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ORIGINAL RESEARCH article

Front. Artif. Intell.
Sec. Medicine and Public Health
Volume 7 - 2024 | doi: 10.3389/frai.2024.1446063

Enzyme Catalytic Efficiency Prediction (ECEP): Employing Convolutional Neural Networks (CNN) and XGBoost

Provisionally accepted
Meshari Alazmi Meshari Alazmi 1,2*
  • 1 University of Hail, Ha'il, Saudi Arabia
  • 2 College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia

The final, formatted version of the article will be published soon.

    In the intricate realm of enzymology, the precise quantification of enzyme efficiency, epitomized by the turnover number (kcat), is a paramount yet elusive objective. Existing methodologies, though sophisticated, often grapple with the inherent stochasticity and multifaceted nature of enzymatic reactions. Thus, there arises a necessity to explore avant-garde computational paradigms. In this context, we introduce "ECEP," leveraging advanced deep learning techniques to enhance the previous implementation, TurnUp, for predicting the enzyme catalase Kcat. Our approach significantly outperforms prior methodologies, incorporating new features derived from enzyme sequences and chemical reaction dynamics. Through ECEP, we unravel the intricate enzyme-substrate interactions, capturing the nuanced interplay of molecular determinants. Preliminary assessments, compared against established models like TurNuP and DLKcat, underscore the superior predictive capabilities of ECEP, marking a pivotal shift in silico enzymatic turnover number estimation. This study enriches the computational toolkit available to enzymologists and lays the groundwork for future explorations in the burgeoning field of bioinformatics. This paper suggested a multi-feature ensemble deep learning-based approach to predict enzyme kinetic parameters using an ensemble Convolution Neural Network and XGBoost by calculating weighted-average of each feature-based model's output to outperform traditional machine learning methods. The proposed "ECEP" model significantly outperformed existing methodologies, achieving a mean squared error (MSE) reduction of 0.35 from 0.81 to 0.46 and Rsquared Score from 0.44 to 0.54, thereby demonstrating its superior accuracy and effectiveness in enzyme catalytic efficiency prediction. This improvement underscores the model's potential to enhance the field of bioinformatics, setting a new benchmark for performance.

    Keywords: deep learning, CNN, Turnover number, Kcat, enzyme efficiency

    Received: 08 Jun 2024; Accepted: 07 Oct 2024.

    Copyright: © 2024 Alazmi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Meshari Alazmi, University of Hail, Ha'il, Saudi Arabia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.