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Protein Structure Prediction. (Lecture for CS397-CXZ Algorithms in Bioinformatics) April 23, 2004 ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign. Topics in Bioinformatics. Function (Protein). Gene (DNA). Gene expression & regulation. - PowerPoint PPT Presentation
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Protein Structure Prediction
(Lecture for CS397-CXZ Algorithms in Bioinformatics)
April 23, 2004
ChengXiang Zhai
Department of Computer Science
University of Illinois, Urbana-Champaign
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Topics in Bioinformatics
> DNA sequenceAATTCATGAAAATCGTATACTGGTCTGGTACCGGCTGAGAAAATGGCAGAGCTCATCGCTAAAGGTATCTGGTAAAGACGTCAACACCATCAACGTGTCACATCGATGAACTGCTGAACGAAGATATCCTGTTGCTCTGCCATGGGCGATGAAGTTCTCGAGG
> Protein sequenceMKIVYWSGTGNTEKMAELIAKGIIESGKDVDELLNEDILILGCSAMGDEVLEESEFEPFIEKVALFGSYGWGDGKWMRDFEERMNGYGPDEAEQDCIEFGKKIANI
Gene (DNA) Function (Protein)
Gene expression& regulation
Microarray data(Matrix)
Genomics Proteomics
transcriptomics
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Proteomics: Protein Sequence Analysis
• Determine protein sequences (primary structure)– Indirect: Find genes and then translate them to proteins
– Direct: Mass spectrometry data
• Determine 3-D protein structures (secondary, tertiary, quaternary)– Computational: Sequence matching, energy minimization etc.
– Experimental: X-ray Crystallography, Nuclear Magnetic Resonance spectroscopy (NMR), Electron Microscopy/Diffraction
• Determine protein functions– Computational: Profile HMMs, protein classification, motif
analysis
– Experimental: Web lab experiments
• Determine protein-protein interactions– Gene network finding (time series microarray data)
– Metabolic engineering
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Basics of Protein Structures…
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The Building Blocks (Amino Acids)
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The 20 Amino AcidsThe 20 Amino Acids
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Protein structure hierarchical levels
VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH
PRIMARY STRUCTURE (amino acid sequence)
QUATERNARY STRUCTURE (oligomers)
SECONDARY STRUCTURE (helices, strands)
TERTIARY STRUCTURE (fold)(Adapted from Jaap Heringa’s slide)
-helix
-sheet
loop/coil
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Domain and Folds
• A discrete portion of a protein assumed to fold independently of the rest of the protein and possessing its own function.
• Most proteins have multi-domains.
• The core 3D structure of a domain is called a fold. There are only a few thousand possible folds.
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Examples of fold classes (CATH architectures)
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Protein Structure & Function
sequence
structure
function
medicine
Most functionsdepend on structures
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Structure Prediction Methods
(Adapted from a slide by P. Johansson, E. Jakobsson)
Homology modelingHigh sequence similarity
(> 30% identity)Exploit known whole structure
Fold RecognitionMedium sequence similarity
(generally < 30% identity)Exploit known partial structures
(e.g., known folds, secondary structures)
Ab InitioLow sequence similarity
Use “first principles” (e.g., energy minimization)
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First, suppose we have high similarity…
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Homology Modeling
• Simplest, reliable approach
• Basis: proteins with similar sequences tend to fold into similar structures
• Has been observed that even proteins with 30% sequence identity fold into similar structures
• Does not work for remote homologs (< 30% pairwise identity)
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Homology Modeling (cont.)
• Given:
– A query sequence Q
– A database of known protein structures
• Find protein P such that P has high sequence similarity to Q
– Based on sequence alignment (tuned for protein structure matching, less penalty for gaps)
– HMMs, BLAST, etc.
• Return P’s structure as an approximation to Q’s structure
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Now, if we don’t have high similarity, but we have medium
similarity…
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Threading (Fold Recognition)
• Given:
– Sequence of protein P with unknown structure
– Database of known folds (overall structures)
• Find:
– Most plausible fold for P
– Evaluate quality of such arrangement
• Places the residues of unknown P along the backbone of a known structure and determines stability of side chains in that arrangement
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What if we have really low similarity?
