Sep 2026· Proceedings of the National Academy of Sciences of the United States of America· Vol 123· 0 citations· 57 references
Medicine
TL;DR
A variational autoencoder (VAE)-based model is developed that enables more precise clustering of enzymes and identifies functional relationships that traditional tools miss, demonstrating its potential to accelerate enzyme discovery for diverse biocatalytic applications.
Abstract
Significance Understanding how protein sequences relate to their functions is critical for discovering enzymes with applications in biotechnology and medicine. Traditional tools, such as phylogenetic trees and sequence similarity networks, are commonly used but struggle to handle large protein datasets and fail to capture important information about higher-order and complex interresidue interactions in protein sequences. In this study, we developed a variational autoencoder (VAE)-based model to overcome these limitations by mapping protein sequences into a continuous, interpretable space. This approach enables more precise clustering of enzymes and identifies functional relationships that traditional tools miss. By applying this model, we discovered uncharacterized enzymes with distinct functions, demonstrating its potential to accelerate enzyme discovery for diverse biocatalytic applications.
Machine learning has become an increasingly powerful tool in enzyme discovery, enabling the prediction of enzyme activity, substrate scope, and functional specificity across diverse protein families. However, many reported models rely on narrowly sampled datasets and task‐specific benchmarks, limiting their robustness...
Nika Sokolova, K. Haslinger· ChemBioChem· 0 citations
This review summarizes recent progress with an emphasis on benchmark design rather than reported scores alone, and compares representative methods by input modality, training data, split strategy, leakage control, evaluation metric, and validation evidence.
Enzymes are efficient and selective biocatalysts, yet obtaining enzymes with desired properties remains challenging due to vast sequence spaces, incomplete functional annotation, limited structural information, and costly experimental screening. Machine learning (ML) offers a powerful complement to address these limi...
Squidly surpasses state-of-the-art ML annotation methods in catalytic residue prediction while remaining sufficiently fast to enable wide-scale screening of databases.
W. J. Rieger, Mikael Bodén, Frances H. Arnold et al.· eLife· 0 citations
Significance Protein databases contain millions of rapidly accumulating sequences, while increasingly accurate 3D structure predictions provide unprecedented data for analysis. Predicted structures can also be encoded using a structure-focused alphabet alongside standard amino acid (AA) representations. We introduce a...
Divyanshu Shukla, Jonathan Martin, F. Morcos et al.· Proceedings of the National...· 0 citations
It is demonstrated that modern EC predictors largely fail to distinguish catalytically incompetent variants from functional enzymes, and it is proposed that integrating structure-aware negative examples into both training and benchmarking is critical for developing functionally robust models in computational enzymology...
João Sartori, Ana Carolina Ramos Guimarães, Lucas de Almeida Machado· bioRxiv· 0 citations
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