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Analyzing effects of naturally occurring missense mutations. (English) Zbl 1239.92039
Summary: Single-point mutation in genomes, for example, single-nucleotide polymorphism (SNP) or rare genetic mutation, is the change of a single nucleotide for another in the genome sequence. Some of them will produce an amino acid substitution in the corresponding protein sequence (missense mutations); others will not. This paper focuses on genetic mutations resulting in a change in the amino acid sequence of the corresponding proteins and how to assess their effects on protein wild-type characteristics. The existing methods and approaches for predicting the effects of mutation on protein stability, structure, and dynamics are outlined and discussed with respect to their underlying principles. Available resources, either as stand-alone applications or webservers, are pointed out as well. It is emphasized that understanding the molecular mechanisms behind these effects due to these missense mutations is of critical importance for detecting disease-causing mutations. The paper provides several examples of the application of 3 D structure-based methods to model the effects of protein stability and protein-protein interactions caused by missense mutations as well.
MSC:
92C40 Biochemistry, molecular biology
92D15 Problems related to evolution
92-08 Computational methods for problems pertaining to biology
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[1] P. Taillon-Miller, Z. Gu, Q. Li, L. Hillier, and P. Y. Kwok, “Overlapping genomic sequences: a treasure trove of single-nucleotide polymorphisms,” Genome Research, vol. 8, no. 7, pp. 748-754, 1998.
[2] S. Mooney, “Bioinformatics approaches and resources for single nucleotide polymorphism functional analysis,” Briefings in Bioinformatics, vol. 6, no. 1, pp. 44-56, 2005.
[3] M. Hagmann, “Human genome. A good SNP may be hard to find,” Science, vol. 285, no. 5424, pp. 21-22, 1999.
[4] T. Strachan and A. P. Read, Human Molecular Genetics, 1999.
[5] Z. Zhang, S. Teng, L. Wang, C. E. Schwartz, and E. Alexov, “Computational analysis of missense mutations causing Snyder-Robinson syndrome,” Human Mutation, vol. 31, no. 9, pp. 1043-1049, 2010.
[6] Z. Zhang, J. Norris, C. Schwartz, and E. Alexov, “In silico and in vitro investigations of the mutability of disease-causing missense mutation sites in spermine synthase,” PLoS One, vol. 6, no. 5, Article ID e20373, 2011.
[7] S. Akhavan, M. A. Miteva, B. O. Villoutreix et al., “A critical role for Gly25 in the B chain of human thrombin,” Journal of Thrombosis and Haemostasis, vol. 3, no. 1, pp. 139-145, 2005.
[8] M. A. Miteva, J. M. Brugge, J. Rosing, G. A. F. Nicolaes, and B. O. Villoutreix, “Theoretical and experimental study of the D2194G mutation in the C2 domain of coagulation factor V,” Biophysical Journal, vol. 86, no. 1, part 1, pp. 488-498, 2004.
[9] M. Steen, M. Miteva, B. O. Villoutreix, T. Yamazaki, and B. Dahlback, “Factor V new brunswick: Ala221Val associated with FV deficiency reproduced in vitro and functionally characterized,” Blood, vol. 102, no. 4, pp. 1316-1322, 2003.
[10] S. Witham, K. Takano, C. Schwartz, and E. Alexov, “A missense mutation in CLIC2 associated with intellectual disability is predicted by in silico modeling to affect protein stability and dynamics,” Proteins, vol. 79, no. 8, pp. 2444-2454, 2011.
[11] S. Teng, T. Madej, A. Panchenko, and E. Alexov, “Modeling effects of human single nucleotide polymorphisms on protein-protein interactions,” Biophysical Journal, vol. 96, no. 6, pp. 2178-2188, 2009.
[12] C. M. Dobson, “Protein folding and misfolding,” Nature, vol. 426, no. 6968, pp. 884-890, 2003.
[13] R. N. Venkatesan, P. M. Treuting, E. D. Fuller et al., “Mutation at the polymerase active site of mouse DNA polymerase \delta increases genomic instability and accelerates tumorigenesis,” Molecular and Cellular Biology, vol. 27, no. 21, pp. 7669-7682, 2007.
[14] L. M. S. Elles and O. C. Uhlenbeck, “Mutation of the arginine finger in the active site of Escherichia coli DbpA abolishes ATPase and helicase activity and confers a dominant slow growth phenotype,” Nucleic Acids Research, vol. 36, no. 1, pp. 41-50, 2008.
[15] J. D. Wright and C. Lim, “Mechanism of DNA-binding loss upon single-point mutation in p53,” Journal of Biosciences, vol. 32, no. 5, pp. 827-839, 2007.
[16] L. G. Kwa, D. Wegmann, B. Brugger, F. T. Wieland, G. Wanner, and P. Braun, “Mutation of a single residue, \beta -glutamate-20, alters protein-lipid interactions of light harvesting complex II,” Molecular Microbiology, vol. 67, no. 1, pp. 63-77, 2008.
[17] Y. Kariya, Y. Tsubota, T. Hirosaki et al., “Differential regulation of cellular adhesion and migration by recombinant laminin-5 forms with partial deletion or mutation within the G3 domain of \alpha 3 chain,” Journal of Cellular Biochemistry, vol. 88, no. 3, pp. 506-520, 2003.
[18] S. Tiede, M. Cantz, J. Spranger, and T. Braulke, “Missense mutation in the N-acetylglucosamine-1-phosphotransferase gene (GNPTA) in a patient with mucolipidosis II induces changes in the size and cellular distribution of GNPTG,” Human Mutation, vol. 27, no. 8, pp. 830-831, 2006.
[19] M. Krumbholz, K. Koehler, and A. Huebner, “Cellular localization of 17 natural mutant variants of ALADIN protein in triple A syndrome-shedding light on an unexpected splice mutation,” Biochemistry and Cell Biology, vol. 84, no. 2, pp. 243-249, 2006.
