BactProNET: A Structural-Mechanistic Platform for Deciphering Target-Mediated Antimicrobial Resistance

BactProNET: A Structural-Mechanistic Platform for Deciphering Target-Mediated Antimicrobial Resistance | 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 BactProNET: A Structural-Mechanistic Platform for Deciphering Target-Mediated Antimicrobial Resistance Ke Wang, Yiru Liu, Xue Wang, Haitao Zhou, Longfei Chen, Jingyun Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8808185/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 17 You are reading this latest preprint version Abstract Antibiotics are the frontline therapy for bacterial infections, yet their efficacy is critically threatened by antimicrobial resistance (AMR), a crisis largely driven by mutations in protein targets. While this mechanism is prevalent, data on its structural impact remains highly dispersed, and existing resources like CARD and ResFinder prioritize gene identification over mechanistic insight. To address this gap, we developed BactProNET, a bioinformatics platform for the structural and evolutionary analysis of target-mediated resistance. Its core innovation is a multi-level data integration that establishes a "wild-type reference system" for mechanistic inference. The platform integrates curated resistance mutations with AlphaFold 2-predicted 3D structures, molecular docking models defining an "optimal binding" baseline, and integrated phylogenetic and sequence alignment views. Currently housing data on 110 protein targets, 205 mutation sites, and 644 docking models, BactProNET is accessible via an interface with embedded BLAST and MSA tools. As a one-stop platform, it accelerates the understanding of AMR's molecular mechanisms and provides a robust, data-driven foundation for the rational design of next-generation antimicrobial drugs. Biological sciences/Computational biology and bioinformatics Biological sciences/Drug discovery Biological sciences/Microbiology BactProNET antibiotic resistance database target protein amino acid mutation molecular docking Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Since the mid-20th century, the widespread use of antibiotics has dramatically reduced morbidity and mortality from bacterial infectious diseases 1 . However, with the misuse and overuse of antibiotics, the problem of bacterial resistance has become increasingly severe, leading to a continuous decline in the effectiveness of conventional treatment regimens and posing a serious challenge to global public health 2 – 4 . Among the diverse mechanisms of bacterial resistance, genetic mutation of the antibiotic's protein target is one of the most direct and prevalent 5 , 6 . For instance, mutations in Penicillin-Binding Proteins (PBPs) can confer resistance to β-lactam antibiotics 7 , while mutations in DNA gyrase or topoisomerase IV are a well-established cause of resistance to fluoroquinolones 8 . Such alterations of key amino acid residues can impair antibiotic efficacy by reducing drug-binding affinity, inducing conformational changes in the target protein, or creating steric hindrance that obstructs drug binding 5 , 8 . Consequently, a systematic investigation of these mutations at the sequence, structural, and functional levels of the protein target is crucial for a comprehensive understanding of antibiotic resistance. While a wealth of research has reported on resistance mutations, this information remains highly dispersed. Authoritative databases such as the Comprehensive Antibiotic Resistance Database (CARD) 9 and ResFinder 10 excel at the identification and cataloging of resistance genes, but they lack the systematic integration of protein structure, drug interactions, and evolutionary context. This forces researchers investigating specific mechanisms to undertake the tedious task of manually retrieving and integrating data from disparate resources like the National Center for Biotechnology Information (NCBI) 11 , UniProt 12 and PDB 13 , and the primary literature. To address this critical bottleneck, we developed BactProNET. In stark contrast to the "gene identification" role of existing platforms, BactProNET's fundamental advantage lies in its focus on mechanistic elucidation, designed to answer the core question of how an amino acid mutation structurally impairs drug binding. To achieve this, it uniquely integrates mutation sites with high-precision 3D structures and molecular docking models. Crucially, it provides a "wild-type-drug optimal binding model" as an analytical baseline—a feature absent in other databases—enabling users to intuitively assess the disruptive impact of any given mutation. Consequently, BactProNET is not a substitute for existing gene catalogs but a highly complementary analytical platform, specifically designed to bridge the critical mechanistic gap between genotype and resistance phenotype. Materials and Methods Data collection and integration To ensure data accuracy and comprehensiveness, the BactProNET dataset was constructed through a dual strategy of manual literature curation and public database integration. Our curation involved systematically screening 323 research articles from databases like PubMed, from which we manually extracted key data on bacterial species, protein targets, amino acid mutations, and associated antibiotics. This curated information was then standardized and enriched by integrating protein sequences from NCBI and UniProt. Concurrently, comprehensive antibiotic data—including 2D/3D chemical structures, mechanisms of action, and clinical indications—were aggregated from PubChem 14 , ChEMBL 15 , CARD, and DrugBank 16 . This robust integration process, which included annotating essential biological information on the source species and protein targets, yielded a highly reliable and integrated dataset for mechanistic AMR analysis. Bioinformatics analysis pipeline To provide deeper insights beyond raw data, BactProNET integrates a comprehensive bioinformatics analysis pipeline. The process begins with sequence alignment to assess protein conservation and standardize mutation sites. Using the highly conserved quinolone target protein GyrA as a representative example, sequences containing resistance mutations from various bacteria were pair-wise aligned against the well-characterized Escherichia coli GyrA template via the CLUSTALW algorithm. This method enabled the mapping of resistance mutations from diverse sources onto homologous positions in the reference sequence, culminating in the generation of an integrated mutation distribution map. To establish a structural foundation for mechanistic