Keck Center for Science and Engineering, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA.
Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA 92618, USA.
Int J Mol Sci. 2024 Sep 19;25(18):10082. doi: 10.3390/ijms251810082.
Despite the success of AlphaFold2 approaches in predicting single protein structures, these methods showed intrinsic limitations in predicting multiple functional conformations of allosteric proteins and have been challenged to accurately capture the effects of single point mutations that induced significant structural changes. We examined several implementations of AlphaFold2 methods to predict conformational ensembles for state-switching mutants of the ABL kinase. The results revealed that a combination of randomized alanine sequence masking with shallow multiple sequence alignment subsampling can significantly expand the conformational diversity of the predicted structural ensembles and capture shifts in populations of the active and inactive ABL states. Consistent with the NMR experiments, the predicted conformational ensembles for M309L/L320I and M309L/H415P ABL mutants that perturb the regulatory spine networks featured the increased population of the fully closed inactive state. The proposed adaptation of AlphaFold can reproduce the experimentally observed mutation-induced redistributions in the relative populations of the active and inactive ABL states and capture the effects of regulatory mutations on allosteric structural rearrangements of the kinase domain. The ensemble-based network analysis complemented AlphaFold predictions by revealing allosteric hotspots that correspond to state-switching mutational sites which may explain the global effect of regulatory mutations on structural changes between the ABL states. This study suggested that attention-based learning of long-range dependencies between sequence positions in homologous folds and deciphering patterns of allosteric interactions may further augment the predictive abilities of AlphaFold methods for modeling of alternative protein sates, conformational ensembles and mutation-induced structural transformations.
尽管 AlphaFold2 方法在预测单个蛋白质结构方面取得了成功，但这些方法在预测变构蛋白质的多种功能构象方面表现出内在的局限性，并且难以准确捕捉导致显著结构变化的单点突变的影响。我们检查了几种 AlphaFold2 方法的实现，以预测 ABL 激酶的状态切换突变体的构象集合。结果表明，随机丙氨酸序列掩蔽与浅层多重序列比对抽样的结合可以显著扩展预测结构集合的构象多样性，并捕捉到活性和非活性 ABL 状态的种群变化。与 NMR 实验一致，预测的 M309L/L320I 和 M309L/H415P ABL 突变体的构象集合，这些突变体扰乱了调节脊柱网络，其完全封闭的非活性状态的种群增加。所提出的 AlphaFold 适应可以再现实验观察到的突变诱导的活性和非活性 ABL 状态相对种群的重新分布，并捕获调节突变对激酶结构域变构结构重排的影响。基于集合的网络分析通过揭示与状态切换突变位点相对应的变构热点来补充 AlphaFold 预测，这可能解释了调节突变对 ABL 状态之间结构变化的全局影响。这项研究表明，同源折叠中序列位置之间的长程依赖性的基于注意力的学习和解码变构相互作用的模式可能会进一步增强 AlphaFold 方法对替代蛋白质状态、构象集合和突变诱导的结构转变进行建模的预测能力。
