The Loss and Gain of Functional Amino Acid Residues Is a Common Mechanism Causing Human Inherited Disease

PLoS Computational Biology

Volumn 12, Issue 8, 2016, Pages

  Lugo Martinez, Jose ^a   Pejaver, Vikas ^a   Pagel, Kymberleigh A ^a   Jain, Shantanu ^a   Mort, Matthew ^b   Cooper, David N ^b   Mooney, Sean D ^c   Radivojac, Predrag ^a  

^a INDIANA UNIVERSITY   (United States)

^b CARDIFF UNIVERSITY SCHOOL OF MEDICINE   (United Kingdom)

^c UNIVERSITY OF WASHINGTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AMINO ACIDS; PROTEINS;

AMINO ACID RESIDUES; AMINO ACID SUBSTITUTION; GENETIC DATA; GENETIC VARIANTS; INHERITED DISEASE; MOLECULAR DATA; MOLECULAR EVENTS; MOLECULAR FUNCTION; PROTEINS STRUCTURES; STRUCTURAL DATA;

CATALYST ACTIVITY;

CARBONATE DEHYDRATASE II; SUPEROXIDE DISMUTASE; PROTEIN BINDING;

ALLOSTERISM; AMINO ACID ANALYSIS; AMINO ACID SUBSTITUTION; ARTICLE; BINDING SITE; CATALYSIS; COMPUTER MODEL; GENE MUTATION; GENETIC DISORDER; GENETIC VARIABILITY; HUMAN; INFORMATION PROCESSING; LIGAND BINDING; LOSS OF FUNCTION MUTATION; MATHEMATICAL COMPUTING; METAL BINDING; PROTEIN PROCESSING; PROTEIN STRUCTURE; STRUCTURE ACTIVITY RELATION; THREE DIMENSIONAL IMAGING; AMINO ACID SEQUENCE; BIOLOGY; COMPUTER SIMULATION; DISEASES; GENETICS; MOLECULAR MODEL; MUTATION; STATISTICAL MODEL;

AMINO ACID SEQUENCE; AMINO ACID SUBSTITUTION; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DISEASE; HUMANS; MODELS, MOLECULAR; MODELS, STATISTICAL; MUTATION; PROTEIN BINDING;

EID: 84988962696     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1005091     Document Type: Article

References (79)

1. Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29(1):308–311. doi: 10.1093/nar/29.1.30811125122
2. Bairoch A, Apweiler R, Wu CH, Barker WC, Boeckmann B, Ferro S, et al. The Universal Protein Resource (UniProt). Nucleic Acids Res. 2005;33(Database issue):D154–159. doi: 10.1093/nar/gki07015608167
3. Stenson PD, Mort M, Ball EV, Shaw K, Phillips AD, Cooper DN, The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine. Hum Genet. 2014;133(1):1–9. doi: 10.1007/s00439-013-1358-424077912
4. Mooney S, Bioinformatics approaches and resources for single nucleotide polymorphism functional analysis. Brief Bioinform. 2005;6(1):44–56. doi: 10.1093/bib/6.1.4415826356
5. Ng PC, Henikoff S, Predicting the effects of amino acid substitutions on protein function. Annu Rev Genomics Hum Genet. 2006;7(1):61–80. doi: 10.1146/annurev.genom.7.080505.11563016824020
6. Karchin R, Next generation tools for the annotation of human SNPs. Brief Bioinform. 2009;10(1):35–52. doi: 10.1093/bib/bbn04719181721
7. Wang Z, Moult J, SNPs, protein structure, and disease. Hum Mutat. 2001;17(4):263–270. doi: 10.1002/humu.2211295823
8. Yue P, Li Z, Moult J, Loss of protein structure stability as a major causative factor in monogenic disease. J Mol Biol. 2005;353(2):459–473. doi: 10.1016/j.jmb.2005.08.02016169011
9. Vogt G, Chapgier A, Yang K, Chuzhanova N, Feinberg J, Fieschi C, et al. Gains of glycosylation comprise an unexpectedly large group of pathogenic mutations. Nat Genet. 2005;37(7):692–700. doi: 10.1038/ng158115924140
10. Vogt G, Vogt B, N C, Julenius K, Cooper DN, Casanova JL, Gain-of-glycosylation mutations. Curr Opin Genet Dev. 2007;17(3):245–251. doi: 10.1016/j.gde.2007.04.00817467977
11. Li S, Iakoucheva L, Mooney SD, Radivojac P, Loss of post-translational modification sites in disease. Pac Symp Biocomput. 2010;15:337–347. 19908386
12. Pan Y, Karagiannis K, Zhang H, Dingerdissen H, Shamsaddini A, Wan Q, et al. Human germline and pan-cancer variomes and their distinct functional profiles. Nucleic Acids Res. 2014;42(18):11570–11588. doi: 10.1093/nar/gku77225232094
13. Peterson TA, Doughty E, Kann MG, Towards precision medicine: advances in computational approaches for the analysis of human variants. J Mol Biol. 2013;425(21):4047–4063. doi: 10.1016/j.jmb.2013.08.00823962656
14. Radivojac P, Baenziger PH, Kann MG, Mort ME, Hahn MW, Mooney SD, Gain and loss of phosphorylation sites in human cancer. Bioinformatics. 2008;24(16):i241–247. doi: 10.1093/bioinformatics/btn26718689832
15. Radivojac P, Vacic V, Haynes C, Cocklin RR, Mohan A, Heyen JW, et al. Identification, analysis, and prediction of protein ubiquitination sites. Proteins. 2010;78(2):365–380. doi: 10.1002/prot.2255519722269
16. Xin F, Myers S, Li YF, Cooper DN, Mooney SD, Radivojac P, Structure-based kernels for the prediction of catalytic residues and their involvement in human inherited disease. Bioinformatics. 2010;26(16):1975–1982. doi: 10.1093/bioinformatics/btq31920551136
17. Li B, Krishnan VG, Mort ME, Xin F, Kamati KK, Cooper DN, et al. Automated inference of molecular mechanisms of disease from amino acid substitutions. Bioinformatics. 2009;25(21):2744–2750. doi: 10.1093/bioinformatics/btp52819734154
18. Saunders CT, Baker D, Evaluation of structural and evolutionary contributions to deleterious mutation prediction. J Mol Biol. 2002;322(4):891–901. doi: 10.1016/S0022-2836(02)00813-612270722
19. Capriotti E, Altman RB, Improving the prediction of disease-related variants using protein three-dimensional structure. BMC Bioinformatics. 2011;12Suppl 4:S3. doi: 10.1186/1471-2105-12-S4-S321992054
20. Xin F, Radivojac P, Computational methods for identification of functional residues in protein structures. Curr Protein Pept Sci. 2011;12:456–469. doi: 10.2174/13892031179695768521787297