investigation, the cutting-edge AlphaFold 2 algorithm was then utilized to predict high-accuracy three-dimensional structures from wild-type FASTA sequences provided by NCBI. These structural models served as the basis for molecular docking studies, performed with the AutoDock 4 software suite, to elucidate drug-target interaction modes. This workflow involved preparing the receptor protein and antibiotic ligand, defining an active site grid box for affinity calculations, and selecting the lowest-energy conformation from the most populated cluster as the optimal binding model. Finally, to contextualize these findings within an evolutionary framework, phylogenetic analysis was conducted using MEGA 11. A phylogenetic tree was constructed from wild-type amino acid sequences using the Neighbor-Joining (NJ) method, with branch support assessed by 1000 bootstrap replicates. Identified resistance mutation sites were subsequently mapped onto this tree, providing an integrated visualization that reveals the evolutionary patterns of resistance mutations. Database architecture and web interface The BactProNET platform is deployed on a Linux server with a backend integrating an Apache web server (v2.4.58), a MySQL database (v8.0.42), and a Node.js runtime environment (v22.17.0). The front-end user interface was built with HTML5, CSS3, and JavaScript (ES6+), employing the Bootstrap framework (v4.0.0-beta2), jQuery (v3.2.1) for responsive design, and the NGL Viewer library (v0.10.4) for interactive 3D molecular visualization. The platform supports keyword search and category-based browsing and incorporates online BLAST and MSA tools to provide a comprehensive platform for data retrieval, visualization, and preliminary analysis. The workflow of database development is illustrated in Fig. 1 . Results Overall framework and functionality of the BactProNET Database BactProNET is designed to provide users with a clear, intuitive, and information-rich online platform (Figure 2). (1) User-friendly interface and navigation The database homepage features a clean design with a clear navigation bar, an introduction to core functionalities, and a global search box. Users can perform rapid searches using keywords or systematically browse the core data through main entry points such as "Antibiotic", "Bacteria", "Protein", and "Molecular Docking". (2) Deeply integrated information detail pages BactProNET has created dedicated detail pages for each core data type, deeply integrating multi-dimensional data to make information access efficient and convenient. The Protein Target Page includes "Basic Information" displaying the 3D structural model of the wild-type protein (with known mutation sites highlighted), sequence length, and biological function annotations; a "Resistance Mutation Table" systematically listing key mutation sites, corresponding amino acid variations, and associated antibiotics in a tabular format; and an Integrated View of Evolution and Sequence presenting the phylogenetic tree and MSA side-by-side, highlighting all reported resistance-associated mutation sites to intuitively reveal evolutionary relationships and sequence conservation. The Bacteria Page provides "Basic Biological Information" including the taxonomic classification and background of the bacterial species, and a "Resistance Information Overview" listing the protein targets reported to be associated with resistance in that bacterium and, for each target, the specific antibiotics documented in the literature to which mutations confer resistance. The Antibiotic Page systematically integrates the antibiotic's 2D/3D chemical structure, mechanism of action, and clinical indications. The Molecular Docking Page features a "3D Interaction View" visually displaying the optimal binding conformation of the antibiotic within the target's active site pocket, and a "Detailed Docking Results Table" providing a table with detailed energy and structural parameters for quantitative assessment of the binding mode.. Data statistics The current release of BactProNET contains a systematically curated dataset encompassing 110 protein targets from 44 bacterial species and comprehensive information for 323 antibiotics. A central feature is the annotation of 205 key resistance mutation sites, which were identified and consolidated from a review of 323 peer-reviewed publications. To facilitate a structural understanding of these interactions, the database also includes a library of 644 pre-computed protein-antibiotic molecular docking models (Figure 3). Case Study: A One-stop Integrated Analysis of GyrA from Staphylococcus aureus using BactProNET To demonstrate the platform's utility, this case study presents a complete workflow analyzing DNA gyrase subunit A (GyrA) from S. aureus , using two fluoroquinolone antibiotics, Gatifloxacin and Moxifloxacin, as exemplar drugs. The analysis was initiated by querying " Staphylococcus aureus " on the BactProNET platform. The target, "DNA gyrase subunit A [ Staphylococcus aureus ] (3D Model)," was then selected from the results to access its dedicated data page and molecular docking interface. Step 1: Establishing a Wild-Type Interaction Baseline Pre-computed molecular docking established a structural baseline. Gatifloxacin bound strongly (-5.46 kcal/mol) via hydrogen bonds to active site residues ASP813 and ASP815, whereas Moxifloxacin bound weakly (-4.22 kcal/mol) without these key interactions(Figure 4). This provides a clear structural basis for interpreting resistance. Step 2: Correlating Resistance with Evolutionary and Sequence Context An integrated view juxtaposes a phylogenetic tree and a MSA (Figure 5), directly linking evolutionary divergence to functional protein regions. All reported resistance mutations are highlighted on the MSA, providing immediate visual and evolutionary context for their distribution. Step 3: Real-time Validation of Novel Sequences Built-in BLAST and MSA tools enable the real-time validation of user-submitted sequences. A novel GyrA sequence from a resistant strain can be directly compared against references to rapidly identify resistance-conferring mutations, accelerating the research cycle. Discussion The core innovation of BactProNET is that, instead of directly modeling mutant proteins, it first provides researchers with a critical "wild-type reference system". By performing structural prediction on the wild-type protein and molecular docking with antibiotics, we identify and display a single, energetically optimal binding conformation for each drug-target pair. This clear "optimal binding model" forms the cornerstone for subsequent