Deep Generative Models of LDLR Protein Structure to Predict Variant Pathogenicity

The complex structure and function of low-density lipoprotein receptor (LDLR) makes classification of protein-coding missense variants challenging. Deep generative models, including evolutionary model of variant effect (EVE), evolutionary scale modeling (ESM), and AlphaFold 2 (AF2), have enabled sig...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Journal of lipid research Jg. 64; H. 12; S. 100455
Hauptverfasser:	James, Jose K., Norland, Kristjan, Johar, Angad S., Kullo, Iftikhar J.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Inc 01.12.2023 American Society for Biochemistry and Molecular Biology Elsevier
Schlagworte:	Atherosclerosis Cardiovascular Diseases - physiopathology Dyslipidemias Genetic Variation Genomics Humans Lipoproteins/Receptors Models, Molecular Phenotype Physical Biochemistry Protein Structure, Tertiary Proteomics Receptors, LDL - chemistry Receptors, LDL - genetics Virulence - genetics FH Dyslipidemias Genomics GPU AUC Atherosclerosis MSA MI LDLR ESM MAVE ACMG OR AMP EGF Lipoproteins/Receptors GMM AI HMM AF2 UKB VCEP EVE VUS pLDDT LA Proteomics Physical Biochemistry VAE atherosclerosis genomics lipoproteins/receptors dyslipidemias proteomics physical biochemistry
ISSN:	0022-2275, 1539-7262, 1539-7262
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	The complex structure and function of low-density lipoprotein receptor (LDLR) makes classification of protein-coding missense variants challenging. Deep generative models, including evolutionary model of variant effect (EVE), evolutionary scale modeling (ESM), and AlphaFold 2 (AF2), have enabled significant progress in the prediction of protein structure and function. ESM and EVE directly estimate the likelihood of a variant sequence but are purely data-driven and challenging to interpret. AF2 predicts LDLR structures but variant effects are explicitly modeled by estimating changes in stability. We tested the effectiveness of these models for predicting variant pathogenicity compared to established methods. AF2 produced two distinct conformations based on a novel hinge mechanism. Within ESM’s hidden space, benign and pathogenic variants had different distributions. In EVE these distributions were similar. EVE and ESM were comparable to Polyphen-2, SIFT, REVEL and Primate AI for predicting binary classifications in ClinVar. However, they were more strongly correlated with experimental measures of LDL uptake. AF2 poorly performed in these tasks. Using the UK biobank to compare association with clinical phenotypes, ESM and EVE were more strongly associated with serum LDL-C than Polyphen-2. ESM was able to identify variants with more extreme LDL-C levels than EVE and had a significantly stronger association with atherosclerotic cardiovascular disease. In conclusion, AF2 predicted LDLR structures do not accurately model variant pathogenicity. ESM and EVE are competitive with prior scoring methods for prediction based on binary classification in ClinVar but are superior based on correlations with experimental assays and clinical phenotypes.
AbstractList	The complex structure and function of low-density lipoprotein receptor (LDLR) makes classification of protein-coding missense variants challenging. Deep generative models, including evolutionary model of variant effect (EVE), evolutionary scale modeling (ESM), and AlphaFold 2 (AF2), have enabled significant progress in the prediction of protein structure and function. ESM and EVE directly estimate the likelihood of a variant sequence but are purely data-driven and challenging to interpret. AF2 predicts LDLR structures but variant effects are explicitly modeled by estimating changes in stability. We tested the effectiveness of these models for predicting variant pathogenicity compared to established methods. AF2 produced two distinct conformations based on a novel hinge mechanism. Within ESM’s hidden space, benign and pathogenic variants had different distributions. In EVE these distributions were similar. EVE and ESM were comparable to Polyphen-2, SIFT, REVEL and Primate AI for predicting binary classifications in ClinVar. However, they were more strongly correlated with experimental measures of LDL uptake. AF2 poorly performed in these tasks. Using the UK biobank to compare association with clinical phenotypes, ESM and EVE were more strongly associated with serum LDL-C than Polyphen-2. ESM was able to identify variants with more extreme LDL-C levels than EVE and had a significantly stronger association with atherosclerotic cardiovascular disease. In conclusion, AF2 predicted LDLR structures do not accurately model variant pathogenicity. ESM and EVE are competitive with prior scoring methods for prediction based on binary classification in ClinVar but are superior based on correlations with experimental assays and clinical phenotypes. The complex structure and function of low density lipoprotein receptor (LDLR) makes classification of protein-coding missense variants challenging. Deep generative models, including Evolutionary model of Variant Effect (EVE), Evolutionary Scale Modeling (ESM), and AlphaFold 2 (AF2), have enabled significant progress in the prediction of protein structure and function. ESM and EVE directly estimate the likelihood of a variant sequence but are purely data-driven and challenging to interpret. AF2 predicts LDLR structures, but variant effects are explicitly modeled by estimating changes in stability. We tested the effectiveness of these models for predicting variant pathogenicity compared to established methods. AF2 produced two distinct conformations based on a novel hinge mechanism. Within ESM’s hidden space, benign and pathogenic variants had different distributions. In EVE, these distributions were similar. EVE and ESM were comparable to Polyphen-2, SIFT, REVEL, and Primate AI for predicting binary classifications in ClinVar. However, they were more strongly correlated with experimental measures of LDL uptake. AF2 poorly performed in these tasks. Using the UK Biobank to compare association with clinical phenotypes, ESM and EVE were more strongly associated with serum LDL-C than Polyphen-2. ESM was able to identify variants with more extreme LDL-C levels than EVE and had a significantly stronger association with atherosclerotic cardiovascular disease. In conclusion, AF2 predicted LDLR structures do not accurately model variant pathogenicity. ESM and EVE are competitive with prior scoring methods for prediction based on binary classifications in ClinVar but are superior based on correlations with experimental assays and clinical phenotypes. The complex structure and function of low density lipoprotein receptor (LDLR) makes classification of protein-coding missense variants challenging. Deep generative models, including Evolutionary model of Variant Effect (EVE), Evolutionary Scale Modeling (ESM), and AlphaFold 2 (AF2), have enabled significant progress in the prediction of protein structure and function. ESM and EVE directly estimate the likelihood of a variant sequence but are purely data-driven and challenging to interpret. AF2 predicts LDLR structures, but variant effects are explicitly modeled by estimating changes in stability. We tested the effectiveness of these models for predicting variant pathogenicity compared to established methods. AF2 produced two distinct conformations based on a novel hinge mechanism. Within ESM's hidden space, benign and pathogenic variants had different distributions. In EVE, these distributions were similar. EVE and ESM were comparable to Polyphen-2, SIFT, REVEL, and Primate AI for predicting binary classifications in ClinVar. However, they were more strongly correlated with experimental measures of LDL uptake. AF2 poorly performed in these tasks. Using the UK Biobank to compare association with clinical phenotypes, ESM and EVE were more strongly associated with serum LDL-C than Polyphen-2. ESM was able to identify variants with more extreme LDL-C levels than EVE and had a significantly stronger association with atherosclerotic cardiovascular disease. In conclusion, AF2 predicted LDLR structures do not accurately model variant pathogenicity. ESM and EVE are competitive with prior scoring methods for prediction based on binary classifications in ClinVar but are superior based on correlations with experimental assays and clinical phenotypes.The complex structure and function of low density lipoprotein receptor (LDLR) makes classification of protein-coding missense variants challenging. Deep generative models, including Evolutionary model of Variant Effect (EVE), Evolutionary Scale Modeling (ESM), and AlphaFold 2 (AF2), have enabled significant progress in the prediction of protein structure and function. ESM and EVE directly estimate the likelihood of a variant sequence but are purely data-driven and challenging to interpret. AF2 predicts LDLR structures, but variant effects are explicitly modeled by estimating changes in stability. We tested the effectiveness of these models for predicting variant pathogenicity compared to established methods. AF2 produced two distinct conformations based on a novel hinge mechanism. Within ESM's hidden space, benign and pathogenic variants had different distributions. In EVE, these distributions were similar. EVE and ESM were comparable to Polyphen-2, SIFT, REVEL, and Primate AI for predicting binary classifications in ClinVar. However, they were more strongly correlated with experimental measures of LDL uptake. AF2 poorly performed in these tasks. Using the UK Biobank to compare association with clinical phenotypes, ESM and EVE were more strongly associated with serum LDL-C than Polyphen-2. ESM was able to identify variants with more extreme LDL-C levels than EVE and had a significantly stronger association with atherosclerotic cardiovascular disease. In conclusion, AF2 predicted LDLR structures do not accurately model variant pathogenicity. ESM and EVE are competitive with prior scoring methods for prediction based on binary classifications in ClinVar but are superior based on correlations with experimental assays and clinical phenotypes.
