Department of Physiology, "Grigore T. Popa" University of Medicine and Pharmacy of Iasi, Str. Universitatii nr. 16, 700051 Iasi, România; TRANSCEND Centre - Regional Institute of Oncology (IRO) Iasi, Str. General Henri Mathias Berthelot, Nr. 2-4 Iași, România.
University of Cagliari, Department of Chemical and Geological Sciences, Campus Monserrato, SS 554 bivio per Sestu, 09042 Monserrato, Italy.
Colloids Surf B Biointerfaces. 2023 Jan;221:112986. doi: 10.1016/j.colsurfb.2022.112986. Epub 2022 Oct 29.
A highly efficient and robust multiple scales in silico protocol, consisting of atomistic Molecular Dynamics (MD), coarse-grain (CG) MD, and constant-pH CG Monte Carlo (MC), has been developed and used to study the binding affinities of selected antigen-binding fragments of the monoclonal antibody (mAbs) CR3022 and several of its here optimized versions against 11 SARS-CoV-2 variants including the wild type. Totally 235,000 mAbs structures were initially generated using the RosettaAntibodyDesign software, resulting in top 10 scored CR3022-like-RBD complexes with critical mutations and compared to the native one, all having the potential to block virus-host cell interaction. Of these 10 finalists, two candidates were further identified in the CG simulations to be the best against all SARS-CoV-2 variants. Surprisingly, all 10 candidates and the native CR3022 exhibited a higher affinity for the Omicron variant despite its highest number of mutations. The multiscale protocol gives us a powerful rational tool to design efficient mAbs. The electrostatic interactions play a crucial role and appear to be controlling the affinity and complex building. Studied mAbs carrying a more negative total net charge show a higher affinity. Structural determinants could be identified in atomistic simulations and their roles are discussed in detail to further hint at a strategy for designing the best RBD binder. Although the SARS-CoV-2 was specifically targeted in this work, our approach is generally suitable for many diseases and viral and bacterial pathogens, leukemia, cancer, multiple sclerosis, rheumatoid, arthritis, lupus, and more.
一种高效、稳健的多尺度计算方案，包含原子分子动力学（MD）、粗粒化（CG）MD 和恒定 pH 的 CG 蒙特卡罗（MC），已被开发并用于研究单克隆抗体（mAbs）CR3022 的选定抗原结合片段及其几种优化版本与 11 种 SARS-CoV-2 变体（包括野生型）的结合亲和力。总共使用 RosettaAntibodyDesign 软件生成了 235,000 个 mAbs 结构，对具有关键突变的 RBD 复合物进行了前 10 名打分，并与天然 RBD 复合物进行了比较，所有这些复合物都有可能阻断病毒与宿主细胞的相互作用。在这 10 个决赛选手中，有两个候选者在 CG 模拟中被进一步确定为针对所有 SARS-CoV-2 变体的最佳候选者。令人惊讶的是，尽管奥密克戎变体的突变数量最多，但所有 10 个候选者和天然的 CR3022 对其仍表现出更高的亲和力。多尺度方案为我们提供了一种强大的理性设计高效 mAbs 的工具。静电相互作用起着至关重要的作用，似乎控制着亲和力和复合物的形成。携带更多负总净电荷的研究 mAbs 显示出更高的亲和力。在原子模拟中可以确定结构决定因素，并详细讨论其作用，以进一步暗示设计最佳 RBD 结合物的策略。虽然这项工作专门针对 SARS-CoV-2，但我们的方法通常适用于许多疾病以及病毒和细菌病原体、白血病、癌症、多发性硬化症、类风湿关节炎、狼疮等。
