NetSurfP-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning
Technical University of Denmark · University of Copenhagen · +4 more institutions
Abstract
Recent advances in machine learning and natural language processing have made it possible to profoundly advance our ability to accurately predict protein structures and their functions. While such improvements are significantly impacting the fields of biology and biotechnology at large, such methods have the downside of high demands in terms of computing power and runtime, hampering their applicability to large datasets. Here, we present NetSurfP-3.0, a tool for predicting solvent accessibility, secondary structure, structural disorder and backbone dihedral angles for each residue of an amino acid sequence. This NetSurfP update exploits recent advances in pre-trained protein language models to drastically…
Citation impact
- FWCI
- 26.51
- Percentile
- 100%
- References
- 30
Authors
8- MHMagnus Haraldson HøieCorresponding
Technical University of Denmark
- ENErik Nicolas Kiehl
Technical University of Denmark
- BPBent Petersen
University of Copenhagen, AIMST University
- MNMorten Nielsen
Technical University of Denmark
- OWOle Winther
University of Copenhagen, Copenhagen University Hospital, Rigshospitalet, Denmark Technical College, Technical University of Denmark
Topics & keywords
- Biology
- Computational biology
- Protein structure prediction
- Protein structure
- Artificial intelligence
- Biochemistry
- Computer science
- Quality Education