ArticleNumber	100455
Author	James, Jose K. Johar, Angad S. Norland, Kristjan Kullo, Iftikhar J.
Author_xml	– sequence: 1 givenname: Jose K. orcidid: 0000-0003-0482-7307 surname: James fullname: James, Jose K. organization: Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN – sequence: 2 givenname: Kristjan surname: Norland fullname: Norland, Kristjan organization: Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN – sequence: 3 givenname: Angad S. orcidid: 0000-0001-9698-3352 surname: Johar fullname: Johar, Angad S. organization: Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN – sequence: 4 givenname: Iftikhar J. orcidid: 0000-0002-6524-3471 surname: Kullo fullname: Kullo, Iftikhar J. email: kullo.iftikhar@mayo.edu organization: Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/37821076$$D View this record in MEDLINE/PubMed
BookMark	eNp9kUtv1DAURi1URKeFH8AGZckmg-3Ej4gFQi2USoMoz63lx83Uo0w8tZ2R-u_xkBZRFl1Ztu93PumeE3Q0hhEQeknwkmDC32yWmyEuKaZNueOWsSdoQVjT1YJyeoQWGFNaUyrYMTpJaYMxaVtOnqHjRkhKsOAL9PUcYFddwAhRZ7-H6nNwMKQq9NXqfPWtuoohgx-r7zlONk8RqhzKIzhvc_VLR6_HXF3pfB3WMHrr8-1z9LTXQ4IXd-cp-vnxw4-zT_Xqy8Xl2ftVbRmRuQbMKWm7xlGjW930YEA7YokzWltKO2M646AVvCPU4L6VTggtG8ukkxxI35yiy5nrgt6oXfRbHW9V0F79eQhxrXTM3g6giJS4Y5wBF651hnRa8pZw6AywDgtcWO9m1m4yW3AWxhz18AD68Gf012od9opg3nHKeCG8viPEcDNBymrrk4Vh0COEKSkqBecNbyQpo6_-Lfvbci-lDIh5wMaQUoRelb0WO-HQ7YdSqg761UYV_eqgX836S5L8l7yHP5Z5O2eKdth7iCpZD6MtiiPYXLbpH0n_BpS_x7E
CitedBy_id	crossref_primary_10_1177_11779322251358314
Cites_doi	10.1038/s41467-019-13633-0 10.1038/nchembio.232 10.1073/pnas.89.22.10915 10.1038/nmeth0410-248 10.1073/pnas.2016239118 10.1016/j.gene.2022.147084 10.1038/s41588-018-0167-z 10.1038/s41436-021-01171-4 10.1002/prot.22934 10.1371/journal.pone.0282689 10.1016/j.ajhg.2016.08.016 10.1002/pro.4353 10.1038/s41587-021-01146-5 10.1038/s41598-020-72404-w 10.1126/science.aaf7000 10.1007/s00439-021-02411-y 10.1038/embor.2011.205 10.1371/journal.pone.0059004 10.1111/biom.13214 10.1101/gr.176601 10.1016/j.molcel.2004.09.038 10.1126/science.1078124 10.1038/s41525-021-00190-z 10.1038/s41588-023-01465-0 10.1093/nar/gky1049 10.1194/jlr.M000422 10.1038/s41586-021-03819-2 10.1093/protein/gzp030 10.1093/bib/bbac074 10.1038/s41467-022-29443-w 10.1038/s41586-021-03828-1 10.1093/bioinformatics/btz184 10.1093/nar/gki387 10.1038/ejhg.2016.193 10.1038/s41594-021-00714-2 10.1186/1471-2105-12-77 10.1371/journal.pgen.1004855 10.1038/nprot.2009.86 10.1093/nar/gkx1153 10.1006/jmbi.2001.5255 10.1186/1471-2105-10-S8-S8 10.15252/msb.202211474 10.1146/annurev.biochem.74.082803.133354 10.4161/fly.19695 10.1038/nbt.3769 10.1038/nrg3463 10.1371/journal.pcbi.1009818 10.3390/ijms24043330 10.3390/ijms19061676 10.1002/humu.23634 10.1016/j.ajhg.2017.07.014 10.1038/s41592-018-0138-4 10.1038/s41586-021-04043-8 10.1016/j.molcel.2006.02.021 10.1093/bioinformatics/btt473 10.1016/j.gim.2021.09.012 10.1002/humu.22768 10.1038/s41594-022-00849-w 10.1093/bioinformatics/bty880
ContentType	Journal Article
Copyright	2023 The Authors Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved. 2023 The Authors 2023
Copyright_xml	– notice: 2023 The Authors – notice: Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved. – notice: 2023 The Authors 2023
DBID	6I. AAFTH AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 5PM DOA
DOI	10.1016/j.jlr.2023.100455
DatabaseName	ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic
DatabaseTitleList	MEDLINE MEDLINE - Academic
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Anatomy & Physiology Chemistry
EISSN	1539-7262
ExternalDocumentID	oai_doaj_org_article_18809565e67d4db19a86416e9be59070 PMC10696256 37821076 10_1016_j_jlr_2023_100455 S0022227523001281
Genre	Journal Article Research Support, N.I.H., Extramural
GrantInformation_xml	– fundername: NHGRI NIH HHS grantid: U24 HG009650 – fundername: NHGRI NIH HHS grantid: U41 HG009650 – fundername: NHLBI NIH HHS grantid: K24 HL137010
GroupedDBID	--- -~X .55 .GJ 0SF 0VX 18M 29K 2WC 34G 39C 4.4 53G 5GY 5RE 5VS 6I. AAEDW AAFTH AAFWJ AAXUO AAYOK ABCQX ABOCM ACCCW ACGFO ACKIV ACNCT ACPRK ADBBV AENEX AEXQZ AFFNX AFOSN AFPKN AHPSJ AI. ALMA_UNASSIGNED_HOLDINGS AMRAJ AOIJS BAWUL BTFSW C1A CS3 D-I DIK DU5 E3Z EBS EJD F5P FDB FRP GROUPED_DOAJ GX1 H13 HH5 HYE H~9 J5H KQ8 L7B MVM OK1 P2P RHF RHI ROL RPM TBC TR2 TWZ VH1 W8F WH7 WOQ X7M XFK YHG YKV ZA5 ZGI ZXP ~KM 0R~ AALRI AAYWO AAYXX ACVFH ADCNI ADVLN AEUPX AFPUW AIGII AITUG AKBMS AKRWK AKYEP CITATION CGR CUY CVF ECM EIF NPM 7X8 5PM
ID	FETCH-LOGICAL-c518t-e0621493d2ba4a3febead1c1dbaac229bb9bde476912b0f48d77a83c58d86e1f3
IEDL.DBID	DOA
ISICitedReferencesCount	1
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001122300300001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0022-2275 1539-7262
IngestDate	Fri Oct 03 12:46:02 EDT 2025 Thu Aug 21 18:35:57 EDT 2025 Fri Sep 05 10:10:16 EDT 2025 Sat Aug 02 01:41:22 EDT 2025 Sat Nov 29 07:29:58 EST 2025 Tue Nov 18 20:24:48 EST 2025 Fri Feb 23 02:33:57 EST 2024
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	12
Keywords	FH Dyslipidemias Genomics GPU AUC Atherosclerosis MSA MI LDLR ESM MAVE ACMG OR AMP EGF Lipoproteins/Receptors GMM AI HMM AF2 UKB VCEP EVE VUS pLDDT LA Proteomics Physical Biochemistry VAE atherosclerosis genomics lipoproteins/receptors dyslipidemias proteomics physical biochemistry
Language	English
License	This is an open access article under the CC BY license. Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c518t-e0621493d2ba4a3febead1c1dbaac229bb9bde476912b0f48d77a83c58d86e1f3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ORCID	0000-0001-9698-3352 0000-0002-6524-3471 0000-0003-0482-7307
OpenAccessLink	https://doaj.org/article/18809565e67d4db19a86416e9be59070
PMID	37821076
PQID	2876636381
PQPubID	23479
ParticipantIDs	doaj_primary_oai_doaj_org_article_18809565e67d4db19a86416e9be59070 pubmedcentral_primary_oai_pubmedcentral_nih_gov_10696256 proquest_miscellaneous_2876636381 pubmed_primary_37821076 crossref_citationtrail_10_1016_j_jlr_2023_100455 crossref_primary_10_1016_j_jlr_2023_100455 elsevier_sciencedirect_doi_10_1016_j_jlr_2023_100455
PublicationCentury	2000
PublicationDate	2023-12-01
PublicationDateYYYYMMDD	2023-12-01
PublicationDate_xml	– month: 12 year: 2023 text: 2023-12-01 day: 01
