The development of an affinity evaluation and prediction system by using protein-protein docking simulations and parameter tuning

COMP 267

Tatsuya Yoshikawa and Kazuhiko Fukui, Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-42 Aomi, Koto-ku, Tokyo, 135-0064, Japan
To elucidate the partners in protein–protein interactions (PPIs), we previously proposed an affinity prediction method called affinity evaluation and prediction (AEP), which is based on the shape complementarity characteristics between proteins. The structures of the protein complexes obtained in our shape complementarity evaluation were selected by a newly developed clustering method called grouping. In this study, we set a data scale (84×84 = 7056 protein pairs) including 84 biologically relevant complexes and then designed 225 parameter sets based on four key parameters related to the grouping and the calculation of affinity scores. AEP was able to provide prediction accuracy for a maximum F-measure that statistically distinguished 23 target complexes among 84 protein pairs. We have also been developing a workflow for protein-protein docking affinity prediction using the Konstanz Information Miner (KNIME). We apply KNIME to operate graphical/seamless protocols between users and analysis steps in the docking affinity prediction.

Poster Session
6:00 PM-8:00 PM, Tuesday, August 18, 2009 Walter E. Washington Convention Center -- Ballroom A, Poster

Division of Computers in Chemistry

The 238th ACS National Meeting, Washington, DC, August 16-20, 2009