Proteomics pp 235-260 | Cite as

Annotating Mutational Effects on Proteins and Protein Interactions: Designing Novel and Revisiting Existing Protocols

  • Minghui Li
  • Alexander Goncearenco
  • Anna R. PanchenkoEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1550)


In this review we describe a protocol to annotate the effects of missense mutations on proteins, their functions, stability, and binding. For this purpose we present a collection of the most comprehensive databases which store different types of sequencing data on missense mutations, we discuss their relationships, possible intersections, and unique features. Next, we suggest an annotation workflow using the state-of-the art methods and highlight their usability, advantages, and limitations for different cases. Finally, we address a particularly difficult problem of deciphering the molecular mechanisms of mutations on proteins and protein complexes to understand the origins and mechanisms of diseases.

Key words

Protein–protein interactions Databases Mutations 



This work was supported by the Intramural Research Program of the National Library of Medicine.


  1. 1.
    Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA, Hurles ME, McVean GA, Genomes Project C (2010) A map of human genome variation from population-scale sequencing. Nature 467(7319):1061–1073PubMedCrossRefGoogle Scholar
  2. 2.
    Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E, Chipot C, Skeel RD, Kale L, Schulten K (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26(16):1781–1802PubMedPubMedCentralCrossRefGoogle Scholar
  3. 3.
    Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S, Karplus M (1983) Charmm – a program for macromolecular energy, minimization, and dynamics calculations. J Comput Chem 4(2):187–217CrossRefGoogle Scholar
  4. 4.
    Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14(1):33–38, 27-38PubMedCrossRefGoogle Scholar
  5. 5.
    Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem 25(13):1605–1612PubMedCrossRefGoogle Scholar
  6. 6.
    Wang Y, Geer LY, Chappey C, Kans JA, Bryant SH (2000) Cn3D: sequence and structure views for Entrez. Trends Biochem Sci 25(6):300–302PubMedCrossRefGoogle Scholar
  7. 7.
    Mailman MD, Feolo M, Jin Y, Kimura M, Tryka K, Bagoutdinov R, Hao L, Kiang A, Paschall J, Phan L, Popova N, Pretel S, Ziyabari L, Lee M, Shao Y, Wang ZY, Sirotkin K, Ward M, Kholodov M, Zbicz K, Beck J, Kimelman M, Shevelev S, Preuss D, Yaschenko E, Graeff A, Ostell J, Sherry ST (2007) The NCBI dbGaP database of genotypes and phenotypes. Nat Genet 39(10):1181–1186PubMedPubMedCentralCrossRefGoogle Scholar
  8. 8.
    Rubinstein WS, Maglott DR, Lee JM, Kattman BL, Malheiro AJ, Ovetsky M, Hem V, Gorelenkov V, Song G, Wallin C, Husain N, Chitipiralla S, Katz KS, Hoffman D, Jang W, Johnson M, Karmanov F, Ukrainchik A, Denisenko M, Fomous C, Hudson K, Ostell JM (2013) The NIH genetic testing registry: a new, centralized database of genetic tests to enable access to comprehensive information and improve transparency. Nucleic Acids Res 41(Database issue):D925–D935PubMedCrossRefGoogle Scholar
  9. 9.
    Sherry ST, Ward M, Sirotkin K (1999) dbSNP-database for single nucleotide polymorphisms and other classes of minor genetic variation. Genome Res 9(8):677–679PubMedGoogle Scholar
  10. 10.
    Lappalainen I, Lopez J, Skipper L, Hefferon T, Spalding JD, Garner J, Chen C, Maguire M, Corbett M, Zhou G, Paschall J, Ananiev V, Flicek P, Church DM (2013) DbVar and DGVa: public archives for genomic structural variation. Nucleic Acids Res 41(Database issue):D936–D941PubMedCrossRefGoogle Scholar
  11. 11.
    Kibbe WA, Arze C, Felix V, Mitraka E, Bolton E, Fu G, Mungall CJ, Binder JX, Malone J, Vasant D, Parkinson H, Schriml LM (2015) Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data. Nucleic Acids Res 43(Database issue):D1071–D1078PubMedCrossRefGoogle Scholar
  12. 12.