PublicationDecade	2020
PublicationPlace	United States
PublicationPlace_xml	– name: United States
PublicationTitle	Journal of lipid research
PublicationTitleAlternate	J Lipid Res
PublicationYear	2023
Publisher	Elsevier Inc American Society for Biochemistry and Molecular Biology Elsevier
Publisher_xml	– name: Elsevier Inc – name: American Society for Biochemistry and Molecular Biology – name: Elsevier
References	Meier, Rao, Verkuil, Liu, Sercu, Rives (bib22) 2021; 34 Porta-Pardo, Ruiz-Serra, Valentini, Valencia (bib28) 2022; 18 Rives, Meier, Sercu, Goyal, Lin, Liu (bib21) 2021; 118 Akdel, Pires, Pardo, Janes, Zalevsky, Meszaros (bib33) 2022; 29 Rudenko, Henry, Henderson, Ichtchenko, Brown, Goldstein (bib54) 2002; 298 Huang, Henry, Ho, Pownall, Rudenko (bib52) 2010; 51 Chora, Iacocca, Tichy, Wand, Kurtz, Zimmermann (bib3) 2022; 24 Ding, Zou, Brooks (bib19) 2019; 10 Cingolani, Platts, Wang, Coon, Nguyen, Wang (bib44) 2012; 6 Starita, Ahituv, Dunham, Kitzman, Roth, Seelig (bib6) 2017; 101 Landrum, Lee, Benson, Brown, Chao, Chitipiralla (bib43) 2018; 46 McCaw, Lane, Saxena, Redline, Lin (bib56) 2020; 76 Hopf, Ingraham, Poelwijk, Scharfe, Springer, Sander (bib17) 2017; 35 Buel, Walters (bib31) 2022; 29 Kumar, Henikoff, Ng (bib15) 2009; 4 Robin, Turck, Hainard, Tiberti, Lisacek, Sanchez (bib46) 2011; 12 Safarova, Klee, Baudhuin, Winkler, Kluge, Bielinski (bib5) 2017; 25 McInnes L, Healy J, Melville J. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:180203426. 2018. Henikoff, Henikoff (bib45) 1992; 89 Iacocca, Chora, Carrie, Freiberger, Leigh, Defesche (bib12) 2018; 39 Bromberg, Rost (bib60) 2009; 10 Benito-Vicente, Uribe, Jebari, Galicia-Garcia, Ostolaza, Martin (bib7) 2018; 19 Tunyasuvunakool, Adler, Wu, Green, Zielinski, Zidek (bib51) 2021; 596 Hsu, Nisonoff, Fannjiang, Listgarten (bib65) 2022; 40 Livesey, Marsh (bib24) 2023; 19 Sundaram, Gao, Padigepati, McRae, Li, Kosmicki (bib36) 2018; 50 Mariani, Biasini, Barbato, Schwede (bib38) 2013; 29 Delgado, Radusky, Cianferoni, Serrano (bib42) 2019; 35 Ng, Henikoff (bib35) 2001; 11 Jeon, Blacklow (bib48) 2005; 74 Abul-Husn, Manickam, Jones, Wright, Hartzel, Gonzaga-Jauregui (bib2) 2016; 354 Barbosa, Hirata, Ferreira, Borges, Oliveira, Gorjao (bib29) 2023; 853 Marquet, Heinzinger, Olenyi, Dallago, Erckert, Bernhofer (bib34) 2022; 141 Nivon, Moretti, Baker (bib40) 2013; 8 Ioannidis, Rothstein, Pejaver, Middha, McDonnell, Baheti (bib10) 2016; 99 Pfisterer, Brock, Kanerva, Hlushchenko, Paavolainen, Ripatti (bib64) 2022; 2 Miller, Lee, Gordon, Amendola, Adelman, Bale (bib4) 2021; 23 Detlefsen, Hauberg, Boomsma (bib57) 2022; 13 Grimm, Azencott, Aicheler, Gieraths, MacArthur, Samocha (bib13) 2015; 36 Montanucci, Martelli, Ben-Tal, Fariselli (bib59) 2019; 35 Chakravarty, Porter (bib63) 2022; 31 Potapov, Cohen, Schreiber (bib58) 2009; 22 Lo Surdo, Bottomley, Calzetta, Settembre, Cirillo, Pandit (bib53) 2011; 12 Thormaehlen, Schuberth, Won, Blattmann, Joggerst-Thomalla, Theiss (bib8) 2015; 11 Boehr, Nussinov, Wright (bib62) 2009; 5 Frazer, Notin, Dias, Gomez, Min, Brock (bib20) 2021; 599 Gerasimavicius, Liu, Marsh (bib25) 2020; 10 Schymkowitz, Borg, Stricher, Nys, Rousseau, Serrano (bib55) 2005; 33 Pak, Markhieva, Novikova, Petrov, Vorobyev, Maksimova (bib32) 2023; 18 UniProt (bib37) 2019; 47 Balakrishnan, Kamisetty, Carbonell, Lee, Langmead (bib16) 2011; 79 Schrodinger, LLC. The PyMOL Molecular Graphics System, Version 1.8. 2015. Rehm (bib9) 2013; 14 Beglova, Jeon, Fisher, Blacklow (bib50) 2004; 16 Saadatagah, Jose, Dikilitas, Alhalabi, Miller, Fan (bib1) 2021; 6 Adzhubei, Schmidt, Peshkin, Ramensky, Gerasimova, Bork (bib11) 2010; 7 Larrea-Sebal, Trenti, Jebari-Benslaiman, Bertolini, Calandra, Negri (bib30) 2023; 24 Riesselman, Ingraham, Marks (bib18) 2018; 15 Jumper, Evans, Pritzel, Green, Figurnov, Ronneberger (bib26) 2021; 596 Ferrer-Costa, Orozco, de la Cruz (bib14) 2002; 315 Brandes, Goldman, Wang, Ye, Ntranos (bib23) 2023 DeepMind. Frequently asked questions. Chen, Hassan, Jernigan, Jia, Kihara, Kloczkowski (bib61) 2023; 120 Tiberti, Terkelsen, Degn, Beltrame, Cremers, da Piedade (bib41) 2022; 23 Fisher, Beglova, Blacklow (bib49) 2006; 22 Livesey (10.1016/j.jlr.2023.100455_bib24) 2023; 19 Buel (10.1016/j.jlr.2023.100455_bib30) 2022; 29 Gerasimavicius (10.1016/j.jlr.2023.100455_bib25) 2020; 10 Safarova (10.1016/j.jlr.2023.100455_bib5) 2017; 25 Tiberti (10.1016/j.jlr.2023.100455_bib40) 2022; 23 Detlefsen (10.1016/j.jlr.2023.100455_bib56) 2022; 13 UniProt (10.1016/j.jlr.2023.100455_bib36) 2019; 47 Brandes (10.1016/j.jlr.2023.100455_bib23) 2023; 55 Rives (10.1016/j.jlr.2023.100455_bib21) 2021; 118 DeLano (10.1016/j.jlr.2023.100455_bib38) 2002; 40 Bromberg (10.1016/j.jlr.2023.100455_bib59) 2009; 10 Suppl 8 Pfisterer (10.1016/j.jlr.2023.100455_bib63) 2022; 2 Beglova (10.1016/j.jlr.2023.100455_bib49) 2004; 16 Benito-Vicente (10.1016/j.jlr.2023.100455_bib7) 2018; 19 Ioannidis (10.1016/j.jlr.2023.100455_bib10) 2016; 99 Abul-Husn (10.1016/j.jlr.2023.100455_bib2) 2016; 354 Chora (10.1016/j.jlr.2023.100455_bib3) 2022; 24 Porta-Pardo (10.1016/j.jlr.2023.100455_bib27) 2022; 18 Potapov (10.1016/j.jlr.2023.100455_bib57) 2009; 22 Rudenko (10.1016/j.jlr.2023.100455_bib53) 2002; 298 Meier (10.1016/j.jlr.2023.100455_bib22) 2021; 34 Marquet (10.1016/j.jlr.2023.100455_bib33) 2022; 141 Frazer (10.1016/j.jlr.2023.100455_bib20) 2021; 599 Lo Surdo (10.1016/j.jlr.2023.100455_bib52) 2011; 12 Starita (10.1016/j.jlr.2023.100455_bib6) 2017; 101 Huang (10.1016/j.jlr.2023.100455_bib51) 2010; 51 Landrum (10.1016/j.jlr.2023.100455_bib42) 2018; 46 Ding (10.1016/j.jlr.2023.100455_bib19) 2019; 10 Mariani (10.1016/j.jlr.2023.100455_bib37) 2013; 29 Saadatagah (10.1016/j.jlr.2023.100455_bib1) 2021; 6 Montanucci (10.1016/j.jlr.2023.100455_bib58) 2019; 35 Grimm (10.1016/j.jlr.2023.100455_bib13) 2015; 36 Ng (10.1016/j.jlr.2023.100455_bib34) 2001; 11 Kumar (10.1016/j.jlr.2023.100455_bib15) 2009; 4 Tunyasuvunakool (10.1016/j.jlr.2023.100455_bib50) 2021; 596 Boehr (10.1016/j.jlr.2023.100455_bib61) 2009; 5 Schymkowitz (10.1016/j.jlr.2023.100455_bib54) 2005; 33 Chakravarty (10.1016/j.jlr.2023.100455_bib62) 2022; 31 Riesselman (10.1016/j.jlr.2023.100455_bib18) 2018; 15 Fisher (10.1016/j.jlr.2023.100455_bib48) 