Cargando…
Predicting Protein–protein Association Rates using Coarse-grained Simulation and Machine Learning
Protein–protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large benchmark set of 49 protein complexes. The predict...
Autores principales: | Xie, Zhong-Ru, Chen, Jiawen, Wu, Yinghao |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5394550/ https://www.ncbi.nlm.nih.gov/pubmed/28418043 http://dx.doi.org/10.1038/srep46622 |
Ejemplares similares
-
Using Coarse-Grained Simulations to Characterize the Mechanisms of Protein–Protein Association
por: Dhusia, Kalyani, et al.
Publicado: (2020) -
Machine learning coarse-grained potentials of protein thermodynamics
por: Majewski, Maciej, et al.
Publicado: (2023) -
Machine learning coarse grained models for water
por: Chan, Henry, et al.
Publicado: (2019) -
Simulation of FUS Protein Condensates with an Adapted
Coarse-Grained Model
por: Benayad, Zakarya, et al.
Publicado: (2020) -
Coarse Grained Molecular Dynamics Simulations of Transmembrane Protein-Lipid Systems
por: Spijker, Peter, et al.
Publicado: (2010)