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Here, we present TPS-Flow, a physics-guided flow-based generative framework for end point-conditioned conformational path sampling between predefined protein states (not equilibrium ensembles). TPS-Flow represents structures as residue-level SE(3) transforms, uses a spatiotemporal gated attention encoder to learn a flow-matching interpolation velocity field from MD trajectories, and incorporates optional energy and structure-aware constraints together with a short physics-based relaxation step. Across a mycobacterial membrane transporter, a monomeric protein, a protein\u2013protein complex, and a soluble enzyme, TPS-Flow preserves residue-wise fluctuation patterns with damped amplitudes and occupies TICA-projected conformational corridors consistent with reference MD, provides conformational coverage complementary to finite reference MD sampling and generates intermediates with reference-comparable docking scores while reducing model size and computational cost compared to a state-of-the-art trajectory generator (MDGen). In an out-of-distribution structural generalization test using PN-subdomain mutants, TPS-Flow preserved fold continuity and wild-type-like global RMSF patterns when conditioned on AF3-derived mutant end point structures, thereby bridging atomistic simulation and deep generative modeling of protein transition paths.<\/jats:p>","DOI":"10.1021\/acs.jcim.6c00807","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T07:11:41Z","timestamp":1783667501000},"page":"7952-7971","source":"Crossref","is-referenced-by-count":0,"title":["TPS-Flow: Physics-Guided\nFlow-Based Generative Modeling\nof Protein Transition Paths"],"prefix":"10.1021","volume":"66","author":[{"given":"Kai","family":"Xu","sequence":"first","affiliation":[{"name":"Macao Polytechnic University , , ,","place":["Macao, China, 999078"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Likun","family":"Zhao","sequence":"additional","affiliation":[{"name":"Macao Polytechnic University , , ,","place":["Macao, China, 999078"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanan","family":"Tian","sequence":"additional","affiliation":[{"name":"Macao Polytechnic University , , ,","place":["Macao, China, 999078"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kewei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Macao Polytechnic University , , ,","place":["Macao, China, 999078"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jieying","family":"Zang","sequence":"additional","affiliation":[{"name":"Macao Polytechnic University , , ,","place":["Macao, China, 999078"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianmin","family":"Wang","sequence":"additional","affiliation":[{"name":"Yonsei University , , ,","place":["Incheon, Republic of Korea, 21983"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuquan","family":"Li","sequence":"additional","affiliation":[{"name":"Guizhou University , , ,","place":["Guizhou, China, 550025"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shihang","family":"Wang","sequence":"additional","affiliation":[{"name":"Macao Polytechnic University , , ,","place":["Macao, China, 999078"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaolong","family":"Lin","sequence":"additional","affiliation":[{"name":"Macao Polytechnic University , , ,","place":["Macao, China, 999078"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9284-3667","authenticated-orcid":true,"given":"Huanxiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Macao Polytechnic University , , ,","place":["Macao, China, 999078"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9958-8438","authenticated-orcid":true,"given":"Xiaojun","family":"Yao","sequence":"additional","affiliation":[{"name":"Macao Polytechnic University , , ,","place":["Macao, China, 999078"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,7,10]]},"reference":[{"key":"2026072709084106000_cit1","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1038\/s41586-024-07487-w","article-title":"Accurate structure prediction\nof biomolecular interactions with AlphaFold 3","volume":"630","author":"Abramson","year":"2024","journal-title":"Nature"},{"key":"2026072709084106000_cit2","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly accurate protein\nstructure prediction with AlphaFold","volume":"596","author":"Jumper","year":"2021","journal-title":"Nature"},{"key":"2026072709084106000_cit3","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1126\/science.abj8754","article-title":"Accurate prediction\nof protein structures and interactions using a three-track neural\nnetwork","volume":"373","author":"Baek","year":"2021","journal-title":"Science"},{"issue":"24","key":"2026072709084106000_cit4","doi-asserted-by":"publisher","DOI":"10.1101\/2023.05.24.542179","article-title":"Efficient and accurate\nprediction of protein structure using RoseTTAFold2","volume":"05","author":"Baek","year":"2023","journal-title":"bioRxiv"},{"key":"2026072709084106000_cit5","doi-asserted-by":"publisher","first-page":"1514","DOI":"10.1038\/s41592-024-02272-z","article-title":"OpenFold: retraining\nAlphaFold2 yields new insights into its learning mechanisms and capacity\nfor generalization","volume":"21","author":"Ahdritz","year":"2024","journal-title":"Nat. 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