analysis. Users can map known resistance mutations onto this 3D model to intuitively infer how a specific amino acid substitution precisely disrupts this optimal binding—through steric hindrance, charge alteration, or the disruption of key interactions—ultimately leading to resistance. The practical value of BactProNET is further enhanced by its built-in tools and external links. The embedded BLAST and MSA tools allow users to instantly analyze their own sequences, while links to authoritative resources like NCBI and PDB establish BactProNET as a convenient portal to the broader data ecosystem. At the same time, we clearly recognize the limitations of the current platform. First, the accuracy of computationally predicted models cannot be fully equivalent to experimentally resolved structures. Second, the database currently focuses on resistance mediated by target-site amino acid substitutions and does not yet cover other important mechanisms such as drug efflux or enzymatic degradation. Finally, the strategy of presenting a single optimal docking conformation, while enhancing clarity, sacrifices the exploration of molecular dynamics and multiple potential binding modes. Looking ahead, the iterative upgrades for BactProNET will be incremental. The first priority is the continuous updating of resistance mutation data. Longer-term goals include: (1) integrating mutant models by introducing structural models of mutant proteins to perform differential docking analysis between wild-type and mutant forms; (2) introducing advanced simulations by considering the application of methods like molecular dynamics to explore the dynamics of the binding process; and (3) expanding the scope of mechanisms by systematically incorporating a wider variety of resistance mechanisms to build a more comprehensive AMR knowledge graph. As a novel bioinformatics resource, BactProNET builds a critical bridge between genetic mutation data and the structural basis of drug action. By establishing a clear "optimal binding model" for wild-type targets, it provides an intuitive analytical pathway for both research and education. We firmly believe that in the global fight against antibiotic resistance, BactProNET will become an indispensable resource, making a unique contribution to accelerating the discovery cycle from identifying resistance mutations to proposing novel therapeutic strategies. Conclusions In summary, BactProNET represents a significant advancement in bridging the gap between genotype and phenotype in the study of target-mediated antimicrobial resistance. By establishing an innovative "wild-type reference system" that integrates curated mutation data, AlphaFold2-predicted structures, molecular docking baselines, and evolutionary analysis, the platform provides a unique mechanistic lens through which to interpret how amino acid substitutions structurally impair drug binding. This approach addresses a critical bottleneck in AMR research, complementing existing gene-centric databases by enabling direct, structure-based hypothesis generation. The utility of BactProNET is demonstrated through its comprehensive dataset—encompassing 110 targets, 205 mutation sites, and 644 docking models—and its user-centric design, which combines intuitive visualization with embedded analytical tools for real-time sequence analysis. While acknowledging current limitations, such as reliance on computational models and a focus on single-point mutations, the platform establishes a robust and extensible foundation. Ultimately, BactProNET accelerates the deciphering of resistance mechanisms and provides a data-driven scaffold to guide the rational design of next-generation antimicrobials and combination therapies. By transforming dispersed mutation data into actionable structural insights, it serves as an indispensable resource in the ongoing battle against the global antimicrobial resistance crisis. Declarations Acknowledgements The authors would like to thank the NHC Key Laboratory of Parasite and Vector Biology (National Institute of Parasitic Diseases, Chinese Center for Diseases Control and Prevention) (Grant No. NHCKFKT2025-2) and the Key Project in National Science Research in Higher Education Institutions of Anhui Province (Grant No. 2022AH051236) for financial support. We also thank all colleagues and collaborators who contributed to this study. Author contributions Ke Wang : Conceptualization; investigation; methodology; Formal Analysis; Data Curation; Validation; Writing—Original Draft; Writing—Review and Editing; Project Administration. Yiru Liu : Conceptualization; software; Data Curation; Writing—Review and Editing. Xue Wang : Data Curation; Writing—Review and Editing. Haitao Zhou : software; Writing—Review and Editing. Longfei Chen and Jingyun Xu : Writing—review and editing; funding acquisition; project administration; validation; supervision. Data availability statement The BactProNET web platform is freely accessible at http://www.wnmc-bioinfo.com/BactProNET/. The source code is available from the corresponding author upon reasonable request. All data used in this study are derived from publicly available sources, with specific references and identifiers provided in the Materials and Methods section. Funding statement This project was supported by NHC Key Laboratory of Parasite and Vector Biology (National Institute of Parasitic Diseases, Chinese Center for Diseases Control and Prevention) (NHCKFKT2025-2) and Key Project in National Science Reserach in Higher Education Insititutions of Anhui Province (2022AH051236). Competing Interests Statement The authors declare no competing interests. References Aminov, R. I. A brief history of the antibiotic era: lessons learned and challenges for the future. Front. Microbiol. 1 , 134, DOI: 10.3389/fmicb.2010.00134 (2010). Ventola CL. The antibiotic resistance crisis: part 1: causes and threats. P T 2015; 40 :277-83. World Health Organization. Antimicrobial resistance. WHO Fact Sheets, (2024). https://www.who.int/news-room/fact-sheets/detail/antimicrobial-resistance Zhu, T., Lei, Z., Qu, S., Zhuang, J., Tao, Y. & Wei, Y. Comparison of the outer membrane proteomes between clinical carbapenem-resistant and -susceptible Acinetobacter baumannii. Lett. Appl. Microbiol. 74 , 873–882, DOI: 10.1111/lam.13672 (2022). Blair, J. M. A., Webber, M. A., Baylay, A. J., Ogbolu, D. O. & Piddock, L. J. V. Molecular mechanisms of antibiotic resistance. Nat. Rev. Microbiol. 13 , 42–51, DOI: 10.1038/nrmicro3380 (2015). Munita, J. M. & Arias, C. A. Mechanisms of Antibiotic Resistance. Microbiol. Spectr. 