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Advances in Bioinformatics
Volume 2011, Article ID 958129, 9 pages
Research Article

Prediction of Enzyme Mutant Activity Using Computational Mutagenesis and Incremental Transduction

Department of Computer Science, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA

Received 8 May 2011; Revised 27 June 2011; Accepted 4 August 2011

Academic Editor: Sandor Vajda

Copyright © 2011 Nada Basit and Harry Wechsler. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Wet laboratory mutagenesis to determine enzyme activity changes is expensive and time consuming. This paper expands on standard one-shot learning by proposing an incremental transductive method (T2bRF) for the prediction of enzyme mutant activity during mutagenesis using Delaunay tessellation and 4-body statistical potentials for representation. Incremental learning is in tune with both eScience and actual experimentation, as it accounts for cumulative annotation effects of enzyme mutant activity over time. The experimental results reported, using cross-validation, show that overall the incremental transductive method proposed, using random forest as base classifier, yields better results compared to one-shot learning methods. T2bRF is shown to yield 90% on T4 and LAC (and 86% on HIV-1). This is significantly better than state-of-the-art competing methods, whose performance yield is at 80% or less using the same datasets.