Classification of Acid and Alkaline Enzymes Based on Normalized Van der Waals Volume Features.

IF 2.5 4区生物学 Q3 BIOCHEMICAL RESEARCH METHODS

PROTEOMICS – Clinical Applications Pub Date : 2025-07-01 Epub Date: 2025-05-31 DOI:10.1002/prca.70009

Hao Wan, Quan Zou, Yanan Zhang

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引用次数: 0

Abstract

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

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基于归一化范德华体积特征的酸性和碱性酶分类。

目的：酸性酶和碱性酶在食品工业和环境管理中发挥着重要作用。本研究旨在开发一种准确区分酸性和碱性酶的计算方法，以提高其在不同pH环境中的稳定性。方法：采用AutoProp进行特征提取，MRMD3.0算法进行特征选择。识别出最具判别性的特征，即归一化范德华体积（nFeat43），并将其用于分类。结果：所选特征（nFeat43）对酸性酶和碱性酶的分类准确率为76.2%。进一步分析了这一特征在酶鉴别中的物理化学意义。结论：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，可用于工业和环境应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

PROTEOMICS – Clinical Applications 医学-生化研究方法

CiteScore

5.20

自引率

5.00%

发文量

审稿时长

1 months

期刊介绍： PROTEOMICS - Clinical Applications has developed into a key source of information in the field of applying proteomics to the study of human disease and translation to the clinic. With 12 issues per year, the journal will publish papers in all relevant areas including: -basic proteomic research designed to further understand the molecular mechanisms underlying dysfunction in human disease -the results of proteomic studies dedicated to the discovery and validation of diagnostic and prognostic disease biomarkers -the use of proteomics for the discovery of novel drug targets -the application of proteomics in the drug development pipeline -the use of proteomics as a component of clinical trials.