21. Sahni N, Yi S, Taipale M, Fuxman Bass JI, Coulombe-Huntington J, Yang F, et al. Widespread macromolecular interaction perturbations in human genetic disorders. Cell. 2015;161(3):647–660. doi: 10.1016/j.cell.2015.04.01325910212
22. Steward RE, MacArthur MW, Laskowski RA, Thornton JM, Molecular basis of inherited diseases: a structural perspective. Trends Genet. 2003;19(9):505–513. doi: 10.1016/S0168-9525(03)00195-112957544
23. De Baets G, Van Durme J, Reumers J, Maurer-Stroh S, Vanhee P, Dopazo J, et al. SNPeffect 4.0: on-line prediction of molecular and structural effects of protein-coding variants. Nucleic Acids Res. 2012;40(Database issue):D935–939. doi: 10.1093/nar/gkr99622075996
24. Yates CM, Sternberg MJ, The effects of non-synonymous single nucleotide polymorphisms (nsSNPs) on protein-protein interactions. J Mol Biol. 2013;425(21):3949–3963. doi: 10.1016/j.jmb.2013.07.01223867278
25. Nussinov R, Tsai CJ, Xin F, Radivojac P, Allosteric post-translational modification codes. Trends Biochem Sci. 2012;37(10):447–455. doi: 10.1016/j.tibs.2012.07.00122884395
26. Elkan C, Noto K. Learning classifiers from only positive and unlabeled data. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD 2008. New York, NY, USA: ACM; 2008. p. 213–220.
27. Jain S, White M, Trosset MW, Radivojac P. Nonparametric semi-supervised learning of class proportions. arXiv preprint arXiv:160101944. 2016;Available from: http://arxiv.org/abs/1601.01944.
28. Denis F, Gilleron R, Letouzey F, Learning from positive and unlabeled examples. Theor Comput Sci. 2005;348(16):70–83. doi: 10.1016/j.tcs.2005.09.007
29. Lagace TA, Curtis DE, Garuti R, McNutt MC, Park SW, Prather HB, et al. Secreted PCSK9 decreases the number of LDL receptors in hepatocytes and in livers of parabiotic mice. J Clin Invest. 2006;116(11):2995–3005. doi: 10.1172/JCI2938317080197
30. Bromberg Y, Rost B, Correlating protein function and stability through the analysis of single amino acid substitutions. BMC Bioinformatics. 2009;10Suppl 8:S8. doi: 10.1186/1471-2105-10-S8-S819758472
31. Tokuriki N, Tawfik DS, Stability effects of mutations and protein evolvability. Curr Opin Struct Biol. 2009;19(5):596–604. doi: 10.1016/j.sbi.2009.08.00319765975
32. Worth CL, Preissner R, Blundell TL, SDM–a server for predicting effects of mutations on protein stability and malfunction. Nucleic Acids Res. 2011;39(Web Server issue):W215–222. doi: 10.1093/nar/gkr36321593128
33. du Plessis MC, Sugiyama M, Class prior estimation from positive and unlabeled data. IEICE Transactions on Information and Systems. E97–D(5):1358–1362.
34. Przulj N, Corneil DG, Jurisica I, Modeling interactome: scale-free or geometric?Bioinformatics. 2004;20(18):3508–3515. doi: 10.1093/bioinformatics/bth43615284103
35. Przulj N, Biological network comparison using graphlet degree distribution. Bioinformatics. 2007;23(2):e177–e183. doi: 10.1093/bioinformatics/btl30117237089
36. Shervashidze N, Vishwanathan SVN, Petri TH, Mehlhorn K, Borgwardt KM. Efficient graphlet kernels for large graph comparison. In: Proc. 12th International Conference on Artificial Intelligence and Statistics. AISTATS’09; 2009. p. 488–495.
37. Vacic V, Iakoucheva LM, Lonardi S, Radivojac P, Graphlet kernels for prediction of functional residues in protein structures. J Comput Biol. 2010;17(1):55–72. doi: 10.1089/cmb.2009.002920078397
38. Lugo-Martinez J, Radivojac P, Generalized graphlet kernels for probabilistic inference in sparse graphs. Network Science. 2014;2(2):254–276. doi: 10.1017/nws.2014.14
39. Joachims T, Learning to classify text using support vector machines: methods, theory, and algorithms. Kluwer Academic Publishers; 2002.
40. Platt J, Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in Large Margin Classifiers. MIT Press; 1999. p. 61–74.
