{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T08:18:47Z","timestamp":1783671527516,"version":"3.55.0"},"reference-count":61,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"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. Bioinform."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The HSP90\u2013HOP interaction orchestrates transfer of client proteins from HSP70 to HSP90, promoting their conformational maturation and stabilisation and thereby sustaining oncogenic signalling. The study explores a promising alternative for cancer chemotherapy by targeting this interface rather than the traditional ATP-binding site, which may circumvent the toxicity associated with classical HSP90 inhibitors. Despite its therapeutic relevance, the HSP90\u2013HOP interface remains underexplored, particularly in the context of structure-guided peptidomimetic inhibitors, highlighting a critical gap in strategies to modulate proteostasis in cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>This study sought to identify a promising peptidomimetic molecule capable of disrupting the HSP90\u2013HOP interface.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A seven-residue template peptide was engineered from a crucial segment of HOP, with hotspot residues identified through\n                      <jats:italic>in silico<\/jats:italic>\n                      mutagenesis. These residues were subsequently employed to retrieve a library of 200 peptidomimetic molecules. An in-house developed classification-based machine learning model served as the primary screening tool to identify potential HSP90\u2013HOP interaction modulators. The shortlisted compounds were subsequently evaluated by molecular docking, binding free energy estimation, machine learning-assisted scoring, and ADMET profiling to ensure structural stability, binding reliability, and pharmacokinetic suitability. This scrutiny resulted in the selection of five lead compounds, with MMs01053537 emerging as a top candidate.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The ML model achieved an accuracy of 0.9055 and an ROC-AUC of 0.9537, indicating strong predictive performance of the model. The lead molecule MMs01053537 demonstrated a favourable binding score of \u221285.92 kcal\/mol, along with a robust stability profile during molecular dynamics simulations. To further assess the consistency of the predicted binding mode across trajectory-derived conformations, ensemble docking and MD-enhanced binding free-energy analysis, along with statistical evaluation were performed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Collectively, these findings position MMs01053537 as a potential candidate for disrupting the HSP90\u2013HOP interaction. However, experimental validation remains essential to confirm its therapeutic potential and support further biological evaluation of the compound.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fbinf.2026.1847387","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T07:59:01Z","timestamp":1783670341000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Discovery of novel peptidomimetics against HSP90\u2013HOP interactions towards improved cancer therapeutics using machine learning strategies"],"prefix":"10.3389","volume":"6","author":[{"given":"Sarath","family":"Perumal","sequence":"first","affiliation":[{"name":"Department of Biotechnology, School of Bio Sciences and Technology Vellore Institute of Technology","place":["Vellore, Tamil Nadu, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramanathan","family":"Karuppasamy","sequence":"additional","affiliation":[{"name":"Department of Biotechnology, School of Bio Sciences and Technology Vellore Institute of Technology","place":["Vellore, Tamil Nadu, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"11293","DOI":"10.3390\/ijms252011293","article-title":"Geldanamycins: potent hsp90 inhibitors with significant potential in cancer therapy","volume":"25","author":"Abdullah","year":"2024","journal-title":"Int. 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