A Sampling-Based Method for Ranking Protein Structural Models by Integrating Multiple Scores and Features
Year: 2011
Type: article
Abstract: One of the major challenges in protein tertiary structure prediction is structure quality assessment. In many cases, protein structure prediction tools generate good structural models, but fail to select the best models from a huge number of candidates as the final output. In this study, we developed a sampling-based machine-learning method to rank protein structural models by integrating multiple scores and features. First, features such as predicted secondary structure, solvent accessibility and residue-residue contact information are integrated by two Radial Basis Function (RBF) models trained from different datasets. Then, the two RBF scores and five selected scoring functions developed by others, i.e., Opus-CA, Opus-PSP, DFIRE, RAPDF, and Cheng Score are synthesized by a sampling method. At last, another integrated RBF model ranks the structural models according to the features of sampling distribution. We tested the proposed method by using two different datasets, including the CASP server prediction models of all CASP8 targets and a set of models generated by our in-house software MUFOLD. The test result shows that our method outperforms any individual scoring function on both best model selection, and overall correlation between the predicted ranking and the actual ranking of structural quality.
Authors: Xiaohu Shi, Jingfen Zhang, Zhiquan He, Yi Shang, Dong Xu
Institution: Jilin University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Awards: G2564083438, G4198798171, G985362401
OpenAlex ID: W2134500225