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Can Predicted Protein 3D Structures Provide Reliable Insights into whether Missense Variants Are Disease Associated?

Sirawit Ittisoponpisan, Suhail A. Islam, Tarun Khanna and 3 more

Journal of Molecular Biology · 2019 · CC BY

doi:10.1016/j.jmb.2019.04.009

Abstract

Knowledge of protein structure can be used to predict the phenotypic consequence of a missense variant. Since structural coverage of the human proteome can be roughly tripled to over 50% of the residues if homology-predicted structures are included in addition to experimentally determined coordinates, it is important to assess the reliability of using predicted models when analyzing missense variants. Accordingly, we assess whether a missense variant is structurally damaging by using experimental and predicted structures. We considered 606 experimental structures and show that 40% of the 1965 disease-associated missense variants analyzed have a structurally damaging change in the mutant structure. Only 11% of the 2134 neutral variants are structurally damaging. Importantly, similar results are obtained when 1052 structures predicted using Phyre2 algorithm were used, even when the model shares low (<40%) sequence identity to the template. Thus, structure-based analysis of the effects of missense variants can be effectively applied to homology models. Our in-house pipeline, Missense3D, for structurally assessing missense variants was made available at http://www.sbg.bio.ic.ac.uk/~missense3d.

References 70

1.
Richards S., Aziz N., Bale S., Bick D., Das S., Gastier-Foster J. ACMG Laboratory Quality Assurance Committee Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17:405-424.
Not in our library10.1038/gim.2015.30
2.
Ellard S, Baple EL, Owens M, Cannon S, Eccles DM, Abbs S, et al. ACGS best practice guidelines for variant classification 2017. ACGS 2017:1–16.
Not in our library
3.
Mardis E.R. The $1,000 genome, the $100,000 analysis? Genome Med. 2010;2:84.
Not in our library10.1186/gm205
4.
Sim N.-L., Kumar P., Hu J., Henikoff S., Schneider G., Ng P.C. SIFT web server: predicting effects of amino acid substitutions on proteins. Nucleic Acids Res. 2012;40:W452-W7.
Not in our library10.1093/nar/gks539
5.
FATHMM-XF: accurate prediction of pathogenic point mutations via extended features · 2017
No notice
6.
Schwarz J.M., Rödelsperger C., Schuelke M., Seelow D. MutationTaster evaluates disease-causing potential of sequence alterations. Nat. Methods. 2010;7:575-576.
Not in our library10.1038/nmeth0810-575
7.
González-Pérez A., López-Bigas N. Improving the assessment of the outcome of nonsynonymous SNVs with a consensus deleteriousness score, Condel. Am. J. Hum. Genet. 2011;88:440-449.
Not in our library10.1016/j.ajhg.2011.03.004
8.
Miosge L.A., Field M.A., Sontani Y., Cho V., Johnson S., Palkova A. Comparison of predicted and actual consequences of missense mutations. Proc. Natl. Acad. Sci. 2015;112:E5189-E98.
Not in our library10.1073/pnas.1511585112
9.
Ioannidis N.M., Rothstein J.H., Pejaver V., Middha S., McDonnell S.K., Baheti S. REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am. J. Hum. Genet. 2016;99:877-885.
Not in our library10.1016/j.ajhg.2016.08.016
10.
OUP accepted manuscript · 2016
No notice
11.
Mir S., Alhroub Y., Anyango S., Armstrong D.R., Berrisford J.M., Clark A.R. PDBe: towards reusable data delivery infrastructure at protein data bank in Europe. Nucleic Acids Res. 2017;46:D486-D92.
Not in our library10.1093/nar/gkx1070
12.
Somody J.C., MacKinnon S.S., Windemuth A. Structural coverage of the proteome for pharmaceutical applications. Drug Discov. Today. 2017;22:1792-1799.
Not in our library10.1016/j.drudis.2017.08.004
13.
Buchan D.W., Ward S., Lobley A.E., Nugent T., Bryson K., Jones D.T. Protein annotation and modelling servers at University College London. Nucleic Acids Res. 2010;38:W563-W8.
Not in our library10.1093/nar/gkq427
14.
Glusman G., Rose P.W., Prlić A., Dougherty J., Duarte J.M., Hoffman A.S. Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: a proposed framework. Genome medicine. 2017;9:113.
Not in our library10.1186/s13073-017-0509-y
15.
Better prediction of functional effects for sequence variants · 2015
No notice
16.
SuSPect: Enhanced Prediction of Single Amino Acid Variant (SAV) Phenotype Using Network Features · 2014
No notice
17.
Adzhubei I.A., Schmidt S., Peshkin L., Ramensky V.E., Gerasimova A., Bork P. A method and server for predicting damaging missense mutations. Nat. Methods. 2010;7:248-249.
Not in our library10.1038/nmeth0410-248
18.
Van Durme J., Delgado J., Stricher F., Serrano L., Schymkowitz J., Rousseau F. A graphical interface for the FoldX forcefield. Bioinformatics. 2011;27:1711-1712.