2006; 22 Balakrishnan (10.1016/j.jlr.2023.100455_bib16) 2011; 79 Pak (10.1016/j.jlr.2023.100455_bib31) 2023; 18 Rehm (10.1016/j.jlr.2023.100455_bib9) 2013; 14 Henikoff (10.1016/j.jlr.2023.100455_bib44) 1992; 89 Hopf (10.1016/j.jlr.2023.100455_bib17) 2017; 35 Adzhubei (10.1016/j.jlr.2023.100455_bib11) 2010; 7 McCaw (10.1016/j.jlr.2023.100455_bib55) 2020; 76 Jeon (10.1016/j.jlr.2023.100455_bib47) 2005; 74 Hsu (10.1016/j.jlr.2023.100455_bib64) 2022; 40 Thormaehlen (10.1016/j.jlr.2023.100455_bib8) 2015; 11 Chen (10.1016/j.jlr.2023.100455_bib60) 2023; 120 Cingolani (10.1016/j.jlr.2023.100455_bib43) 2012; 6 Ferrer-Costa (10.1016/j.jlr.2023.100455_bib14) 2002; 315 Akdel (10.1016/j.jlr.2023.100455_bib32) 2022; 29 McInnes (10.1016/j.jlr.2023.100455_bib46) 2018 Iacocca (10.1016/j.jlr.2023.100455_bib12) 2018; 39 Sundaram (10.1016/j.jlr.2023.100455_bib35) 2018; 50 Nivon (10.1016/j.jlr.2023.100455_bib39) 2013; 8 Delgado (10.1016/j.jlr.2023.100455_bib41) 2019; 35 Robin (10.1016/j.jlr.2023.100455_bib45) 2011; 12 Larrea-Sebal (10.1016/j.jlr.2023.100455_bib29) 2023; 24 Miller (10.1016/j.jlr.2023.100455_bib4) 2021; 23 Barbosa (10.1016/j.jlr.2023.100455_bib28) 2023; 853 Jumper (10.1016/j.jlr.2023.100455_bib26) 2021; 596
References_xml	– volume: 118 year: 2021 ident: bib21 article-title: Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences publication-title: Proc Natl Acad Sci U S A – volume: 19 year: 2023 ident: bib24 article-title: Updated benchmarking of variant effect predictors using deep mutational scanning publication-title: Mol Syst Biol – volume: 120 year: 2023 ident: bib61 article-title: Opinion: Protein folds vs. protein folding: Differing questions, different challenges publication-title: Proc Natl Acad Sci U S A – reference: Schrodinger, LLC. The PyMOL Molecular Graphics System, Version 1.8. 2015. – volume: 34 start-page: 29287 year: 2021 end-page: 29303 ident: bib22 article-title: Language models enable zero-shot prediction of the effects of mutations on protein function publication-title: Advances in Neural Information Processing Systems – volume: 10 start-page: 5644 year: 2019 ident: bib19 article-title: Deciphering protein evolution and fitness landscapes with latent space models publication-title: Nat Commun – volume: 19 year: 2018 ident: bib7 article-title: Validation of LDLr Activity as a Tool to Improve Genetic Diagnosis of Familial Hypercholesterolemia: A Retrospective on Functional Characterization of LDLr Variants publication-title: Int J Mol Sci – volume: 76 start-page: 1262 year: 2020 end-page: 1272 ident: bib56 article-title: Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies publication-title: Biometrics – volume: 99 start-page: 877 year: 2016 end-page: 885 ident: bib10 article-title: REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants publication-title: Am J Hum Genet – volume: 596 start-page: 590 year: 2021 end-page: 596 ident: bib51 article-title: Highly accurate protein structure prediction for the human proteome publication-title: Nature – volume: 11 start-page: 863 year: 2001 end-page: 874 ident: bib35 article-title: Predicting deleterious amino acid substitutions publication-title: Genome Res – volume: 101 start-page: 315 year: 2017 end-page: 325 ident: bib6 article-title: Variant Interpretation: Functional Assays to the Rescue publication-title: Am J Hum Genet – volume: 13 start-page: 1914 year: 2022 ident: bib57 article-title: Learning meaningful representations of protein sequences publication-title: Nat Commun – volume: 23 start-page: 1391 year: 2021 end-page: 1398 ident: bib4 article-title: Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2021 update: a policy statement of the American College of Medical Genetics and Genomics (ACMG) publication-title: Genet Med – volume: 29 start-page: 1056 year: 2022 end-page: 1067 ident: bib33 article-title: A structural biology community assessment of AlphaFold2 applications publication-title: Nat Struct Mol Biol – volume: 14 start-page: 295 year: 2013 end-page: 300 ident: bib9 article-title: Disease-targeted sequencing: a cornerstone in the clinic publication-title: Nat Rev Genet – volume: 10 year: 2020 ident: bib25 article-title: Identification of pathogenic missense mutations using protein stability predictors publication-title: Sci Rep – volume: 31 start-page: e4353 year: 2022 ident: bib63 article-title: AlphaFold2 fails to predict protein fold switching publication-title: Protein Sci – volume: 35 start-page: 128 year: 2017 end-page: 135 ident: bib17 article-title: Mutation effects predicted from sequence co-variation publication-title: Nat Biotechnol – volume: 39 start-page: 1631 year: 2018 end-page: 1640 ident: bib12 article-title: ClinVar database of global familial hypercholesterolemia-associated DNA variants publication-title: Hum Mutat – year: 2023 ident: bib23 article-title: Genome-wide prediction of disease variant effects with a deep protein language model publication-title: Nat Genet – volume: 315 start-page: 771 year: 2002 end-page: 786 ident: bib14 article-title: Characterization of disease-associated single amino acid polymorphisms in terms of sequence and structure properties publication-title: J Mol Biol – volume: 89 start-page: 10915 year: 1992 end-page: 10919 ident: bib45 article-title: Amino acid substitution matrices from protein blocks publication-title: Proc Natl Acad Sci U S A – volume: 46 start-page: D1062 year: 2018 end-page: D1067 ident: bib43 article-title: ClinVar: improving access to variant interpretations and supporting evidence publication-title: Nucleic Acids Res – volume: 6 start-page: 28 year: 2021 ident: bib1 article-title: Genetic basis of hypercholesterolemia in adults publication-title: NPJ Genom Med – volume: 24 year: 2023 ident: bib30 article-title: Functional Characterization of p.