Accurate and efficient in silico ranking of protein-protein binding affinities is useful for protein design with applications in biological therapeutics. One popular approach to rank binding affinities is to apply the molecular mechanics Poisson Boltzmann/generalized Born surface area (MMPB/GBSA) method to molecular dynamics trajectories. Here, we identify protocols that enable the reliable evaluation of T-cell receptor (TCR) variants binding to their target, peptide-human leukocyte antigens (pHLAs). We suggest different protocols for variant sets with few (≤4) or many mutations, with entropy corrections important for the latter. We demonstrate how potential outliers could be identified in advance and that just 5-10 replicas of short (4 ns) MD simulations may be sufficient for reproducible and accurate ranking of TCR variants. The protocols developed here can be applied towards in silico screening during the optimization of therapeutic TCRs, potentially reducing both the cost and time taken for biologic development.

中文翻译：

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用 MMPB/GBSA 对工程治疗性 TCR 结合亲和力进行可靠的计算机排序

蛋白质-蛋白质结合亲和力的准确和有效的计算机排序可用于蛋白质设计和生物治疗中的应用。一种流行的对结合亲和力进行排序的方法是将分子力学泊松玻尔兹曼/广义玻恩表面积 (MMPB/GBSA) 方法应用于分子动力学轨迹。在这里，我们确定了能够可靠评估与其靶标肽-人类白细胞抗原 (pHLAs) 结合的 T 细胞受体 (TCR) 变体的协议。我们建议对具有很少（≤4）或许多突变的变体集使用不同的协议，熵校正对后者很重要。我们演示了如何提前识别潜在的异常值，并且只需 5-10 个短 (4 ns) MD 模拟副本就足以对 TCR 变体进行可重复和准确的排序。