Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China; Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
Sci Bull (Beijing). 2023 Nov 30;68(22):2793-2805. doi: 10.1016/j.scib.2023.09.039. Epub 2023 Sep 29.
The demand for high efficiency glycoside hydrolases (GHs) is on the rise due to their various industrial applications. However, improving the catalytic efficiency of an enzyme remains a challenge. This investigation showcases the capability of a deep neural network and method for enhancing the catalytic efficiency (MECE) platform to predict mutations that improve catalytic activity in GHs. The MECE platform includes DeepGH, a deep learning model that is able to identify GH families and functional residues. This model was developed utilizing 119 GH family protein sequences obtained from the Carbohydrate-Active enZYmes (CAZy) database. After undergoing ten-fold cross-validation, the DeepGH models exhibited a predictive accuracy of 96.73%. The utilization of gradient-weighted class activation mapping (Grad-CAM) was used to aid us in comprehending the classification features, which in turn facilitated the creation of enzyme mutants. As a result, the MECE platform was validated with the development of CHIS1754-MUT7, a mutant that boasts seven amino acid substitutions. The k/K of CHIS1754-MUT7 was found to be 23.53 times greater than that of the wild type CHIS1754. Due to its high computational efficiency and low experimental cost, this method offers significant advantages and presents a novel approach for the intelligent design of enzyme catalytic efficiency. As a result, it holds great promise for a wide range of applications.
由于糖苷水解酶（GHs）在各种工业应用中的广泛需求，对其高效性的要求也在不断提高。然而，提高酶的催化效率仍然是一个挑战。本研究展示了深度神经网络和增强催化效率（MECE）平台的能力，用于预测提高 GHs 催化活性的突变。MECE 平台包括 DeepGH，这是一种深度学习模型，能够识别 GH 家族和功能残基。该模型是利用从碳水化合物活性酶（CAZy）数据库中获得的 119 个 GH 家族蛋白序列开发的。经过十折交叉验证，DeepGH 模型的预测准确率达到 96.73%。梯度加权类激活映射（Grad-CAM）的使用有助于我们理解分类特征，进而促进了酶突变体的产生。结果，MECE 平台通过开发 CHIS1754-MUT7 得到了验证，这是一种具有七个氨基酸取代的突变体。CHIS1754-MUT7 的 k/K 值比野生型 CHIS1754 高 23.53 倍。由于该方法具有计算效率高、实验成本低的优点，为酶催化效率的智能设计提供了一种新的方法，具有广泛的应用前景。
