{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T05:09:09Z","timestamp":1778476149745,"version":"3.51.4"},"reference-count":40,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T00:00:00Z","timestamp":1778457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiol."],"abstract":"<jats:p>\n                    <jats:italic>Methicillin-resistant Staphylococcus aureus (MRSA)<\/jats:italic>\n                    remains a major cause of morbidity and mortality worldwide, largely due to penicillin-binding protein 2a (PBP2A), a low-affinity transpeptidase encoded by mecA. To address the challenge of this highly flexible target, we developed an integrated discovery framework combining conformational ensemble analysis with deep generative modeling. Extensive molecular dynamics simulations were used to map the dynamic landscape of PBP2A and identify cryptic binding pockets. A target-conditioned generative workflow then explored these states to produce novel candidate scaffolds, followed by multi-objective optimization and structural prioritization. Among the generated molecules, Compound 1 showed a conserved binding mode, sustained stability over 1000\u202fns MD simulations, and more favorable MMGBSA binding energies than native ligands. Whole-cell assays showed dose-dependent growth inhibition against\n                    <jats:italic>S. aureus<\/jats:italic>\n                    ATCC 29213 and\n                    <jats:italic>MRSA<\/jats:italic>\n                    ATCC 43300, with stronger activity after 24\u202fh exposure. Additional propidium iodide staining revealed a concentration-dependent increase in PI uptake in both strains, consistent with compound-induced envelope perturbation. These findings support Compound 1 as an antibacterial lead emerging from a PBP2A-guided discovery strategy, while direct target engagement remains to be established.\n                  <\/jats:p>","DOI":"10.3389\/fmicb.2026.1811950","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T04:26:58Z","timestamp":1778473618000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrated framework for targeting dynamic penicillin-binding protein 2a via ensemble structural biology and deep generative modeling"],"prefix":"10.3389","volume":"17","author":[{"given":"Tianshu","family":"Pang","sequence":"first","affiliation":[{"name":"Department of General Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine","place":["Hangzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of General Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine","place":["Hangzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of General Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine","place":["Hangzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of General Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine","place":["Hangzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,11]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1126\/science.adn1369","article-title":"Two codependent routes lead to high-level Mrsa","volume":"386","author":"Adedeji-Olulana","year":"2024","journal-title":"Science"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"103000","DOI":"10.1016\/j.sbi.2025.103000","article-title":"Modeling Boltzmann-weighted structural ensembles of proteins using artificial intelligence\u2013based methods","volume":"91","author":"Aranganathan","year":"2025","journal-title":"Curr. 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