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Secondary Structure Prediction
• Given an amino acid sequence
• Predict a secondary structure state (, , coil) for each residue in the sequence
• Secondary structures can help– Determine 3D structures (e.g., help threading)
– Provide insights about functions
• Evaluation: Q3 = percentage of correct assignments
• Accuracy – 64% -75% based on primary sequence only (recent
methods perform better)
– Higher accuracy for -helices than strands
– Accuracy is dependent on protein family
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Typical Secondary Structure Prediction Results
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Secondary Structure Prediction Methods
• Early approaches (Chou and Fasman 1978)
– Make prediction for a given residue by considering a window of n (13 – 21) neighboring residues
– Learn model that performs mapping from window of residues to secondary structure state
• Later methods utilize evolutionary information (e.g., PHD system (Rost & Sander, 1993) ) and consider related sequences when making prediction
• Most recent approaches: Neural networks (PSIPRED, 77%) (Altschul et al., 1997)
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Chou-Fasman Method
• Developed by Chou & Fasman in 1974 & 1978
• Based on frequencies of residues in -helices, -sheets and turns
• Assumptions:– The entire information for forming secondary structure is
contained in the primary sequence
– Side groups of residues will determine structure
– Examining windows of 13 - 17 residues is sufficient to predict structure
– Basis for window size selection: -helices 5 – 40 residues long -strands 5 – 10 residues long
• Accuracy ~50 - 60% Q3
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Chou-Fasman Pij-valuesName P(H) P(E) P(turn)
Alanine 142 83 66
Arginine 98 93 95
Aspartic Acid 101 54 146
Asparagine 67 89 156
Cysteine 70 119 119
Glutamic Acid 151 37 74
Glutamine 111 110 98
Glycine 57 75 156
Histidine 100 87 95
Isoleucine 108 160 47
Leucine 121 130 59
Lysine 114 74 101
Methionine 145 105 60
Phenylalanine 113 138 60
Proline 57 55 152
Serine 77 75 143
Threonine 83 119 96
Tryptophan 108 137 96
Tyrosine 69 147 114
Valine 106 170 50
Values indicate how likely an amino acid occurs in one secondary structure
as opposed to others
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Improved Chou-Fasman
1. Assign all of the residues the appropriate set of parameters
2. Identify -helix and -sheet regions. Extend the regions in both directions.
3. If structures overlap compare average values for P(H) and P(E) and assign secondary structure based on best scores.
4. Turns are modeled as tetrapeptides using 2 different probability values.
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Assign Pij values
1. Assign all of the residues the appropriate set of parameters
T S P T A E L M R S T GP(H) 69 77 57 69 142 151 121 145 98 77 69 57P(E) 147 75 55 147 83 37 130 105 93 75 147 75
P(turn) 114 143 152 114 66 74 59 60 95 143 114 156
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Scan peptide for helix regions
2. Identify regions where 4/6 have a
P(H) >100 “alpha-helix nucleus”
T S P T A E L M R S T GP(H) 69 77 57 69 142 151 121 145 98 77 69 57
T S P T A E L M R S T GP(H) 69 77 57 69 142 151 121 145 98 77 69 57
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Extend -helix nucleus
3. Extend helix in both directions until a set of four residues have an average P(H) <100.
T S P T A E L M R S T GP(H) 69 77 57 69 142 151 121 145 98 77 69 57
Repeat steps 1 – 3 for entire peptide
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Scan peptide for -sheet regions
4. Identify regions where 3/5 have a
P(E) >100 “-sheet nucleus”
5. Extend -sheet until 4 continuous residues an have an average P(E) < 100
6. If region average > 105 and the average P(E) > average P(H) then “-sheet”
T S P T A E L M R S T GP(H) 69 77 57 69 142 151 121 145 98 77 69 57P(E) 147 75 55 147 83 37 130 105 93 75 147 75
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Visit
http://fasta.bioch.virginia.edu/fasta_www/chofas.htm
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Neural Network Predictors
• All current state of the art methods for secondary structure prediction (except consensus methods) employ neural network classifiers.
• (Large) data sets are used to train the neural net
• A sequence window centered on the amino acid to predict is presented to the classifier
• Homologous sequences (e.g. -Blast profile) are used to augment prediction capability
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What about exploit physical principles?
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Ab Initio Prediction
Solve a complex optimization Problem:- Measure “goodness” based on energy etc- Randomly start with some conformation- Heuristically propose a next conformation
- Search for the best conformation
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Best so far…
http://depts.washington.edu/bakerpg/
Using Rosetta for Ab Initio Structure Prediction in the Fourth Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP4)
Group of David Baker, Univ. of Washington
Visit their website and read the paper if you are interested…