41. Capriotti E, Fariselli P, Casadio R, A neural-network-based method for predicting protein stability changes upon single point mutations. Bioinformatics. 2004;20(1):i63–i68. doi: 10.1093/bioinformatics/bth92815262782
42. Cheng J, Randall A, Baldi P, Prediction of protein stability changes for single-site mutations using support vector machines. Proteins. 2006;62(4):1125–1132. doi: 10.1002/prot.20810
43. Kyte J, Doolittle RF, A simple method for displaying the hydropathic character of a protein. J Mol Biol. 1982;157(1):105–132. doi: 10.1016/0022-2836(82)90515-07108955
44. Janin J, Surface and inside volumes in globular proteins. Nature. 1979;277(5696):491–492. doi: 10.1038/277491a0763335
45. Vihinen M, Torkkila E, Riikonen P, Accuracy of protein flexibility predictions. Proteins. 1994;19(2):141–149. doi: 10.1002/prot.3401902078090708
46. Jones S, Thornton JM, Principles of protein-protein interactions. Proc Natl Acad Sci USA. 1996;93(1):13–20. doi: 10.1073/pnas.93.1.138552589
47. Jones S, Thornton JM, Analysis of protein-protein interaction sites using surface patches. J Mol Biol. 1997;272(1):121–132. doi: 10.1006/jmbi.1997.12349299342
48. Zimmerman JM, Eliezer N, Simha R, The characterization of amino acid sequences in proteins by statistical methods. J Theor Biol. 1968;21(2):170–201. doi: 10.1016/0022-5193(68)90069-65700434
49. George RA, Heringa J, An analysis of protein domain linkers: their classification and role in protein folding. Protein Eng. 2002;15(11):871–879. doi: 10.1093/protein/15.11.87112538906
50. Nagano K, Logical analysis of the mechanism of protein folding. I. Predictions of helices, loops and beta-structures from primary structure. J Mol Biol. 1973;75(2):401–420. doi: 10.1016/0022-2836(73)90030-24728695
51. Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25(17):3389–3402. doi: 10.1093/nar/25.17.33899254694
52. Xin F. Methods for predicting functional residues in protein structures and understanding molecular mechanisms of disease [Ph.D. Thesis]; 2012.
53. Porter CT, Bartlett GJ, Thornton JM, The Catalytic Site Atlas: a resource of catalytic sites and residues identified in enzymes using structural data. Nucleic Acids Res. 2004;32:D129–33. doi: 10.1093/nar/gkh02814681376
54. Yan C, Terribilini M, Wu F, Jernigan RL, Dobbs D, Honavar V, Predicting DNA-binding sites of proteins from amino acid sequence. BMC Bioinformatics. 2006;7:262–271. doi: 10.1186/1471-2105-7-26216712732
55. Singh H, Chauhan JS, Gromiha MM, Consortium OSDD, Raghava GPS, ccPDB: compilation and creation of data sets from Protein Data Bank. Nucleic Acids Res. 2012;40(D1):D486–D489. doi: 10.1093/nar/gkr115022139939
56. Chung JL, Wang W, Bourne PE, Exploiting sequence and structure homologs to identify protein–protein binding sites. Proteins. 2006;62(3):630–640. doi: 10.1002/prot.2074116329107
57. Lise S, Archambeau C, Pontil M, Jones D, Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods. BMC Bioinformatics. 2009;10(1):365–381. doi: 10.1186/1471-2105-10-36519878545
58. Thorn KS, Bogan AA, ASEdb: a database of alanine mutations and their effects on the free energy of binding in protein interactions. Bioinformatics. 2001;17(3):284–285. doi: 10.1093/bioinformatics/17.3.28411294795
59. Huang Z, Mou L, Shen Q, Lu S, Li C, Liu X, et al. ASD v2.0: updated content and novel features focusing on allosteric regulation. Nucleic Acids Res. 2014;42(D1):D510–D516. doi: 10.1093/nar/gkt124724293647
60. Gromiha M, An J, Kono H, Oobatake M, Uedaira H, Prabakaran P, et al. ProTherm, version 2.0: thermodynamic database for proteins and mutants. Nucleic Acids Research. 2000;28(1):283–285. doi: 10.1093/nar/28.1.28310592247
61. Iakoucheva LM, Radivojac P, Brown CJ, O’Connor TR, Sikes JG, Obradovic Z, et al. The importance of intrinsic disorder for protein phosphorylation. Nucleic Acids Res. 2004;32(3):1037–1049. doi: 10.1093/nar/gkh25314960716
