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 Penicillium verruculosum 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.

纤维素酶是工业上重要的酶，例如在生物乙醇的生产、纸浆和造纸工业、原料和纺织中。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特定活动的情况下提高热稳定性具有挑战性且通常耗时。结合实验和计算的蛋白质工程策略正在出现，以减少实验筛选工作并加速酶工程活动。约束网络分析 (CNA) 是一种很有前景的计算方法，可识别酶中有益的位置以提高热稳定性。在这项研究中，我们比较了 CNA 和定向进化在有利位置识别中的作用，以评估 CNA 在蛋白质工程活动中的潜力（例如，在 KnowVolution 的识别阶段）。我们从疣状青霉中设计了工业相关的内切葡聚糖酶 EGLII ，以提高热稳定性。通过 CNA 方法，获得了 6 种变体，其热稳定性提高了 2 倍。与定向进化相比，使用 CNA 方法的总体实验负担减少了 40%。在变体水平上，两种策略的成功率相似，epPCR 和 CNA 引导文库中的变体分别提高了 0.27% 和 0.18%。从本质上讲，CNA 是一种识别提高热稳定性的位置的有效方法。