Developing optimal nonlinear scoring function for protein design

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dc.creatorHu, Changyu
dc.creatorLi, Xiang
dc.creatorLiang, Jie
dc.date2004-07-29
dc.date.accessioned2026-07-07T05:58:34Z
dc.date.available2026-07-07T05:58:34Z
dc.descriptionMotivation. Protein design aims to identify sequences compatible with a given protein fold but incompatible to any alternative folds. To select the correct sequences and to guide the search process, a design scoring function is critically important. Such a scoring function should be able to characterize the global fitness landscape of many proteins simultaneously. Results. To find optimal design scoring functions, we introduce two geometric views and propose a formulation using mixture of nonlinear Gaussian kernel functions. We aim to solve a simplified protein sequence design problem. Our goal is to distinguish each native sequence for a major portion of representative protein structures from a large number of alternative decoy sequences, each a fragment from proteins of different fold. Our scoring function discriminate perfectly a set of 440 native proteins from 14 million sequence decoys. We show that no linear scoring function can succeed in this task. In a blind test of unrelated proteins, our scoring function misclassfies only 13 native proteins out of 194. This compares favorably with about 3-4 times more misclassifications when optimal linear functions reported in literature are used. We also discuss how to develop protein folding scoring function.
dc.description25 pages, 6 figures, 7 tables. Accepted by Bioinformatics
dc.identifierhttps://arxiv.org/abs/q-bio/0407040
dc.identifierhttp://arxiv.org/abs/q-bio/0407040
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/88406
dc.subjectBiomolecules
dc.titleDeveloping optimal nonlinear scoring function for protein design
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