Medvedev Kirill E, Schaeffer R Dustin, Grishin Nick V
Department of Biophysics, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390, USA.
Department of Biochemistry, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390, USA.
J Cheminform. 2025 May 4;17(1):67. doi: 10.1186/s13321-025-01019-y.
Post-translational modifications (PTMs) play a crucial role in allowing cells to expand the functionality of their proteins and adaptively regulate their signaling pathways. Defects in PTMs have been linked to numerous developmental disorders and human diseases, including cancer, diabetes, heart, neurodegenerative and metabolic diseases. PTMs are important targets in drug discovery, as they can significantly influence various aspects of drug interactions including binding affinity. The structural consequences of PTMs, such as phosphorylation-induced conformational changes or their effects on ligand binding affinity, have historically been challenging to study on a large scale, primarily due to reliance on experimental methods. Recent advancements in computational power and artificial intelligence, particularly in deep learning algorithms and protein structure prediction tools like AlphaFold3, have opened new possibilities for exploring the structural context of interactions between PTMs and drugs. These AI-driven methods enable accurate modeling of protein structures including prediction of PTM-modified regions and simulation of ligand-binding dynamics on a large scale. In this work, we identified small molecule binding-associated PTMs that can influence drug binding across all human proteins listed as small molecule targets in the DrugDomain database, which we developed recently. 6,131 identified PTMs were mapped to structural domains from Evolutionary Classification of Protein Domains (ECOD) database.Scientific contribution: Using recent AI-based approaches for protein structure prediction (AlphaFold3, RoseTTAFold All-Atom, Chai-1), we generated 14,178 models of PTM-modified human proteins with docked ligands. Our results demonstrate that these methods can predict PTM effects on small molecule binding, but precise evaluation of their accuracy requires a much larger benchmarking set. We also found that phosphorylation of NADPH-Cytochrome P450 Reductase, observed in cervical and lung cancer, causes significant structural disruption in the binding pocket, potentially impairing protein function. All data and generated models are available from DrugDomain database v1.1 ( http://prodata.swmed.edu/DrugDomain/ ) and GitHub ( https://github.com/kirmedvedev/DrugDomain ). This resource is the first to our knowledge in offering structural context for small molecule binding-associated PTMs on a large scale.
翻译后修饰（PTMs）在使细胞扩展其蛋白质功能并适应性调节其信号通路方面发挥着关键作用。PTMs的缺陷与多种发育障碍和人类疾病相关，包括癌症、糖尿病、心脏病、神经退行性疾病和代谢疾病。PTMs是药物发现中的重要靶点，因为它们可显著影响药物相互作用的各个方面，包括结合亲和力。PTMs的结构后果，如磷酸化诱导的构象变化或其对配体结合亲和力的影响，历来在大规模研究中具有挑战性，主要是由于依赖实验方法。计算能力和人工智能的最新进展，特别是深度学习算法和诸如AlphaFold3等蛋白质结构预测工具，为探索PTMs与药物之间相互作用的结构背景开辟了新的可能性。这些由人工智能驱动的方法能够对蛋白质结构进行精确建模，包括预测PTM修饰区域并大规模模拟配体结合动力学。在这项工作中，我们在我们最近开发的DrugDomain数据库中确定了可影响所有列为小分子靶点的人类蛋白质上药物结合的小分子结合相关PTMs。6131个确定的PTMs被映射到来自蛋白质结构域进化分类（ECOD）数据库的结构域。科学贡献：使用基于人工智能的最新蛋白质结构预测方法（AlphaFold3、RoseTTAFold全原子、Chai-1），我们生成了14178个带有对接配体的PTM修饰人类蛋白质模型。我们的结果表明，这些方法可以预测PTMs对小分子结合的影响，但要精确评估其准确性需要大得多的基准测试集。我们还发现，在宫颈癌和肺癌中观察到的NADPH-细胞色素P450还原酶的磷酸化会导致结合口袋中的显著结构破坏，可能损害蛋白质功能。所有数据和生成的模型可从DrugDomain数据库v1.1（http://prodata.swmed.edu/DrugDomain/）和GitHub（https://github.com/kirmedvedev/DrugDomain）获得。据我们所知，该资源是首个大规模提供小分子结合相关PTMs结构背景的资源。
