![\"\"](\"http:\/\/sites.unica.it\/pe2026\/files\/2025\/09\/cropped-00_Profile_Tondo-Social__Spilla_PE26_1200px.png\")
<\/figure>PC20 – Antonio Laus<\/strong><\/p>\n\n\n\n CRS4 (Center for Advanced Studies, Research and Development in Sardinia), Pula, Italy<\/p>\n\n\n\n laus@crs4.it<\/strong><\/p>\n<\/div><\/div>\n\n\n\n PC20 – Antonio Laus CRS4 (Center for Advanced Studies, Research and Development in Sardinia), Pula, Italy laus@crs4.it A Bayesian-optimized, shape-driven oneOPES framework for resolving membrane-dependent nucleation landscapes of human IAPP toward mechanism-guided design Laus Antonio1, Ferro […]<\/p>\n","protected":false},"author":10371,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-2284","post","type-post","status-publish","format-standard","hentry","category-senza-categoria"],"_links":{"self":[{"href":"https:\/\/sites.unica.it\/pe2026\/wp-json\/wp\/v2\/posts\/2284","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.unica.it\/pe2026\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sites.unica.it\/pe2026\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sites.unica.it\/pe2026\/wp-json\/wp\/v2\/users\/10371"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.unica.it\/pe2026\/wp-json\/wp\/v2\/comments?post=2284"}],"version-history":[{"count":2,"href":"https:\/\/sites.unica.it\/pe2026\/wp-json\/wp\/v2\/posts\/2284\/revisions"}],"predecessor-version":[{"id":2343,"href":"https:\/\/sites.unica.it\/pe2026\/wp-json\/wp\/v2\/posts\/2284\/revisions\/2343"}],"wp:attachment":[{"href":"https:\/\/sites.unica.it\/pe2026\/wp-json\/wp\/v2\/media?parent=2284"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.unica.it\/pe2026\/wp-json\/wp\/v2\/categories?post=2284"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.unica.it\/pe2026\/wp-json\/wp\/v2\/tags?post=2284"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}A<\/strong> Bayesian-optimized, shape-driven oneOPES framework for resolving membrane-dependent nucleation landscapes of human IAPP toward mechanism-guided design<\/strong><\/td><\/tr> Laus Antonio1<\/sup>, Ferro Elsi1<\/sup>, Atzeni Rossano1<\/sup>, Pieroni Enrico1<\/sup>, Valentini Maria1<\/sup>, Massimo Pisu1<\/sup><\/em>
1<\/sup><\/em> CRS4 (Center for Advanced Studies, Research and Development in Sardinia), Pula, Italy<\/td><\/tr>Abstract<\/strong>
Human islet amyloid polypeptide (hIAPP) aggregation is a key molecular event in type 2 diabetes. Yet early nucleation remains difficult to characterize due to heterogeneous conformational ensembles and metastable intermediates, further modulated by membrane proximity. To address this problem, we developed an enhanced-sampling framework based on oneOPES (On-the-fly Probability Enhanced Sampling) to reconstruct the free-energy landscape of hIAPP nucleation in membrane-near and membrane-far conditions [1].
The method is centered on a custom collective variable (CV) designed to encode peptide shape and conformational identity. This variable is built from structurally interpretable descriptors, including hydrogen-bond patterns and side-chain contacts, selected across reference basins spanning beta-like, alpha-like, and disordered states. The descriptor space is then compressed into a one-dimensional discriminant through an HLDA-based formulation further refined by state-specific gating terms, improving local state resolution while reducing degeneracy along the projected coordinate.
To increase robustness and transferability, collective-variable construction is coupled to an AI-assisted Bayesian hyperparameter optimization strategy, in which weights, cutoffs, and state-specific thresholds are tuned through a multi-rung workflow with progressively stricter selection criteria. This enables the systematic identification of a balanced and physically meaningful CV that preserves structural interpretability while maximizing discrimination among nucleation-relevant metastable states.
We aim to obtain a reliable free-energy landscape describing how hIAPP explores distinct metastable regions in membrane-near and membrane-far conditions, and to identify the key intermediates and barrier regions controlling the onset of aggregation [2]. These metastable states act as mechanistic gatekeepers for rational modulation of nucleation pathways, since selectively stabilizing off-pathway states or destabilizing nucleation-competent intermediates may reshape the associated free-energy barriers. Overall, this work establishes a computational framework that links advanced CV engineering, AI-assisted Bayesian optimization, and enhanced sampling to the mechanistic dissection of hIAPP nucleation and to the identification of structurally actionable states for anti-aggregation design [3].<\/td><\/tr>References <\/strong>
[1] Rizzi, V.; H\u00e9ritier, M.; Piasentin, N.; Aureli, S.; Gervasio, F.L. The Arch from the Stones: Understanding Protein Folding Energy Landscapes via Bioinspired Collective Variables. J. Phys. Chem. Lett.<\/strong> 2025, 16<\/strong>, 9636\u20139645. https:\/\/doi.org\/10.1021\/acs.jpclett.5c02079
[2] Elenbaas, B.O.W.; Khemtemourian, L.; Killian, J.A.; Sinnige, T. Membrane-Catalyzed Aggregation of Islet Amyloid Polypeptide Is Dominated by Secondary Nucleation. Biochemistry<\/strong> 2022, 61<\/strong>, 1465\u20131472. https:\/\/doi.org\/10.1021\/acs.biochem.2c00184
[3] Xu, Y.; Maya-Martinez, R.; Guthertz, N.; Heath, G.R.; Manfield, I.W.; Breeze, A.L.; Sobott, F.; Foster, R.; Radford, S.E. Tuning the Rate of Aggregation of hIAPP into Amyloid Using Small-Molecule Modulators of Assembly. Nat. Commun.<\/strong> 2022, 13<\/strong>, 1040. https:\/\/doi.org\/10.1038\/s41467-022-28660-7
<\/td><\/tr><\/tbody><\/table><\/div><\/figure>\n","protected":false},"excerpt":{"rendered":"