[20] C. O. Hanemann, D. D’Urso, A. A. W. M. Gabreels-Festen, and H. W. Muller, “Mutation-dependent alteration in cellular distribution of peripheral myelin protein 22 in nerve biopsies from Charcot-Marie-Tooth type 1A,” Brain, vol. 123, no. 5, pp. 1001-1006, 2000.
[21] S. T. Sherry, M. H. Ward, M. Kholodov et al., “DbSNP: the NCBI database of genetic variation,” Nucleic Acids Research, vol. 29, no. 1, pp. 308-311, 2001.
[22] D. Fredman, G. Munns, D. Rios et al., “HGVbase: a curated resource describing human DNA variation and phenotype relationships,” Nucleic Acids Research, vol. 32, pp. D516-D519, 2004.
[23] N. O. Stitziel, T. A. Binkowski, Y. Y. Tseng, S. Kasif, and J. Liang, “topoSNP: a topographic database of non-synonymous single nucleotide polymorphisms with and without known disease association,” Nucleic Acids Research, vol. 32, no. Database issue, pp. D520-D522, 2004.
[24] A. Hamosh, A. F. Scott, J. Amberger, D. Valle, and V. A. McKusick, “Online mendelian inheritance in man (OMIM),” Human Mutation, vol. 15, no. 1, pp. 57-61, 2000.
[25] A. Hamosh, A. F. Scott, J. Amberger, C. Bocchini, D. Valle, and V. A. McKusick, “Onlined mendelian inheritance in man (OMIM), a knowledgebase of human genes and genetic disorders,” Nucleic Acids Research, vol. 30, no. 1, pp. 52-55, 2002.
[26] A. Hamosh, A. F. Scott, J. S. Amberger, C. A. Bocchini, and V. A. McKusick, “Online mendelian inheritance in man (OMIM), a knowledgebase of human genes and genetic disorders,” Nucleic Acids Research, vol. 33, pp. D514-D517, 2005. · Zbl 05437466
[27] S. Teng, E. Michonova-Alexova, and E. Alexov, “Approaches and resources for prediction of the effects of non-synonymous single nucleotide polymorphism on protein function and interactions,” Current Pharmaceutical Biotechnology, vol. 9, no. 2, pp. 123-133, 2008.
[28] E. W. Sayers, T. Barrett, D. A. Benson et al., “Database resources of the national center for biotechnology information,” Nucleic Acids Research, vol. 39, supplement 1, no. Database issue, pp. D38-D51, 2011. · Zbl 05892183
[29] E. M. Smigielski, K. Sirotkin, M. Ward, and S. T. Sherry, “dbSNP: a database of single nucleotide polymorphisms,” Nucleic Acids Research, vol. 28, no. 1, pp. 352-355, 2000.
[30] S. T. Sherry, M. Ward, and K. Sirotkin, “dbSNP-database for single nucleotide polymorphisms and other classes of minor genetic variation,” Genome Research, vol. 9, no. 8, pp. 677-679, 1999.
[31] D. N. Cooper, E. V. Ball, and M. Krawczak, “The human gene mutation database,” Nucleic Acids Research, vol. 26, no. 1, pp. 285-287, 1998. · Zbl 05435310
[32] N. O. Stitziel, Y. Y. Tseng, D. Pervouchine, D. Goddeau, S. Kasif, and J. Liang, “Structural location of disease-associated single-nucleotide polymorphisms,” Journal of Molecular Biology, vol. 327, no. 5, pp. 1021-1030, 2003.
[33] R. B. Altman, “PharmGKB: a logical home for knowledge relating genotype to drug response phenotype,” Nature Genetics, vol. 39, no. 4, article 426, 2007.
[34] B. R. Packer, M. Yeager, L. Burdett et al., “SNP500Cancer: a public resource for sequence validation, assay development, and frequency analysis for genetic variation in candidate genes,” Nucleic acids research., vol. 34, pp. D617-D621, 2006.
[35] J. Bidwell, L. Keen, G. Gallagher et al., “Cytokine gene polymorphism in human disease: on-line databases,” Genes and Immunity, vol. 1, no. 1, pp. 3-19, 1999.
[36] P. A. Bash, U. C. Singh, R. Langridge, and P. A. Kollman, “Free energy calculations by computer simulation,” Science, vol. 236, no. 4801, pp. 564-568, 1987.
[37] Y. Duan and P. A. Kollman, “Pathways to a protein folding intermediate observed in a 1-microsecond simulation in aqueous solution,” Science, vol. 282, no. 5389, pp. 740-744, 1998.
[38] S. D. Khare, M. Caplow, and N. V. Dokholyan, “FALS mutations in Cu, Zn superoxide dismutase destabilize the dimer and increase dimer dissociation propensity: a large-scale thermodynamic analysis,” Amyloid, vol. 13, no. 4, pp. 226-235, 2006.
[39] B. Kuhlman and D. Baker, “Native protein sequences are close to optimal for their structures,” Proceedings of the National Academy of Sciences of the United States of America, vol. 97, no. 19, pp. 10383-10388, 2000.
[40] C. Lee, “Testing homology modeling on mutant proteins: predicting structural and thermodynamic effects in the Ala\rightarrow Val mutants of T4 lysozyme,” Folding and Design, vol. 1, no. 1, pp. 1-12, 1996.
[41] C. Lee and M. Levitt, “Accurate prediction of the stability and activity effects of site-directed mutagenesis on a protein core,” Nature, vol. 352, no. 6334, pp. 448-451, 1991.
[42] S. Miyazawa and R. L. Jernigan, “Protein stability for single substitution mutants and the extent of local compactness in the denatured state,” Protein Engineering, vol. 7, no. 10, pp. 1209-1220, 1994.
