15个靶点上Martini 3绝对蛋白质-配体结合自由能的可预测性

中文摘要 AI 辅助

炼金术自由能计算可预测蛋白质-配体结合亲和力，精度接近实验值，但计算成本限制其仅适用于小配体集。Martini 3等粗粒化（CG）模型以远低于前者的成本保留严格的统计力学处理，但其在蛋白质-配体结合方面的精度尚未得到系统评估。本研究采用Martini 3双解耦协议，计算15个蛋白质靶点上172种配体的绝对结合自由能，并与原子级FEP+和OpenFE参考值进行比较。结果显示，Martini对每个靶点均存在结合不足，偏差为2.7至8.7 kcal/mol，且该偏差具有系统性：其包含随口袋中带电残基数增加的均匀贡献。仅需蛋白质结构的线性模型（基于该残基数）在留一交叉验证中将平均绝对误差从5.7降至1.2 kcal/mol，接近采用精确靶点偏移得到的1.0 kcal/mol。同一靶点内配体的排名差异更大（中位数Spearman ρ为0.32，范围从-0.41至0.89），且可通过各系列的构效关系解释：当配体亲和力由疏水性、π堆积或大小驱动时，Martini对其排名较好；而当由氢键或电荷驱动时，排名较差。这两种趋势均源于Martini针对体相分配的校准，该校准既未捕捉结合口袋的介电环境，也未捕捉氢键的方向性。虽然MMGBSA保留了对同系配体进行排名所需的原子级细节，但Martini可产生具有物理意义的自由能，且其粗粒化映射将许多原子级不同的分子映射为单一表示，使其成为快速覆盖化学空间的互补工具。

英文摘要

Alchemical free-energy calculations predict protein-ligand binding affinities with close-to-experimental accuracy, but their cost restricts them to small sets of ligands. Coarse-grained (CG) models such as Martini 3 retain a rigorous statistical-mechanical treatment at a fraction of this cost, yet their accuracy for protein-ligand binding has not been assessed systematically. Here, we compute absolute binding free energies for 172 ligands across 15 protein targets with a Martini 3 double-decoupling protocol and compare them against atomistic FEP+ and OpenFE references. Martini underbinds for every target, by 2.7 to 8.7 kcal/mol. This offset is systematic: it comprises a uniform contribution that grows with the number of charged pocket residues. A linear model in this number, which requires only the protein structure, reduces the mean absolute error from 5.7 to 1.2 kcal/mol in leave-one-out cross-validation, close to the 1.0 kcal/mol obtained with exact per-target offsets. The ranking of ligands within a target varies more widely (median Spearman $ρ$ of 0.32, from $-$0.41 to 0.89) and is rationalized by the structure-activity relationship of each series: Martini ranks ligands well when their affinity is driven by hydrophobicity, $π$-stacking, or size, and poorly when it is driven by hydrogen bonding or charge. Both trends follow from Martini's calibration against bulk partitioning, which captures neither the dielectric environment of a binding pocket nor the directionality of hydrogen bonds. While MMGBSA retains the atomistic detail needed to rank congeneric ligands, Martini yields physically meaningful free energies, and its coarse-graining maps many atomistically distinct molecules onto one representation, making it a complementary tool for rapidly spanning chemical space.

发表机构

• Institute for Theoretical Physics, Heidelberg University（海德堡大学理论物理研究所）
• Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University（海德堡大学跨学科科学计算中心）

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