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.

Improving Prediction of Zinc Binding Sites by Modeling the Linkage between Residues Close in Sequence / SAURO MENCHETTI; ANDREA PASSERINI; P. FRASCONI; CLAUDIA ANDREINI; ANTONIO ROSATO. - STAMPA. - (2006), pp. 309-320. [10.1007/11732990_26]

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

MENCHETTI, SAURO;PASSERINI, ANDREA;FRASCONI, PAOLO;ANDREINI, CLAUDIA;ROSATO, ANTONIO
2006

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.
2006
978-3-540-33296-1
Research in Computational Molecular Biology
309
320
SAURO MENCHETTI; ANDREA PASSERINI; P. FRASCONI; CLAUDIA ANDREINI; ANTONIO ROSATO
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/238734
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