xTrimoABFold: De novo Antibody Structure Prediction without MSA

要約

タイトル：MSAなしでのde novo抗体構造予測のxTrimoABFold

要約：
– AlphaFold2はタンパク質構造を予測することができるが、多数の配列の共進化的多重配列アライメント（MSA）を必要とする。
– xTrimoABFoldは、深層抗体言語モデルを使って、効率的なEvoformerと構造モジュールを組み合わせた新しいモデルである。ALMはPDBから収集された抗体の観測された領域のカリキュレータによって、アルゴリズム用の強力な表現を学ぶために使用された。
– xTrimoABFoldはAlphaFold2よりも151倍速く、CDRに関する焦点損失とフレームアラインドポイント損失のアンサンブル損失を最小化することで、抗体構造を予測するために訓練された。そして、他のタンパク質言語モデルに比べて、大幅な改善（RMSDの30+\%)がある。
– xTrimoABFoldはde novo抗体設計のために貴重なツールであり、免疫理論のさらなる改善につながる可能性がある。

要約(オリジナル)

In the field of antibody engineering, an essential task is to design a novel antibody whose paratopes bind to a specific antigen with correct epitopes. Understanding antibody structure and its paratope can facilitate a mechanistic understanding of its function. Therefore, antibody structure prediction from its sequence alone has always been a highly valuable problem for de novo antibody design. AlphaFold2, a breakthrough in the field of structural biology, provides a solution to predict protein structure based on protein sequences and computationally expensive coevolutionary multiple sequence alignments (MSAs). However, the computational efficiency and undesirable prediction accuracy of antibodies, especially on the complementarity-determining regions (CDRs) of antibodies limit their applications in the industrially high-throughput drug design. To learn an informative representation of antibodies, we employed a deep antibody language model (ALM) on curated sequences from the observed antibody space database via a transformer model. We also developed a novel model named xTrimoABFold to predict antibody structure from antibody sequence based on the pretrained ALM as well as efficient evoformers and structural modules. The model was trained end-to-end on the antibody structures in PDB by minimizing the ensemble loss of domain-specific focal loss on CDR and the frame-aligned point loss. xTrimoABFold outperforms AlphaFold2 and other protein language model based SOTAs, e.g., OmegaFold, HelixFold-Single, and IgFold with a large significant margin (30+\% improvement on RMSD) while performing 151 times faster than AlphaFold2. To the best of our knowledge, xTrimoABFold achieved state-of-the-art antibody structure prediction. Its improvement in both accuracy and efficiency makes it a valuable tool for de novo antibody design and could make further improvements in immuno-theory.

arxiv情報

提供元, 利用サービス

arxiv.jp, OpenAI