Department of Chemistry, University of California Davis, Davis, California, United States of America.
Department of Biochemistry and Molecular Medicine, University of California, Davis, Davis, California, United States of America.
PLoS One. 2019 Apr 4;14(4):e0214126. doi: 10.1371/journal.pone.0214126. eCollection 2019.
Enzymes play a critical role in a wide array of industrial, medical, and research applications and with the recent explosion of genomic sequencing, we now have sequences for millions of enzymes for which there is no known structure. In order to utilize modern computational design tools for constructing inhibitors or engineering novel catalysts, the ability to accurately model enzymes is critical. A popular approach for modeling enzymes are comparative modeling techniques which can often accurately predict the global structural features. However, achieving atomic accuracy of an active site remains a challenge and is an issue when trying to utilize the molecular details for designing inhibitors or enhanced catalysts. Here we explore integrating knowledge about the required geometric orientation of conserved catalytic residues into the comparative modeling process in order to improve modeling accuracy. In order to investigate the utility of adding this information, we first carefully construct a benchmark set of reference structures to use. Consistent with previous findings, our benchmark demonstrates that the geometry between catalytic residues across an enzyme family is conserved and does not tend to deviate by more than 0.5Å. We then find that by integrating these geometric constraints during modeling, we can double the number of atomic level accuracy models (<1Å RMSD to the crystal structure ligand) within our benchmarking dataset, even for targets with templates as low as 20-30% sequence identity. Catalytic residues within an enzyme family are highly conserved and can often be readily identified through comparative sequence analysis to a known structure within the enzyme family. Therefore utilizing this readily available information has the potential to significantly improve drug design and enzyme engineering efforts for which there is no known structure for the enzyme of interest.
酶在广泛的工业、医学和研究应用中起着至关重要的作用，随着基因组测序的迅速发展，我们现在拥有了数以百万计的酶的序列，而这些酶的结构尚不清楚。为了利用现代计算设计工具来构建抑制剂或工程新型催化剂，准确建模酶的能力至关重要。一种用于建模酶的流行方法是比较建模技术，它通常可以准确地预测全局结构特征。然而，实现活性位点的原子精度仍然是一个挑战，当试图利用分子细节来设计抑制剂或增强催化剂时，这是一个问题。在这里，我们探索将关于保守催化残基所需几何取向的知识集成到比较建模过程中，以提高建模准确性。为了研究添加此信息的效用，我们首先仔细构建了一个参考结构的基准集来使用。与先前的发现一致，我们的基准表明，酶家族中催化残基之间的几何形状是保守的，并且不太可能偏离超过 0.5Å。然后，我们发现通过在建模过程中整合这些几何约束，可以将基准数据集内原子水平精度模型（与晶体结构配体的 RMSD 小于 1Å）的数量增加一倍，即使对于模板序列同一性低至 20-30%的目标也是如此。酶家族中的催化残基高度保守，通常可以通过与酶家族中已知结构的比较序列分析来轻易识别。因此，利用这种现成的信息有可能显著改善药物设计和酶工程的努力，对于感兴趣的酶没有已知结构。
