PT  - JOURNAL ARTICLE
AU  - Jiaxiang Wu
AU  - Fandi Wu
AU  - Biaobin Jiang
AU  - Wei Liu
AU  - Peilin Zhao
TI  - tFold-Ab: Fast and Accurate Antibody Structure Prediction without Sequence Homologs
AID  - 10.1101/2022.11.10.515918
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.11.10.515918
4099  - http://biorxiv.org/content/early/2022/11/13/2022.11.10.515918.short
4100  - http://biorxiv.org/content/early/2022/11/13/2022.11.10.515918.full
AB  - Accurate prediction of antibody structures is critical in analyzing the function of antibodies, thus enabling the rational design of antibodies. However, existing antibody structure prediction methods often only formulate backbone atoms and rely on additional tools for side-chain conformation prediction. In this work, we propose a fully end-to-end architecture for simultaneous prediction of backbone and side-chain conformations, namely tFold-Ab. Pre-trained language models are adopted for fast structure prediction by avoiding the time-consuming search for sequence homologs. The model firstly predicts monomer structures of each chain, and then refines them into heavy-light chain complex structure prediction, which enables multi-level supervision for model training. Evaluation results verify the effectiveness of tFold-Ab for both antibody and nanobody structure prediction. In addition, we provide a public web service for antibody structure prediction at https://drug.ai.tencent.com/en.Competing Interest StatementThe authors have declared no competing interest.