    Ramos EM, Hoffman D, Junkins HA, Maglott D, Phan L, Sherry ST, Feolo M, Hindorff LA (2014) Phenotype-Genotype Integrator (PheGenI): synthesizing genome-wide association study (GWAS) data with existing genomic resources. Eur J Hum Genet 22(1):144–147PubMedCrossRefGoogle Scholar
  13. 13.
    Stenson PD, Ball EV, Mort M, Phillips AD, Shiel JA, Thomas NS, Abeysinghe S, Krawczak M, Cooper DN (2003) Human Gene Mutation Database (HGMD): 2003 update. Hum Mutat 21(6):577–581PubMedCrossRefGoogle Scholar
  14. 14.
    Amberger JS, Bocchini CA, Schiettecatte F, Scott AF, Hamosh A (2015) Online Mendelian Inheritance in Man (OMIM(R)), an online catalog of human genes and genetic disorders. Nucleic Acids Res 43(Database issue):D789–D798PubMedCrossRefGoogle Scholar
  15. 15.
    Landrum MJ, Lee JM, Riley GR, Jang W, Rubinstein WS, Church DM, Maglott DR (2014) ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res 42(Database issue):D980–D985PubMedCrossRefGoogle Scholar
  16. 16.
    Forbes SA, Bindal N, Bamford S, Cole C, Kok CY, Beare D, Jia M, Shepherd R, Leung K, Menzies A, Teague JW, Campbell PJ, Stratton MR, Futreal PA (2011) COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res 39(suppl 1):D945–D950PubMedCrossRefGoogle Scholar
  17. 17.
    Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, Shmulevich I, Sander C, Stuart JM, Cancer Genome Atlas Research N (2013) The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45(10):1113–1120PubMedPubMedCentralCrossRefGoogle Scholar
  18. 18.
    Singh A, Olowoyeye A, Baenziger PH, Dantzer J, Kann MG, Radivojac P, Heiland R, Mooney SD (2008) MutDB: update on development of tools for the biochemical analysis of genetic variation. Nucleic Acids Res 36(Database issue):D815–D819PubMedGoogle Scholar
  19. 19.
    Mottaz A, David FP, Veuthey AL, Yip YL (2010) Easy retrieval of single amino-acid polymorphisms and phenotype information using SwissVar. Bioinformatics 26(6):851–852PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    Li M, Kales SC, Ma K, Shoemaker BA, Crespo-Barreto J, Cangelosi AL, Lipkowitz S, Panchenko AR (2015) Balancing protein stability and activity in cancer: a new approach for identifying driver mutations affecting CBL ubiquitin ligase activation. Cancer Res 76(3):561–571PubMedPubMedCentralCrossRefGoogle Scholar
  21. 21.
    NR Coordinators (2014) Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 42(Database issue):D7–D17Google Scholar
  22. 22.
    Perez B, Mechinaud F, Galambrun C, Ben Romdhane N, Isidor B, Philip N, Derain-Court J, Cassinat B, Lachenaud J, Kaltenbach S, Salmon A, Desiree C, Pereira S, Menot ML, Royer N, Fenneteau O, Baruchel A, Chomienne C, Verloes A, Cave H (2010) Germline mutations of the CBL gene define a new genetic syndrome with predisposition to juvenile myelomonocytic leukaemia. J Med Genet 47(10):686–691PubMedCrossRefGoogle Scholar
  23. 23.
    Marchler-Bauer A, Derbyshire MK, Gonzales NR, Lu S, Chitsaz F, Geer LY, Geer RC, He J, Gwadz M, Hurwitz DI, Lanczycki CJ, Lu F, Marchler GH, Song JS, Thanki N, Wang Z, Yamashita RA, Zhang D, Zheng C, Bryant SH (2015) CDD: NCBI’s conserved domain database. Nucleic Acids Res 43(D1):D222–D226PubMedCrossRefGoogle Scholar
  24. 24.