4 , VMBF-0016-2015, DOI: 10.1128/microbiolspec.VMBF-0016-2015 (2016). Bush, K. & Bradford, P. A. β-Lactams and β-Lactamase Inhibitors: An Overview. Cold Spring Harb. Perspect. Med. 6 , a025247, DOI: 10.1101/cshperspect.a025247 (2016). Hooper, D. C. & Jacoby, G. A. Topoisomerase Inhibitors: Fluoroquinolone Mechanisms of Action and Resistance. Cold Spring Harb. Perspect. Med. 6 , a025320, DOI: 10.1101/cshperspect.a025320 (2016). Alcock, B. P. et al. CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. Nucleic Acids Res. 51 , D690–D699, DOI: 10.1093/nar/gkac920 (2023). Bortolaia, V. et al. ResFinder 4.0 for predictions of phenotypes from genotypes. J. Antimicrob. Chemother. 75 , 3491–3500, DOI: 10.1093/jac/dkaa345 (2020). Sayers, E. W. et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 52 , D17–D25, DOI: 10.1093/nar/gkad1044 (2024). The UniProt Consortium. UniProt: the universal protein knowledgebase in 2023. Nucleic Acids Res. 51 , D523–D531, DOI: 10.1093/nar/gkac1052 (2023). Burley, S. K. et al. RCSB Protein Data Bank: biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy. Nucleic Acids Res. 47 , D464–D474, DOI: 10.1093/nar/gky1004 (2019). Kim, S. et al. PubChem 2023 update. Nucleic Acids Res. 51 , D1373–D1380, DOI: 10.1093/nar/gkac956 (2023). Bento, A. P. et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 52 , D1253–D1267, DOI: 10.1093/nar/gkr777 (2024). Wishart, D. S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 50 , D1285–D1293, DOI: 10.1093/nar/gkx1037 (2022). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Mar, 2026 Reviews received at journal 08 Mar, 2026 Reviews received at journal 02 Mar, 2026 Reviews received at journal 01 Mar, 2026 Reviews received at journal 01 Mar, 2026 Reviewers agreed at journal 17 Feb, 2026 Reviewers agreed at journal 16 Feb, 2026 Reviewers agreed at journal 16 Feb, 2026 Reviewers agreed at journal 15 Feb, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers invited by journal 11 Feb, 2026 Editor invited by journal 11 Feb, 2026 Editor assigned by journal 09 Feb, 2026 Submission checks completed at journal 09 Feb, 2026 First submitted to journal 06 Feb, 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. 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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-8808185","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":592306738,"identity":"b5897a24-c67d-47f9-b41a-6b806e628ea9","order_by":0,"name":"Ke Wang","email":"","orcid":"","institution":"Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Wang","suffix":""},{"id":592306739,"identity":"fbbf3821-3011-4aab-a232-d6a584a9b4fd","order_by":1,"name":"Yiru Liu","email":"","orcid":"","institution":"Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yiru","middleName":"","lastName":"Liu","suffix":""},{"id":592306740,"identity":"eedca6c1-b298-4f99-a33e-2f8fdb6ff2b8","order_by":2,"name":"Xue Wang","email":"","orcid":"","institution":"Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Wang","suffix":""},{"id":592306743,"identity":"cbfec340-c0e3-4332-9a42-20a1434b84c8","order_by":3,"name":"Haitao Zhou","email":"","orcid":"","institution":"Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Haitao","middleName":"","lastName":"Zhou","suffix":""},{"id":592306745,"identity":"2ddf8bf7-968b-471c-b2d7-bd82d2f337d9","order_by":4,"name":"Longfei Chen","email":"","orcid":"","institution":"Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Longfei","middleName":"","lastName":"Chen","suffix":""},{"id":592306747,"identity":"cc7eb4e0-69e9-4f09-a98d-31e2119ddab5","order_by":5,"name":"Jingyun Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYHCChANgir0Byj9AtBYemFIitECBRAKRWgxuJDw8XPDrcOJ2yTdmj27UMMjx3Uhg/FyAR4vkjISEwzP7DifunJ1jbpxzjMFY8kYCs/QMPFr4JYBaeHsOJ264nbtNOreBIXHDjQQ2Zh48WtjgWm6eBWupJ6gFbAvPD6CWG7xgLQkGhLRI9jwA2tKQbrzhTP436ZxjEoYzzzxslsanxeB4TvJnnj/WshuOH0uTzqmxkec7nnzwMz4twChMYGBsa4bxJICYsQGvBmBCOcDA8KeOgKJRMApGwSgY0QAAczBT7j1RedcAAAAASUVORK5CYII=","orcid":"","institution":"Wannan Medical College","correspondingAuthor":true,"prefix":"","firstName":"Jingyun","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2026-02-06 14:25:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8808185/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8808185/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102835924,"identity":"a857cdc3-58e3-40af-a912-625d12855ff1","added_by":"auto","created_at":"2026-02-17 10:56:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":460866,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe construction workflow of the BactProNET database.\u003c/strong\u003e Data acquired from literature and public repositories were processed using a bioinformatics pipeline for sequence alignment, structure prediction, molecular docking, and phylogenetic analysis. The resulting curated data were then integrated to build the BactProNET web platform, featuring tools for data query, browsing, and online analysis.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8808185/v1/4724762f15fee7aa98379ea8.png"},{"id":102835917,"identity":"8d6e6222-bef1-4105-b8fe-f49a7333df2b","added_by":"auto","created_at":"2026-02-17 10:56:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":478415,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional architecture of the BactProNET web platform. \u003c/strong\u003eThis schematic outlines the logical structure of the BactProNET platform, which is built upon five interconnected core modules: Homepage, Data Retrieval \u0026amp; Browsing, Online Analysis Tools, Detailed View \u0026amp; Analysis, and Auxiliary Pages.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8808185/v1/859346be31c5224dc13f8dd5.png"},{"id":102835928,"identity":"4ee3fcd2-90d6-4571-ba18-3096199a1bc3","added_by":"auto","created_at":"2026-02-17 10:57:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":274365,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHierarchical structure of the BactProNET database.\u003c/strong\u003e The sunburst chart illustrates the database architecture, comprising 44 bacterial species, 110 protein targets, 205 mutation sites, 323 antibiotics, and 644 docking models. Data were compiled from 323 manually curated articles, public repositories, and molecular docking simulations using AlphaFold 2-predicted structures.