(Arg160Gln) PCSK9 Variant Accidentally Found in a Hypercholesterolemic Subject publication-title: Int J Mol Sci – volume: 2 year: 2022 ident: bib64 article-title: Multiparametric platform for profiling lipid trafficking in human leukocytes publication-title: Cell Rep Methods – volume: 24 start-page: 293 year: 2022 end-page: 306 ident: bib3 article-title: The Clinical Genome Resource (ClinGen) Familial Hypercholesterolemia Variant Curation Expert Panel consensus guidelines for LDLR variant classification publication-title: Genet Med – volume: 16 start-page: 281 year: 2004 end-page: 292 ident: bib50 article-title: Cooperation between fixed and low pH-inducible interfaces controls lipoprotein release by the LDL receptor publication-title: Mol Cell – volume: 15 start-page: 816 year: 2018 end-page: 822 ident: bib18 article-title: Deep generative models of genetic variation capture the effects of mutations publication-title: Nat Methods – reference: DeepMind. Frequently asked questions. – volume: 4 start-page: 1073 year: 2009 end-page: 1081 ident: bib15 article-title: Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm publication-title: Nat Protoc – volume: 51 start-page: 297 year: 2010 end-page: 308 ident: bib52 article-title: Mechanism of LDL binding and release probed by structure-based mutagenesis of the LDL receptor publication-title: J Lipid Res – volume: 5 start-page: 789 year: 2009 end-page: 796 ident: bib62 article-title: The role of dynamic conformational ensembles in biomolecular recognition publication-title: Nat Chem Biol – volume: 596 start-page: 583 year: 2021 end-page: 589 ident: bib26 article-title: Highly accurate protein structure prediction with AlphaFold publication-title: Nature – volume: 29 start-page: 1 year: 2022 end-page: 2 ident: bib31 article-title: Can AlphaFold2 predict the impact of missense mutations on structure? publication-title: Nat Struct Mol Biol – volume: 29 start-page: 2722 year: 2013 end-page: 2728 ident: bib38 article-title: lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests publication-title: Bioinformatics – volume: 12 start-page: 77 year: 2011 ident: bib46 article-title: pROC: an open-source package for R and S+ to analyze and compare ROC curves publication-title: BMC Bioinformatics – volume: 33 start-page: W382 year: 2005 end-page: W388 ident: bib55 article-title: The FoldX web server: an online force field publication-title: Nucleic Acids Res – volume: 354 year: 2016 ident: bib2 article-title: Genetic identification of familial hypercholesterolemia within a single U.S. health care system publication-title: Science – volume: 35 start-page: 4168 year: 2019 end-page: 4169 ident: bib42 article-title: FoldX 5.0: working with RNA, small molecules and a new graphical interface publication-title: Bioinformatics – volume: 599 start-page: 91 year: 2021 end-page: 95 ident: bib20 article-title: Disease variant prediction with deep generative models of evolutionary data publication-title: Nature – volume: 6 start-page: 80 year: 2012 end-page: 92 ident: bib44 article-title: A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3 publication-title: Fly (Austin) – volume: 36 start-page: 513 year: 2015 end-page: 523 ident: bib13 article-title: The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity publication-title: Hum Mutat – volume: 18 year: 2023 ident: bib32 article-title: Using AlphaFold to predict the impact of single mutations on protein stability and function publication-title: PLoS One – volume: 40 start-page: 1114 year: 2022 end-page: 1122 ident: bib65 article-title: Learning protein fitness models from evolutionary and assay-labeled data publication-title: Nat Biotechnol – volume: 25 start-page: 410 year: 2017 end-page: 415 ident: bib5 article-title: Variability in assigning pathogenicity to incidental findings: insights from LDLR sequence linked to the electronic health record in 1013 individuals publication-title: Eur J Hum Genet – volume: 23 year: 2022 ident: bib41 article-title: MutateX: an automated pipeline for in silico saturation mutagenesis of protein structures and structural ensembles publication-title: Brief Bioinform – volume: 18 year: 2022 ident: bib28 article-title: The structural coverage of the human proteome before and after AlphaFold publication-title: PLoS Comput Biol – volume: 12 start-page: 1300 year: 2011 end-page: 1305 ident: bib53 article-title: Mechanistic implications for LDL receptor degradation from the PCSK9/LDLR structure at neutral pH publication-title: EMBO Rep – volume: 11 year: 2015 ident: bib8 article-title: Systematic cell-based phenotyping of missense alleles empowers rare variant association studies: a case for LDLR and myocardial infarction publication-title: PLoS Genet – volume: 298 start-page: 2353 year: 2002 end-page: 2358 ident: bib54 article-title: Structure of the LDL receptor extracellular domain at endosomal pH publication-title: Science – volume: 141 start-page: 1629 year: 2022 end-page: 1647 ident: bib34 article-title: Embeddings from protein language models predict conservation and variant effects publication-title: Hum Genet – volume: 47 start-page: D506 year: 2019 end-page: D515 ident: bib37 article-title: UniProt: a worldwide hub of protein knowledge publication-title: Nucleic Acids Res – volume: 22 start-page: 277 year: 2006 end-page: 283 ident: bib49 article-title: Structure of an LDLR-RAP complex reveals a general mode for ligand recognition by lipoprotein receptors publication-title: Mol Cell – reference: McInnes L, Healy J, Melville J. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:180203426. 2018. – volume: 22 start-page: 553 year: 2009 end-page: 560 ident: bib58 article-title: Assessing computational methods for predicting protein stability upon mutation: good on average but not in the details publication-title: Protein Eng Des Sel – volume: 7 start-page: 248 year: 2010 end-page: 249 ident: bib11 article-title: A method and server for predicting damaging missense mutations publication-title: Nat Methods – volume: 35 start-page: 1513 year: 2019 end-page: 1517 ident: bib59 article-title: A natural upper bound to the accuracy of predicting protein stability changes upon mutations publication-title: Bioinformatics – volume: 79 start-page: 1061 year: 2011 end-page: 1078 ident: bib16 article-title: Learning generative models for protein fold families publication-title: Proteins – volume: 50 start-page: 1161 year: 2018 end-page: 1170 ident: bib36 article-title: Predicting the clinical impact of human mutation with deep neural networks publication-title: Nat Genet – volume: 74 start-page: 