{"id":"https://openalex.org/W4410944829","doi":"https://doi.org/10.1021/acs.jctc.5c00044","title":"Model Selection Using Replica Averaging with Bayesian Inference of Conformational Populations","display_name":"Model Selection Using Replica Averaging with Bayesian Inference of Conformational Populations","publication_year":2025,"publication_date":"2025-06-02","ids":{"openalex":"https://openalex.org/W4410944829","doi":"https://doi.org/10.1021/acs.jctc.5c00044","pmid":"https://pubmed.ncbi.nlm.nih.gov/40456681"},"language":"en","primary_location":{"id":"doi:10.1021/acs.jctc.5c00044","doi":"https://doi.org/10.1021/acs.jctc.5c00044","is_oa":false,"landing_page_url":"https://doi.org/10.1021/acs.jctc.5c00044","pdf_url":null,"source":{"id":"https://openalex.org/S189701308","display_name":"Journal of Chemical Theory and Computation","issn_l":"1549-9618","issn":["1549-9618","1549-9626"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-b","jufo-2","ki-jl-2","medline","norway-2","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320006","host_organization_name":"American Chemical Society","host_organization_lineage":["https://openalex.org/P4310320006"],"host_organization_lineage_names":["American Chemical Society"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Chemical Theory and Computation","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/64db8445dfabaf06ff507b5b/original/model-selection-using-replica-averaging-with-bayesian-inference-of-conformational-populations.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5028185893","display_name":"Robert M. Raddi","orcid":"https://orcid.org/0000-0001-7139-5028","observed_orcids":["https://orcid.org/0000-0001-7139-5028"]},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Robert M. Raddi","raw_affiliation_strings":["Department of Chemistry","Temple University"],"raw_orcid":"https://orcid.org/0000-0001-7139-5028","affiliations":[{"raw_affiliation_string":"Department of Chemistry","institution_ids":[]},{"raw_affiliation_string":"Temple University","institution_ids":["https://openalex.org/I84392919"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068649670","display_name":"Tim Marshall","orcid":"https://orcid.org/0000-0001-5126-942X","observed_orcids":["https://orcid.org/0000-0001-5126-942X"]},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tim Marshall","raw_affiliation_strings":["Department of Chemistry","Temple University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Chemistry","institution_ids":[]},{"raw_affiliation_string":"Temple University","institution_ids":["https://openalex.org/I84392919"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088783242","display_name":"Yunhui Ge","orcid":"https://orcid.org/0000-0002-3946-1440","observed_orcids":["https://orcid.org/0000-0002-3946-1440"]},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yunhui Ge","raw_affiliation_strings":["Department of Chemistry","Temple University"],"raw_orcid":"https://orcid.org/0000-0002-3946-1440","affiliations":[{"raw_affiliation_string":"Department of Chemistry","institution_ids":[]},{"raw_affiliation_string":"Temple University","institution_ids":["https://openalex.org/I84392919"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074806960","display_name":"Vincent A. Voelz","orcid":"https://orcid.org/0000-0002-1054-2124","observed_orcids":["https://orcid.org/0000-0002-1054-2124"]},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Vincent A. Voelz","raw_affiliation_strings":["Department of Chemistry","Temple University"],"raw_orcid":"https://orcid.org/0000-0002-1054-2124","affiliations":[{"raw_affiliation_string":"Department of Chemistry","institution_ids":[]},{"raw_affiliation_string":"Temple University","institution_ids":["https://openalex.org/I84392919"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5074806960"],"corresponding_institution_ids":["https://openalex.org/I84392919"],"apc_list":{"value":4500,"currency":"USD","value_usd":4500},"apc_paid":null,"fwci":2.2077,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.87722237,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":87,"max":99},"biblio":{"volume":"21","issue":"12","first_page":"5880","last_page":"5889"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10044","display_name":"Protein