Protein Structure Prediction With Evolutionary Algorithms

Natalio Krasnogor, U of the West of England

William Hart, Sandia National Laboratories

Jim Smith, U of the West of England

David Pelta, Universidad de Granada

Presenter: Elena Zheleva

Introduction

Problem Description Biology Background

– Protein Folding– HP Protein Folding Model

Genetic Algorithm (GA) Design Factors– Encodings for Internal Coordinates– Potential Energy Formulation– Constraint Management

Methods and Results Conclusion

Problem Description

Computational Biology open problem: protein structure prediction

Genetic algorithms have been used in the research literature

Authors analyze 3 algorithm parameters that impact performance and behavior of GAs

Goal: make suggestions for future algorithm design

Outline

Problem Description Biology Background

– Protein Folding– HP Protein Folding Model

GA Design Factors– Encodings for Internal Coordinates– Potential Energy Formulation– Constraint Management

Methods and Results Conclusion

Protein Folding

Proteins: driving force behind all of the biochemical reactions which make biology work

Protein is an amino acid chain! Amino acid chain -> Structure of a protein Structure of a protein -> Function of a protein

Protein Folding

Protein Folding: connection between the genome (sequence) and what the proteins actually do (their function).

Currently, no reliable computational solution for protein folding (3D structure) problem.

Chemistry, Physics, Biology, CS

Outline

Problem Description Biology Background

– Protein Folding– HP Protein Folding Model

GA Design Factors– Encodings for Internal Coordinates– Potential Energy Formulation– Constraint Management

Methods and Results Conclusion

HP Protein Folding Model

Amino acid chains (proteins) are represented as connected beads on a 2D or 3D lattice

HP: hydrophobic – hydrophilic property

Hydrophobic amino acids can form a hydrophobic core w/ energy potential

HP Protein Folding Model

Model adds energy value e to each pair of hydrophobics that are adjacent on lattice AND not consecutive in the sequence

Goal of GA: find low energy configurations!

Outline

Problem Description Biology Background

– Protein Folding– HP Protein Folding Model

GA Design Factors– Encodings for Internal Coordinates– Potential Energy Formulation– Constraint Management

Methods and Results Conclusion

Encodings for Internal Coordinates

Proteins are represented using internal coordinates (vs. Cartesian)

Absolute vs. Relative encoding Absolute Encoding: specifies an absolute

direction cubic lattice: {U,D,L,R,F,B} Relative Encoding: specifies direction relative

to the previous amino acid cubic lattice: {U,D,L,R,F}

n-1

n-1

Encodings for Internal Coordinates

Encoding impacts global search behavior of GA Example: One-point Mutations Relative Encoding:

FLLFRRLRLLR->

FLLFRFLRLLR Absolute Encoding:

RULLURURULU->

RULLUULULDL

Outline

Problem Description Biology Background

– Protein Folding– HP Protein Folding Model

GA Design Factors– Encodings for Internal Coordinates– Potential Energy Formulation– Constraint Management

Methods and Results Conclusion

Potential Energy Formulation

Problem: same energy but different potential

(Picture )

Augment energy function to allow a distance-dependent hydrophobic-hydrophobic potential

(Formula)

Outline

Problem Description Biology Background

– Protein Folding– HP Protein Folding Model

GA Design Factors– Encodings for Internal Coordinates– Potential Energy Formulation– Constraint Management

Methods and Results Conclusion

Constraint Management

Methods for penalizing infeasible conformations Method 1: Consider only feasible conformations

– Weakness: shortest path from one feasible conformation to another may be very long

Method 2: Fixed Penalty Approach– Violations:

2 amino acids lying on the same lattice point Lattice point at which there are 2 or more amino acids

– Penalty per violation = 2*number of hydrophobics + 2

(any infeasible conformation has positive energy)

Outline

Problem Description Biology Background

– Protein Folding– HP Protein Folding Model

GA Design Factors– Encodings for Internal Coordinates– Potential Energy Formulation– Constraint Management

Methods and Results Conclusion

Methods and Results

1-point and 2-point Mutation operators 1-point, 2-point and Uniform Crossover

operators 5 polymer sequences (< 50 amino acids) Each run of GA: 200 generations

Methods and Results

Relative vs. Absolute Encoding

(Diagram )

Distribution of relative ranks on the 3 lattices

Methods and Results

Standard vs. Distant Energy Does the modified energy potential improve the

search capabilities of the GA? No significant difference on test sequences A guess: there might be on longer sequences

Conclusion

GAs applied to Protein Structure Prediction problem have 3 important factors to consider

Relative encoding is at least as good as absolute encoding, in some cases much better

Modified energy potential does not improve search capabilities of GA

The proposed constraint/penalty method ensures feasibility of the optimal solution

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