Title: Leveraging Sequence Purification for Accurate Prediction of Multiple Conformational States with AlphaFold2 Show Chinese title

Title: 利用序列纯化进行AlphaFold2的多个构象状态的准确预测

Abstract: AlphaFold2 (AF2) has transformed protein structure prediction by harnessing co-evolutionary constraints embedded in multiple sequence alignments (MSAs). MSAs not only encode static structural information, but also hold critical details about protein dynamics, which underpin biological functions. However, these subtle co-evolutionary signatures, which dictate conformational state preferences, are often obscured by noise within MSA data and thus remain challenging to decipher. Here, we introduce AF-ClaSeq, a systematic framework that isolates these co-evolutionary signals through sequence purification and iterative enrichment. By extracting sequence subsets that preferentially encode distinct structural states, AF-ClaSeq enables high-confidence predictions of alternative conformations. Our findings reveal that the successful sampling of alternative states depends not on MSA depth but on sequence purity. Intriguingly, purified sequences encoding specific structural states are distributed across phylogenetic clades and superfamilies, rather than confined to specific lineages. Expanding upon AF2's transformative capabilities, AF-ClaSeq provides a powerful approach for uncovering hidden structural plasticity, advancing allosteric protein and drug design, and facilitating dynamics-based protein function annotation. Show Chinese abstract

Abstract: AlphaFold2（AF2）通过利用多重序列比对（MSAs）中嵌入的共进化约束，彻底改变了蛋白质结构预测。MSAs不仅编码静态结构信息，还包含关于蛋白质动态的关键细节，这些细节是生物功能的基础。然而，这些微妙的共进化特征，决定了构象状态的偏好，常常被MSA数据中的噪声所掩盖，因此难以解析。在此，我们介绍了AF-ClaSeq，这是一种系统框架，通过序列纯化和迭代增强来隔离这些共进化信号。通过提取优先编码不同结构状态的序列子集，AF-ClaSeq能够实现对替代构象的高置信度预测。我们的研究发现，替代状态的成功采样并不取决于MSA的深度，而是取决于序列的纯度。有趣的是，编码特定结构状态的纯化序列分布在系统发育分支和超家族中，而不是局限于特定谱系。在AF2的变革性能力基础上，AF-ClaSeq提供了一种强大的方法，用于揭示隐藏的结构可塑性，推动别构蛋白和药物设计，并促进基于动态的蛋白质功能注释。