{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T06:20:11Z","timestamp":1791613211080,"version":"4.3.4"},"reference-count":53,"publisher":"National Academy of Sciences","issue":"48","license":[{"start":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T00:00:00Z","timestamp":1764028800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["22203001"],"award-info":[{"award-number":["22203001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["22025304 22033007"],"award-info":[{"award-number":["22025304 22033007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"University Synergy Innovation Program of Anhui Province","award":["GXXT-2022-062"],"award-info":[{"award-number":["GXXT-2022-062"]}]},{"DOI":"10.13039\/501100003995","name":"\u5b89\u5fbd\u7701\u79d1\u5b66\u6280\u672f\u5385 | Natural Science Foundation of Anhui Province","doi-asserted-by":"publisher","award":["2208085Y04"],"award-info":[{"award-number":["2208085Y04"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Laboratory of Precision and Intelligent Chemistry","award":["open research fund"],"award-info":[{"award-number":["open research fund"]}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["CHE-2246379"],"award-info":[{"award-number":["CHE-2246379"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.pnas.org"],"crossmark-restriction":true},"short-container-title":["Proc. 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U.S.A."],"published-print":{"date-parts":[[2025,12,2]]},"abstract":"<jats:p>\n                    Protein misfolding plays a central role in diseases such as Alzheimer\u2019s disease, Parkinson\u2019s disease, type 2 diabetes, and transthyretin amyloidosis (ATTR), often driven by specific aggregation-prone segments such as A\n                    <jats:italic toggle=\"yes\">\u03b2<\/jats:italic>\n                    <jats:sub>17\u201323<\/jats:sub>\n                    and A\n                    <jats:italic toggle=\"yes\">\u03b2<\/jats:italic>\n                    <jats:sub>37\u201342<\/jats:sub>\n                    of amyloid-\n                    <jats:italic toggle=\"yes\">\u03b2<\/jats:italic>\n                    42 (A\n                    <jats:italic toggle=\"yes\">\u03b2<\/jats:italic>\n                    42),\n                    <jats:italic toggle=\"yes\">\u03b1<\/jats:italic>\n                    -Syn\n                    <jats:sub>66\u201374<\/jats:sub>\n                    and\n                    <jats:italic toggle=\"yes\">\u03b1<\/jats:italic>\n                    -Syn\n                    <jats:sub>71\u201382<\/jats:sub>\n                    of\n                    <jats:italic toggle=\"yes\">\u03b1<\/jats:italic>\n                    -synuclein (\n                    <jats:italic toggle=\"yes\">\u03b1<\/jats:italic>\n                    -syn), hIAPP\n                    <jats:sub>22\u201327<\/jats:sub>\n                    of human islet amyloid polypeptide (hIAPP), and TTR\n                    <jats:sub>105\u2013115<\/jats:sub>\n                    of transthyretin (TTR). Capturing the atomic-level structural features of these transient and dynamically fluctuating regions remains challenging. Two-dimensional infrared (2DIR) spectroscopy provides rich vibrational fingerprints that are highly sensitive to protein conformational dynamics, but extracting atomic-resolution structural information from these complex signals is nontrivial. In this study, we present a machine learning framework that integrates 2DIR spectra with deep structural modeling to reconstruct the three-dimensional atomic structures of monomeric intrinsically disordered aggregation-prone segments of amyloidogenic proteins. Using this model, we were able to predict the conformational ensembles of aggregation-prone segments from amyloid-\n                    <jats:italic toggle=\"yes\">\u03b2<\/jats:italic>\n                    42,\n                    <jats:italic toggle=\"yes\">\u03b1<\/jats:italic>\n                    -synuclein, human islet amyloid polypeptide, and transthyretin, as well as the structural evolution of A\n                    <jats:italic toggle=\"yes\">\u03b2<\/jats:italic>\n                    42 bound to a small-molecule inhibitor, directly from computationally derived 2DIR spectra. An attention module highlights the most informative spectral features associated with local structural variations, providing interpretable links between spectra and structure. This generalizable strategy paves the way for interpreting time-resolved spectroscopic studies and offers a promising computational framework for probing misfolding-related structural dynamics and therapeutic mechanisms.\n                  <\/jats:p>","DOI":"10.1073\/pnas.2522772122","type":"journal-article","created":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T19:02:44Z","timestamp":1764097364000},"update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":2,"title":["A machine learning protocol for predicting structural distributions of amyloid-forming proteins from 2D IR spectra"],"prefix":"10.1073","volume":"122","author":[{"given":"Sheng","family":"Ye","sequence":"first","affiliation":[{"name":"Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology, School of AI, Anhui University","place":["Hefei, 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