[43] J. W. Pitera and P. A. Kollman, “Exhaustive mutagenesis in silico: multicoordinate free energy calculations on proteins and peptides,” Proteins, vol. 41, no. 3, pp. 385-397, 2000.
[44] M. Prevost, S. J. Wodak, B. Tidor, and M. Karplus, “Contribution of the hydrophobic effect to protein stability: analysis based on simulations of the Ile-96\rightarrow Ala mutation in barnase,” Proceedings of the National Academy of Sciences of the United States of America, vol. 88, no. 23, pp. 10880-10884, 1991.
[45] B. Tidor, “Simulation analysis of the stability mutant R96h of T4 lysozyme,” Biochemistry, vol. 30, no. 13, pp. 3217-3228, 1991.
[46] Y. N. Vorobjev and J. Hermans, “ES/IS: estimation of conformational free energy by combining dynamics simulations with explicit solvent with an implicit solvent continuum model,” Biophysical Chemistry, vol. 78, no. 1-2, pp. 195-205, 1999.
[47] A. Ben-Naim, “Statistical potentials extracted from protein structures: are these meaningful potentials?” Journal of Chemical Physics, vol. 107, no. 9, pp. 3698-3706, 1997.
[48] P. D. Thomas and K. A. Dill, “Statistical potentials extracted from protein structures: how accurate are they?” Journal of Molecular Biology, vol. 257, no. 2, pp. 457-469, 1996.
[49] D. Gilis and M. Rooman, “Stability changes upon mutation of solvent-accessible residues in proteins evaluated by database-derived potentials,” Journal of Molecular Biology, vol. 257, no. 5, pp. 1112-1126, 1996.
[50] D. Gilis and M. Rooman, “Predicting protein stability changes upon mutation using database-derived potentials: solvent accessibility determines the importance of local versus non-local interactions along the sequence,” Journal of Molecular Biology, vol. 272, no. 2, pp. 276-290, 1997.
[51] D. Gilis and M. Rooman, “PoPMuSiC, an algorithm for predicting protein mutant stability changes. Application to prion proteins,” Protein Engineering, vol. 13, no. 12, pp. 849-856, 2000.
[52] C. Hoppe and D. Schomburg, “Prediction of protein thermostability with a direction- and distance-dependent knowledge-based potential,” Protein Science, vol. 14, no. 10, pp. 2682-2692, 2005.
[53] M. Ota, Y. Isogai, and K. Nishikawa, “Knowledge-based potential defined for a rotamer library to design protein sequences,” Protein Engineering, vol. 14, no. 8, pp. 557-564, 2001.
[54] C. M. Topham, N. Srinivasan, and T. L. Blundell, “Prediction of the stability of protein mutants based on structural environment-dependent amino acid substitution and propensity tables,” Protein Engineering, vol. 10, no. 1, pp. 7-21, 1997.
[55] H. Zhou and Y. Zhou, “Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction,” Protein Science, vol. 11, no. 11, pp. 2714-2726, 2002.
[56] H. M. Berman, J. Westbrook, Z. Feng et al., “The protein data bank,” Nucleic Acids Research, vol. 28, no. 1, pp. 235-242, 2000.
[57] A. J. Bordner and R. A. Abagyan, “Large-scale prediction of protein geometry and stability changes for arbitrary single point mutations,” Proteins, vol. 57, no. 2, pp. 400-413, 2004.
[58] H. Domingues, J. Peters, K. H. Schneider et al., “Improving the refolding yield of interleukin-4 through the optimization of local interactions,” Journal of Biotechnology, vol. 84, no. 3, pp. 217-230, 2000.
[59] V. Munoz and L. Serrano, “Development of the multiple sequence approximation within the AGADIR Model of \alpha -helix formation: comparison with zimm-bragg and lifson-roig formalisms,” Biopolymers, vol. 41, no. 5, pp. 495-509, 1997.
[60] K. Takano, M. Ota, K. Ogasahara, Y. Yamagata, K. Nishikawa, and K. Yutani, “Experimental verification of the “stability profile of mutant protein” (SPMP) data using mutant human lysozymes,” Protein Engineering, vol. 12, no. 8, pp. 663-672, 1999.
[61] D. Verma, D. J. Jacobs, and D. R. Livesay, “Predicting the melting point of human C-type lysozyme mutants,” Current Protein and Peptide Science, vol. 11, no. 7, pp. 562-572, 2010.
[62] V. Villegas, A. R. Viguera, F. X. Aviles, and L. Serrano, “Stabilization of proteins by rational design of \alpha -helix stability using helix/coil transition theory,” Folding and Design, vol. 1, no. 1, pp. 29-34, 1996.
[63] J. Schymkowitz, J. Borg, F. Stricher, R. Nys, F. Rousseau, and L. Serrano, “The FoldX web server: an online force field,” Nucleic Acids Research, vol. 33, no. 2, pp. W382-W388, 2005. · Zbl 05437685
[64] R. Guerois, J. E. Nielsen, and L. Serrano, “Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations,” Journal of Molecular Biology, vol. 320, no. 2, pp. 369-387, 2002.
[65] Z. Zhang, L. Wang, Y. Gao, J. Zhang, M. Zhenirovskyy, and E. Alexov, “Predicting folding free energy changes upon single point mutations,” Bioinformatics, vol. 28, no. 5, pp. 664-671, 2012.
[66] E. Capriotti, P. Fariselli, and R. Casadio, “A neural-network-based method for predicting protein stability changes upon single point mutations,” Bioinformatics, vol. 20, supplement 1, pp. i63-i68, 2004.
[67] R. Casadio, M. Compiani, P. Fariselli, and F. Vivarelli, “Predicting free energy contributions to the conformational stability of folded proteins from the residue sequence with radial basis function networks,” Proceedings International Conference on Intelligent Systems for Molecular Biology, vol. 3, pp. 81-88, 1995.
