Protein Structural Analysis and Design Lab, Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824-1319, USA.
Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA.
J Comput Aided Mol Des. 2018 Apr;32(4):511-528. doi: 10.1007/s10822-018-0105-2. Epub 2018 Feb 12.
Understanding how proteins encode ligand specificity is fascinating and similar in importance to deciphering the genetic code. For protein-ligand recognition, the combination of an almost infinite variety of interfacial shapes and patterns of chemical groups makes the problem especially challenging. Here we analyze data across non-homologous proteins in complex with small biological ligands to address observations made in our inhibitor discovery projects: that proteins favor donating H-bonds to ligands and avoid using groups with both H-bond donor and acceptor capacity. The resulting clear and significant chemical group matching preferences elucidate the code for protein-native ligand binding, similar to the dominant patterns found in nucleic acid base-pairing. On average, 90% of the keto and carboxylate oxygens occurring in the biological ligands formed direct H-bonds to the protein. A two-fold preference was found for protein atoms to act as H-bond donors and ligand atoms to act as acceptors, and 76% of all intermolecular H-bonds involved an amine donor. Together, the tight chemical and geometric constraints associated with satisfying donor groups generate a hydrogen-bonding lock that can be matched only by ligands bearing the right acceptor-rich key. Measuring an index of H-bond preference based on the observed chemical trends proved sufficient to predict other protein-ligand complexes and can be used to guide molecular design. The resulting Hbind and Protein Recognition Index software packages are being made available for rigorously defining intermolecular H-bonds and measuring the extent to which H-bonding patterns in a given complex match the preference key.
理解蛋白质如何编码配体特异性既令人着迷,又与破译遗传密码同等重要。对于蛋白质-配体识别,界面形状和化学基团模式的几乎无限多样性的组合使得这个问题特别具有挑战性。在这里,我们分析了与小生物配体结合的非同源蛋白质的数据,以解决我们在抑制剂发现项目中观察到的问题:蛋白质倾向于向配体提供氢键,避免使用同时具有氢键供体和受体能力的基团。由此产生的清晰而显著的化学基团匹配偏好阐明了蛋白质-天然配体结合的密码,类似于在核酸碱基配对中发现的主要模式。平均而言,生物配体中出现的酮和羧酸氧 90%直接与蛋白质形成氢键。发现蛋白质原子作为氢键供体和配体原子作为受体的两倍偏好,并且所有分子间氢键中有 76%涉及胺供体。与满足供体基团相关的紧密化学和几何约束共同产生了一个氢键锁定,只有带有正确富受体键的配体才能匹配。基于观察到的化学趋势测量氢键偏好指数足以预测其他蛋白质-配体复合物,并可用于指导分子设计。由此产生的 Hbind 和 Protein Recognition Index 软件包可用于严格定义分子间氢键,并测量给定复合物中氢键模式与偏好键的匹配程度。