{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T13:18:20Z","timestamp":1776863900982,"version":"3.51.2"},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"W1","license":[{"start":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T00:00:00Z","timestamp":1713139200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["SFRH\/BD\/136226\/2018"],"award-info":[{"award-number":["SFRH\/BD\/136226\/2018"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["CEECIND\/02300\/2017"],"award-info":[{"award-number":["CEECIND\/02300\/2017"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UIDB\/04046\/2020"],"award-info":[{"award-number":["UIDB\/04046\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UIDP\/04046\/2020"],"award-info":[{"award-number":["UIDP\/04046\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"name":"European Union\u2019s Horizon 2020","award":["101017567"],"award-info":[{"award-number":["101017567"]}]},{"name":"CESGA"},{"name":"Advanced Computing Project","award":["2021.09635.CPCA"],"award-info":[{"award-number":["2021.09635.CPCA"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>When preparing biomolecular structures for molecular dynamics simulations, pKa calculations are required to provide at least a representative protonation state at a given pH value. Neglecting this step and adopting the reference protonation states of the amino acid residues in water, often leads to wrong electrostatics and nonphysical simulations. Fortunately, several methods have been developed to prepare structures considering the protonation preference of residues in their specific environments (pKa values), and some are even available for online usage. In this work, we present the PypKa server, which allows users to run physics-based, as well as ML-accelerated methods suitable for larger systems, to obtain pKa values, isoelectric points, titration curves, and structures with representative pH-dependent protonation states compatible with commonly used force fields (AMBER, CHARMM, GROMOS). The user may upload a custom structure or submit an identifier code from PBD or UniProtKB. The results for over 200k structures taken from the Protein Data Bank and the AlphaFold DB have been precomputed, and their data can be retrieved without extra calculations. All this information can also be obtained from an application programming interface (API) facilitating its usage and integration into existing pipelines as well as other web services. The web server is available at pypka.org.<\/jats:p>","DOI":"10.1093\/nar\/gkae255","type":"journal-article","created":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T10:47:37Z","timestamp":1713178057000},"page":"W294-W298","source":"Crossref","is-referenced-by-count":8,"title":["PypKa server: online <b>p<i>K<\/i>a<\/b> predictions and biomolecular structure preparation with precomputed data from PDB and AlphaFold DB"],"prefix":"10.1093","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3563-6239","authenticated-orcid":false,"given":"Pedro B P S","family":"Reis","sequence":"first","affiliation":[{"name":"BioISI \u2013 Instituto de Biossistemas e Ci\u00eancias Integrativas, Faculdade de Ci\u00eancias, Universidade de Lisboa , 1749-016 Lisboa, Portugal"},{"name":"Machine Learning Research, Bayer AG , M\u00fcllerstra\u00dfe 178, 13353 Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4191-2156","authenticated-orcid":false,"given":"Djork-Arn\u00e9","family":"Clevert","sequence":"additional","affiliation":[{"name":"Machine Learning Research, Bayer AG , M\u00fcllerstra\u00dfe 178, 13353 Berlin, Germany"},{"name":"Machine Learning Research, Pfizer , Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6923-8744","authenticated-orcid":false,"given":"Miguel","family":"Machuqueiro","sequence":"additional","affiliation":[{"name":"BioISI \u2013 Instituto de Biossistemas e Ci\u00eancias Integrativas, Faculdade de Ci\u00eancias, Universidade de Lisboa , 1749-016 Lisboa, Portugal"}]}],"member":"286","published-online":{"date-parts":[[2024,4,15]]},"reference":[{"key":"2024070423564036500_B1","first-page":"19","article-title":"GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers","volume":"1","author":"Abraham","year":"2015","journal-title":"Software X"},{"key":"2024070423564036500_B2","doi-asserted-by":"crossref","first-page":"1781","DOI":"10.1002\/jcc.20289","article-title":"Scalable molecular dynamics with NAMD","volume":"26","author":"Phillips","year":"2005","journal-title":"J. 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