[68] C. M. Frenz, “Neural network-based prediction of mutation-induced protein stability changes in staphylococcal nuclease at 20 residue positions,” Proteins, vol. 59, no. 2, pp. 147-151, 2005.
[69] T. Joachims, Learning to Classify Text Using Support Vector Machines, Springer, 2002.
[70] M. Masso and I. I. Vaisman, “Accurate prediction of stability changes in protein mutants by combining machine learning with structure based computational mutagenesis,” Bioinformatics, vol. 24, no. 18, pp. 2002-2009, 2008. · Zbl 05511798
[71] E. Capriotti, P. Fariselli, R. Calabrese, and R. Casadio, “Predicting protein stability changes from sequences using support vector machines,” Bioinformatics, vol. 21, supplement 2, pp. ii54-ii58, 2005.
[72] E. Capriotti, P. Fariselli, and R. Casadio, “I-mutant2.0: predicting stability changes upon mutation from the protein sequence or structure,” Nucleic Acids Research, vol. 33, no. 2, pp. W306-W310, 2005. · Zbl 05437636
[73] Y. Yamada, Y. Banno, H. Yoshida et al., “Catalytic inactivation of human phospholipase D2 by a naturally occurring Gly901Asp mutation,” Archives of Medical Research, vol. 37, no. 6, pp. 696-699, 2006.
[74] G. Stevanin, V. Hahn, E. Lohmann et al., “Mutation in the catalytic domain of protein kinase C \gamma and extension of the phenotype associated with spinocerebellar ataxia type 14,” Archives of Neurology, vol. 61, no. 8, pp. 1242-1248, 2004.
[75] O. Takamiya, M. Seta, K. Tanaka, and F. Ishida, “Human factor VII deficiency caused by S339C mutation located adjacent to the specificity pocket of the catalytic domain,” Clinical and Laboratory Haematology, vol. 24, no. 4, pp. 233-238, 2002.
[76] S. B. Koukouritaki, M. T. Poch, M. C. Henderson et al., “Identification and functional analysis of common human flavin-containing monooxygenase 3 genetic variants,” Journal of Pharmacology and Experimental Therapeutics, vol. 320, no. 1, pp. 266-273, 2007.
[77] R. de Cristofaro, A. Carotti, S. Akhavan et al., “The natural mutation by deletion of Lys9 in the thrombin A-chain affects the PKa value of catalytic residues, the overall enzyme’s stability and conformational transitions linked to Na+ binding,” The FEBS Journal, vol. 273, no. 1, pp. 159-169, 2006.
[78] E. Alexov, “Numerical calculations of the pH of maximal protein stability: the effect of the sequence composition and three-dimensional structure,” European Journal of Biochemistry, vol. 271, no. 1, pp. 173-185, 2004.
[79] H. Fujiwara, K. I. Tatsumi, S. Tanaka, M. Kimura, O. Nose, and N. Amino, “A novel V59E missense mutation in the sodium iodide symporter gene in a family with iodide transport defect,” Thyroid, vol. 10, no. 6, pp. 471-474, 2000.
[80] K. A. Dill, K. M. Fiebig, and H. S. Chan, “Cooperativity in protein-folding kinetics,” Proceedings of the National Academy of Sciences of the United States of America, vol. 90, no. 5, pp. 1942-1946, 1993.
[81] K. A. Dill, S. B. Ozkan, T. R. Weikl, J. D. Chodera, and V. A. Voelz, “The protein folding problem: when will it be solved?” Current Opinion in Structural Biology, vol. 17, no. 3, pp. 342-346, 2007.
[82] Y. Ye, Z. Li, and A. Godzik, “Modeling and analyzing three-dimensional structures of human disease proteins,” Pacific Symposium on Biocomputing, pp. 439-450, 2006.
[83] R. Karchin, M. Diekhans, L. Kelly et al., “LS-SNP: large-scale annotation of coding non-synonymous SNPs based on multiple information sources,” Bioinformatics, vol. 21, no. 12, pp. 2814-2820, 2005.
[84] Z. Wang and J. Moult, “SNPs, protein structure, and disease,” Human Mutation, vol. 17, no. 4, pp. 263-270, 2001.
[85] Z. Wang and J. Moult, “Three-dimensional structural location and molecular functional effects of missense SNPs in the T cell receptor V\beta domain,” Proteins, vol. 53, no. 3, pp. 748-757, 2003.
[86] V. Ramensky, P. Bork, and S. Sunyaev, “Human non-synonymous SNPs: server and survey,” Nucleic Acids Research, vol. 30, no. 17, pp. 3894-3900, 2002.
[87] H. Ode, S. Matsuyama, M. Hata et al., “Computational characterization of structural role of the non-active site mutation M36I of human immunodeficiency virus type 1 protease,” Journal of Molecular Biology, vol. 370, no. 3, pp. 598-607, 2007.
[88] B. A. Shirley, P. Stanssens, U. Hahn, and C. N. Pace, “Contribution of hydrogen bonding to the conformational stability of ribonuclease T1,” Biochemistry, vol. 31, no. 3, pp. 725-732, 1992.
[89] M. Karplus and J. Kuriyan, “Molecular dynamics and protein function,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 19, pp. 6679-6685, 2005.
[90] M. A. Young, S. Gonfloni, G. Superti-Furga, B. Roux, and J. Kuriyan, “Dynamic coupling between the SH2 and SH3 domains of c-Src and Hck underlies their inactivation by C-terminal tyrosine phosphorylation,” Cell, vol. 105, no. 1, pp. 115-126, 2001.