Structure and Dynamics","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10044","display_name":"Protein Structure and Dynamics","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12044","display_name":"Advanced Physical and Chemical Molecular Interactions","score":0.00039999998989515007,"subfield":{"id":"https://openalex.org/subfields/1606","display_name":"Physical and Theoretical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10521","display_name":"RNA and protein synthesis mechanisms","score":0.0003000000142492354,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/biceps-brachii","display_name":"biceps brachii","score":0.890999972820282},{"id":"https://openalex.org/keywords/model-selection","display_name":"model selection","score":0.8889999985694885},{"id":"https://openalex.org/keywords/force-field-evaluation","display_name":"force field evaluation","score":0.8009999990463257},{"id":"https://openalex.org/keywords/conformational-populations","display_name":"conformational populations","score":0.7279999852180481},{"id":"https://openalex.org/keywords/chignolin","display_name":"chignolin","score":0.8180000185966492},{"id":"https://openalex.org/keywords/chemical-shifts","display_name":"chemical shifts","score":0.8820000290870667},{"id":"https://openalex.org/keywords/tip3p-water-model","display_name":"TIP3P water model","score":0.7260000109672546},{"id":"https://openalex.org/keywords/maximum-entropy","display_name":"maximum entropy","score":0.6019999980926514},{"id":"https://openalex.org/keywords/protein-folding","display_name":"protein folding","score":0.6439999938011169}],"concepts":[{"id":"https://openalex.org/C10803110","wikidata":"https://www.wikidata.org/wiki/Q1341441","display_name":"Force field (fiction)","level":2,"score":0.7016066908836365},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.650409996509552},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5278453826904297},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4924412667751312},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.482384592294693},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.44940537214279175},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.4105801582336426},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.4086756408214569},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38346701860427856},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3777264952659607},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.353189617395401},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.20692408084869385},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009842","descriptor_name":"Oligopeptides","qualifier_ui":"Q000737","qualifier_name":"chemistry","is_major_topic":true},{"descriptor_ui":"D011487","descriptor_name":"Protein Conformation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D013816","descriptor_name":"Thermodynamics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D056004","descriptor_name":"Molecular Dynamics Simulation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":3,"locations":[{"id":"doi:10.1021/acs.jctc.5c00044","doi":"https://doi.org/10.1021/acs.jctc.5c00044","is_oa":false,"landing_page_url":"https://doi.org/10.1021/acs.jctc.5c00044","pdf_url":null,"source":{"id":"https://openalex.org/S189701308","display_name":"Journal of Chemical Theory and Computation","issn_l":"1549-9618","issn":["1549-9618","1549-9626"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-b","jufo-2","ki-jl-2","medline","norway-2","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320006","host_organization_name":"American Chemical Society","host_organization_lineage":["https://openalex.org/P4310320006"],"host_organization_lineage_names":["American Chemical Society"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Chemical Theory and