Wan Hao, Zou Quan, Zhang Yanan
Institute of Advanced Cross-Field Science, College of Life Science, Qingdao University, Qingdao, China.
Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China.
Proteomics Clin Appl. 2025 Jul;19(4):e70009. doi: 10.1002/prca.70009. Epub 2025 May 31.
Acidic and alkaline enzymes play crucial roles in the food industry and environmental management. This study aims to develop a computational method for accurately distinguishing between acidic and alkaline enzymes to enhance their stability in varying pH environments.
We employed AutoProp for feature extraction and the MRMD3.0 algorithm for feature selection. The most discriminative feature, the normalized Van der Waals volume (nFeat43), was identified and used for classification.
The selected feature (nFeat43) achieved a classification accuracy of 76.2% in distinguishing acidic from alkaline enzymes. Further analysis was conducted to interpret the physicochemical significance of this feature in enzyme discrimination.
Our findings demonstrate that nFeat43 is a key determinant in differentiating acidic and alkaline enzymes. This method provides a rapid and reliable computational approach for enzyme characterization, which could aid in industrial and environmental applications.
酸性和碱性酶在食品工业和环境管理中发挥着关键作用。本研究旨在开发一种计算方法，用于准确区分酸性和碱性酶，以提高它们在不同pH环境中的稳定性。
我们采用AutoProp进行特征提取，并使用MRMD算法进行特征选择。确定了最具区分性的特征，即归一化范德华体积（nFeat43），并将其用于分类。
所选特征（nFeat43）在区分酸性和碱性酶方面的分类准确率达到了76.2%。进一步分析以解释该特征在酶鉴别中的物理化学意义。
我们的研究结果表明，nFeat43是区分酸性和碱性酶的关键决定因素。该方法为酶的表征提供了一种快速可靠的计算方法，有助于工业和环境应用。