535 year: 2005 end-page: 562 ident: bib48 article-title: Structure and physiologic function of the low-density lipoprotein receptor publication-title: Annu Rev Biochem – volume: 8 year: 2013 ident: bib40 article-title: A Pareto-optimal refinement method for protein design scaffolds publication-title: PLoS One – volume: 10 start-page: S8 year: 2009 ident: bib60 article-title: Correlating protein function and stability through the analysis of single amino acid substitutions publication-title: BMC Bioinformatics – volume: 853 year: 2023 ident: bib29 article-title: LDLR missense variants disturb structural conformation and LDLR activity in T-lymphocytes of Familial hypercholesterolemia patients publication-title: Gene – year: 2018 ident: 10.1016/j.jlr.2023.100455_bib46 article-title: Umap: uniform manifold approximation and projection for dimension reduction publication-title: arXiv – volume: 10 start-page: 5644 year: 2019 ident: 10.1016/j.jlr.2023.100455_bib19 article-title: Deciphering protein evolution and fitness landscapes with latent space models publication-title: Nat. Commun. doi: 10.1038/s41467-019-13633-0 – volume: 5 start-page: 789 year: 2009 ident: 10.1016/j.jlr.2023.100455_bib61 article-title: The role of dynamic conformational ensembles in biomolecular recognition publication-title: Nat. Chem. Biol. doi: 10.1038/nchembio.232 – volume: 89 start-page: 10915 year: 1992 ident: 10.1016/j.jlr.2023.100455_bib44 article-title: Amino acid substitution matrices from protein blocks publication-title: Proc. Natl. Acad. Sci. U. S. A. doi: 10.1073/pnas.89.22.10915 – volume: 7 start-page: 248 year: 2010 ident: 10.1016/j.jlr.2023.100455_bib11 article-title: A method and server for predicting damaging missense mutations publication-title: Nat. Methods doi: 10.1038/nmeth0410-248 – volume: 118 year: 2021 ident: 10.1016/j.jlr.2023.100455_bib21 article-title: Biological stsructure and function emerge from scaling unsupervised learning to 250 million protein sequences publication-title: Proc. Natl. Acad. Sci. U. S. A. doi: 10.1073/pnas.2016239118 – volume: 853 year: 2023 ident: 10.1016/j.jlr.2023.100455_bib28 article-title: LDLR missense variants disturb structural conformation and LDLR activity in T-lymphocytes of familial hypercholesterolemia patients publication-title: Gene doi: 10.1016/j.gene.2022.147084 – volume: 2 year: 2022 ident: 10.1016/j.jlr.2023.100455_bib63 article-title: Multiparametric platform for profiling lipid trafficking in human leukocytes publication-title: Cell Rep. Methods – volume: 50 start-page: 1161 year: 2018 ident: 10.1016/j.jlr.2023.100455_bib35 article-title: Predicting the clinical impact of human mutation with deep neural networks publication-title: Nat. Genet. doi: 10.1038/s41588-018-0167-z – volume: 23 start-page: 1391 year: 2021 ident: 10.1016/j.jlr.2023.100455_bib4 article-title: Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2021 update: a policy statement of the American College of Medical Genetics and Genomics (ACMG) publication-title: Genet. Med. doi: 10.1038/s41436-021-01171-4 – volume: 79 start-page: 1061 year: 2011 ident: 10.1016/j.jlr.2023.100455_bib16 article-title: Learning generative models for protein fold families publication-title: Proteins doi: 10.1002/prot.22934 – volume: 18 year: 2023 ident: 10.1016/j.jlr.2023.100455_bib31 article-title: Using AlphaFold to predict the impact of single mutations on protein stability and function publication-title: PLoS One doi: 10.1371/journal.pone.0282689 – volume: 99 start-page: 877 year: 2016 ident: 10.1016/j.jlr.2023.100455_bib10 article-title: REVEL: an ensemble method for predicting the pathogenicity of rare missense variants publication-title: Am. J. Hum. Genet. doi: 10.1016/j.ajhg.2016.08.016 – volume: 31 start-page: e4353 year: 2022 ident: 10.1016/j.jlr.2023.100455_bib62 article-title: AlphaFold2 fails to predict protein fold switching publication-title: Protein Sci. doi: 10.1002/pro.4353 – volume: 40 start-page: 1114 year: 2022 ident: 10.1016/j.jlr.2023.100455_bib64 article-title: Learning protein fitness models from evolutionary and assay-labeled data publication-title: Nat. Biotechnol. doi: 10.1038/s41587-021-01146-5 – volume: 10 year: 2020 ident: 10.1016/j.jlr.2023.100455_bib25 article-title: Identification of pathogenic missense mutations using protein stability predictors publication-title: Sci. Rep. doi: 10.1038/s41598-020-72404-w – volume: 354 year: 2016 ident: 10.1016/j.jlr.2023.100455_bib2 article-title: Genetic identification of familial hypercholesterolemia within a single U.S. health care system publication-title: Science doi: 10.1126/science.aaf7000 – volume: 141 start-page: 1629 year: 2022 ident: 10.1016/j.jlr.2023.100455_bib33 article-title: Embeddings from protein language models predict conservation and variant effects publication-title: Hum. Genet. doi: 10.1007/s00439-021-02411-y – volume: 12 start-page: 1300 year: 2011 ident: 10.1016/j.jlr.2023.100455_bib52 article-title: Mechanistic implications for LDL receptor degradation from the PCSK9/LDLR structure at neutral pH publication-title: EMBO Rep. doi: 10.1038/embor.2011.205 – volume: 8 year: 2013 ident: 10.1016/j.jlr.2023.100455_bib39 article-title: A Pareto-optimal refinement method for protein design scaffolds publication-title: PLoS One doi: 10.1371/journal.pone.0059004 – volume: 76 start-page: 1262 year: 2020 ident: 10.1016/j.jlr.2023.100455_bib55 article-title: Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies publication-title: Biometrics doi: 10.1111/biom.13214 – volume: 11 start-page: 863 year: 2001 ident: 10.1016/j.jlr.2023.100455_bib34 article-title: Predicting deleterious amino acid substitutions publication-title: Genome Res. doi: 10.1101/gr.176601 – volume: 16 start-page: 281 year: 2004 ident: 10.1016/j.jlr.2023.100455_bib49 article-title: Cooperation between fixed and low pH-inducible interfaces controls lipoprotein release by the LDL receptor publication-title: Mol. Cell doi: 10.1016/j.molcel.2004.09.038 – volume: 40 start-page: 82 year: 2002 ident: 10.1016/j.jlr.2023.100455_bib38 article-title: Pymol: An open-source molecular graphics tool publication-title: CCP4 Newsl Protein Crystallogr – volume: 298 start-page: 2353 