无监督提示AlphaFold2用于准确折叠景观和蛋白质结构预测的少样本学习
Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure Prediction
AI总结：
针对AlphaFold2在少样本MSA下性能不足的问题，提出元生成模型EvoGen，通过无监督提示校准或虚拟同源序列，提升低数据下的折叠准确率，并支持概率性结构生成与蛋白质设计。
摘要：
数据驱动的预测方法能够高效准确地将蛋白质序列转化为具有生物活性的结构，这对科学研究和医学发展具有重要价值。利用共进化信息确定准确的折叠景观是现代蛋白质结构预测方法成功的基础。作为最先进的方法，AlphaFold2在不进行显式共进化分析的情况下大幅提高了预测精度。然而，其性能仍然强烈依赖于可用的序列同源物。基于对这种依赖性原因的探究，我们提出了EvoGen，一种元生成模型，以弥补AlphaFold2在MSA目标质量较差时的性能不足。通过向模型提示经过校准或虚拟生成的同源序列，EvoGen帮助AlphaFold2在低数据条件下准确折叠，甚至在使用单序列预测时也能取得令人鼓舞的性能。能够利用少样本MSA进行准确预测，不仅使AlphaFold2更好地泛化于孤儿序列，还使其在高通量应用中的使用更加普及。此外，EvoGen与AlphaFold2结合产生了一种概率性结构生成方法，能够探索蛋白质序列的替代构象，而用于序列生成的任务感知可微算法将有益于包括蛋白质设计在内的其他相关任务。
英文摘要：
Data-driven predictive methods which can efficiently and accurately transform protein sequences into biologically active structures are highly valuable for scientific research and medical development. Determining accurate folding landscape using co-evolutionary information is fundamental to the success of modern protein structure prediction methods. As the state of the art, AlphaFold2 has dramatically raised the accuracy without performing explicit co-evolutionary analysis. Nevertheless, its performance still shows strong dependence on available sequence homologs. Based on the interrogation on the cause of such dependence, we presented EvoGen, a meta generative model, to remedy the underperformance of AlphaFold2 for poor MSA targets. By prompting the model with calibrated or virtually generated homologue sequences, EvoGen helps AlphaFold2 fold accurately in low-data regime and even achieve encouraging performance with single-sequence predictions. Being able to make accurate predictions with few-shot MSA not only generalizes AlphaFold2 better for orphan sequences, but also democratizes its use for high-throughput applications. Besides, EvoGen combined with AlphaFold2 yields a probabilistic structure generation method which could explore alternative conformations of protein sequences, and the task-aware differentiable algorithm for sequence generation will benefit other related tasks including 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。
