COMPUTATIONAL DESIGN AND EVALUATION OF NOVEL ENZYME CATALYSTS

这个子项目是许多利用资源的研究子项目之一 由NIH/NCRR资助的中心拨款提供。子项目的主要支持 而子项目的主要调查员可能是由其他来源提供的， 包括其它NIH来源。 列出的子项目总成本可能 代表子项目使用的中心基础设施的估计数量， 而不是由NCRR赠款提供给子项目或子项目工作人员的直接资金。 在2008年，一个成功的计算设计程序的报道，产生活性酶催化剂的肯普消除和逆羟醛反应。我们研究了这些蛋白质以及一组以前未发表的无活性设计，以确定活性或缺乏活性的来源，并预测哪些设计的结构最有可能是催化的。从截断模型系统的量子力学（QM）到ONIOM QM/MM和AMBER分子动力学（MD）对全蛋白质的处理方法进行了探索。最有效的程序涉及明确的溶剂，边界分子动力学，并建立了一个通用的MD协议（见支持信息“分子动力学的酶设计的评价和排名”）。观察到许多设计与理想的催化几何形状有很大的偏差。水渗透到催化位点和活性位点周围的残留物填充不足是导致酶设计失活的主要因素。在过去，设计的酶的计算评估对于实际考虑来说时间太长，现在已经可以在实验之前并结合实验对候选物进行计算排名，从而显着提高酶设计过程的效率。利用DESRES Anton机器的计算资源将有助于进一步开发和完善我们的MD辅助酶设计方案，并促进下一代催化剂的生产，用于从替代燃料到再生人体组织弹性到新型被动免疫和基因治疗等众多有用应用。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. In 2008, a successful computational design procedure was reported that yielded active enzyme catalysts for the Kemp elimination and the retro-aldol reaction. We studied these proteins together with a set of previously unpublished inactive designs in order to determine the sources of activity or lack thereof, and to predict which of the designed structures are most likely to be catalytic. Methods that range from quantum mechanics (QM) on truncated model systems to the treatment of the full protein with ONIOM QM/MM and AMBER molecular dynamics (MD) were explored. The most effective procedure involved explicit-solvent, periodic-boundary molecular dynamics, and a general MD protocol was established (see supporting information "Evaluation and Ranking of Enzyme Designs with Molecular Dynamics"). Substantial deviations from the ideal catalytic geometries were observed for a number of designs. Penetration of water into the catalytic site and insufficient residue-packing around the active site are the main factors that can cause enzyme designs to be inactive. Where in the past, computational evaluations of designed enzymes were too time-extensive for practical consideration, it has now become feasible to rank candidates computationally prior to and in conjunction with experimentation, thus markedly increasing the efficiency of the enzyme design process. Employing computational resources from the DESRES Anton machine will be instrumental to further develop and refine our MD-assisted enzyme design protocol and to facilitate the production of next-generation catalysts for a plethora of useful applications that range from alternative fuels to regenerating human tissue elasticity to novel passive immunizations and gene therapy.