Improving Prediction of Zinc Binding Sites by Modeling the Linkage Between Residues Close in Sequence

Abstract. We describe and empirically evaluate machine learning methods for the prediction of zinc binding sites from protein sequences. We start by observing that a data set consisting of single residues as examples is affected by autocorrelation and we propose an ad-hoc remedy in which sequentially close pairs of candidate residues are classified as being jointly involved in the coordination of a zinc ion. We develop a kernel for this particular type of data that can handle variable length gaps between candidate coordinating residues. Our empirical evaluation on a data set of non redundant protein chains shows that explicit modeling the correlation between residues close in sequence allows us to gain a significant improvement in the prediction performance.

• Type: Conference
• Year: 2006
• Where: RECOMB
• Authors: Sauro Menchetti, Andrea Passerini, Paolo Frasconi, Claudia Andreini, Antonio Rosato
• Keywords: Computational Biology, Machine Learning Methods, RECOMB 2006, Variable Length Gaps, Zinc Binding Sites