Developing optimal non-linear scoring function for protein design.
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.


RESULTS
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 develop protein folding scoring function.
Hu
Changyu
C
Department of Bioengineering, SEO, MC-063, University of Illinois at Chicago, 851 S. Morgan Street, Room 218, Chicago, IL 60607-7052, USA.
Li
Xiang
X
Liang
Jie
J
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D015195:Drug Design; D008956:Models, Chemical; D008958:Models, Molecular; D017711:Nonlinear Dynamics; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000138:chemical synthesis / Q000737:chemistry* / Q000145:classification*; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*; D013329:Structure-Activity Relationship
D011506:Proteins