Not in our library10.1093/bioinformatics/btr254
19.
PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality · 2011
No notice
20.
mCSM: predicting the effects of mutations in proteins using graph-based signatures · 2013
No notice
21.
DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach · 2014
No notice
22.
Savojardo C., Fariselli P., Martelli P.L., Casadio R. INPS-MD: a web server to predict stability of protein variants from sequence and structure. Bioinformatics. 2016;32:2542-2544.
Not in our library10.1093/bioinformatics/btw192
23.
Zhou H., Gao M., Skolnick J. ENTPRISE: an algorithm for predicting human disease-associated amino acid substitutions from sequence entropy and predicted protein structures. PLoS One. 2016;11.
Not in our library10.1371/journal.pone.0150965
24.
Bowie J.U., Reidhaar-Olson J.F., Lim W.A., Sauer R.T. Deciphering the message in protein sequences: tolerance to amino acid substitutions. Science. 1990;247:1306-1310.
Not in our library10.1126/science.2315699
25.
Wang Z., Moult J. SNPs, protein structure, and disease. Hum. Mutat. 2001;17:263-270.
Not in our library10.1002/humu.22
26.
Yue P., Li Z., Moult J. Loss of protein structure stability as a major causative factor in monogenic disease. J. Mol. Biol. 2005;353:459-473.
Not in our library10.1016/j.jmb.2005.08.020
27.
SNPs3D: Candidate gene and SNP selection for association studies · 2006
No notice
28.
Kucukkal T.G., Petukh M., Li L., Alexov E. Structural and physico-chemical effects of disease and non-disease nsSNPs on proteins. Curr. Opin. Struct. Biol. 2015;32:18-24.
Not in our library10.1016/j.sbi.2015.01.003
29.
Gao M., Zhou H., Skolnick J. Insights into disease-associated mutations in the human proteome through protein structural analysis. Structure. 2015;23:1362-1369.
Not in our library10.1016/j.str.2015.03.028
30.
Bhattacharya R., Rose P.W., Burley S.K., Prlić A. Impact of genetic variation on three dimensional structure and function of proteins. PLoS One. 2017;12.
Not in our library10.1371/journal.pone.0171355
31.
Laddach A., Ng J.C.-F., Chung S.S., Fraternali F. Genetic variants and protein–protein interactions: a multidimensional network-centric view. Curr. Opin. Struct. Biol. 2018;50:82-90.
Not in our library10.1016/j.sbi.2017.12.006
32.
David A., Razali R., Wass M.N., Sternberg M.J.E. Protein–protein interaction sites are hot spots for disease-associated nonsynonymous SNPs. Hum. Mutat. 2012;33:359-363.
Not in our library10.1002/humu.21656
33.
David A., Sternberg M.J. The contribution of missense mutations in core and rim residues of protein–protein interfaces to human disease. J. Mol. Biol. 2015;427:2886-2898.
Not in our library10.1016/j.jmb.2015.07.004
34.
Protein structure analysis of mutations causing inheritable diseases. An e-Science approach with life scientist friendly interfaces · 2010
No notice
35.
SDM: a server for predicting effects of mutations on protein stability · 2017
No notice
36.
Al-Numair N.S., Martin A.C. The SAAP pipeline and database: tools to analyze the impact and predict the pathogenicity of mutations. BMC Genomics. 2013;14:S4.
Not in our library10.1186/1471-2164-14-s3-s4
37.
Williams C.J., Headd J.J., Moriarty N.W., Prisant M.G., Videau L.L., Deis L.N. MolProbity: more and better reference data for improved all-atom structure validation. Protein Sci. 2018;27:293-315.
Not in our library10.1002/pro.3330
38.
UniProt: the universal protein knowledgebase · 2018
No notice
39.
Landrum M.J., Lee J.M., Benson M., Brown G.R., Chao C., Chitipiralla S. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 2017;46:D1062-D7.
Not in our library10.1093/nar/gkx1153
40.
The ExAC browser: displaying reference data information from over 60 000 exomes · 2016
No notice
41.
Krivov G.G., Shapovalov M.V., Dunbrack R.L. Improved prediction of protein side-chain conformations with SCWRL4. Proteins. 2009;77:778-795.
Not in our library10.1002/prot.22488
42.
Benjamini Y., Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 1995;289-300.
Not in our library
43.
Jabs A., Weiss M.S., Hilgenfeld R. Non-proline Cis peptide bonds in proteins. J. Mol. Biol. 1999;286(1):291-304.
Not in our library10.1006/jmbi.1998.2459
44.
Lins L., Thomas A., Brasseur R. Analysis of accessible surface of residues in proteins. Protein Sci. 2003;12:1406-1417.
Not in our library10.1110/ps.0304803
45.
Sanghera D.K., Wagenknecht D.R., McIntyre J.A., Kamboh M.I. Identification of structural mutations in the fifth domain of apolipoprotein H (β2-glycoprotein I) which affect phospholipid binding. Hum. Mol. Genet. 1997;6:311-316.
Not in our library10.1093/hmg/6.2.311
