课题组研究人员成功地在七个不同的数据集中验证了CB，鉴定了K-Ras、SARS-CoV-2刺突、β2肾上腺素能受体和Src激酶的变体，这些变体具有改善的构象特异性功能，例如增强的结合或酶活性。将CB应用于硫辛酸连接酶（LplA），研究组发现了一个以前未知的控制其混杂活性的机制。偏向于“开放”构象状态的变体变得更加混杂，而“封闭”偏倚的变体更具选择性，增强了LplA在活细胞中使用荧光团标记位点特异性蛋白质的效用。CB的速度和简单性使其成为工程蛋白动力学的通用工具，在基础研究、生物技术和医学中有着广泛的应用。
据介绍，构象偏倚（CB）是一种快速、简化的计算方法，通过反折叠模型的对比评分来预测蛋白质变异偏向于期望的构象状态。
附：英文原文
Title: Computational design of conformation-biasing mutations to alter protein functions
Author: Peter E. Cavanagh, Andrew G. Xue, Shizhong A. Dai, Albert Qiang, Tsutomu Matsui, Alice Y. Ting
Issue&Volume: 2026-01-08
Abstract: Conformational biasing (CB) is a rapid and streamlined computational method that uses contrastive scoring by inverse folding models to predict protein variants biased toward desired conformational states. We successfully validated CB across seven diverse datasets, identifying variants of K-Ras, SARS-CoV-2 spike, β2 adrenergic receptor, and Src kinase with improved conformation-specific functions, such as enhanced binding or enzymatic activity. Applying CB to the enzyme lipoic acid ligase (LplA), we uncovered a previously unknown mechanism controlling its promiscuous activity. Variants biased toward an “open” conformation state became more promiscuous, whereas “closed”-biased variants were more selective, enhancing LplA’s utility for site-specific protein labeling with fluorophores in living cells. The speed and simplicity of CB make it a versatile tool for engineering protein dynamics with broad applications in basic research, biotechnology, and medicine.
DOI: adv7953
Source: https://www.science.org/doi/10.1126/science.adv7953