Developing optimal non-linear scoring function for protein design

Bioinformatics
Changyu HuJie Liang

Abstract

Motivation. 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. To find optimal design scoring functions, we introduce two geometric views and propose a formulation using a mixture of non-linear 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 folds. Our scoring function discriminates 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 three-four times more misclassifications when optimal linear functions reported in the literature are used. We also discuss how to devel...Continue Reading

Citations

Jul 23, 2009·Proceedings of the National Academy of Sciences of the United States of America·Hammad NaveedJie Liang
Mar 24, 2007·BMC Structural Biology·Federico FogolariPaolo Viglino
Oct 4, 2005·Human Genomics·Yanlong O XuDavid D Pollock
Aug 15, 2009·PLoS Computational Biology·Mati CohenGideon Schreiber
Aug 17, 2011·The Journal of Chemical Physics·D V S Ravikant, Ron Elber
Nov 21, 2009·Proteins·Jingfen ZhangDong Xu
Sep 8, 2010·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Qiwen Dong, Shuigeng Zhou
Dec 24, 2019·Proteins·Yuan ZhangJinfeng Zhang

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