Contreras Francisca, Nutschel Christina, Beust Laura, Davari Mehdi D, Gohlke Holger, Schwaneberg Ulrich
Institute of Biotechnology, RWTH Aachen University, Worringerweg 3, 52074 Aachen, Germany.
John von Neumann Institute for Computing (NIC), Jülich Supercomputing Centre (JSC) and Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany.
Comput Struct Biotechnol J. 2020 Dec 28;19:743-751. doi: 10.1016/j.csbj.2020.12.034. eCollection 2021.
Cellulases are industrially important enzymes, e.g., in the production of bioethanol, in pulp and paper industry, feedstock, and textile. Thermostability is often a prerequisite for high process stability and improving thermostability without affecting specific activities at lower temperatures is challenging and often time-consuming. Protein engineering strategies that combine experimental and computational are emerging in order to reduce experimental screening efforts and speed up enzyme engineering campaigns. Constraint Network Analysis (CNA) is a promising computational method that identifies beneficial positions in enzymes to improve thermostability. In this study, we compare CNA and directed evolution in the identification of beneficial positions in order to evaluate the potential of CNA in protein engineering campaigns (e.g., in the identification phase of KnowVolution). We engineered the industrially relevant endoglucanase EGLII from towards increased thermostability. From the CNA approach, six variants were obtained with an up to 2-fold improvement in thermostability. The overall experimental burden was reduced to 40% utilizing the CNA method in comparison to directed evolution. On a variant level, the success rate was similar for both strategies, with 0.27% and 0.18% improved variants in the epPCR and CNA-guided library, respectively. In essence, CNA is an effective method for identification of positions that improve thermostability.
纤维素酶是具有重要工业价值的酶，例如在生物乙醇生产、纸浆和造纸工业、原料以及纺织业中。热稳定性通常是高工艺稳定性的先决条件，而在不影响较低温度下的比活性的情况下提高热稳定性具有挑战性且往往耗时。结合实验和计算的蛋白质工程策略正在兴起，以减少实验筛选工作量并加速酶工程进程。约束网络分析（CNA）是一种很有前景的计算方法，可识别酶中有助于提高热稳定性的有利位点。在本研究中，我们比较了CNA和定向进化在识别有利位点方面的效果，以评估CNA在蛋白质工程进程（例如在KnowVolution的识别阶段）中的潜力。我们对具有工业相关性的内切葡聚糖酶EGLII进行改造，以提高其热稳定性。通过CNA方法，获得了六个变体，其热稳定性提高了两倍。与定向进化相比，使用CNA方法可将总体实验负担降低至40%。在变体水平上，两种策略的成功率相似，易错PCR和CNA指导文库中分别有0.27%和0.18%的变体得到了改善。本质上，CNA是一种识别有助于提高热稳定性位点的有效方法。
