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Modifying inhibitor specificity for homologous enzymes by machine learning

ABSTRACT: Selective inhibitors are essential for targeted therapeutics and for probing enzyme functions in various biological systems. The two main challenges in identifying such inhibitors lie in the extensive experimental effort required, including the generation of large libraries, and in tailoring the selectivity of inhibitors to enzymes with homologous structures. To address these challenges, machine learning (ML) is being used to improve protein design by training on targeted libraries and identifying key interface mutations that enhance affinity and specificity. However, such ML-based methods are limited by inaccurate energy calculations and difficulties in predicting the structural impacts of multiple mutations. Here, we present an ML-based method that leverages HTS data to streamline the design of selective inhibitors. To demonstrate its utility, we applied our new method to finding inhibitors of matrix metalloproteinases (MMPs), a family of homologous enzymes involved in both physiological and pathological processes. By training ML models on binding data for three MMPs (MMP-1, MMP-3, and MMP-9), we successfully designed a novel N-TIMP2 variant with a differential specificity profile, namely, high affinity for MMP-9, moderate affinity for MMP-3, and low affinity for MMP-1. Our experimental validation showed that this novel variant exhibited a significant specificity shift and enhanced selectivity compared to wild-type N-TIMP2. Through molecular modeling and energy minimization, we obtained structural insights into the variant’s enhanced selectivity. Our findings highlight the power of ML-based methods to reduce experimental workloads, facilitate the rational design of selective inhibitors, and advance the understanding of specific inhibitor-enzyme interactions in homologous enzyme systems.

ORGANISM(S): Saccharomyces cerevisiae

PROVIDER: GSE290918 | GEO | 2025/03/10

REPOSITORIES: GEO

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Project description:This a model from the article: A theoretical model of type I collagen proteolysis by matrix metalloproteinase (MMP) 2 and membrane type 1 MMP in the presence of tissue inhibitor of metalloproteinase 2. Karagiannis ED, Popel AS. J Biol Chem 2004 Sep 10;279(37):39105-14 15252025 , Abstract: One well documented family of enzymes responsible for the proteolytic processes that occur in the extracellular matrix is the soluble and membrane-associated matrix metalloproteinases. Here we present the first theoretical model of the biochemical network describing the proteolysis of collagen I by matrix metalloproteinases 2 (MMP2) and membrane type 1 matrix metalloproteinases (MT1-MMP) in the presence of the tissue inhibitor of metalloproteinases 2 (TIMP2) in a bulk, cell-free, well stirred environment. The model can serve as a tool for describing quantitatively the activation of the MMP2 proenzyme (pro-MMP2), the ectodomain shedding of MT1-MMP, and the collagenolysis arising from both of the enzymes. We show that pro-MMP2 activation, a process that involves a trimer formation of the proenzyme with TIMP2 and MT1-MMP, is suppressed at high inhibitor levels and paradoxically attains maximum only at intermediate TIMP2 concentrations. We also calculate the conditions for which pro-MMP2 activation is maximal. Furthermore we demonstrate that the ectodomain shedding of MT1-MMP can serve as a mechanism controlling the MT1-MMP availability and therefore the pro-MMP2 activation. Finally the proteolytic synergism of MMP2 and MT1-MMP is introduced and described quantitatively. The model provides us a tool to determine the conditions under which the synergism is optimized. Our approach is the first step toward a more complete description of the proteolytic processes that occur in the extracellular matrix and include a wider spectrum of enzymes and substrates as well as naturally occurring or artificial inhibitors. This model was taken from the CellML repository and automatically converted to SBML. The original model was: Karagiannis ED, Popel AS. (2004) - version01 This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Modifying inhibitor specificity for homologous enzymes by machine learning

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