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8808185/v1/55276728f75c817d6caf60fb.png"},{"id":102835926,"identity":"e94d3178-2128-4bfe-b6a0-47195abe3faf","added_by":"auto","created_at":"2026-02-17 10:57:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":840613,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular docking of gatifloxacin (A) and moxifloxacin (B) with \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eStaphylococcus aureus\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e GyrA. \u003c/strong\u003eLeft panels show the overall structure; right panels detail the active site. \u003cstrong\u003e(A)\u003c/strong\u003e Gatifloxacin (marine sticks) forms two hydrogen bonds (yellow dashes) with active site residues ASP-815 and ASP-813 (lightmagenta sticks), with a binding energy of -5.46 kcal/mol. \u003cstrong\u003e(B)\u003c/strong\u003e Moxifloxacin (marine sticks) does not form these hydrogen bonds, resulting in a weaker binding energy of -4.22 kcal/mol.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8808185/v1/5817c3b0c0cbd3af73d85f5b.png"},{"id":102835929,"identity":"2dc7cd48-1c3b-4b01-92fd-f4f9dd434508","added_by":"auto","created_at":"2026-02-17 10:57:03","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":543622,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvolutionary Distribution of Resistance Mutations in DNA Gyrase (GyrA). \u003c/strong\u003eThe phylogenetic tree (left), constructed from an alignment of wild-type GyrA protein sequences, is presented alongside a corresponding profile of literature-curated resistance mutations (right). All mutation sites are numbered according to the homologous positions in the \u003cem\u003eEscherichia coli\u003c/em\u003e GyrA reference sequence.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8808185/v1/ea41a5e9426dbd92bad1241d.jpg"},{"id":102835939,"identity":"a1d8f378-ee8d-4399-a581-c083cc5a6727","added_by":"auto","created_at":"2026-02-17 10:57:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3016354,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8808185/v1/deb512a1-8599-4eae-a5c5-78b1eac78d73.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"BactProNET: A Structural-Mechanistic Platform for Deciphering Target-Mediated Antimicrobial Resistance","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSince the mid-20th century, the widespread use of antibiotics has dramatically reduced morbidity and mortality from bacterial infectious diseases \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. However, with the misuse and overuse of antibiotics, the problem of bacterial resistance has become increasingly severe, leading to a continuous decline in the effectiveness of conventional treatment regimens and posing a serious challenge to global public health \u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAmong the diverse mechanisms of bacterial resistance, genetic mutation of the antibiotic's protein target is one of the most direct and prevalent \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. For instance, mutations in Penicillin-Binding Proteins (PBPs) can confer resistance to β-lactam antibiotics \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, while mutations in DNA gyrase or topoisomerase IV are a well-established cause of resistance to fluoroquinolones \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Such alterations of key amino acid residues can impair antibiotic efficacy by reducing drug-binding affinity, inducing conformational changes in the target protein, or creating steric hindrance that obstructs drug binding \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Consequently, a systematic investigation of these mutations at the sequence, structural, and functional levels of the protein target is crucial for a comprehensive understanding of antibiotic resistance.\u003c/p\u003e \u003cp\u003eWhile a wealth of research has reported on resistance mutations, this information remains highly dispersed. Authoritative databases such as the Comprehensive Antibiotic Resistance Database (CARD) \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e and ResFinder \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e excel at the identification and cataloging of resistance genes, but they lack the systematic integration of protein structure, drug interactions, and evolutionary context. This forces researchers investigating specific mechanisms to undertake the tedious task of manually retrieving and integrating data from disparate resources like the National Center for Biotechnology Information (NCBI) \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, UniProt \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e and PDB \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and the primary literature. To address this critical bottleneck, we developed BactProNET. In stark contrast to the \"gene identification\" role of existing platforms, BactProNET's fundamental advantage lies in its focus on mechanistic elucidation, designed to answer the core question of how an amino acid mutation structurally impairs drug binding. To achieve this, it uniquely integrates mutation sites with high-precision 3D structures and molecular docking models. Crucially, it provides a \"wild-type-drug optimal binding model\" as an analytical baseline\u0026mdash;a feature absent in other databases\u0026mdash;enabling users to intuitively assess the disruptive impact of any given mutation. Consequently, BactProNET is not a substitute for existing gene catalogs but a highly complementary analytical platform, specifically designed to bridge the critical mechanistic gap between genotype and resistance phenotype.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection and integration\u003c/h2\u003e \u003cp\u003eTo ensure data accuracy and comprehensiveness, the BactProNET dataset was constructed through a dual strategy of manual literature curation and public database integration. Our curation involved systematically screening 323 research articles from databases like PubMed, from which we manually extracted key data on bacterial species, protein targets, amino acid mutations, and associated antibiotics. This curated information was then standardized and enriched by integrating protein sequences from NCBI and UniProt. Concurrently, comprehensive antibiotic data\u0026mdash;including 2D/3D chemical structures, mechanisms of action, and clinical indications\u0026mdash;were aggregated from PubChem \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, ChEMBL \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, CARD, and DrugBank \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. This robust integration process, which included annotating essential biological information on the source species and protein targets, yielded a highly reliable and integrated dataset for mechanistic AMR analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBioinformatics analysis pipeline\u003c/h3\u003e\n\u003cp\u003eTo