Rustamov Khondamir R, Baev Artyom Y
Laboratory of Experimental Biophysics, Center for Advanced Technologies, Tashkent, 100174, Uzbekistan.
Department of Biophysics, National University of Uzbekistan, Tashkent, 100174, Uzbekistan.
Bioinform Adv. 2024 Dec 6;5(1):vbae197. doi: 10.1093/bioadv/vbae197. eCollection 2025.
Understanding the conformational landscape of protein-ligand interactions is critical for elucidating the binding mechanisms that govern these interactions. Traditional methods like molecular dynamics (MD) simulations are computationally intensive, leading to a demand for more efficient approaches. This study explores how multiple sequence alignment (MSA) clustering enhance AF-Multimer's ability to predict conformational landscapes, particularly for proteins with multiple conformational states.
We verified this approach by predicting the conformational landscapes of chemokine receptor 4 (CXCR4) and glucagon receptor (GCGR) in the presence of their agonists and antagonists. In our experiments, AF-Multimer predicted the structures of CXCR4 and GCGR predominantly in active state in the presence of agonists and in inactive state in the presence of antagonists. Moreover, we tested our approach with proteins known to switch between monomeric and dimeric states, such as lymphotactin, SH3, and thermonuclease. AFcluster-Multimer accurately predicted conformational states during oligomerization, which AFcluster with AlphaFold2 alone fails to achieve. In conclusion, MSA clustering enhances AF-Multimer's ability to predict protein conformational landscapes and mechanistic effects of ligand binding, offering a robust tool for understanding protein-ligand interactions.
Code for running AFcluster-Multimer is available at https://github.com/KhondamirRustamov/AF-Multimer-cluster.
了解蛋白质-配体相互作用的构象景观对于阐明控制这些相互作用的结合机制至关重要。像分子动力学（MD）模拟这样的传统方法计算量很大，因此需要更有效的方法。本研究探讨了多序列比对（MSA）聚类如何增强AF-Multimer预测构象景观的能力，特别是对于具有多种构象状态的蛋白质。
我们通过预测趋化因子受体4（CXCR4）和胰高血糖素受体（GCGR）在其激动剂和拮抗剂存在下的构象景观来验证这种方法。在我们的实验中，AF-Multimer在激动剂存在下主要预测CXCR4和GCGR的结构处于活性状态，而在拮抗剂存在下则处于非活性状态。此外，我们用已知在单体和二聚体状态之间转换的蛋白质，如淋巴细胞趋化因子、SH3和热核酸酶，测试了我们的方法。AFcluster-Multimer准确地预测了寡聚化过程中的构象状态，而仅使用AlphaFold2的AFcluster则无法做到这一点。总之，MSA聚类增强了AF-Multimer预测蛋白质构象景观和配体结合机制效应的能力，为理解蛋白质-配体相互作用提供了一个强大的工具。
运行AFcluster-Multimer的代码可在https://github.com/KhondamirRustamov/AF-Multimer-cluster获取。