62. Daily KM, Radivojac P, Dunker AK. Intrinsic disorder and protein modifications: building an SVM predictor for methylation. In: Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE; 2005. p. 475–481.
63. Gsponer J, Futschik ME, Teichmann SA, Babu MM, Tight regulation of unstructured proteins: from transcript synthesis to protein degradation. Science. 2008;322(5906):1365–1368. doi: 10.1126/science.116358119039133
64. Pejaver V, Hsu WL, Xin F, Dunker AK, Uversky VN, Radivojac P, The structural and functional signatures of proteins that undergo multiple events of post-translational modification. Protein Sci. 2014;23(8):1077–1093. doi: 10.1002/pro.249424888500
65. Roberts BR, Tainer JA, Getzoff ED, Malencik DA, Anderson SR, Bomben VC, et al. Structural characterization of zinc-deficient human superoxide dismutase and implications for ALS. J Mol Biol. 2007;373(4):877–890. doi: 10.1016/j.jmb.2007.07.04317888947
66. Seetharaman SV, Taylor AB, Holloway S, Hart PJ, Structures of mouse SOD1 and human/mouse SOD1 chimeras. Arch Biochem Biophys. 2010;503(2):183–190. doi: 10.1016/j.abb.2010.08.01420727846
67. Alexander MD, Traynor BJ, Miller N, Corr B, Frost E, McQuaid S, et al. “True” sporadic ALS associated with a novel SOD-1 mutation. Ann Neurol. 2002;52(5):680–683. doi: 10.1002/ana.1036912402272
68. Krishnan U, Son M, Rajendran B, Elliott JL, Novel mutations that enhance or repress the aggregation potential of SOD1. Mol Cell Biochem. 2006;287(1–2):201–211. doi: 10.1007/s11010-005-9112-416583143
69. Joyce PI, Mcgoldrick P, Saccon RA, Weber W, Fratta P, West SJ, et al. A novel SOD1-ALS mutation separates central and peripheral effects of mutant SOD1 toxicity. Hum Mol Genet. 2015;24(7):1883–1897. doi: 10.1093/hmg/ddu60525468678
70. Ippolito JA, Christianson DW, Structure of an engineered His3 Cys zinc binding site in human carbonic anhydrase II. Biochemistry. 1993;32(38):9901–9905. doi: 10.1021/bi00089a0058399159
71. Krebs JF, Ippolito JA, Christianson DW, Fierke CA, Structural and functional importance of a conserved hydrogen bond network in human carbonic anhydrase II*. J Biol Chem. 1993;268(36):27458–27466. 8262987
72. Xue Y, Liljas A, Jonsson BH, Lindskog S, Structural analysis of the zinc hydroxide-Thr-199-Glu-106 hydrogen-bond network in human carbonic anhydrase II. Proteins. 1993;17(1):93–106. doi: 10.1002/prot.3401701127901850
73. Ippolito JA, Baird TT, JrM SA, C DW, F CA, Structure-assisted redesign of a protein-zinc-binding site with femtomolar affinity. Proc Natl Acad Sci USA. 1995;92:5017–5021. doi: 10.1073/pnas.92.11.50177761440
74. Huang S, Sjöblom Sauer-Eriksson AE, Jonsson BH, Organization of an efficient carbonic anhydrase: Implications for the mechanism based on structure-function studies of a T199P/C206S mutant. Biochemistry. 2002;41(24):7628–7635. doi: 10.1021/bi020053o12056894
75. DePristo MA, Weinreich DM, Hartl DL, Missense meanderings in sequence space: a biophysical view of protein evolution. Nat Rev Genet. 2005;6(9):678–687. doi: 10.1038/nrg167216074985
76. Schymkowitz J, Borg J, Stricher F, Nys R, Rousseau F, Serrano L, The FoldX web server: an online force field. Nucleic Acids Res. 2005;33(Web Server issue):W382–388. doi: 10.1093/nar/gki38715980494
77. Casadio R, Vassura M, Tiwari S, Fariselli P, Luigi Martelli P, Correlating disease-related mutations to their effect on protein stability: a large-scale analysis of the human proteome. Hum Mutat. 2011;32(10):1161–1170. doi: 10.1002/humu.2155521853506
78. Gray VE, Kukurba KR, Kumar S, Performance of computational tools in evaluating the functional impact of laboratory-induced amino acid mutations. Bioinformatics. 2012;28(16):2093–2096. doi: 10.1093/bioinformatics/bts33622685075
79. Mohan A, Uversky VN, Radivojac P, Influence of sequence changes and environment on intrinsically disordered proteins. PLoS Comput Biol. 2009;5(9):e1000497. doi: 10.1371/journal.pcbi.100049719730682