Computation","raw_type":"journal-article"},{"id":"doi:10.26434/chemrxiv-2023-396mm","doi":"https://doi.org/10.26434/chemrxiv-2023-396mm","is_oa":true,"landing_page_url":"https://doi.org/10.26434/chemrxiv-2023-396mm","pdf_url":"https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/64db8445dfabaf06ff507b5b/original/model-selection-using-replica-averaging-with-bayesian-inference-of-conformational-populations.pdf","source":{"id":"https://openalex.org/S4393918830","display_name":"ChemRxiv","issn_l":"2573-2293","issn":["2573-2293"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},{"id":"pmid:40456681","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40456681","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of chemical theory and computation","raw_type":"Journal Article"}],"best_oa_location":{"id":"doi:10.26434/chemrxiv-2023-396mm","doi":"https://doi.org/10.26434/chemrxiv-2023-396mm","is_oa":true,"landing_page_url":"https://doi.org/10.26434/chemrxiv-2023-396mm","pdf_url":"https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/64db8445dfabaf06ff507b5b/original/model-selection-using-replica-averaging-with-bayesian-inference-of-conformational-populations.pdf","source":{"id":"https://openalex.org/S4393918830","display_name":"ChemRxiv","issn_l":"2573-2293","issn":["2573-2293"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G9000902234","display_name":"Markov State Model approaches for folding, binding and design","funder_award_id":"R01GM123296","funder_id":"https://openalex.org/F4320337354","funder_display_name":"National Institute of General Medical Sciences"}],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337354","display_name":"National Institute of General Medical Sciences","ror":"https://ror.org/04q48ey07"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410944829.pdf","grobid_xml":"https://content.openalex.org/works/W4410944829.grobid-xml"},"referenced_works_count":84,"referenced_works":["https://openalex.org/W161994183","https://openalex.org/W641640508","https://openalex.org/W1031578623","https://openalex.org/W1540687752","https://openalex.org/W1967025687","https://openalex.org/W1967734507","https://openalex.org/W1968735445","https://openalex.org/W1974030600","https://openalex.org/W1976238793","https://openalex.org/W1984892764","https://openalex.org/W1987027200","https://openalex.org/W2011510351","https://openalex.org/W2015078526","https://openalex.org/W2023511762","https://openalex.org/W2029120587","https://openalex.org/W2034016778","https://openalex.org/W2039588355","https://openalex.org/W2050961477","https://openalex.org/W2051091550","https://openalex.org/W2052338665","https://openalex.org/W2053160116","https://openalex.org/W2054881399","https://openalex.org/W2060757799","https://openalex.org/W2060872117","https://openalex.org/W2061827156","https://openalex.org/W2071827681","https://openalex.org/W2078162783","https://openalex.org/W2093030571","https://openalex.org/W2129762799","https://openalex.org/W2131288855","https://openalex.org/W2136849653","https://openalex.org/W2143592395","https://openalex.org/W2144288821","https://openalex.org/W2147988069","https://openalex.org/W2156313302","https://openalex.org/W2161605421","https://openalex.org/W2241089358","https://openalex.org/W2265101181","https://openalex.org/W2315297180","https://openalex.org/W2315961248","https://openalex.org/W2315962322","https://openalex.org/W2322017037","https://openalex.org/W2327421019","https://openalex.org/W2512849379","https://openalex.org/W2554113655","https://openalex.org/W2570104014","https://openalex.org/W2784103526","https://openalex.org/W2790474068","https://openalex.org/W2790940744","https://openalex.org/W2793701070","https://openalex.org/W2803396657","https://openalex.org/W2904163250","https://openalex.org/W2906718052","https://openalex.org/W2925663094","https://openalex.org/W2964106113","https://openalex.org/W2967420157","https://openalex.org/W2972332521","https://openalex.org/W2973748343","https://openalex.org/W2990103831","https://openalex.org/W3013283700","https://openalex.org/W3100810942","https://openalex.org/W3163051848","https://openalex.org/W3164065259","https