基于多序列比对训练的蛋白质语言模型的生成能力
Generative power of a protein language model trained on multiple sequence alignments
AI总结：
本文提出并验证了一种基于 MSA Transformer 掩码语言建模目标的迭代序列生成方法，证明其在同源性、共进化、结构度量及高阶统计分布上优于或媲美 Potts 模型，展示了该模型在蛋白质序列生成与设计中的强大能力。
摘要：
从大规模进化相关蛋白质序列集合出发的计算模型能够捕捉蛋白质家族的表征，并学习与蛋白质结构和功能相关的约束，从而为生成属于蛋白质家族的新序列提供了可能。基于多序列比对训练的蛋白质语言模型（如 MSA Transformer）是实现这一目标极具吸引力的候选模型。我们提出并测试了一种迭代方法，该方法直接利用掩码语言建模目标，使用 MSA Transformer 生成序列。我们证明，所生成序列在同源性、共进化和基于结构的度量上表现与天然序列相当。对于大型蛋白质家族，我们的合成序列与 Potts 模型生成的序列（包括经过实验验证的序列）相比具有相似或更优的性质。此外，对于小型蛋白质家族，我们基于 MSA Transformer 的生成方法优于 Potts 模型。与 Potts 模型相比，我们的方法还能更准确地再现自然数据的高阶统计量和序列在序列空间中的分布。因此，MSA Transformer 是蛋白质序列生成和蛋白质设计的有力候选模型。
英文摘要：
Computational models starting from large ensembles of evolutionarily related protein sequences capture a representation of protein families and learn constraints associated to protein structure and function. They thus open the possibility for generating novel sequences belonging to protein families. Protein language models trained on multiple sequence alignments, such as MSA Transformer, are highly attractive candidates to this end. We propose and test an iterative method that directly employs the masked language modeling objective to generate sequences using MSA Transformer. We demonstrate that the resulting sequences score as well as natural sequences, for homology, coevolution and structure-based measures. For large protein families, our synthetic sequences have similar or better properties compared to sequences generated by Potts models, including experimentally-validated ones. Moreover, for small protein families, our generation method based on MSA Transformer outperforms Potts models. Our method also more accurately reproduces the higher-order statistics and the distribution of sequences in sequence space of natural data than Potts models. MSA Transformer is thus a strong candidate for protein sequence generation and protein design.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