    Ashkenazy H, Erez E, Martz E, Pupko T, Ben-Tal N (2010) ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids. Nucleic Acids Res 38(suppl 2):W529–W533PubMedPubMedCentralCrossRefGoogle Scholar
  25. 25.
    Shoemaker BA, Zhang D, Thangudu RR, Tyagi M, Fong JH, Marchler-Bauer A, Bryant SH, Madej T, Panchenko AR (2010) Inferred Biomolecular Interaction Server – a web server to analyze and predict protein interacting partners and binding sites. Nucleic Acids Res 38(Database issue):D518–D524PubMedCrossRefGoogle Scholar
  26. 26.
    Goncearenco A, Shaytan AK, Shoemaker BA, Panchenko AR (2015) Structural perspectives on the evolutionary expansion of unique protein-protein binding sites. Biophys J 109(6):1295–1306PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Shoemaker BA, Zhang D, Tyagi M, Thangudu RR, Fong JH, Marchler-Bauer A, Bryant SH, Madej T, Panchenko AR (2012) IBIS (Inferred Biomolecular Interaction Server) reports, predicts and integrates multiple types of conserved interactions for proteins. Nucleic Acids Res 40(Database issue):D834–D840PubMedCrossRefGoogle Scholar
  28. 28.
    Dou H, Buetow L, Hock A, Sibbet GJ, Vousden KH, Huang DT (2012) Structural basis for autoinhibition and phosphorylation-dependent activation of c-Cbl. Nat Struct Mol Biol 19(2):184–192PubMedCrossRefGoogle Scholar
  29. 29.
    Hernansaiz-Ballesteros RD, Salavert F, Sebastian-Leon P, Aleman A, Medina I, Dopazo J (2015) Assessing the impact of mutations found in next generation sequencing data over human signaling pathways. Nucleic Acids Res 43(W1):W270–W275PubMedPubMedCentralCrossRefGoogle Scholar
  30. 30.
    Choi Y, Sims GE, Murphy S, Miller JR, Chan AP (2012) Predicting the functional effect of amino acid substitutions and indels. PLoS One 7(10):e46688PubMedPubMedCentralCrossRefGoogle Scholar
  31. 31.
    Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR (2010) A method and server for predicting damaging missense mutations. Nat Methods 7(4):248–249PubMedPubMedCentralCrossRefGoogle Scholar
  32. 32.
    Thusberg J, Olatubosun A, Vihinen M (2011) Performance of mutation pathogenicity prediction methods on missense variants. Hum Mutat 32(4):358–368PubMedCrossRefGoogle Scholar
  33. 33.
    Hashimoto K, Rogozin IB, Panchenko AR (2012) Oncogenic potential is related to activating effect of cancer single and double somatic mutations in receptor tyrosine kinases. Hum Mutat 33(11):1566–1575PubMedPubMedCentralCrossRefGoogle Scholar
  34. 34.
    Schlebach JP, Narayan M, Alford C, Mittendorf KF, Carter BD, Li J, Sanders CR (2015) Conformational stability and pathogenic misfolding of the integral membrane protein PMP22. J Am Chem Soc 137(27):8758–8768PubMedPubMedCentralCrossRefGoogle Scholar
  35. 35.
    Potapov V, Cohen M, Schreiber G (2009) Assessing computational methods for predicting protein stability upon mutation: good on average but not in the details. Protein Eng Des Sel 22(9):553–560PubMedCrossRefGoogle Scholar
  36. 36.
    Khan S, Vihinen M (2010) Performance of protein stability predictors. Hum Mutat 31(6):675–684PubMedCrossRefGoogle Scholar
  37. 37.
    Zhang Z, Wang L, Gao Y, Zhang J, Zhenirovskyy M, Alexov E (2012) Predicting folding free energy changes upon single point mutations. Bioinformatics 28(5):664–671PubMedPubMedCentralCrossRefGoogle Scholar
  38. 38.
