A Solution to Protein Folding Problem Using a Genetic Algorithm By Pavan Kumar Goduguluru

1. A Solution to Protein Folding Problem Using a Genetic Algorithm By Pavan Kumar Goduguluru

2. Introduction Amino Acids are the building blocks of Proteins These amino acids are in the form of poly peptide chains The primary structure of proteins folds itself in to 3-Dimensioanl form tertiary structure These structures defines the functional properties of proteins Approximately 40,000 proteins primary structure is known In these only few of the tertiary structures are known Transition of a protein into its functional structure is called Protein Folding

3. Protein Folding problem mainly deals with three Questions a)What is the Folding code? b)What is the Folding Mechanism? c)Whether the native structure of a protein can be predicted from amino acids sequence? • Genetic Algorithms are used to solve protein folding problem

4. HP Model • In this amino acids are divided into two categories a) Hydrophobic ( H ) b) Hydrophilic ( p ) • The Primary sequence of a protein is S ∑ {H,P}* • In HP lattice vertices represent amino acids and edges represent the bonds • Black squares are hydrophobes and white squares are hydrophilic amino acids • The H-H contacts are basis for evaluation function.

5. The pair of hydrophobes which are adjacent on lattice are assigned with energy value • Sum of all these energy values gives the energy of the conformation. • The energy of the above 20 length HP sequence is -5

6. Traditional Genetic Algorithm • In each generation each structure is subjected number of mutations • These number of mutation steps range from 0.01 to 0.20 • After the mutation the crossover process is performed • For a structure to be selected for cross over is proportional to its energy P(Si) = Lower the energy conformations have the higher chance of being selected

7. A pair of structures are been selected and are divided into individual structures randomly • These residual structures are joined in three ways 0”,90” and 270” • The best structure is selected and its energy is calculated • This energy is compared with the average energy of its parent structures • The resultant structure is accepted if its energy is less than its parents average • This cross operation is performed until the N-1 hybrid structures are obtained

8. The cross over operation

9. Modified keep best reproduction strategy (MKBR) • MKBR was the intermediate selection strategy implemented in place of KBR • In KBR best of the two off springs is selected and the other is replaced by the best parent • This approach has potential danger • So MKBR has been implemented • In MKBR in selection process includes parents also • With this we get best next generation

10. Tested Sequences

11. Energy Evalations

12. Optimal conformation for sequences

13. Conclusion • MKBR outperforms the standard generational replacement technique significantly on protein folding problems, especially as the problem size increases in terms of time an optimality. • Our selection strategy benefits from higher mutation rates, since we always keep the best parent conformation, thus limiting the disruptive effect that mutation can have. • The collected data also demonstrates that modified keep-best reproduction are best suited for problems with higher genetic operator probabilities, especially the mutation probability.