://openalex.org/W3169673449","https://openalex.org/W3204102301","https://openalex.org/W4205246014","https://openalex.org/W4210278374","https://openalex.org/W4220658536","https://openalex.org/W4225998725","https://openalex.org/W4226337067","https://openalex.org/W4229369034","https://openalex.org/W4229504054","https://openalex.org/W4233253307","https://openalex.org/W4238663480","https://openalex.org/W4245107345","https://openalex.org/W4246447715","https://openalex.org/W4252222576","https://openalex.org/W4319931814","https://openalex.org/W4328102269","https://openalex.org/W4362691831","https://openalex.org/W4379376242","https://openalex.org/W4388247078","https://openalex.org/W4393043891","https://openalex.org/W6828914598"],"related_works":["https://openalex.org/W4319930464","https://openalex.org/W6960165440","https://openalex.org/W4362691831","https://openalex.org/W2949879779","https://openalex.org/W2790940744","https://openalex.org/W3163051848","https://openalex.org/W3004962603","https://openalex.org/W7119526315","https://openalex.org/W4392011804","https://openalex.org/W2011510351"],"abstract_inverted_index":{"Bayesian":[0],"Inference":[1],"of":[2,25,31,56,66,102,112,128,169,238],"Conformational":[3],"Populations":[4],"(BICePs)":[5],"is":[6],"a":[7,90,126,179,206,211,235],"reweighting":[8,245],"algorithm":[9],"that":[10,209],"reconciles":[11],"simulated":[12,116],"ensembles":[13,111],"with":[14,52,74,79,122,175,205],"sparse":[15],"and/or":[16],"noisy":[17],"observables":[18],"by":[19],"sampling":[20],"the":[21,29,53,63,95,100,113,157,166,187],"full":[22],"posterior":[23,64],"distribution":[24,65],"conformational":[26,110,172],"populations":[27,155,173],"in":[28,40,117,240],"presence":[30],"experimental":[32,130,176],"restraints.":[33],"By":[34],"modifying":[35],"BICePs":[36,44,96,107,162,239],"to":[37,47,60,69,77,108,181],"use":[38],"replica-averaging":[39],"its":[41],"forward":[42],"model,":[43],"becomes":[45],"similar":[46],"other":[48],"MaxEnt":[49],"approaches,":[50],"but":[51],"significant":[54],"advantages":[55],"(1)":[57],"being":[58],"able":[59],"sample":[61],"over":[62],"uncertainties":[67],"due":[68],"random":[70],"and":[71,81,138,147,222,246],"systematic":[72],"error,":[73],"improved":[75],"likelihoods":[76],"deal":[78],"outliers,":[80],"(2)":[82],"having":[83],"an":[84],"objective":[85],"score":[86],"for":[87,144,216,219],"model":[88,217,247],"selection,":[89],"free":[91,167],"energy-like":[92],"quantity":[93],"called":[94],"score.":[97],"To":[98],"demonstrate":[99],"power":[101],"our":[103],"approach,":[104],"we":[105,201],"used":[106,210],"reweight":[109],"mini-protein":[114],"chignolin":[115],"nine":[118,188],"different":[119],"force":[120,184,189],"fields":[121,190],"TIP3P":[123],"water,":[124],"using":[125],"set":[127],"158":[129],"measurements":[131],"(139":[132],"NOE":[133],"distances,":[134],"13":[135],"chemical":[136],"shifts,":[137],"6":[139],"vicinal":[140],"J":[141],"-coupling":[142],"constants":[143],"H":[145,148],"N":[146],"\u03b1":[149],").":[150],"In":[151],"all":[152],"cases,":[153],"reweighted":[154],"favor":[156],"correctly":[158],"folded":[159],"conformation.":[160],"The":[161],"score,":[163],"which":[164],"reports":[165],"energy":[168],"\u201cturning":[170],"on\u201d":[171],"along":[174],"restraints,":[177],"provides":[178],"metric":[180,215],"evaluate":[182],"each":[183],"field.":[185],"For":[186],"tested":[191],"(A14SB,":[192],"A99SB-ildn,":[193],"A99,":[194],"A99SBnmr1-ildn,":[195],"A99SB,":[196],"C22star,":[197],"C27,":[198],"C36,":[199],"OPLS-aa),":[200],"obtain":[202],"results":[203,233],"consistent":[204],"previous":[207],"work":[208],"conventional":[212],"\u03c7":[213],"2":[214],"selection":[218],"small":[220],"polypeptides":[221],"ubiquitin":[223],"(":[224],"J.":[225],"Chem.":[226],"Theory":[227],"Comput.,":[228],"2012,":[229],"8,":[230],"1409\u20131414).":[231],"These":[232],"suggest":[234],"powerful":[236],"role":[237],"future":[241],"applications":[242],"requiring":[243],"ensemble":[244],"selection.":[248]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2025-10-10T00:00:00"}
