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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…