MUFold-SSW: a new web server for predicting protein secondary structures, torsion angles and turns
Year: 2019
Type: article
Abstract: MOTIVATION: Protein secondary structure and backbone torsion angle prediction can provide important information for predicting protein 3D structures and protein functions. Our new methods MUFold-SS, MUFold-Angle, MUFold-BetaTurn and MUFold-GammaTurn, developed based on advanced deep neural networks, achieved state-of-the-art performance for predicting secondary structures, backbone torsion angles, beta-turns and gamma-turns, respectively. An easy-to-use web service will provide the community a convenient way to use these methods for research and development. RESULTS: MUFold-SSW, a new web server, is presented. It provides predictions of protein secondary structures, torsion angles, beta-turns and gamma-turns for a given protein sequence. This server implements MUFold-SS, MUFold-Angle, MUFold-BetaTurn and MUFold-GammaTurn, which performed well for both easy targets (proteins with weak sequence similarity in PDB) and hard targets (proteins without detectable similarity in PDB) in various experimental tests, achieving results better than or comparable with those of existing methods. AVAILABILITY AND IMPLEMENTATION: MUFold-SSW is accessible at http://mufold.org/mufold-ss-angle. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Source: Bioinformatics
Authors: Chao Fang, Zhaoyu Li, Dong Xu, Yi Shang
Institution: University of Missouri
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Awards: G2592954042, G9000005980
OpenAlex ID: W2973280817