    Nishi H, Tyagi M, Teng S, Shoemaker BA, Hashimoto K, Alexov E, Wuchty S, Panchenko AR (2013) Cancer missense mutations alter binding properties of proteins and their interaction networks. PLoS One 8(6):e66273PubMedPubMedCentralCrossRefGoogle Scholar
  39. 39.
    Teng S, Madej T, Panchenko A, Alexov E (2009) Modeling effects of human single nucleotide polymorphisms on protein-protein interactions. Biophys J 96(6):2178–2188PubMedPubMedCentralCrossRefGoogle Scholar
  40. 40.
    Ghersi D, Singh M (2014) Interaction-based discovery of functionally important genes in cancers. Nucleic Acids Res 42(3):e18PubMedCrossRefGoogle Scholar
  41. 41.
    Li M, Petukh M, Alexov E, Panchenko AR (2014) Predicting the impact of missense mutations on protein-protein binding affinity. J Chem Theory Comput 10(4):1770–1780PubMedPubMedCentralCrossRefGoogle Scholar
  42. 42.
    Porollo A, Meller J (2007) Prediction-based fingerprints of protein–protein interactions. Proteins 66(3):630–645PubMedCrossRefGoogle Scholar
  43. 43.
    Qin S, Zhou H-X (2007) meta-PPISP: a meta web server for protein-protein interaction site prediction. Bioinformatics 23(24):3386–3387PubMedCrossRefGoogle Scholar
  44. 44.
    Zhou H-X, Qin S (2007) Interaction-site prediction for protein complexes: a critical assessment. Bioinformatics 23(17):2203–2209PubMedCrossRefGoogle Scholar
  45. 45.
    Porollo A, Meller J (2012) Computational methods for prediction of protein-protein interaction sites. Protein-Protein Interactions – Computational and Experimental Tools 472:3–26Google Scholar
  46. 46.
    Li M, Simonetti FL, Goncearenco A, Panchenko AR (2016) MutaBind estimates and interprets the effects of sequence variants on protein-protein interactions. Nucleic Acids Res. Jul 8;44(W1):W494-501.Google Scholar
  47. 47.
    Li M, Shoemaker BA, Thangudu RR, Ferraris JD, Burg MB, Panchenko AR (2013) Mutations in DNA-binding loop of NFAT5 transcription factor produce unique outcomes on protein-DNA binding and dynamics. J Phys Chem B 117(42):13226–13234PubMedPubMedCentralCrossRefGoogle Scholar
  48. 48.
    Case DA, Cheatham TE, Darden T, Gohlke H, Luo R, Merz KM, Onufriev A, Simmerling C, Wang B, Woods RJ (2005) The Amber biomolecular simulation programs. J Comput Chem 26(16):1668–1688PubMedPubMedCentralCrossRefGoogle Scholar
  49. 49.
    Stefl S, Nishi H, Petukh M, Panchenko AR, Alexov E (2013) Molecular mechanisms of disease-causing missense mutations. J Mol Biol 425(21):3919–3936PubMedPubMedCentralCrossRefGoogle Scholar
  50. 50.
    Mizuguchi K, Deane CM, Blundell TL, Johnson MS, Overington JP (1998) JOY: protein sequence-structure representation and analysis. Bioinformatics 14(7):617–623PubMedCrossRefGoogle Scholar
  51. 51.
    Tina KG, Bhadra R, Srinivasan N (2007) PIC: protein interactions calculator. Nucleic Acids Res 35(suppl 2):W473–W476PubMedPubMedCentralCrossRefGoogle Scholar
  52. 52.
    Stenson P, Mort M, Ball E, Shaw K, Phillips A, Cooper D (2014) The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine. Hum Genet 133(1):1–9PubMedCrossRefGoogle Scholar
  53. 53.
    Thorn CF, Klein TE, Altman RB (2010) Pharmacogenomics and bioinformatics: PharmGKB. Pharmacogenomics 11(4):501–505PubMedPubMedCentralCrossRefGoogle Scholar
  54. 54.
    Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E, Antipin Y, Reva B, Goldberg AP, Sander C, Schultz N (2012) The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2(5):401–404PubMedCrossRefGoogle Scholar
  55. 55.