[91] K. E. S. Tang and K. A. Dill, “Native protein fluctuations: the conformational-motion temperature and the inverse correlation of protein flexibility with protein stability,” Journal of Biomolecular Structure and Dynamics, vol. 16, no. 2, pp. 397-411, 1998.
[92] E. S. Song, A. Daily, M. G. Fried, M. A. Juliano, L. Juliano, and L. B. Hersh, “Mutation of active site residues of insulin-degrading enzyme alters allosteric interactions,” The Journal of Biological Chemistry, vol. 280, no. 18, pp. 17701-17706, 2005.
[93] M. Valerio, A. Colosimo, F. Conti et al., “Early events in protein aggregation: molecular flexibility and hydrophobicity/charge interaction in amyloid peptides as studied by molecular dynamics simulations,” Proteins, vol. 58, no. 1, pp. 110-118, 2005.
[94] P. G. Board, K. Pierce, and M. Coggan, “Expression of functional coagulation factor XIII in Escherichia coli,” Thrombosis and Haemostasis, vol. 63, no. 2, pp. 235-240, 1990.
[95] S. E. A. Ozbabacan, A. Gursoy, O. Keskin, and R. Nussinov, “Conformational ensembles, signal transduction and residue hot spots: application to drug discovery,” Current Opinion in Drug Discovery and Development, vol. 13, no. 5, pp. 527-537, 2010.
[96] A. Dixit, A. Torkamani, N. J. Schork, and G. Verkhivker, “Computational modeling of structurally conserved cancer mutations in the RET and MET kinases: the impact on protein structure, dynamics, and stability,” Biophysical Journal, vol. 96, no. 3, pp. 858-874, 2009.
[97] R. Jones, M. Ruas, F. Gregory et al., “A CDKN2A mutation in familial melanoma that abrogates binding of p16 INK4a to CDK4 but not CDK6,” Cancer Research, vol. 67, no. 19, pp. 9134-9141, 2007.
[98] T. R. Rignall, J. O. Baker, S. L. McCarter et al., “Effect of single active-site cleft mutation on product specificity in a thermostable bacterial cellulase,” Applied Biochemistry and Biotechnology A, vol. 98-100, pp. 383-394, 2002.
[99] R. van Wijk, G. Rijksen, E. G. Huizinga, H. K. Nieuwenhuis, and W. W. van Solinge, “HK Utrecht: missense mutation in the active site of human hexokinase associated with hexokinase deficiency and severe nonspherocytic hemolytic anemia,” Blood, vol. 101, no. 1, pp. 345-347, 2003.
[100] M. Hardt and R. A. Laine, “Mutation of active site residues in the chitin-binding domain ChBDChiA1 from chitinase A1 of Bacillus circulans alters substrate specificity: use of a green fluorescent protein binding assay,” Archives of Biochemistry and Biophysics, vol. 426, no. 2, pp. 286-297, 2004.
[101] M. A. Ortiz, J. Light, R. A. Maki, and N. Assa-Munt, “Mutation analysis of the pip interaction domain reveals critical residues for protein-protein interactions,” Proceedings of the National Academy of Sciences of the United States of America, vol. 96, no. 6, pp. 2740-2745, 1999.
[102] E. Kim, K. L. Hyrc, J. Speck et al., “Missense mutations in Otopetrin 1 affect subcellular localization and inhibition of purinergic signaling in vestibular supporting cells,” Molecular and Cellular Neuroscience, vol. 46, no. 3, pp. 655-661, 2011.
[103] M. Castella, R. Pujol, E. Callen et al., “Origin, functional role, and clinical impact of fanconi anemia fanca mutations,” Blood, vol. 117, no. 14, pp. 3759-3769, 2011.
[104] A. Boulling, C. le Marechal, P. Trouve, O. Raguenes, J. M. Chen, and C. Ferec, “Functional analysis of pancreatitis-associated missense mutations in the pancreatic secretory trypsin inhibitor (SPINK1) gene,” European Journal of Human Genetics, vol. 15, no. 9, pp. 936-942, 2007.
[105] F. Niel-Butschi, B. Kantelip, J. Iwaszkiewicz et al., “Genotype-phenotype correlations of TGFBI p.Leu509Pro, p.Leu509Arg, p.Val613Gly, and the allelic association of p.Met502Val-p.Arg555Gln mutations,” Molecular Vision, vol. 17, pp. 1192-11202, 2011.
[106] B. J. Henriques, P. Bross, and C. M. Gomes, “Mutational hotspots in electron transfer flavoprotein underlie defective folding and function in multiple acyl-CoA dehydrogenase deficiency,” Biochimica et Biophysica Acta, vol. 1802, no. 11, pp. 1070-1077, 2010.
[107] S. Khan and M. Vihinen, “Performance of protein stability predictors,” Human Mutation, vol. 31, no. 6, pp. 675-684, 2010.
[108] R. Battiti, “Using mutual information for selecting features in supervised neural net learning,” IEEE Transactions on Neural Networks, vol. 5, no. 4, pp. 537-550, 1994.
[109] R. Karchin, L. Kelly, and A. Sali, “Improving functional annotation of non-synonomous SNPs with information theory,” Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, pp. 397-408, 2005.
[110] D. Chasman and R. M. Adams, “Predicting the functional consequences of non-synonymous single nucleotide polymorphisms: structure-based assessment of amino acid variation,” Journal of Molecular Biology, vol. 307, no. 2, pp. 683-706, 2001.
[111] P. Yue and J. Moult, “Identification and analysis of deleterious human SNPs,” Journal of Molecular Biology, vol. 356, no. 5, pp. 1263-1274, 2006.
[112] S. Yin, F. Ding, and N. V. Dokholyan, “Eris: an automated estimator of protein stability,” Nature Methods, vol. 4, no. 6, pp. 466-467, 2007.
