NetSurfP‐2.0: Improved prediction of protein structural features by integrated deep learning
Novo Nordisk Foundation · Technical University of Denmark · +4 more institutions
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…
Citation impact
- FWCI
- 30.59
- Percentile
- 100%
- References
- 36
Authors
11Topics & keywords
- Deep learning
- Artificial intelligence
- Computer science
- Protein structure prediction
- Chemistry
- Protein structure