DataSheet1_Solvated interaction energy: from small-molecule to antibody drug design.PDF

Scoring functions are ubiquitous in structure-based drug design as an aid to predicting binding modes and estimating binding affinities. Ideally, a scoring function should be broadly applicable, obviating the need to recalibrate and refit its parameters for every new target and class of ligands. Traditionally, drugs have been small molecules, but in recent years biologics, particularly antibodies, have become an increasingly important if not dominant class of therapeutics. This makes the goal of having a transferable scoring function, i.e., one that spans the range of small-molecule to protein ligands, even more challenging. One such broadly applicable scoring function is the Solvated Interaction Energy (SIE), which has been developed and applied in our lab for the last 15 years, leading to several important applications. This physics-based method arose from efforts to understand the physics governing binding events, with particular care given to the role played by solvation. SIE has been used by us and many independent labs worldwide for virtual screening and discovery of novel small-molecule binders or optimization of known drugs. Moreover, without any retraining, it is found to be transferrable to predictions of antibody-antigen relative binding affinities and as accurate as functions trained on protein-protein binding affinities. SIE has been incorporated in conjunction with other scoring functions into ADAPT (Assisted Design of Antibody and Protein Therapeutics), our platform for affinity modulation of antibodies. Application of ADAPT resulted in the optimization of several antibodies with 10-to-100-fold improvements in binding affinity. Further applications included broadening the specificity of a single-domain antibody to be cross-reactive with virus variants of both SARS-CoV-1 and SARS-CoV-2, and the design of safer antibodies by engineering of a pH switch to make them more selective towards acidic tumors while sparing normal tissues at physiological pH.

评分函数（Scoring Function）在基于结构的药物设计中应用广泛，可辅助预测结合模式并估算结合亲和力。理想状态下，评分函数应具备广泛适用性，无需针对每一个新靶点和配体（Ligand）类别重新校准与拟合其参数。传统药物多为小分子化合物，但近年来生物制品（Biologics）尤其是抗体（Antibody）已成为一类愈发重要——即便尚未占据主导地位——的治疗性药物类别。这使得开发可迁移评分函数（即可覆盖从小分子到蛋白质配体全范围的评分函数）的目标更具挑战性。溶剂化相互作用能（Solvated Interaction Energy，SIE）便是这类广泛适用的评分函数之一。本实验室在过去15年间对其开展了开发与应用研究，衍生出多项重要应用场景。该基于物理原理的方法源自对结合事件背后物理机制的探索，尤其关注溶剂化作用所发挥的功能。本团队与全球众多独立实验室均已将SIE应用于虚拟筛选（Virtual Screening）、新型小分子结合剂发现以及已知药物的优化工作中。此外，无需任何重新训练，该函数即可用于预测抗体-抗原的相对结合亲和力，其准确度与基于蛋白质-蛋白质结合亲和力训练得到的评分函数相当。SIE已与其他评分函数相结合，集成至我们研发的抗体亲和力调控平台ADAPT（Assisted Design of Antibody and Protein Therapeutics）中。通过ADAPT的应用，已实现多款抗体的优化，使其结合亲和力提升10至100倍。其进一步应用包括：将单域抗体的特异性拓宽至可与SARS-CoV-1和SARS-CoV-2的多种病毒变体产生交叉反应；以及通过工程化改造pH开关设计更安全的抗体，使其对酸性肿瘤具备更高选择性，同时在生理pH值条件下不损伤正常组织。