[113] J. Cai, L. Q. Cai, Y. Hong, and Y. S. Zhu, “Functional characterisation of a natural androgen receptor missense mutation (N771H) causing human androgen insensitivity syndrome,” submitted to Andrologia.
[114] D. M. Hunt, J. W. Saldanha, J. F. Brennan et al., “Single nucleotide polymorphisms that cause structural changes in the cyclic AMP receptor protein transcriptional regulator of the tuberculosis vaccine strain Mycobacterium bovis BCG alter global gene expression without attenuating growth,” Infection and Immunity, vol. 76, no. 5, pp. 2227-2234, 2008.
[115] P. Nicolao, M. Carella, B. Giometto et al., “Missense polymorphism in the human carboxypeptidase E gene alters enzymatic activity,” Human Mutation, vol. 18, no. 2, pp. 120-131, 2001.
[116] P. A. Kollman, I. Massova, C. Reyes et al., “Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models,” Accounts of Chemical Research, vol. 33, no. 12, pp. 889-897, 2000.
[117] A. Cavallo and A. C. R. Martin, “Mapping SNPs to protein sequence and structure data,” Bioinformatics, vol. 21, no. 8, pp. 1443-1450, 2005.
[118] P. C. Ng and S. Henikoff, “SIFT: predicting amino acid changes that affect protein function,” Nucleic Acids Research, vol. 31, no. 13, pp. 3812-3814, 2003. · Zbl 05434816
[119] L. Bao and Y. Cui, “Prediction of the phenotypic effects of non-synonymous single nucleotide polymorphisms using structural and evolutionary information,” Bioinformatics, vol. 21, no. 10, pp. 2185-2190, 2005.
[120] C. T. Saunders and D. Baker, “Evaluation of structural and evolutionary contributions to deleterious mutation prediction,” Journal of Molecular Biology, vol. 322, no. 4, pp. 891-901, 2002.
[121] S. Sunyaev, V. Ramensky, and P. Bork, “Towards a structural basis of human non-synonymous single nucleotide polymorphisms,” Trends in Genetics, vol. 16, no. 5, pp. 198-200, 2000.
[122] S. R. Sunyaev, W. C. Lathe III, V. E. Ramensky, and P. Bork, “SNP frequencies in human genes: an excess of rare alleles and differing modes of selection,” Trends in Genetics, vol. 16, no. 8, pp. 335-337, 2000.
[123] S. Sunyaev, V. Ramensky, I. Koch, W. Lathe III, A. S. Kondrashov, and P. Bork, “Prediction of deleterious human alleles,” Human Molecular Genetics, vol. 10, no. 6, pp. 591-597, 2001.
[124] M. W. Dimmic, S. Sunyaev, and C. D. Bustamante, “Inferring SNP function using evolutionary, structural, and computational methods,” Pacific Symposium on Biocomputing, pp. 382-384, 2005.
[125] R. Landgraf, I. Xenarios, and D. Eisenberg, “Three-dimensional cluster analysis identifies interfaces and functional residue clusters in proteins,” Journal of Molecular Biology, vol. 307, no. 5, pp. 1487-1502, 2001.
[126] O. Lichtarge and M. E. Sowa, “Evolutionary predictions of binding surfaces and interactions,” Current Opinion in Structural Biology, vol. 12, no. 1, pp. 21-27, 2002.
[127] C. A. Innis, J. Shi, and T. L. Blundell, “Evolutionary trace analysis of TGF-\beta and related growth factors: implications for site-directed mutagenesis,” Protein Engineering, vol. 13, no. 12, pp. 839-847, 2000.
[128] O. Lichtarge, H. R. Bourne, and F. E. Cohen, “An evolutionary trace method defines binding surfaces common to protein families,” Journal of Molecular Biology, vol. 257, no. 2, pp. 342-358, 1996.
[129] C. T. Porter, G. J. Bartlett, and J. M. Thornton, “The catalytic site atlas: a resource of catalytic sites and residues identified in enzymes using structural data,” Nucleic Acids Research, vol. 32, pp. D129-D133, 2004.
[130] V. Chelliah, L. Chen, T. L. Blundell, and S. C. Lovell, “Distinguishing structural and functional restraints in evolution in order to identify interaction sites,” Journal of Molecular Biology, vol. 342, no. 5, pp. 1487-1504, 2004.
[131] F. Pazos and M. J. E. Sternberg, “Automated prediction of protein function and detection of functional sites from structure,” Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. 41, pp. 14754-14759, 2004.
[132] F. Ding and N. V. Dokholyan, “Emergence of protein fold families through rational design,” PLoS Computational Biology, vol. 2, no. 7, pp. 0725-0733, 2006.
[133] S. Yin, F. Ding, and N. V. Dokholyan, “Modeling backbone flexibility improves protein stability estimation,” Structure, vol. 15, no. 12, pp. 1567-1576, 2007.
[134] E. Capriotti, P. Fariselli, I. Rossi, and R. Casadio, “A three-state prediction of single point mutations on protein stability changes,” BMC Bioinformatics, vol. 9, supplement 2, article S6, 2008.
[135] J. L. Cheng, A. Randall, and P. Baldi, “Prediction of protein stability changes for single-site mutations using support vector machines,” Proteins, vol. 62, no. 4, pp. 1125-1132, 2006.
[136] V. Alva, D. P. Syamala Devi, and R. Sowdhamini, “COILCHECK: an interactive server for the analysis of interface regions in coiled coils,” Protein and Peptide Letters, vol. 15, no. 1, pp. 33-38, 2008.
[137] D. M. Kruger and H. Gohlke, “DrugScorePPI webserver: fast and accurate in silico alanine scanning for scoring protein-protein interactions,” Nucleic Acids Research, vol. 38, supplement 2, pp. W480-W486, 2010. · Zbl 05892115
[138] P. Yue, E. Melamud, and J. Moult, “SNPs3D: candidate gene and SNP selection for association studies,” BMC Bioinformatics, vol. 7, article no. 166, 2006.