provide deeper insights beyond raw data, BactProNET integrates a comprehensive bioinformatics analysis pipeline. The process begins with sequence alignment to assess protein conservation and standardize mutation sites. Using the highly conserved quinolone target protein GyrA as a representative example, sequences containing resistance mutations from various bacteria were pair-wise aligned against the well-characterized Escherichia coli GyrA template via the CLUSTALW algorithm. This method enabled the mapping of resistance mutations from diverse sources onto homologous positions in the reference sequence, culminating in the generation of an integrated mutation distribution map. To establish a structural foundation for mechanistic investigation, the cutting-edge AlphaFold 2 algorithm was then utilized to predict high-accuracy three-dimensional structures from wild-type FASTA sequences provided by NCBI. These structural models served as the basis for molecular docking studies, performed with the AutoDock 4 software suite, to elucidate drug-target interaction modes. This workflow involved preparing the receptor protein and antibiotic ligand, defining an active site grid box for affinity calculations, and selecting the lowest-energy conformation from the most populated cluster as the optimal binding model. Finally, to contextualize these findings within an evolutionary framework, phylogenetic analysis was conducted using MEGA 11. A phylogenetic tree was constructed from wild-type amino acid sequences using the Neighbor-Joining (NJ) method, with branch support assessed by 1000 bootstrap replicates. Identified resistance mutation sites were subsequently mapped onto this tree, providing an integrated visualization that reveals the evolutionary patterns of resistance mutations.\u003c/p\u003e\n\u003ch3\u003eDatabase architecture and web interface\u003c/h3\u003e\n\u003cp\u003eThe BactProNET platform is deployed on a Linux server with a backend integrating an Apache web server (v2.4.58), a MySQL database (v8.0.42), and a Node.js runtime environment (v22.17.0). The front-end user interface was built with HTML5, CSS3, and JavaScript (ES6+), employing the Bootstrap framework (v4.0.0-beta2), jQuery (v3.2.1) for responsive design, and the NGL Viewer library (v0.10.4) for interactive 3D molecular visualization. The platform supports keyword search and category-based browsing and incorporates online BLAST and MSA tools to provide a comprehensive platform for data retrieval, visualization, and preliminary analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe workflow of database development is illustrated in\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eOverall framework and functionality of the BactProNET Database\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBactProNET is designed to provide users with a clear, intuitive, and information-rich online platform (Figure 2).\u003c/p\u003e\n\u003cp\u003e(1)\u0026nbsp;\u0026nbsp;User-friendly interface and navigation\u003c/p\u003e\n\u003cp\u003eThe database homepage features a clean design with a clear navigation bar, an introduction to core functionalities, and a global search box. Users can perform rapid searches using keywords or systematically browse the core data through main entry points such as \"Antibiotic\", \"Bacteria\", \"Protein\", and \"Molecular Docking\".\u003c/p\u003e\n\u003cp\u003e(2) Deeply integrated information detail pages\u003c/p\u003e\n\u003cp\u003eBactProNET has created dedicated detail pages for each core data type, deeply integrating multi-dimensional data to make information access efficient and convenient. The Protein Target Page includes \"Basic Information\" displaying the 3D structural model of the wild-type protein (with known mutation sites highlighted), sequence length, and biological function annotations; a \"Resistance Mutation Table\" systematically listing key mutation sites, corresponding amino acid variations, and associated antibiotics in a tabular format; and an Integrated View of Evolution and Sequence presenting the phylogenetic tree and MSA side-by-side, highlighting all reported resistance-associated mutation sites to intuitively reveal evolutionary relationships and sequence conservation. The Bacteria Page provides \"Basic Biological Information\" including the taxonomic classification and background of the bacterial species, and a \"Resistance Information Overview\" listing the protein targets reported to be associated with resistance in that bacterium and, for each target, the specific antibiotics documented in the literature to which mutations confer resistance. The Antibiotic Page systematically integrates the antibiotic's 2D/3D chemical structure, mechanism of action, and clinical indications. The Molecular Docking Page features a \"3D Interaction View\" visually displaying the optimal binding conformation of the antibiotic within the target's active site pocket, and a \"Detailed Docking Results Table\" providing a table with detailed energy and structural parameters for quantitative assessment of the binding mode..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current release of BactProNET contains a systematically curated dataset encompassing 110 protein targets from 44 bacterial species and comprehensive information for 323 antibiotics. A central feature is the annotation of 205 key resistance mutation sites, which were identified and consolidated from a review of 323 peer-reviewed publications. To facilitate a structural understanding of these interactions, the database also includes a library of 644 pre-computed protein-antibiotic molecular docking models (Figure 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCase Study: A One-stop Integrated Analysis of GyrA from \u003cem\u003eStaphylococcus aureus\u003c/em\u003e using BactProNET\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo demonstrate the platform's utility, this case study presents a complete workflow analyzing DNA gyrase subunit A (GyrA) from \u003cem\u003eS. aureus\u003c/em\u003e, using two fluoroquinolone antibiotics, Gatifloxacin and Moxifloxacin, as exemplar drugs. The analysis was initiated by querying \"\u003cem\u003eStaphylococcus aureus\u003c/em\u003e\" on the BactProNET platform. The target, \"DNA gyrase subunit A [\u003cem\u003eStaphylococcus aureus\u003c/em\u003e] (3D Model),\" was then selected from the results to access its dedicated data page and molecular docking interface.