    Peri S, Navarro JD, Amanchy R, Kristiansen TZ, Jonnalagadda CK, Surendranath V, Niranjan V, Muthusamy B, Gandhi TK, Gronborg M, Ibarrola N, Deshpande N, Shanker K, Shivashankar HN, Rashmi BP, Ramya MA, Zhao Z, Chandrika KN, Padma N, Harsha HC, Yatish AJ, Kavitha MP, Menezes M, Choudhury DR, Suresh S, Ghosh N, Saravana R, Chandran S, Krishna S, Joy M, Anand SK, Madavan V, Joseph A, Wong GW, Schiemann WP, Constantinescu SN, Huang L, Khosravi-Far R, Steen H, Tewari M, Ghaffari S, Blobe GC, Dang CV, Garcia JG, Pevsner J, Jensen ON, Roepstorff P, Deshpande KS, Chinnaiyan AM, Hamosh A, Chakravarti A, Pandey A (2003) Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Res 13(10):2363–2371PubMedPubMedCentralCrossRefGoogle Scholar
  56. 56.
    Kerrien S, Aranda B, Breuza L, Bridge A, Broackes-Carter F, Chen C, Duesbury M, Dumousseau M, Feuermann M, Hinz U, Jandrasits C, Jimenez RC, Khadake J, Mahadevan U, Masson P, Pedruzzi I, Pfeiffenberger E, Porras P, Raghunath A, Roechert B, Orchard S, Hermjakob H (2012) The IntAct molecular interaction database in 2012. Nucleic Acids Res 40(Database issue):D841–D846PubMedCrossRefGoogle Scholar
  57. 57.
    Joshi-Tope G, Gillespie M, Vastrik I, D'Eustachio P, Schmidt E, de Bono B, Jassal B, Gopinath GR, Wu GR, Matthews L, Lewis S, Birney E, Stein L (2005) Reactome: a knowledgebase of biological pathways. Nucleic Acids Res 33(Database issue):D428–D432PubMedCrossRefGoogle Scholar
  58. 58.
    Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S, Katayama T, Araki M, Hirakawa M (2006) From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res 34(Database issue):D354–D357PubMedCrossRefGoogle Scholar
  59. 59.
    Niknafs N, Kim D, Kim R, Diekhans M, Ryan M, Stenson PD, Cooper DN, Karchin R (2013) MuPIT interactive: webserver for mapping variant positions to annotated, interactive 3D structures. Hum Genet 132(11):1235–1243PubMedPubMedCentralCrossRefGoogle Scholar
  60. 60.
    Peterson TA, Adadey A, Santana-Cruz I, Sun Y, Winder A, Kann MG (2010) DMDM: domain mapping of disease mutations. Bioinformatics 26(19):2458–2459PubMedPubMedCentralCrossRefGoogle Scholar
  61. 61.
    Jegga AG, Gowrisankar S, Chen J, Aronow BJ (2007) PolyDoms: a whole genome database for the identification of non-synonymous coding SNPs with the potential to impact disease. Nucleic Acids Res 35(Database issue):D700–D706PubMedCrossRefGoogle Scholar
  62. 62.
    Ng PC, Henikoff S (2003) SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res 31(13):3812–3814PubMedPubMedCentralCrossRefGoogle Scholar
  63. 63.
    Yue P, Melamud E, Moult J (2006) SNPs3D: candidate gene and SNP selection for association studies. BMC Bioinformatics 7:166PubMedPubMedCentralCrossRefGoogle Scholar
  64. 64.
    Ferrer-Costa C, Gelpi JL, Zamakola L, Parraga I, de la Cruz X, Orozco M (2005) PMUT: a web-based tool for the annotation of pathological mutations on proteins. Bioinformatics 21(14):3176–3178PubMedCrossRefGoogle Scholar
  65. 65.
    Bromberg Y, Rost B (2007) SNAP: predict effect of non-synonymous polymorphisms on function. Nucleic Acids Res 35(11):3823–3835PubMedPubMedCentralCrossRefGoogle Scholar
  66. 66.
