Raisinghani Nishank, Parikh Vedant, Foley Brandon, Verkhivker Gennady
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 Dec 2;25(23):12968. doi: 10.3390/ijms252312968.
Proteins often exist in multiple conformational states, influenced by the binding of ligands or substrates. The study of these states, particularly the apo (unbound) and holo (ligand-bound) forms, is crucial for understanding protein function, dynamics, and interactions. In the current study, we use AlphaFold2, which combines randomized alanine sequence masking with shallow multiple sequence alignment subsampling to expand the conformational diversity of the predicted structural ensembles and capture conformational changes between apo and holo protein forms. Using several well-established datasets of structurally diverse apo-holo protein pairs, the proposed approach enables robust predictions of apo and holo structures and conformational ensembles, while also displaying notably similar dynamics distributions. These observations are consistent with the view that the intrinsic dynamics of allosteric proteins are defined by the structural topology of the fold and favor conserved conformational motions driven by soft modes. Our findings provide evidence that AlphaFold2 combined with randomized alanine sequence masking can yield accurate and consistent results in predicting moderate conformational adjustments between apo and holo states, especially for proteins with localized changes upon ligand binding. For large hinge-like domain movements, the proposed approach can predict functional conformations characteristic of both apo and ligand-bound holo ensembles in the absence of ligand information. These results are relevant for using this AlphaFold adaptation for probing conformational selection mechanisms according to which proteins can adopt multiple conformations, including those that are competent for ligand binding. The results of this study indicate that robust modeling of functional protein states may require more accurate characterization of flexible regions in functional conformations and the detection of high-energy conformations. By incorporating a wider variety of protein structures in training datasets, including both apo and holo forms, the model can learn to recognize and predict the structural changes that occur upon ligand binding.
蛋白质通常存在于多种构象状态中，受配体或底物结合的影响。对这些状态的研究，特别是无配体（未结合）和有配体（配体结合）形式的研究，对于理解蛋白质功能、动力学和相互作用至关重要。在当前的研究中，我们使用了AlphaFold2，它将随机丙氨酸序列掩蔽与浅度多序列比对抽样相结合，以扩大预测结构集合的构象多样性，并捕捉无配体和有配体蛋白质形式之间的构象变化。使用几个结构多样的无配体-有配体蛋白质对的成熟数据集，所提出的方法能够对无配体和有配体结构以及构象集合进行可靠预测，同时还显示出明显相似的动力学分布。这些观察结果与变构蛋白的内在动力学由折叠的结构拓扑定义并有利于由软模式驱动的保守构象运动的观点一致。我们的研究结果证明，AlphaFold2与随机丙氨酸序列掩蔽相结合，能够在预测无配体和有配体状态之间的适度构象调整时产生准确且一致的结果，特别是对于那些在配体结合时发生局部变化的蛋白质。对于大的类似铰链结构域的运动，所提出的方法可以在没有配体信息的情况下预测无配体和配体结合的有配体集合所特有的功能构象。这些结果对于使用这种AlphaFold变体来探究构象选择机制具有重要意义，根据该机制，蛋白质可以采用多种构象，包括那些能够结合配体的构象。这项研究的结果表明，对功能性蛋白质状态进行可靠建模可能需要更准确地表征功能构象中的柔性区域并检测高能构象。通过在训练数据集中纳入更广泛的蛋白质结构，包括无配体和有配体形式，该模型可以学会识别和预测配体结合时发生的结构变化。
