Enzyme catalytic efficiency prediction: employing convolutional neural networks and XGBoost

Abstract

IntroductionIn 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.MethodsIn this context, we introduce “enzyme catalytic efficiency prediction (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.ResultsPreliminary 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 R-squared score from 0.44 to 0.54, thereby demonstrating its superior accuracy and effectiveness in enzyme catalytic efficiency prediction.DiscussionThis improvement underscores the model’s potential to enhance the field of bioinformatics, setting a new benchmark for performance.