Molecular Modeling Lab, Food and Drug Department, University of Parma, Parco Area delle Scienze 17/A, 43121 Parma, Italy.
Department of Mathematical, Physical and Computer Sciences, University of Parma, 43121 Parma, Italy.
Molecules. 2023 Oct 14;28(20):7082. doi: 10.3390/molecules28207082.
The biological target identification process, a pivotal phase in the drug discovery workflow, becomes particularly challenging when mutations affect proteins' mechanisms of action. COVID-19 Spike glycoprotein mutations are known to modify the affinity toward the human angiotensin-converting enzyme ACE2 and several antibodies, compromising their neutralizing effect. Predicting new possible mutations would be an efficient way to develop specific and efficacious drugs, vaccines, and antibodies. In this work, we developed and applied a computational procedure, combining constrained logic programming and careful structural analysis based on the Structural Activity Relationship (SAR) approach, to predict and determine the structure and behavior of new future mutants. "Mutations rules" that would track statistical and functional types of substitutions for each residue or combination of residues were extracted from the GISAID database and used to define constraints for our software, having control of the process step by step. A careful molecular dynamics analysis of the predicted mutated structures was carried out after an energy evaluation of the intermolecular and intramolecular interactions using the HINT (Hydrophatic INTeraction) force field. Our approach successfully predicted, among others, known Spike mutants.
生物靶标鉴定过程是药物发现工作流程中的一个关键阶段，当突变影响蛋白质的作用机制时，这个过程会变得特别具有挑战性。已知 COVID-19 刺突糖蛋白突变会改变其与人血管紧张素转化酶 ACE2 和几种抗体的亲和力，从而降低其中和效果。预测新的可能突变将是开发特异性和有效的药物、疫苗和抗体的有效方法。在这项工作中，我们开发并应用了一种计算程序，结合约束逻辑编程和基于结构活性关系 (SAR) 方法的仔细结构分析，以预测和确定新未来突变体的结构和行为。从 GISAID 数据库中提取了“突变规则”，这些规则跟踪每个残基或残基组合的统计和功能类型的取代，并用它们来定义我们软件的约束，逐步控制过程。使用 HINT（亲水性相互作用）力场对分子间和分子内相互作用进行能量评估后，对预测的突变结构进行了仔细的分子动力学分析。我们的方法成功预测了已知的 Spike 突变体等。
