Computational predictions of thermostability and binding affinity changes in enzymes

酶用于化学合成和回收往往受到热稳定性低的限制，因为许多反应需要高温。因此，寻找提高蛋白质稳定性的突变的高性能计算程序将代表着绿色产业的竞争优势。该项目旨在使用分子动力学模拟来确定点突变对不同酶的热稳定性的影响。这些目标包括文献中描述良好的用于基准的例子(RNase SA)，以及目前正在CEI进行实验评估的塑料降解酶的新突变体(例如，PETase和角质酶)。模拟折叠和未折叠状态下的突变，以确定相对自由能的变化。折叠状态是基于酶的晶体结构来模拟的，而去折叠状态可以由水中的小肽来模拟。理想情况下，模拟将提供一个等级，根据所有典型氨基酸对蛋白质稳定性的影响对它们进行排名。这可以用来训练快速的生物信息学方法。每种氨基酸类型的水合自由能和构象熵随温度变化的数据有助于理解嗜热生物进化过程中的突变模式。酶功能正常的另一个重要因素是在高温下与底物结合的能力。蛋白质与配体的结合通常是由疏水效应驱动的，而疏水效应依赖于温度。因此，通常有必要优化结合袋，以允许在高温下进行底物结合(例如，PET塑料与PETase的结合)。这样的计算在计算药物设计中已经很常见，所以所需的工具在计算化学中已经存在，只需修改以优化结合口袋而不是配体。可能的应用包括设计耐热的酶，这种酶可以降解塑料回收利用。

The use of enzymes for chemical synthesis and recycling is often limited by their low thermostability, as many reactions require high temperatures. Therefore, high performance computing procedures to find mutations that increase protein stability would represent a competitive advantage for the green industry. This project aims at using molecular dynamics simulations to determine the effect of point mutations on the thermostability of different enzymes. The targets include well-characterized examples from the literature (RNAse SA) for benchmarking, and new mutants of plastic-degrading enzymes that are currently being experimentally evaluated at the CEI (e.g., PETase and cutinases). The mutations are simulated in the folded and the unfolded state to determine the relative free energy changes deltadeltaG. The folded state is modelled based on the crystal structure of the enzyme, and the unfolded state can be modelled by small peptides in water. Ideally, the simulations will provide a scale that ranks all canonical amino acids in terms of their influence on protein stability. This can be used to train fast bioinformatics approaches. The temperature-dependent data of hydration free energies and conformational entropies of each amino acid type can help to understand the mutation patterns during the evolution of thermophilic organisms. Another important factor for proper enzyme function is the capability to bind to the substrate at elevated temperatures. Protein-ligand binding is often driven by the hydrophobic effect, which depends on the temperature. Therefore, it is often necessary to optimize the binding pocket to allow substrate binding at high temperatures (e.g., the binding of PET plastics to PETase). Such calculations are already common in computational drug design, so the required tools already exist in computational chemistry, and only have to be modified to optimize the binding pocket instead of the ligand. Possible applications include the design of thermostable enzymes that can degrade plastics for recycling.