    Shihab HA, Gough J, Cooper DN, Stenson PD, Barker GL, Edwards KJ, Day IN, Gaunt TR (2013) Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Hum Mutat 34(1):57–65PubMedCrossRefGoogle Scholar
  67. 67.
    Reva B, Antipin Y, Sander C (2011) Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res 39(17):e118PubMedPubMedCentralCrossRefGoogle Scholar
  68. 68.
    Carter H, Chen S, Isik L, Tyekucheva S, Velculescu VE, Kinzler KW, Vogelstein B, Karchin R (2009) Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations. Cancer Res 69(16):6660–6667PubMedPubMedCentralCrossRefGoogle Scholar
  69. 69.
    Capriotti E, Calabrese R, Fariselli P, Martelli PL, Altman RB, Casadio R (2013) WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation. BMC Genomics 14(Suppl 3):S6PubMedPubMedCentralCrossRefGoogle Scholar
  70. 70.
    Wang M, Zhao XM, Takemoto K, Xu H, Li Y, Akutsu T, Song J (2012) FunSAV: predicting the functional effect of single amino acid variants using a two-stage random forest model. PLoS One 7(8):e43847PubMedPubMedCentralCrossRefGoogle Scholar
  71. 71.
    Bao L, Zhou M, Cui Y (2005) nsSNPAnalyzer: identifying disease-associated nonsynonymous single nucleotide polymorphisms. Nucleic Acids Res 33(Web Server Issue):W480–482PubMedPubMedCentralCrossRefGoogle Scholar
  72. 72.
    Mi H, Muruganujan A, Thomas PD (2013) PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees. Nucleic Acids Res 41(Database issue):D377–D386PubMedCrossRefGoogle Scholar
  73. 73.
    Capriotti E, Calabrese R, Casadio R (2006) Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. Bioinformatics 22(22):2729–2734PubMedCrossRefGoogle Scholar
  74. 74.
    Al-Numair NS, Martin AC (2013) The SAAP pipeline and database: tools to analyze the impact and predict the pathogenicity of mutations. BMC Genomics 14(Suppl 3):S4PubMedPubMedCentralGoogle Scholar
  75. 75.
    Yates CM, Filippis I, Kelley LA, Sternberg MJ (2014) SuSPect: enhanced prediction of single amino acid variant (SAV) phenotype using network features. J Mol Biol 426(14):2692–2701PubMedPubMedCentralCrossRefGoogle Scholar
  76. 76.
    Simonetti FL, Tornador C, Nabau-Moreto N, Molina-Vila MA, Marino-Buslje C (2014) Kin-Driver: a database of driver mutations in protein kinases. Database 2014:bau104.Google Scholar
  77. 77.
    McSkimming DI, Dastgheib S, Talevich E, Narayanan A, Katiyar S, Taylor SS, Kochut K, Kannan N (2015) ProKinO: a unified resource for mining the cancer kinome. Hum Mutat 36(2):175–186PubMedCrossRefGoogle Scholar
  78. 78.
    Guerois R, Nielsen JE, Serrano L (2002) Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. J Mol Biol 320(2):369–387PubMedCrossRefGoogle Scholar
  79. 79.
    Dehouck Y, Grosfils A, Folch B, Gilis D, Bogaerts P, Rooman M (2009) Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: PoPMuSiC-2.0. Bioinformatics 25(19):2537–2543PubMedCrossRefGoogle Scholar
  80. 80.
    Yin S, Ding F, Dokholyan NV (2007) Eris: an automated estimator of protein stability. Nat Methods 4(6):466–467PubMedCrossRefGoogle Scholar
  81. 81.
    Parthiban V, Gromiha MM, Schomburg D (2006) CUPSAT: prediction of protein stability upon point mutations. Nucleic Acids Res 34(Web Server Issue):W239–242PubMedPubMedCentralCrossRefGoogle Scholar
  82. 82.
