ECRECer: Enzyme Commission Number Recommendation and Benchmarking based on Multiagent Dual-core Learning

Abstract

Enzyme Commission (EC) numbers, which associate a protein sequence with the biochemical reactions it catalyzes, are essential for the accurate understanding of enzyme functions and cellular metabolism. Many ab-initio computational approaches were proposed to predict EC numbers for given input sequences directly. However, the prediction performance (accuracy, recall, precision), usability, and efficiency of existing methods still have much room to be improved. Here, we report ECRECer, a cloud platform for accurately predicting EC numbers based on novel deep learning techniques. To build ECRECer, we evaluate different protein representation methods and adopt a protein language model for protein sequence embedding. After embedding, we propose a multi-agent hierarchy deep learning-based framework to learn the proposed tasks in a multi-task manner. Specifically, we used an extreme multilabel classifier to perform the EC prediction and employed a greedy strategy to integrate and fine-tune the final model. Comparative analyses against four representative methods demonstrate that ECRECer delivers the highest performance, which improves accuracy and F1 score by 70% and 20% over the state-of-the-the-art, respectively. With ECRECer, we can annotate numerous enzymes in the Swiss-Prot database with incomplete EC numbers to their full fourth level. Take UniPort protein "A0A0U5GJ41" as an example (1.14.-.-), ECRECer annotated it with "1.14.11.38", which supported by further protein structure analysis based on AlphaFold2. Finally, we established a webserver (https://ecrecer.biodesign.ac.cn) and provided an offline bundle to improve usability.

Interpretable and explainable predictive machine learning models for data-driven protein engineering

“…ML methods have found invaluable applications in diverse biotechnology areas, including drug discovery (Rickerby et al, 2020), assessing various aspects of protein fitness such as thermostability (Csicsery-Ronay et al, 2022), stereoselectivity (Moon et al, 2021;Li et al, 2021), fluorescence properties (Somermeyer et al, 2022), predicting affinity in protein complex interactions (Medina-Ortiz et al, 2023;Liu et al, 2021), functional classification based on Enzyme Commission numbers (Shi et al, 2022;Fernández et al, 2023), recognition of biological activities in peptide sequences (Quiroz et al, 2021), photoreceptor adduct lifetime (Hemmer et al, 2023), and assessing DNA-binding proteins (Qu et al, 2019). The versatility of ML methods has resulted in their integration with traditional experimental techniques, such as DE and RD (Yang et al, 2019;Wittmann et al, 2021).…”