Katalyst: Knowledge-Guided Semantic Alignment via Contrastive Learning for Enzyme Turnover Prediction

Clicks: 13
ID: 331505
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #946 of 983 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 983 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
MOTIVATION: The turnover number (kcat) is crucial for understanding enzyme kinetics, yet its experimental determination remains costly and low-throughput. Existing deep learning approaches often rely on single-substrate assumptions and overlook full reaction contexts or language-informed semantic representations, limiting their accuracy and generalization across diverse reactions. RESULTS: To address these challenges, we propose Katalyst, a knowledge-guided semantic alignment framework for kcat prediction based on contrastive learning. Katalyst integrates enzyme sequences, protein textual descriptions, substrates and products into a shared latent space using multi-channel encoders and contrastive alignment. An interaction-aware fusion module is designed to capture complementary cross-modal information. Experimental results demonstrate that Katalyst achieved the best overall performance, with the RMSE of 0.9880, R2 of 0.5829, and SRCC of 0.7603. Compared with the State-Of-The-Art (SOTA) baselines, Katalyst reduced RMSE by 3.01% and improved R2 and SRCC by 4.72% and 2.31%, respectively. Moreover, ablation studies and evaluations across sequence scales further confirmed the effectiveness of semantic alignment and multimodal feature fusion. Overall, Katalyst provides a robust and accurate framework for kcat prediction. AVAILABILITY AND IMPLEMENTATION: The code and data are available at https://github.com/liruilin-SUAT/Katalyst. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7220392455 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ruilin Li, Senyu Tang, Jiaqi Deng, 邱太兴, Fei Guo, Jijun Tang, Xiaoyi Liu
Are you one of these authors? Claim your profile
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag709
URL
https://doi.org/10.1093/bioinformatics/btag709
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.