Identifying Near-Native Protein Structures via Anomaly Detection

摘要

Discriminating biologically-activeative tertiary protein structures from non-native ones is an outstanding challenge in computational structural biology. Computationally, the task involves teasing out near-native structures out of several thousands generated in silico. In this paper we build on the concept of anomaly detection in machine learning and propose several methods for discriminating near-native structures. Evaluations on benchmark datasets demonstrate that the proposed methods advance the state of the art and warrant further research on adapting concepts and techniques from machine learning to improve recognition of near-native structures in template-free protein structure prediction.