\u003c/p\u003e\n\u003cp\u003eStep 1: Establishing a Wild-Type Interaction Baseline\u003c/p\u003e\n\u003cp\u003ePre-computed molecular docking established a structural baseline. Gatifloxacin bound strongly (-5.46 kcal/mol) via hydrogen bonds to active site residues ASP813 and ASP815, whereas Moxifloxacin bound weakly (-4.22 kcal/mol) without these key interactions(Figure 4). This provides a clear structural basis for interpreting resistance.\u003c/p\u003e\n\u003cp\u003eStep 2: Correlating Resistance with Evolutionary and Sequence Context\u003c/p\u003e\n\u003cp\u003eAn integrated view juxtaposes a phylogenetic tree and a MSA (Figure 5), directly linking evolutionary divergence to functional protein regions. All reported resistance mutations are highlighted on the MSA, providing immediate visual and evolutionary context for their distribution.\u003c/p\u003e\n\u003cp\u003eStep 3: Real-time Validation of Novel Sequences\u003c/p\u003e\n\u003cp\u003eBuilt-in BLAST and MSA tools enable the real-time validation of user-submitted sequences. A novel GyrA sequence from a resistant strain can be directly compared against references to rapidly identify resistance-conferring mutations, accelerating the research cycle.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe core innovation of BactProNET is that, instead of directly modeling mutant proteins, it first provides researchers with a critical \"wild-type reference system\". By performing structural prediction on the wild-type protein and molecular docking with antibiotics, we identify and display a single, energetically optimal binding conformation for each drug-target pair. This clear \"optimal binding model\" forms the cornerstone for subsequent analysis. Users can map known resistance mutations onto this 3D model to intuitively infer how a specific amino acid substitution precisely disrupts this optimal binding\u0026mdash;through steric hindrance, charge alteration, or the disruption of key interactions\u0026mdash;ultimately leading to resistance.\u003c/p\u003e \u003cp\u003eThe practical value of BactProNET is further enhanced by its built-in tools and external links. The embedded BLAST and MSA tools allow users to instantly analyze their own sequences, while links to authoritative resources like NCBI and PDB establish BactProNET as a convenient portal to the broader data ecosystem. At the same time, we clearly recognize the limitations of the current platform. First, the accuracy of computationally predicted models cannot be fully equivalent to experimentally resolved structures. Second, the database currently focuses on resistance mediated by target-site amino acid substitutions and does not yet cover other important mechanisms such as drug efflux or enzymatic degradation. Finally, the strategy of presenting a single optimal docking conformation, while enhancing clarity, sacrifices the exploration of molecular dynamics and multiple potential binding modes.\u003c/p\u003e \u003cp\u003eLooking ahead, the iterative upgrades for BactProNET will be incremental. The first priority is the continuous updating of resistance mutation data. Longer-term goals include: (1) integrating mutant models by introducing structural models of mutant proteins to perform differential docking analysis between wild-type and mutant forms; (2) introducing advanced simulations by considering the application of methods like molecular dynamics to explore the dynamics of the binding process; and (3) expanding the scope of mechanisms by systematically incorporating a wider variety of resistance mechanisms to build a more comprehensive AMR knowledge graph.\u003c/p\u003e \u003cp\u003eAs a novel bioinformatics resource, BactProNET builds a critical bridge between genetic mutation data and the structural basis of drug action. By establishing a clear \"optimal binding model\" for wild-type targets, it provides an intuitive analytical pathway for both research and education. We firmly believe that in the global fight against antibiotic resistance, BactProNET will become an indispensable resource, making a unique contribution to accelerating the discovery cycle from identifying resistance mutations to proposing novel therapeutic strategies.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, BactProNET represents a significant advancement in bridging the gap between genotype and phenotype in the study of target-mediated antimicrobial resistance. By establishing an innovative \"wild-type reference system\" that integrates curated mutation data, AlphaFold2-predicted structures, molecular docking baselines, and evolutionary analysis, the platform provides a unique mechanistic lens through which to interpret how amino acid substitutions structurally impair drug binding. This approach addresses a critical bottleneck in AMR research, complementing existing gene-centric databases by enabling direct, structure-based hypothesis generation.\u003c/p\u003e \u003cp\u003eThe utility of BactProNET is demonstrated through its comprehensive dataset\u0026mdash;encompassing 110 targets, 205 mutation sites, and 644 docking models\u0026mdash;and its user-centric design, which combines intuitive visualization with embedded analytical tools for real-time sequence analysis. While acknowledging current limitations, such as reliance on computational models and a focus on single-point mutations, the platform establishes a robust and extensible foundation.\u003c/p\u003e \u003cp\u003eUltimately, BactProNET accelerates the deciphering of resistance mechanisms and provides a data-driven scaffold to guide the rational design of next-generation antimicrobials and combination therapies. By transforming dispersed mutation data into actionable structural insights, it serves as an indispensable resource in the ongoing battle against the global antimicrobial resistance crisis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the NHC Key Laboratory of Parasite and Vector Biology (National Institute of Parasitic Diseases, Chinese Center for Diseases Control and Prevention) (Grant No. NHCKFKT2025-2) and the Key Project in National Science Research in Higher Education Institutions of Anhui Province (Grant No. 2022AH051236) for financial support. We also thank all colleagues and collaborators who contributed to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKe Wang\u003c/strong\u003e: Conceptualization; investigation; methodology; Formal Analysis; Data Curation; Validation; Writing—Original Draft; Writing—Review and Editing; Project Administration.\u0026nbsp;\u003cstrong\u003eYiru Liu\u003c/strong\u003e:\u0026nbsp;Conceptualization; software; Data Curation; Writing—Review and Editing.\u0026nbsp;\u003cstrong\u003eXue Wang\u003c/strong\u003e:\u0026nbsp;Data Curation; Writing—Review and Editing.