[139] G. de Baets, J. van Durme, J. Reumers et al., “Snpeffect 4.0: on-line prediction of molecular and structural effects of protein-coding variants,” Nucleic Acids Research, vol. 40, pp. D935-D939, 2012, submitted to Nucleic Acids Research.
[140] A. M. Fernandez-Escamilla, F. Rousseau, J. Schymkowitz, and L. Serrano, “Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins,” Nature Biotechnology, vol. 22, no. 10, pp. 1302-1306, 2004.
[141] S. Maurer-Stroh, M. Debulpaep, N. Kuemmerer et al., “Exploring the sequence determinants of amyloid structure using position-specific scoring matrices,” Nature Methods, vol. 7, no. 3, pp. 237-242, 2010.
[142] J. van Durme, S. Maurer-Stroh, R. Gallardo, H. Wilkinson, F. Rousseau, and J. Schymkowitz, “Accurate prediction of DnaK-peptide binding via homology modelling and experimental data,” PLoS Computational Biology, vol. 5, no. 8, Article ID e1000475, 2009.
[143] P. Kumar, S. Henikoff, and P. C. Ng, “Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm,” Nature Protocols, vol. 4, no. 7, pp. 1073-1082, 2009.
[144] I. A. Adzhubei, S. Schmidt, L. Peshkin et al., “A method and server for predicting damaging missense mutations,” Nature Methods, vol. 7, no. 4, pp. 248-249, 2010.
[145] J. Tian, N. Wu, X. Guo, J. Guo, J. Zhang, and Y. Fan, “Predicting the phenotypic effects of non-synonymous single nucleotide polymorphisms based on support vector machines,” BMC Bioinformatics, vol. 8, article 450, 2007. · Zbl 05326145
[146] A. Uzun, C. M. Leslin, A. Abyzov, and V. Ilyin, “Structure SNP (StSNP): a web server for mapping and modeling nsSNPs on protein structures with linkage to metabolic pathways,” Nucleic Acids Research, vol. 35, pp. W384-392, 2007. · Zbl 05438339
[147] M. Masso and I. I. Vaisman, “Knowledge-based computational mutagenesis for predicting the disease potential of human non-synonymous single nucleotide polymorphisms,” Journal of Theoretical Biology, vol. 266, no. 4, pp. 560-568, 2010.
[148] B. R. Brooks, C. L. Brooks III, A. D. Mackerell Jr. et al., “CHARMM: the biomolecular simulation program,” Journal of Computational Chemistry, vol. 30, no. 10, pp. 1545-1614, 2009. · Zbl 05745476
[149] A. L. Cason, Y. Ikeguchi, C. Skinner et al., “X-linked spermine synthase gene (SMS) defect: the first polyamine deficiency syndrome,” European Journal of Human Genetics, vol. 11, no. 12, pp. 937-944, 2003.
[150] G. de Alencastro, D. E. McCloskey, S. E. Kliemann et al., “New SMS mutation leads to a striking reduction in spermine synthase protein function and a severe form of Snyder-Robinson X-linked recessive mental retardation syndrome,” Journal of Medical Genetics, vol. 45, no. 8, pp. 539-543, 2008.
[151] L. E. Becerra-Solano, J. Butler, G. Castaneda-Cisneros et al., “A missense mutation, p.V132G, in the X-linked spermine synthase gene (SMS) causes Snyder-Robinson syndrome,” American Journal of Medical Genetics A, vol. 149, no. 3, pp. 328-335, 2009.
[152] E. W. Gerner and F. L. Meyskens Jr., “Polyamines and cancer: old molecules, new understanding,” Nature Reviews Cancer, vol. 4, no. 10, pp. 781-792, 2004.
[153] Y. Ikeguchi, M. C. Bewley, and A. E. Pegg, “Aminopropyltransferases: function, structure and genetics,” Journal of Biochemistry, vol. 139, no. 1, pp. 1-9, 2006.
[154] A. E. Pegg, “Mammalian polyamine metabolism and function,” IUBMB Life, vol. 61, no. 9, pp. 880-894, 2009.
[155] D. Geerts, J. Koster, D. Albert et al., “The polyamine metabolism genes ornithine decarboxylase and antizyme 2 predict aggressive behavior in neuroblastomas with and without MYCN amplification,” International Journal of Cancer, vol. 126, no. 9, pp. 2012-2024, 2010.
[156] H. Wu, J. Min, H. Zeng et al., “Crystal structure of human spermine synthase: implications of substrate binding and catalytic mechanism,” The Journal of Biological Chemistry, vol. 283, no. 23, pp. 16135-16146, 2008.
[157] Z. Xiang and B. Honig, “Extending the accuracy limits of prediction for side-chain conformations,” Journal of Molecular Biology, vol. 311, no. 2, pp. 421-430, 2001.
[158] J. W. Ponder, Tinker-Software Tools for Molecular Design, 1999.
[159] V. Rajendran, R. Purohit, and R. Sethumadhavan, “In silico investigation of molecular mechanism of laminopathy caused by a point mutation (R482W) in lamin A/C protein,” submitted to Amino Acids.
[160] D. van der Spoel, E. Lindahl, B. Hess, G. Groenhof, A. E. Mark, and H. J. C. Berendsen, “GROMACS: fast, flexible, and free,” Journal of Computational Chemistry, vol. 26, no. 16, pp. 1701-1718, 2005. · Zbl 05429671
[161] C. Minutolo, A. D. Nadra, C. Fernandez et al., “Structure-based analysis of five novel disease-causing mutations in 21-hydroxylase-deficient patients,” PLoS One, vol. 6, no. 1, Article ID e15899, 2011.
