Abstract—A protein and a ligand have a process of recognizing each other when they are remote before approaching for binding. In this process, there should be a local portion corresponding to a target of the recognition (a recognition spot) on the protein molecular surface. In this paper, we proposed a new biclustering algorithm BISERS for predicting recognition spots on protein molecular surfaces, in which a portion of the molecular surface of a query protein that frequently shows the similarity to other specific proteins binding to the similar ligand is extracted by biclustering. In BISERS, the similarity between rows as well as the similarity between columns is introduced into the evaluation function for updating the biclusters. In addition, the random sampling is applied to the process of exclusive selection of the column for reducing the computational cost effectively. Experimental results for 60 proteins show the effectiveness of our BISERS algorithm.

Index Terms—Biclustering, data mining, protein, recognition spot.

The authors are with Graduate School of System Informatics, Kobe University, 1-1, Rokkodai, Nada, Kobe, 657-8501 Japan (email: ohkawa@kobe-u.ac.jp).

Cite: Hiroto Nishimura, Takenao Ohkawa, "A New Biclustering Algorithm with Exclusive Random Selection of Columns for Predicting Recognition Spots on Protein Molecular Surfaces," International Journal of Bioscience, Biochemistry and Bioinformatics vol. 8, no. 1, pp. 11-19, 2018.