Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies.
Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies.
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DOI：
10.1038/s41467-023-38063-x
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发表时间：
2023-04-25
影响因子：
16.6
通讯作者：
Gray, Jeffrey J.
中科院分区：
文献类型：
--
作者：
Ruffolo, Jeffrey A.;Chu, Lee-Shin;Mahajan, Sai Pooja;Gray, Jeffrey J.
抗体具有结合多种抗原的能力，它们已成为关键的治疗和诊断分子。抗体的结合是由一组六个通过基因重组和突变而多样化的高变环促进的。即使有了最近的进展，对这些环的准确结构预测仍然是一个挑战。在这里，我们提出了IgFold，一种用于抗体结构预测的快速深度学习方法。IgFold由一个预先训练的语言模型组成，该模型训练了5.58亿个天然抗体序列，然后是直接预测主链原子坐标的图网络。IgFold比其他方法（包括AlphaFold）在更短的时间内（低于25秒）预测相似或更好质量的结构。在这个时间尺度上精确的结构预测使以前无法实现的研究途径成为可能。为了证明IgFold的能力，我们预测了140万个配对抗体序列的结构，提供了比实验确定的结构多500倍的抗体结构见解。抗体结构的预测对于理解和设计新的治疗和诊断分子至关重要。在这里，作者提出了IgFold：一种使用端到端深度学习模型进行抗体结构预测的快速，准确的方法。
Antibodies have the capacity to bind a diverse set of antigens, and they have become critical therapeutics and diagnostic molecules. The binding of antibodies is facilitated by a set of six hypervariable loops that are diversified through genetic recombination and mutation. Even with recent advances, accurate structural prediction of these loops remains a challenge. Here, we present IgFold, a fast deep learning method for antibody structure prediction. IgFold consists of a pre-trained language model trained on 558 million natural antibody sequences followed by graph networks that directly predict backbone atom coordinates. IgFold predicts structures of similar or better quality than alternative methods (including AlphaFold) in significantly less time (under 25 s). Accurate structure prediction on this timescale makes possible avenues of investigation that were previously infeasible. As a demonstration of IgFold’s capabilities, we predicted structures for 1.4 million paired antibody sequences, providing structural insights to 500-fold more antibodies than have experimentally determined structures. Prediction of antibody structures is critical for understanding and designing novel therapeutic and diagnostic molecules. Here, the authors present IgFold: a fast, accurate method for antibody structure prediction using an end-to-end deep learning model.
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影响因子：
64.8
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Jumper J;Evans R;Pritzel A;Green T;Figurnov M;Ronneberger O;Tunyasuvunakool K;Bates R;Žídek A;Potapenko A;Bridgland A;Meyer C;Kohl SAA;Ballard AJ;Cowie A;Romera-Paredes B;Nikolov S;Jain R;Adler J;Back T;Petersen S;Reiman D;Clancy E;Zielinski M;Steinegger M;Pacholska M;Berghammer T;Bodenstein S;Silver D;Vinyals O;Senior AW;Kavukcuoglu K;Kohli P;Hassabis D
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通讯作者：
Løset GÅ
影响因子：
14.9
作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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