Cargando…
PASSer2.0: Accurate Prediction of Protein Allosteric Sites Through Automated Machine Learning
Allostery is a fundamental process in regulating protein activities. The discovery, design, and development of allosteric drugs demand better identification of allosteric sites. Several computational methods have been developed previously to predict allosteric sites using static pocket features and...
Autores principales: | Xiao, Sian, Tian, Hao, Tao, Peng |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9309527/ https://www.ncbi.nlm.nih.gov/pubmed/35898310 http://dx.doi.org/10.3389/fmolb.2022.879251 |
Ejemplares similares
-
PASSer: fast and accurate prediction of protein allosteric sites
por: Tian, Hao, et al.
Publicado: (2023) -
PASSerRank: Prediction of Allosteric Sites with Learning to Rank
por: Tian, Hao, et al.
Publicado: (2023) -
Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning
por: Verkhivker, Gennady M., et al.
Publicado: (2020) -
Allosterically Linked Binding Sites in Serotonin Transporter Revealed by Single Molecule Force Spectroscopy
por: Zhu, Rong, et al.
Publicado: (2020) -
Validation of an Accurate Automated Multiplex Immunofluorescence Method for Immuno-Profiling Melanoma
por: Yaseen, Zarwa, et al.
Publicado: (2022)