46.
Lovell S.C., Davis I.W., Arendall W.B., De Bakker P.I., Word J.M., Prisant M.G. Structure validation by Cα geometry: ϕ, ψ and Cβ deviation. Proteins. 2003;50:437-450.
Not in our library10.1002/prot.10286
47.
Lee B., Vasmatzis G. Stabilization of protein structures. Curr. Opin. Biotechnol. 1997;8:423-428.
Not in our library10.1016/s0958-1669(97)80063-8
48.
Rost B., Sander C. Conservation and prediction of solvent accessibility in protein families. Proteins. 1994;20:216-226.
Not in our library10.1002/prot.340200303
49.
Cirulli E.T., Goldstein D.B. Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nat. Rev. Genet. 2010;11:415-425.
Not in our library10.1038/nrg2779
50.
Ittisoponpisan S., Alhuzimi E., Sternberg M.J., David A. Landscape of pleiotropic proteins causing human disease: structural and system biology insights. Hum. Mutat. 2017;38:289-296.
Not in our library10.1002/humu.23155
51.
Ittisoponpisan S., David A. Structural biology helps interpret variants of uncertain significance in genes causing endocrine and metabolic disorders. Journal of the Endocrine Society. 2018;2:842-854.
Not in our library10.1210/js.2018-00077
52.
Flores T., Orengo C., Moss D., Thornton J. Comparison of conformational characteristics in structurally similar protein pairs. Protein Sci. 1993;2:1811-1826.
Not in our library10.1002/pro.5560021104
53.
Almstedt K., Mårtensson L.-G., Carlsson U., Hammarström P. Thermodynamic interrogation of a folding disease. Mutant mapping of position 107 in human carbonic anhydrase II linked to marble brain disease. Biochemistry. 2008;47:1288-1298.
Not in our library10.1021/bi701720p
54.
DeLano W.L. The PyMOL Molecular Graphics System. 2002.
Not in our library
55.
Lukas J., Scalia S., Eichler S., Pockrandt A.M., Dehn N., Cozma C. Functional and clinical consequences of novel α-galactosidase a mutations in Fabry disease. Hum. Mutat. 2016;37:43-51.
Not in our library10.1002/humu.22910
56.
Kelley L.A., Mezulis S., Yates C.M., Wass M.N., Sternberg M.J. The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc. 2015;10:845-858.
Not in our library10.1038/nprot.2015.053
57.
Grimm D.G., Azencott C.A., Aicheler F., Gieraths U., MacArthur D.G., Samocha K.E. The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity. Hum. Mutat. 2015;36:513-523.
Not in our library10.1002/humu.22768
58.
Yang J., Yan R., Roy A., Xu D., Poisson J., Zhang Y. The I-TASSER Suite: protein structure and function prediction. Nat. Methods. 2015;12:7.
Not in our library10.1038/nmeth.3213
59.
Conchúir S.Ó., Barlow K.A., Pache R.A., Ollikainen N., Kundert K., O'Meara M.J. A web resource for standardized benchmark datasets, metrics, and Rosetta protocols for macromolecular modeling and design. PLoS One. 2015;10.
Not in our library10.1371/journal.pone.0130433
60.
Zhang C., Mortuza S., He B., Wang Y., Zhang Y. Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12. Proteins. 2018;86:136-151.
Not in our library10.1002/prot.25414
61.
UniProt: the universal protein knowledgebase · 2016
No notice
62.
Alhuzimi E., Leal L.G., Sternberg M.J., David A. Properties of human genes guided by their enrichment in rare and common variants. Hum. Mutat. 2018;39:365-370.
Not in our library10.1002/humu.23377
63.
Larkin M.A., Blackshields G., Brown N., Chenna R., McGettigan P.A., McWilliam H. Clustal W and Clustal X version 2.0. bioinformatics. 2007;23:2947-2948.
Not in our library10.1093/bioinformatics/btm404
64.
Morris A.L., MacArthur M.W., Hutchinson E.G., Thornton J.M. Stereochemical quality of protein structure coordinates. Proteins. 1992;12:345-364.
Not in our library10.1002/prot.340120407
65.
Sticke D.F., Presta L.G., Dill K.A., Rose G.D. Hydrogen bonding in globular proteins. J. Mol. Biol. 1992;226:1143-1159.
Not in our library10.1016/0022-2836(92)91058-w
66.
Kumar S., Nussinov R. Close-range electrostatic interactions in proteins. ChemBioChem. 2002;3:604-617.
Not in our library10.1002/1439-7633(20020703)3:7<604::aid-cbic604>3.0.co;2-x
67.
Kabsch W., Sander C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers. 1983;22:2577-2637.
Not in our library10.1002/bip.360221211
68.
Oliveira S.H., Ferraz F.A., Honorato R.V., Xavier-Neto J., Sobreira T.J., de Oliveira P.S. KVFinder: steered identification of protein cavities as a PyMOL plugin. BMC bioinformatics. 2014;15:197.
Not in our library10.1186/1471-2105-15-197
69.
Hubbard S.J., Gross K.-H., Argos P. Intramolecular cavities in globular proteins. Protein Eng. Des. Sel. 1994;7:613-626.
Not in our library10.1093/protein/7.5.613
70.
Altman D.G. Practical Statistics for Medical Research. 1990.
Not in our library

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