Side-chain Packing Using SE(3)-Transformer
By Akhil Jindal, S. Kotelnikov, D. Padhorny, Dima Kozakov, Yimin Zhu, Rezaul Alam Chowdhury, Sandor Vajda. 2021
7 citations recorded by the source corpus.
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.
Subfields: Materials Chemistry, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Materials Chemistry
- Tommi S. Jaakkola
- Regina Barzilay
- Octavian-Eugen Ganea
- Lagnajit Pattanaik
- Hannes Stärk
- Wengong Jin
- José Miguel Hernández-Lobato
- William L. Hamilton
In Molecular Biology
- Tommi S. Jaakkola
- Regina Barzilay
- Hannes Stärk
- Lagnajit Pattanaik
- Octavian-Eugen Ganea
- Gabriele Corso
- Bowen Jing
- Shuya Nakata
Citations in the corpus
- EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction (2022, background): “…have performed direct prediction of the complex structure from the two concatenated input sequences using evolutionary information (Evans et al., 2021), or have leveraged geometric deep learning to model rigid body docking (Ganea et al., 2021a) or side-chains structures (Jindal et al., 2021).”