Developing optimal non-linear scoring function for protein design

By Changyu Hu, Xiang Li, Jie Liang. 2004

35 citations recorded by the source corpus.

UNLABELLED 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.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiuwen Liu
2. Ivan V. Anishchenko
3. Song-Chun Zhu

In Computational Theory and Mathematics

1. Petras J. Kundrotas
2. Ivan V. Anishchenko
3. Ilya A. Vakser
4. Torsten Schwede
5. Yang Zhang

In Management Science and Operations Research

1. Ivan V. Anishchenko

Citations in the corpus

• ProDCoNN: Protein design using a convolutional neural network (2019, methodology): “Hu and coworkers developed a nonlinear scoring function for selecting sequences comparable with a given structure.(19) Most of the above methods used energy-based method, which starts”
• Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model. (2018, background): “Thus, they are successfully applied to many problems, such as discrimination of the native structure from decoys (9,10), fold recognition (11), structure prediction (12), protein docking (13–15) and design (16,17), and prediction of protein stability and affinity (18–20).”