| Literature DB >> 34890135 |
Akhil Jindal1, Sergei Kotelnikov, Dzmitry Padhorny, Dima Kozakov, Yimin Zhu, Rezaul Chowdhury, Sandor Vajda.
Abstract
Predicting protein side-chains is important for both protein structure prediction and protein design. Modeling approaches to predict side-chains such as SCWRL4 have become one of the most widely used tools of its type due to fast and highly accurate predictions. Motivated by the recent success of AlphaFold2 in CASP14, our group adapted a 3D equivariant neural network architecture to predict protein side-chain conformations, specifically within a protein-protein interface, a problem that has not been fully addressed by AlphaFold2.Entities:
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Year: 2022 PMID: 34890135 PMCID: PMC8887833
Source DB: PubMed Journal: Pac Symp Biocomput ISSN: 2335-6928