NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning

Proteins
Michael Schantz KlausenPaolo Marcatili

Abstract

The ability to predict local structural features of a protein from the primary sequence is of paramount importance for unraveling its function in absence of experimental structural information. Two main factors affect the utility of potential prediction tools: their accuracy must enable extraction of reliable structural information on the proteins of interest, and their runtime must be low to keep pace with sequencing data being generated at a constantly increasing speed. Here, we present NetSurfP-2.0, a novel tool that can predict the most important local structural features with unprecedented accuracy and runtime. NetSurfP-2.0 is sequence-based and uses an architecture composed of convolutional and long short-term memory neural networks trained on solved protein structures. Using a single integrated model, NetSurfP-2.0 predicts solvent accessibility, secondary structure, structural disorder, and backbone dihedral angles for each residue of the input sequences. We assessed the accuracy of NetSurfP-2.0 on several independent test datasets and found it to consistently produce state-of-the-art predictions for each of its output features. We observe a correlation of 80% between predictions and experimental data for solvent accessi...Continue Reading

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Citations

Aug 2, 2019·Antioxidants & Redox Signaling·Jason M Held
Dec 24, 2019·Briefings in Bioinformatics·Qiang ShiZhidong Xue
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Mar 2, 2021·Frontiers in Microbiology·Hengwei QianWenxing Liang

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