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.I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.