Year: 2026
Type: preprint
Abstract: SUMMARY Enzyme kinetic parameters inform metabolic models, yet experimental measurements are sparse. A growing body of work predicts them from protein and substrate features, but software fragmentation hinders adoption, so downstream tools lock into the most accessible method. We present OpenKinetics Predictor ( predictor.openkinetics.org ), an open-source platform integrating thirteen kinetic parameter prediction methods accessible via one interface. The platform optionally reports similarity between query proteins and the training data of each method to provide context. A shared framework for featurisation and prediction keeps it extensible, and independent parties, including original authors, contributed many methods. A paired data portal ( data.openkinetics.org ) exposes a curated kinetic dataset with precomputed embeddings, predicted binding sites, and standardised splits. Both offer a web interface and an API, and the GECKO modelling toolbox already calls the predictor API. As a case study, we predict across an E. coli model and find inter-predictor agreement varies with metabolic context and data availability.
Authors: Saleh Alwer, Hugues Escoffier, Karim Taha, Veda Boorla, Han Yu, Somtirtha Santra, Zechen Wang, Chidi Egwu, Abraham Osinuga, Supantha Dey, Vaishnavey Srinivasan Raghunath, Farid Zare, Jack McGoldrick, Jan-Niklas Weder, Eduard Kerkhoven, Xiaozhou Luo, Costas D. Maranas, Liangzhen Zheng, Ulrike Wittig, Ratul Chowdhury, Rajib Saha, Nadine Töpfer, Thomas Sauter, Ronan M. T. Fleming
Institutions: Ollscoil na Gaillimhe – University of Galway, University of Luxembourg, University of Cologne, Cluster of Excellence on Plant Sciences, Pennsylvania State University, Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, Shenzhen Institute of Synthetic Biology, Nanyang Technological University, University of Nebraska–Lincoln, Iowa State University, Science for Life Laboratory, Novo Nordisk Foundation, Chalmers University of Technology, Technical University of Denmark, Heidelberg Institute for Theoretical Studies
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7214639976