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...

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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
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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
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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
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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...
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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
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