year: 2002 ident: 10.1016/j.jlr.2023.100455_bib53 article-title: Structure of the LDL receptor extracellular domain at endosomal pH publication-title: Science doi: 10.1126/science.1078124 – volume: 6 start-page: 28 year: 2021 ident: 10.1016/j.jlr.2023.100455_bib1 article-title: Genetic basis of hypercholesterolemia in adults publication-title: NPJ Genom. Med. doi: 10.1038/s41525-021-00190-z – volume: 55 start-page: 1512 year: 2023 ident: 10.1016/j.jlr.2023.100455_bib23 article-title: Genome-wide prediction of disease variant effects with a deep protein language model publication-title: Nat. Genet. doi: 10.1038/s41588-023-01465-0 – volume: 47 start-page: D506 year: 2019 ident: 10.1016/j.jlr.2023.100455_bib36 article-title: UniProt: a worldwide hub of protein knowledge publication-title: Nucleic Acids Res. doi: 10.1093/nar/gky1049 – volume: 51 start-page: 297 year: 2010 ident: 10.1016/j.jlr.2023.100455_bib51 article-title: Mechanism of LDL binding and release probed by structure-based mutagenesis of the LDL receptor publication-title: J. Lipid Res. doi: 10.1194/jlr.M000422 – volume: 596 start-page: 583 year: 2021 ident: 10.1016/j.jlr.2023.100455_bib26 article-title: Highly accurate protein structure prediction with AlphaFold publication-title: Nature doi: 10.1038/s41586-021-03819-2 – volume: 22 start-page: 553 year: 2009 ident: 10.1016/j.jlr.2023.100455_bib57 article-title: Assessing computational methods for predicting protein stability upon mutation: good on average but not in the details publication-title: Protein Eng. Des. Sel. doi: 10.1093/protein/gzp030 – volume: 23 year: 2022 ident: 10.1016/j.jlr.2023.100455_bib40 article-title: MutateX: an automated pipeline for in silico saturation mutagenesis of protein structures and structural ensembles publication-title: Brief. Bioinform. doi: 10.1093/bib/bbac074 – volume: 13 start-page: 1914 year: 2022 ident: 10.1016/j.jlr.2023.100455_bib56 article-title: Learning meaningful representations of protein sequences publication-title: Nat. Commun. doi: 10.1038/s41467-022-29443-w – volume: 596 start-page: 590 year: 2021 ident: 10.1016/j.jlr.2023.100455_bib50 article-title: Highly accurate protein structure prediction for the human proteome publication-title: Nature doi: 10.1038/s41586-021-03828-1 – volume: 35 start-page: 4168 year: 2019 ident: 10.1016/j.jlr.2023.100455_bib41 article-title: FoldX 5.0: working with RNA, small molecules and a new graphical interface publication-title: Bioinformatics doi: 10.1093/bioinformatics/btz184 – volume: 33 start-page: W382 year: 2005 ident: 10.1016/j.jlr.2023.100455_bib54 article-title: The FoldX web server: an online force field publication-title: Nucleic Acids Res. doi: 10.1093/nar/gki387 – volume: 25 start-page: 410 year: 2017 ident: 10.1016/j.jlr.2023.100455_bib5 article-title: Variability in assigning pathogenicity to incidental findings: insights from LDLR sequence linked to the electronic health record in 1013 individuals publication-title: Eur. J. Hum. Genet. doi: 10.1038/ejhg.2016.193 – volume: 29 start-page: 1 year: 2022 ident: 10.1016/j.jlr.2023.100455_bib30 article-title: Can AlphaFold2 predict the impact of missense mutations on structure? publication-title: Nat. Struct. Mol. Biol. doi: 10.1038/s41594-021-00714-2 – volume: 12 start-page: 77 year: 2011 ident: 10.1016/j.jlr.2023.100455_bib45 article-title: pROC: an open-source package for R and S+ to analyze and compare ROC curves publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-12-77 – volume: 11 year: 2015 ident: 10.1016/j.jlr.2023.100455_bib8 article-title: Systematic cell-based phenotyping of missense alleles empowers rare variant association studies: a case for LDLR and myocardial infarction publication-title: PLOS Genet. doi: 10.1371/journal.pgen.1004855 – volume: 4 start-page: 1073 year: 2009 ident: 10.1016/j.jlr.2023.100455_bib15 article-title: Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm publication-title: Nat. Protoc. doi: 10.1038/nprot.2009.86 – volume: 46 start-page: D1062 year: 2018 ident: 10.1016/j.jlr.2023.100455_bib42 article-title: ClinVar: improving access to variant interpretations and supporting evidence publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkx1153 – volume: 315 start-page: 771 year: 2002 ident: 10.1016/j.jlr.2023.100455_bib14 article-title: Characterization of disease-associated single amino acid polymorphisms in terms of sequence and structure properties publication-title: J. Mol. Biol. doi: 10.1006/jmbi.2001.5255 – volume: 10 Suppl 8 start-page: S8 issue: Suppl 8 year: 2009 ident: 10.1016/j.jlr.2023.100455_bib59 article-title: Correlating protein function and stability through the analysis of single amino acid substitutions publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-10-S8-S8 – volume: 19 year: 2023 ident: 10.1016/j.jlr.2023.100455_bib24 article-title: Updated benchmarking of variant effect predictors using deep mutational scanning publication-title: Mol. Syst. Biol. doi: 10.15252/msb.202211474 – volume: 74 start-page: 535 year: 2005 ident: 10.1016/j.jlr.2023.100455_bib47 article-title: Structure and physiologic function of the low-density lipoprotein receptor publication-title: Annu. Rev. Biochem. doi: 10.1146/annurev.biochem.74.082803.133354 – volume: 6 start-page: 80 year: 2012 ident: 10.1016/j.jlr.2023.100455_bib43 article-title: A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3 publication-title: Fly (Austin) doi: 10.4161/fly.19695 – volume: 35 start-page: 128 year: 2017 ident: 10.1016/j.jlr.2023.100455_bib17 article-title: Mutation effects predicted from sequence co-variation publication-title: Nat. Biotechnol. doi: 10.1038/nbt.3769 – volume: 14 start-page: 295 year: 2013 ident: 10.1016/j.jlr.2023.100455_bib9 article-title: Disease-targeted sequencing: a cornerstone in the clinic publication-title: Nat. Rev. Genet. doi: 10.1038/nrg3463 – volume: 18 year: 2022 ident: 10.1016/j.jlr.2023.100455_bib27 article-title: The structural coverage of the human proteome before and after AlphaFold publication-title: PLOS Comput. Biol. doi: 10.1371/journal.pcbi.1009818 – volume: 24 start-page: 3330 year: 2023 ident: 10.1016/j.jlr.2023.100455_bib29 article-title: Functional Characterization of p.