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Protein Structure Alignment using a Genetic algorithm. By Szustakowski et al Proteins:Structure, Function, and Genetics(38):428-440,2000 Presented by Nannan Li. Introduction. To establish evolutionary relationships between the proteins. Biological problem.

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Protein structure alignment using a genetic algorithm

Protein Structure Alignment using a Genetic algorithm

By Szustakowski et al

Proteins:Structure, Function, and Genetics(38):428-440,2000

Presented by Nannan Li


Introduction
Introduction

  • To establish evolutionary relationships between the proteins


Biological problem
Biological problem

  • For many protein pairs, distinct alignments could be generated that are indistinguishable in terms of number of equivalent residues and root mean square error of superposition

  • Protein structures are more conserved in the core than in exposed loops and turns


Motivation
Motivation

  • To develop a structure alignment algorithm with the goal of generating high-quality, biologically meaningful alignments by first aligning the protein’s cores (secondary structure elements)


Method
Method

  • Target Function--resulting in correct pairing of SSEs. “Elastic similarity score” has adopted to simultaneously maximize the number of equivalent residue pairs and minimize the distance between these pairs

  • Treating each protein as a collection SSEs to avoid exhaustive search for regions of similarity shared by two distance matrices


Genetic algorithm
Genetic Algorithm

  • Use genetic algorithm to search optimal solution to target function

  • Algorithm starts from a population of completely random pairs of alignment and happens in generations. Multiple SSE alignment are stochastically selected from the current population, modified (mutated or recombined) to from a new population, which becomes current in the next iteration of the algorithm


Genetic algorithm steps
Genetic Algorithm Steps

  • Generate an initial population for possible SSE alignments

  • Alter each alignment using “mutate"," hop”, and “swap” operators

  • Carry out “recombination” between randomly assigned pairs of alignments using the “crossover” operator

  • Accept or reject the alterations made to each alignment

  • Exit if certain conditions are met. Otherwise go to step 2


Initial population
Initial Population

  • Since SSE alignment search space is very large, we biased the initial population toward SSE pair doublets

  • Similarity scores are then calculated for all SSE pair doublets based on target function (Population size is set to 100)


Genetic algorithm operators
Genetic algorithm operators

  • “mutate”– with a mutation probability, mutate the individual SSE pairs at each residue pairs

  • “hop”– with a hop probability, two SSE pairs in one selected alignment trade places

  • “swap”– with equal probability, an alignment is swapped with its parter


Genetic algorithm operator contd
Genetic algorithm operator(‘contd)

  • Crossover– each alignment is randomly assigned a crossover partner from the rest of the population


Availability
Availability

  • C++ program called KENOBI http://zlab.bu.edu/k2/documents.shtml


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