Page 1 of 1 (page number not for citation purposes) Chemistry Central Journal Open Access Poster presentation A new approach for flexible protein-ligand docking based on Particle Swarm Optimisation Rene Meier* 1 , Frank Brandt 2 , Teresa M Pisabarro 2 , Carsten Baldauf 2 and Wolfgang Sippl 1 Address: 1 Institute for Pharmaceutical Chemistry, Martin-Luther-University Halle-Wittenberg Wolfgang-Langenbeckstr. 4, 06120 Halle (Saale), Germany and 2 Structural Bioinformatics, Biotec TU Dresden, Tatzberg 47-51, 01307 Dresden, Germany Email: Rene Meier* - [email protected] * Corresponding author Particle Swarm Optimiser (PSO) uses a general-purpose, iterative, heuristic search algorithm. It considers a population of individuals to probe promising regions of the search space in an effective manner. In this context, the population of solutions is called a swarm, and the individuals are called particles. Each particle moves within the search space and retains in its memory the best position and the overall best position that has been encountered. The velocity of each particle is adjusted during each itera- tion toward the personal best position as well as the overall best position, thus mimicking swarm intelligence. In our recent work we have implemented PSO in a ligand- docking program. The fitness landscape of the docking program is modeled by a modified version of the scoring function X-Score [1]. X-Score is an empirical scoring function which shows a significant correlation between calcu- lated docking scores and experimentally derived ligand geometries. Preliminary investigations show promising results in terms of speed and accuracy. Special attention during the development will be paid to a modular design of the program in order to easily implement different scoring functions as well as to perform parallel computing. References 1. Wang R, Lai L, Wang S: J Comput-Aided Mol Des 2002, 16:11-26. from 3rd German Conference on Chemoinformatics Goslar, Germany. 11-13 November 2007 Published: 26 March 2008 Chemistry Central Journal 2008, 2(Suppl 1):P6 doi:10.1186/1752-153X-2-S1-P6 <supplement> <title> <p>3rd German Conference on Chemoinformatics: 21. CIC-Workshop</p> </title> <note>Meeting abstracts - A single PDF containing all abstracts in this Supplement is available <a href="http://www.biomedcentral.com/content/files/pdf/1752-153X-2-S1-full.pdf">here</a>.</note> <url>http://www.biomedcentral.com/content/pdf/1752-153X-2-S1-info.pdf</url> </supplement> This abstract is available from: http://www.journal.chemistrycentral.com/content/2/S1/P6 © 2008 Meier et al.

A new approach for flexible protein-ligand docking based on Particle Swarm Optimisation

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Page 1: A new approach for flexible protein-ligand docking based on Particle Swarm Optimisation

Page 1 of 1(page number not for citation purposes)

Chemistry Central Journal

Open AccessPoster presentationA new approach for flexible protein-ligand docking based on Particle Swarm OptimisationRene Meier*1, Frank Brandt2, Teresa M Pisabarro2, Carsten Baldauf2 and Wolfgang Sippl1

Address: 1Institute for Pharmaceutical Chemistry, Martin-Luther-University Halle-Wittenberg Wolfgang-Langenbeckstr. 4, 06120 Halle (Saale), Germany and 2Structural Bioinformatics, Biotec TU Dresden, Tatzberg 47-51, 01307 Dresden, Germany

Email: Rene Meier* - [email protected]

* Corresponding author

Particle Swarm Optimiser (PSO) uses a general-purpose,iterative, heuristic search algorithm. It considers a popula-tion of individuals to probe promising regions of thesearch space in an effective manner. In this context, thepopulation of solutions is called a swarm, and the individ-uals are called particles. Each particle moves within thesearch space and retains in its memory the best positionand the overall best position that has been encountered.The velocity of each particle is adjusted during each itera-tion toward the personal best position as well as the over-all best position, thus mimicking swarm intelligence. Inour recent work we have implemented PSO in a ligand-docking program. The fitness landscape of the dockingprogram is modeled by a modified version of the scoringfunction X-Score [1]. X-Score is an empirical scoring func-tion which shows a significant correlation between calcu-lated docking scores and experimentally derived ligandgeometries. Preliminary investigations show promisingresults in terms of speed and accuracy. Special attentionduring the development will be paid to a modular designof the program in order to easily implement different scor-ing functions as well as to perform parallel computing.

References1. Wang R, Lai L, Wang S: J Comput-Aided Mol Des 2002, 16:11-26.

from 3rd German Conference on ChemoinformaticsGoslar, Germany. 11-13 November 2007

Published: 26 March 2008

Chemistry Central Journal 2008, 2(Suppl 1):P6 doi:10.1186/1752-153X-2-S1-P6

<supplement> <title> <p>3rd German Conference on Chemoinformatics: 21. CIC-Workshop</p> </title> <note>Meeting abstracts - A single PDF containing all abstracts in this Supplement is available <a href="http://www.biomedcentral.com/content/files/pdf/1752-153X-2-S1-full.pdf">here</a>.</note> <url>http://www.biomedcentral.com/content/pdf/1752-153X-2-S1-info.pdf</url> </supplement>

This abstract is available from: http://www.journal.chemistrycentral.com/content/2/S1/P6

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