(Arg160Gln) PCSK9 variant accidently found in a hypercholesterolemic subject publication-title: Int. J. Mol. Sci. doi: 10.3390/ijms24043330 – volume: 120 year: 2023 ident: 10.1016/j.jlr.2023.100455_bib60 article-title: Opinion: protein folds vs. protein folding: Differing questions, different challenges publication-title: Proc. Natl. Acad. Sci. U. S. A. – volume: 19 start-page: 1676 year: 2018 ident: 10.1016/j.jlr.2023.100455_bib7 article-title: Validation of LDLr activity as a tool to improve genetic diagnosis of familial hypercholesterolemia: a retrospective on functional characterization of LDLr variants publication-title: Int. J. Mol. Sci. doi: 10.3390/ijms19061676 – volume: 39 start-page: 1631 year: 2018 ident: 10.1016/j.jlr.2023.100455_bib12 article-title: ClinVar database of global familial hypercholesterolemia-associated DNA variants publication-title: Hum. Mutat. doi: 10.1002/humu.23634 – volume: 34 start-page: 29287 year: 2021 ident: 10.1016/j.jlr.2023.100455_bib22 article-title: Language models enable zero-shot prediction of the effects of mutations on protein function publication-title: Adv. Neural Inf. Process. Syst. – volume: 101 start-page: 315 year: 2017 ident: 10.1016/j.jlr.2023.100455_bib6 article-title: Variant interpretation: functional assays to the rescue publication-title: Am. J. Hum. Genet. doi: 10.1016/j.ajhg.2017.07.014 – volume: 15 start-page: 816 year: 2018 ident: 10.1016/j.jlr.2023.100455_bib18 article-title: Deep generative models of genetic variation capture the effects of mutations publication-title: Nat. Methods doi: 10.1038/s41592-018-0138-4 – volume: 599 start-page: 91 year: 2021 ident: 10.1016/j.jlr.2023.100455_bib20 article-title: Disease variant prediction with deep generative models of evolutionary data publication-title: Nature doi: 10.1038/s41586-021-04043-8 – volume: 22 start-page: 277 year: 2006 ident: 10.1016/j.jlr.2023.100455_bib48 article-title: Structure of an LDLR-RAP complex reveals a general mode for ligand recognition by lipoprotein receptors publication-title: Mol. Cell doi: 10.1016/j.molcel.2006.02.021 – volume: 29 start-page: 2722 year: 2013 ident: 10.1016/j.jlr.2023.100455_bib37 article-title: lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests publication-title: Bioinformatics doi: 10.1093/bioinformatics/btt473 – volume: 24 start-page: 293 year: 2022 ident: 10.1016/j.jlr.2023.100455_bib3 article-title: The clinical genome resource (ClinGen) familial hypercholesterolemia variant curation expert Panel consensus guidelines for LDLR variant classification publication-title: Genet. Med. doi: 10.1016/j.gim.2021.09.012 – volume: 36 start-page: 513 year: 2015 ident: 10.1016/j.jlr.2023.100455_bib13 article-title: The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity publication-title: Hum. Mutat. doi: 10.1002/humu.22768 – volume: 29 start-page: 1056 year: 2022 ident: 10.1016/j.jlr.2023.100455_bib32 article-title: A structural biology community assessment of AlphaFold2 applications publication-title: Nat. Struct. Mol. Biol. doi: 10.1038/s41594-022-00849-w – volume: 35 start-page: 1513 year: 2019 ident: 10.1016/j.jlr.2023.100455_bib58 article-title: A natural upper bound to the accuracy of predicting protein stability changes upon mutations publication-title: Bioinformatics doi: 10.1093/bioinformatics/bty880
SSID	ssj0014461
Score	2.4459994
Snippet	The complex structure and function of low-density lipoprotein receptor (LDLR) makes classification of protein-coding missense variants challenging. Deep... The complex structure and function of low density lipoprotein receptor (LDLR) makes classification of protein-coding missense variants challenging. Deep...
SourceID	doaj pubmedcentral proquest pubmed crossref elsevier
SourceType	Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher
StartPage	100455
SubjectTerms	Atherosclerosis Cardiovascular Diseases - physiopathology Dyslipidemias Genetic Variation Genomics Humans Lipoproteins/Receptors Models, Molecular Phenotype Physical Biochemistry Protein Structure, Tertiary Proteomics Receptors, LDL - chemistry Receptors, LDL - genetics Virulence - genetics
Title	Deep Generative Models of LDLR Protein Structure to Predict Variant Pathogenicity
URI	https://dx.doi.org/10.1016/j.jlr.2023.100455 https://www.ncbi.nlm.nih.gov/pubmed/37821076 https://www.proquest.com/docview/2876636381 https://pubmed.ncbi.nlm.nih.gov/PMC10696256 https://doaj.org/article/18809565e67d4db19a86416e9be59070
Volume	64
WOSCitedRecordID	wos001122300300001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1539-7262 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014461 issn: 0022-2275 databaseCode: DOA dateStart: 20210101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nb9QwEB1BhQQXBC3Q5aNypaoHpIjYsWPnWFoqDquqoBbtzYo_QlO1SdXdVuLfM7aT1S5I5cI1m3jXfpP4vczsG4A9IQq8DwxHbSLzjOMemClLRcZs0-TeOlXGRPuPqTw5UbNZdbrS6ivUhCV74LRwn4JfGHJ44UvpuDO0qlWJJMJXxgsUdlGtI-sZxdSQP0CRQ0efcMakGPOZsbLr8ioYgbIiVAjw8B-_lR0pGvevbUx_E88_6ydXNqTjF_B8YJLkIM3gJTzy3SZsHXSooq9_kX0SazvjS_NNeHo49nXbgm9H3t-Qn9FuOjzrSGyGMyd9Q6ZH0-8kOje0HUnOsne3nix6PBgSOgtyj9IasSChkXGPY7QWWfwrOD_-cnb4NRsaK2RWULXIfF4yVEaFY6bmddEgkLWjljpT15axypjKOM9lWVFm8oYrJ2WtCisUYudpU7yGja7v_DYQoYwtJMoiJz3PrTXCKZpbYahTprTlBPJxcbUdXMdD84srPZaXXWrEQwc8dMJjAh-Xl9wky42HTv4cEFueGNyy4wGMIT3EkP5XDE2Aj3jrgXgkQoFDtQ999-4YGxoxDJmWuvP93VyjDEUmh482OoE3KVaWvxBXC2W2xJVRa1G0NoX1T7r2Ihp_o3yvUK-Wb__HpN_BszCXVJrzHjYwqPwHeGLvF-38dgcey5naiTfVb_63Iuc
linkProvider	Directory of Open Access Journals
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Deep+generative+models+of+LDLR+protein+structure+to+predict+variant+pathogenicity&rft.jtitle=Journal+of+lipid+research&rft.au=James%2C+Jose+K.&rft.au=Norland%2C+Kristjan&rft.au=Johar%2C+Angad+S.&rft.au=Kullo%2C+Iftikhar+J.&rft.date=2023-12-01&rft.pub=American+Society+for+Biochemistry+and+Molecular+Biology&rft.issn=0022-2275&rft.eissn=1539-7262&rft.volume=64&rft.issue=12&rft_id=info:doi/10.1016%2Fj.jlr.2023.100455&rft_id=info%3Apmid%2F37821076&rft.externalDocID=PMC10696256
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0022-2275&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0022-2275&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0022-2275&client=summon