Evolution Shapes Enzyme Turnover Numbers to Support Cellular Objectives

Evolution Shapes Enzyme Turnover Numbers to Support Cellular Objectives | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Evolution Shapes Enzyme Turnover Numbers to Support Cellular Objectives Samira L. van den Bogaard, Lorenzo Wormer, Nadja A. Henke, Lars M. Blank, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9383378/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Microbial growth is constrained by physicochemical and spatial limitations that shape how cells utilize available nutrients. Over evolutionary timescales, microorgansims have optimized the allocation of protein resources to thrive across diverse environments, with ranging nutrient availabilities. This raises the question of whether extracellular metabolite exchange rates, i.e. all molecules consumed or produced by the cell, can serve as quantitative fingerprints of the intracellular catalytic efficiencies that evolution has shaped. To explore the relation between the environment and enzyme efficiencies, we combined sEnz, a method to quantify the flux control of enzymes, with a genetic algorithm that evolves kcat values in Protein Allocation Models (PAMs) toward experimentally observed fluxes. The PAMparametrizer framework reproduced key physiological traits and accurately reflected environmental influences on intracellular metabolism, generating an ensemble of improved models. Applied to Escherichia coli and Corynebacterium glutamicum, the resulting PAMs better captured metabolic behavior than the initial models. For the metabolically versatile Pseudomonas putida, limited experimental measurements allowed only recovery of the extracellular phenotype , emphasizing the value of complete physiological datasets for complex metabolic systems. The PAMparametrizer, by linking exchange fluxes to enzyme kinetics, not only deepens our understanding of metabolic refinement over evolutionary timescales but also establishes a foundation for scalable, evolution-informed model parametrization across organisms and conditions. Biological sciences/Biochemistry Biological sciences/Biological techniques Biological sciences/Computational biology and bioinformatics Biological sciences/Systems biology metabolic modeling resource allocation protein allocation models kinetic parameters evolution Full Text Additional Declarations No competing interests reported. Supplementary Files Bogaard2026SIEvolutionShapesEnzymeTurnoverNumberstoSupportCellularObjectives.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 May, 2026 Reviews received at journal 18 May, 2026 Reviewers agreed at journal 27 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviews received at journal 22 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers invited by journal 22 Apr, 2026 Editor assigned by journal 21 Apr, 2026 Submission checks completed at journal 21 Apr, 2026 First submitted to journal 10 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9383378","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":627723022,"identity":"a4d9670e-0fe5-4471-bd9c-5c75636bd27d","order_by":0,"name":"Samira L. van den Bogaard","email":"","orcid":"","institution":"RWTH Aachen University","correspondingAuthor":false,"prefix":"","firstName":"Samira","middleName":"L. van den","lastName":"Bogaard","suffix":""},{"id":627723023,"identity":"67910aeb-cb4c-4d26-927a-132f3367a3d0","order_by":1,"name":"Lorenzo Wormer","email":"","orcid":"","institution":"Karlsruhe Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Lorenzo","middleName":"","lastName":"Wormer","suffix":""},{"id":627723026,"identity":"ecba380c-e200-4892-b933-c13f17fad75c","order_by":2,"name":"Nadja A. 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