    Potapov V, Cohen M, Inbar Y, Schreiber G (2010) Protein structure modelling and evaluation based on a 4-distance description of side-chain interactions. BMC Bioinformatics 11:374–374PubMedPubMedCentralCrossRefGoogle Scholar
  83. 83.
    Deutsch C, Krishnamoorthy B (2007) Four-body scoring function for mutagenesis. Bioinformatics 23(22):3009–3015PubMedCrossRefGoogle Scholar
  84. 84.
    Willard L, Ranjan A, Zhang H, Monzavi H, Boyko RF, Sykes BD, Wishart DS (2003) VADAR: a web server for quantitative evaluation of protein structure quality. Nucleic Acids Res 31(13):3316–3319PubMedPubMedCentralCrossRefGoogle Scholar
  85. 85.
    Giollo M, Martin AJ, Walsh I, Ferrari C, Tosatto SC (2014) NeEMO: a method using residue interaction networks to improve prediction of protein stability upon mutation. BMC Genomics 15(Suppl 4):S7PubMedPubMedCentralCrossRefGoogle Scholar
  86. 86.
    Pires DE, Ascher DB, Blundell TL (2014) DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach. Nucleic Acids Res 42(Web Server Issue):W314–319PubMedPubMedCentralCrossRefGoogle Scholar
  87. 87.
    Laimer J, Hofer H, Fritz M, Wegenkittl S, Lackner P (2015) MAESTRO – multi agent stability prediction upon point mutations. BMC Bioinformatics 16(1):116PubMedPubMedCentralCrossRefGoogle Scholar
  88. 88.
    Capriotti E, Fariselli P, Rossi I, Casadio R (2008) A three-state prediction of single point mutations on protein stability changes. BMC Bioinformatics 9(Suppl 2):S6PubMedPubMedCentralCrossRefGoogle Scholar
  89. 89.
    Cheng J, Randall A, Baldi P (2006) Prediction of protein stability changes for single-site mutations using support vector machines. Proteins 62(4):1125–1132PubMedCrossRefGoogle Scholar
  90. 90.
    Chen CW, Lin J, Chu YW (2013) iStable: off-the-shelf predictor integration for predicting protein stability changes. BMC Bioinformatics 14(Suppl 2):S5CrossRefGoogle Scholar
  91. 91.
    Teng S, Srivastava A, Wang L (2010) Sequence feature-based prediction of protein stability changes upon amino acid substitutions. BMC Genomics 11(Suppl 2):1–8CrossRefGoogle Scholar
  92. 92.
    Huang L-T, Gromiha MM, Ho S-Y (2007) iPTREE-STAB: interpretable decision tree based method for predicting protein stability changes upon mutations. Bioinformatics 23(10):1292–1293PubMedCrossRefGoogle Scholar
  93. 93.
    Dehouck Y, Kwasigroch JM, Rooman M, Gilis D (2013) BeAtMuSiC: prediction of changes in protein–protein binding affinity on mutations. Nucleic Acids Res 41(W1):W333–W339PubMedPubMedCentralCrossRefGoogle Scholar
  94. 94.
    Berliner N, Teyra J, Çolak R, Garcia Lopez S, Kim PM (2014) Combining structural modeling with ensemble machine learning to accurately predict protein fold stability and binding affinity effects upon mutation. PLoS One 9(9):e107353PubMedPubMedCentralCrossRefGoogle Scholar
  95. 95.
    Kruger DM, Gohlke H (2010) DrugScorePPI webserver: fast and accurate in silico alanine scanning for scoring protein-protein interactions. Nucleic Acids Res 38(Web Server Issue):W480–486PubMedPubMedCentralCrossRefGoogle Scholar
  96. 96.
    Zhao N, Han JG, Shyu CR, Korkin D (2014) Determining effects of non-synonymous SNPs on protein-protein interactions using supervised and semi-supervised learning. PLoS Comput Biol 10(5):e1003592PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Minghui Li
    • 1
  • Alexander Goncearenco
    • 1
  • Anna R. Panchenko
    • 1
    Email author
  1. 1.National Center for Biotechnology InformationNational Institutes of HealthBethesdaUSA

Personalised recommendations