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From a prediction standpoint, the rates have been a long\u2010standing puzzle. We have known for decades that folding rates empirically correlate with properties of the native three dimensional (3D) structures and that both, folding and unfolding rates, scale with protein size. Whereas such rate correlations are too rough for being of practical use, no significant progress in prediction accuracy has occurred since then, despite many efforts even including machine learning approaches. Here, we retake on this challenge by expanding the simple one\u2010dimensional free energy surface (1D\u2010FES) model that originally led to demonstrate the size scaling of both rates, and a curated database with rates for 75 single\u2010domain proteins. We define the weighted sequence order (WSO) as a novel parameter that allows incorporating structural information into the 1D\u2010FES model explicitly. Via the WSO, we examine the role of global structural properties such as fold topology and core packing in defining the (un)folding rates within the context of a physics\u2010based model of protein folding. After introducing fold topology and packing at a coarse\u2010grained level, the model uses three floating parameters to predict the folding and unfolding rates within 6.5\u2010 and 10\u2010fold, respectively, resulting in \u00b16.5\u2009kJ\/mol accuracy in native stability, equivalent to the typical perturbation induced by one single\u2010point mutation. The net improvement over the 2\u2010parameter size\u2010only prediction is of 2.5\u2010fold. These new rate predictions are significantly closer to the threshold of usefulness for engineering and design. More importantly, this WSO\u2010modified 1D\u2010FES model can now directly accommodate atomistic, high\u2010resolution, force\u2010fields to further optimize the rate predictions, and\/or to use rate information as a testbed for force\u2010field refinement. Finally, the WSO\u20101D\u2010FES model could also serve as foundation for developing more complex models capable of dealing with multi\u2010domain proteins as well as with the evolutionary information cryptically encoded in natural protein sequences.<\/jats:p>","DOI":"10.1002\/pro.70705","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T01:08:42Z","timestamp":1785287322000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Physics\u2010based prediction of protein folding and unfolding rates: Examining the roles of fold topology and core packing"],"prefix":"10.1002","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1521-9480","authenticated-orcid":false,"given":"Mohammad","family":"Abdulqader","sequence":"first","affiliation":[{"name":"Department of Bioengineering University of California Merced  Merced California USA"},{"name":"NSF\u2010CREST Center for Cellular and Biomolecular Machines University of California Merced  Merced California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5683-1482","authenticated-orcid":false,"given":"Victor","family":"Mu\u00f1oz","sequence":"additional","affiliation":[{"name":"Department of Bioengineering University of California Merced  Merced California USA"},{"name":"NSF\u2010CREST Center for Cellular and Biomolecular Machines University of California Merced  Merced California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,28]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.1974.1100705"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/prot.20172"},{"key":"e_1_2_9_4_1","doi-asserted-by":"crossref","unstructured":"AtsavapraneeB SundenF HerschlagD FordyceP.Quantifying protein unfolding kinetics with a high\u2010throughput microfluidic platform. bioRxiv.2025https:\/\/doi.org\/10.1101\/2025.01.15.633299","DOI":"10.1101\/2025.01.15.633299"},{"key":"e_1_2_9_5_1","doi-asserted-by":"crossref","unstructured":"BakerD HassabisD JumperJ.Nobel prize in chemistry 2024. 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