[162] Y. Tan and R. Luo, “Structural and functional implications of p53 missense cancer mutations,” PMC Biophysics, vol. 2, no. 1, article 5, 2009.
[163] W. H. Lee, A. Raas-Rotschild, M. A. Miteva et al., “Noonan syndrome type I with PTPN11 3 bp deletion: structure-function implications,” Proteins, vol. 58, no. 1, pp. 7-13, 2005.
[164] R. Abagyan and M. Totrov, “Biased probability Monte Carlo conformational searches and electrostatic calculations for peptides and proteins,” Journal of Molecular Biology, vol. 235, no. 3, pp. 983-1002, 1994.
[165] W. Rocchia, E. Alexov, and B. Honig, “Extending the applicability of the nonlinear Poisson-Boltzmann equation: multiple dielectric constants and multivalent ions,” Journal of Physical Chemistry B, vol. 105, no. 28, pp. 6507-6514, 2001.
[166] E. G. Alexov and M. R. Gunner, “Calculated protein and proton motions coupled to electron transfer: electron transfer from QA- to QB in bacterial photosynthetic reaction centers,” Biochemistry, vol. 38, no. 26, pp. 8253-8270, 1999.
[167] R. E. Georgescu, E. G. Alexov, and M. R. Gunner, “Combining conformational flexibility and continuum electrostatics for calculating PKas in proteins,” Biophysical Journal, vol. 83, no. 4, pp. 1731-1748, 2002.
[168] Y. Song, J. Mao, and M. R. Gunner, “MCCE2: improving protein PKa calculations with extensive side chain rotamer sampling,” Journal of Computational Chemistry, vol. 30, no. 14, pp. 2231-2247, 2009. · Zbl 05745536
[169] B. M. Tynan-Connolly and J. E. Nielsen, “pKD: re-designing protein PKa values,” Nucleic Acids Research, vol. 34, pp. W48-W51, 2006. · Zbl 05437879
[170] Q. Wei, L. Wang, Q. Wang, W. D. Kruger, and R. L. Dunbrack Jr., “Testing computational prediction of missense mutation phenotypes: functional characterization of 204 mutations of human cystathionine beta synthase,” Proteins, vol. 78, no. 9, pp. 2058-2074, 2010.
[171] R. Rajasekaran and R. Sethumadhavan, “Exploring the cause of drug resistance by the detrimental missense mutations in KIT receptor: computational approach,” Amino Acids, vol. 39, no. 3, pp. 651-660, 2010.
[172] S. M. Abdur Rauf, M. Ismael, K. K. Sahu et al., “A graph theoretical approach to the effect of mutation on the flexibility of the DNA binding domain of p53 protein,” Chemical Papers, vol. 63, no. 6, pp. 654-661, 2009.
[173] T. M. K. Cheng, Y. E. Lu, M. Vendruscolo, P. Lio, and T. L. Blundell, “Prediction by graph theoretic measures of structural effects in proteins arising from non-synonymous single nucleotide polymorphisms,” PLoS Computational Biology, vol. 4, no. 7, Article ID e1000135, 2008.
[174] S. Danielian, J. El-Hakeh, G. Basilico et al., “Bruton tyrosine kinase gene mutations in Argentina,” Human Mutation, vol. 21, no. 4, p. 451, 2003.
[175] H. Iqbal, A. Mir, and R. Faryal, “Molecular modeling of mutant kinase domain of Btk, a Tec family member, for structure prediction,” African Journal of Biotechnology, vol. 10, no. 17, pp. 3274-3289, 2011.
[176] J. Leandro, N. Simonsen, J. Saraste, P. Leandro, and T. Flatmark, “Phenylketonuria as a protein misfolding disease: the mutation pG46S in phenylalanine hydroxylase promotes self-association and fibril formation,” Biochimica et Biophysica Acta, vol. 1812, no. 1, pp. 106-120, 2011.
[177] Y. Yang and Y. Zhou, “Specific interactions for ab initio folding of protein terminal regions with secondary structures,” Proteins, vol. 72, no. 2, pp. 793-803, 2008.
[178] Y. Yang and Y. Zhou, “Ab initio folding of terminal segments with secondary structures reveals the fine difference between two closely related all-atom statistical energy functions,” Protein Science, vol. 17, no. 7, pp. 1212-1219, 2008.
[179] Y. Dehouck, A. Grosfils, B. Folch, D. Gilis, P. Bogaerts, and M. Rooman, “Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: PoPMuSiC-2.0,” Bioinformatics, vol. 25, no. 19, pp. 2537-2543, 2009. · Zbl 05744236
[180] Y. Dehouck, J. M. Kwasigroch, D. Gilis, and M. Rooman, “PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality,” BMC Bioinformatics, vol. 12, article 151, 2011. · Zbl 05921002
[181] V. Parthiban, M. M. Gromiha, and D. Schomburg, “CUPSAT: prediction of protein stability upon point mutations,” Nucleic Acids Research, vol. 34, pp. W239-W242, 2006. · Zbl 05437987
[182] V. Parthiban, M. M. Gromiha, C. Hoppe, and D. Schomburg, “Structural analysis and prediction of protein mutant stability using distance and torsion potentials: role of secondary structure and solvent accessibility,” Proteins, vol. 66, no. 1, pp. 41-52, 2007.
[183] V. Parthiban, M. M. Gromiha, M. Abhinandan, and D. Schomburg, “Computational modeling of protein mutant stability: analysis and optimization of statistical potentials and structural features reveal insights into prediction model development,” BMC Structural Biology, vol. 7, article 54, 2007.
[184] G. Wainreb, L. Wolf, H. Ashkenazy, Y. Dehouck, and N. Ben-Tal, “Protein stability: a single recorded mutation aids in predicting the effects of other mutations in the same amino acid site,” Bioinformatics, vol. 27, no. 23, pp. 3286-3292, 2011.
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