Rosetta custom score functions accurately predict ΔΔG of mutations at protein–protein interfaces using machine learning
Work
Year: 2020
Type: article
Abstract: Reweighting Rosetta energy terms <italic>via</italic> machine learning improves prediction of ΔΔ<italic>G</italic> values for mutations at protein interfaces, providing insight into biological process... more
Source: Chemical Communications
Institutions University of Pennsylvania, California University of Pennsylvania, Philadelphia University
Cites: 40
Cited by: 16
Related to: 10
FWCI: 1.027
Citation percentile (by year/subfield): 100
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Funders National Science Foundation, National Science Foundation, Parkinson's Disease Foundation, University of Pennsylvania
Grant IDS CHE-1150351, DGE-1321851, PF-RVSASFW-1754,