein Structure Determination?High-resolution structure determination
• X-ray crystallography (<1A°)• Nuclear magnetic resonance (NMR) (~1-2.5A°)
Lower-resolution structure determination• Cryo-EM (electron-microscropy) ~10-15A°
Theoretical Models?• Highly variable - now, some equiv to X-ray!
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Tertiary Structure PredictionFold or tertiary structure prediction
problem can be formulated as a searchfor minimum energy conformation• search space is defined by psi/phi angles of backbone
and side-chain rotamers• search space is enormous even for small proteins!• number of local minima increases exponentially
of the number of residues
Computationally it is an exceedingly difficult problem!
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Ab Initio Prediction1. Develop energy function
• bond energy• bond angle energy• dihedral angle energy• van der Waals energy• electrostatic energy
2. Calculate structure by minimizing energy function(usually Molecular Dynamics or Monte Carlo methods)
Ab initio prediction - not practical in general• Computationally? very expensive• Accuracy? Usually poor for all but short peptides
(but see Baker review!)
Provides both folding pathway & folded structure
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Comparative Modeling
Provide folded structure only
Two primary methods1) Homology modeling2) Threading (fold recognition)
Note: both rely on availability of experimentallydetermined structures that are "homologous" orat least structurally very similar to target
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Homology Modeling1. Identify homologous protein sequences
• PSI-BLAST• multiple sequence alignment (MSA)
2. Among those with available structures, chooseclosest sequence match for template
3. Build model by placing residues into correspondingpositions of homologous structure models & refineby "tweaking"
Homology modeling - works "well"• Computationally? not very expensive• Accuracy? higher sequence identity ⇒ better model
Requires >30% sequence identity
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Threading - Fold RecognitionIdentify “best” fit between target sequence & template structure
1. Develop energy function2. Develop template library3. Align target sequence with each template & score4. Determine best score (1D to 3D alignment)5. Build refine structure as in homology modeling Threading - works "sometimes"
• Computationally? Can be expensive or cheap, depends on energyfunction & whether "all atom" or "backbone only" threading
• Accuracy? in theory, should not depend on sequenceidentity (should depend on quality of template library & "luck")
• But, usually higher sequence identity ⇒ better model
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1. Align target sequence with template structures(fold library) from the Protein Data Bank (PDB)
2. Calculate energy (score) to evaluate goodness of fitbetween target sequence & template structure
3. Rank models based on energy scores
TargetSequence
StructureTemplates
ALKKGF…HFDTSE
Threading - a "local" example
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Threading Goals & Issues
Structure database - must be complete: no decent model if nogood template in library!
Sequence-structure alignment algorithm:Bad alignment ⇒ Bad score!
Energy function (scoring scheme):• must distinguish correct sequence-fold alignment from
incorrect sequence-fold alignments• must distinguish “correct” fold from close decoys
Prediction reliability assessment - how determine whetherpredicted structure is correct (or even close?)
Find “correct” sequence-structure alignment of atarget sequence with its native-like fold in PDB
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Threading – Structure database
Build a template database (e.g., ASTRAL domain library derived from PDB)
Supplement with additional decoys, e.g., generated usingab initio approach such as Rosetta (Baker)
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Threading - Energy function
Two main methods (and combinations of these)
• Structural profile (environmental) physico-chemical properties of aa’s
• Contact potential (statistical)based on contact statistics from PDB
(Miyazawa & Jernigan - Jernigan now at ISU)
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Protein Threading – typical energy functionMTYKLILNGKTKGETTTEAVDAATAEKVFQYANDNGVDGEWTYTE
How well does aspecific residue fitstructural environment?
What is "probability"that two specificresidues are incontact?
Alignment gappenalty?
Total energy: E_p + E_s + E_g
Find a sequence-structure alignment that minimizingthe energy function
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A Rapid Threading Approach forProtein Structure Prediction
Kai-Ming Ho, PhysicsHaibo Cao Yungok Ihm
Zhong GaoJames MorrisCai-zhuang Wang
Drena Dobbs, GDCBJae-Hyung LeeMichael TerribiliniJeff Sander
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Performance Evaluation? "Blind Test"
CASP5 Competition (Critical Assessment of Protein Structure Prediction)
Given: Amino acid sequence Goal: Predict 3-D structure (before experimental results published)
Typical Results: well, actually, BEST Results:
HO = #1 ranked CASP prediction for this target
Target 174PDB ID = 1MG7
Actual Structure
Predicted Structure
T174_1
T174_2
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FR Fold Recognition(targets manually assessed by Nick Grishin)
-----------------------------------------------------------
Rank Z-Score Ngood Npred NgNW NpNW Group-name 1 24.26 9.00 12.00 9 12 Ginalski 2 21.64 7.00 12.00 7 12 Skolnick Kolinski 3 19.55 8.00 12.50 9 14 Baker 4 16.88 6.00 10.00 6 10 BIOINFO.PL 5 15.25 7.00 7.00 7 7 Shortle 6 14.56 6.50 11.50 7 13 BAKER-ROBETTA 7 13.49 4.00 11.00 4 11 Brooks 8 11.34 3.00 6.00 3 6 Ho-Kai-Ming 9 10.45 3.00 5.50 3 6 Jones-NewFold -----------------------------------------------------------
FR NgNW - number of good predictions without weighting for multiple modelsFR NpNW - number of total predictions without weighting for multiple models
Overall Performance in CASP5 Contest (M. Levitt, Stanford)