\u0026nbsp;\u003cstrong\u003eHaitao Zhou\u003c/strong\u003e:\u0026nbsp;software; Writing—Review and Editing.\u0026nbsp;\u003cstrong\u003eLongfei Chen\u003c/strong\u003e and\u0026nbsp;\u003cstrong\u003eJingyun Xu\u003c/strong\u003e: Writing—review and editing; funding acquisition; project administration; validation; supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe BactProNET web platform is freely accessible at http://www.wnmc-bioinfo.com/BactProNET/. The source code is available from the corresponding author upon reasonable request. All data used in this study are derived from publicly available sources, with specific references and identifiers provided in the Materials and Methods section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was supported by NHC Key Laboratory of Parasite and Vector Biology (National Institute of Parasitic Diseases, Chinese Center for Diseases Control and Prevention) (NHCKFKT2025-2) and Key Project in National Science Reserach in Higher Education Insititutions of Anhui Province (2022AH051236).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAminov, R. I. 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P. et al. CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. Nucleic Acids Res. \u003cstrong\u003e51\u003c/strong\u003e, D690\u0026ndash;D699, DOI: 10.1093/nar/gkac920 (2023).\u003c/li\u003e\n\u003cli\u003eBortolaia, V. et al. ResFinder 4.0 for predictions of phenotypes from genotypes. J. Antimicrob. Chemother. \u003cstrong\u003e75\u003c/strong\u003e, 3491\u0026ndash;3500, DOI: 10.1093/jac/dkaa345 (2020).\u003c/li\u003e\n\u003cli\u003eSayers, E. W. et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. \u003cstrong\u003e52\u003c/strong\u003e, D17\u0026ndash;D25, DOI: 10.1093/nar/gkad1044 (2024).\u003c/li\u003e\n\u003cli\u003eThe UniProt Consortium. UniProt: the universal protein knowledgebase in 2023. Nucleic Acids Res. \u003cstrong\u003e51\u003c/strong\u003e, D523\u0026ndash;D531, DOI: 10.1093/nar/gkac1052 (2023).\u003c/li\u003e\n\u003cli\u003eBurley, S. K. et al. RCSB Protein Data Bank: biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy. Nucleic Acids Res. \u003cstrong\u003e47\u003c/strong\u003e, D464\u0026ndash;D474, DOI: 10.1093/nar/gky1004 (2019).\u003c/li\u003e\n\u003cli\u003eKim, S. et al. PubChem 2023 update. Nucleic Acids Res. \u003cstrong\u003e51\u003c/strong\u003e, D1373\u0026ndash;D1380, DOI: 10.1093/nar/gkac956 (2023).\u003c/li\u003e\n\u003cli\u003eBento, A. P. et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. \u003cstrong\u003e52\u003c/strong\u003e, D1253\u0026ndash;D1267, DOI: 10.1093/nar/gkr777 (2024).\u003c/li\u003e\n\u003cli\u003eWishart, D. S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. \u003cstrong\u003e50\u003c/strong\u003e, D1285\u0026ndash;D1293, DOI: 10.1093/nar/gkx1037 (2022).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"BactProNET, antibiotic resistance, database, target protein, amino acid mutation, molecular docking","lastPublishedDoi":"10.21203/rs.3.rs-8808185/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8808185/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAntibiotics are the frontline therapy for bacterial infections, yet their efficacy is critically threatened by antimicrobial resistance (AMR), a crisis largely driven by mutations in protein targets. While this mechanism is prevalent, data on its structural impact remains highly dispersed, and existing resources like CARD and ResFinder prioritize gene identification over mechanistic insight. To address this gap, we developed BactProNET, a bioinformatics platform for the structural and evolutionary analysis of target-mediated resistance. Its core innovation is a multi-level data integration that establishes a \"wild-type reference system\" for mechanistic inference. The platform integrates curated resistance mutations with AlphaFold 2-predicted 3D structures, molecular docking models defining an \"optimal binding\" baseline, and integrated phylogenetic and sequence alignment views. Currently housing data on 110 protein targets, 205 mutation sites, and 644 docking models, BactProNET is accessible via an interface with embedded BLAST and MSA tools. As a one-stop platform, it accelerates the understanding of AMR's molecular mechanisms and provides a robust, data-driven foundation for the rational design of next-generation antimicrobial drugs.\u003c/p\u003e","manuscriptTitle":"BactProNET: A Structural-Mechanistic Platform for Deciphering Target-Mediated Antimicrobial Resistance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 10:55:54","doi":"10.21203/rs.3.rs-8808185/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-16T04:51:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-08T09:11:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-02T16:06:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-01T21:11:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-01T08:01:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339898508679736876220311598458146138825","date":"2026-02-17T09:00:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144557107828999893063414833893871064030","date":"2026-02-17T02:37:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23723419347157732281004289030072139114","date":"2026-02-16T17:04:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86180301219289807156311764855934455938","date":"2026-02-15T06:08:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"222327710821340692941311451517207458349","date":"2026-02-14T03:34:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218056394757589007854329094454548522727","date":"2026-02-12T09:26:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96451413069326419460644957172557712000","date":"2026-02-12T03:12:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-12T02:34:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-11T18:57:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-09T14:44:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-09T14:38:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-06T13:56:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d854f1be-8938-4527-9758-763f6a1adf0d","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":63025600,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":63025601,"name":"Biological sciences/Drug discovery"},{"id":63025602,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-05-13T10:26:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-17 10:55:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8808185","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8808185","identity":"rs-8808185","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}