Computational and experimental approaches to reveal the effects of single nucleotide polymorphisms with respect to disease diagnostics

International Journal of Molecular Sciences

Volumn 15, Issue 6, 2014, Pages 9670-9717

  Kucukkal, Tugba G ^a   Yang, Ye ^a   Chapman, Susan C ^a   Cao, Weiguo ^a   Alexov, Emil ^a  

^a CLEMSON UNIVERSITY   (United States)

Author keywords

Disease diagnostics; Missense mutations; Pathogenic mutation; Protein interactions; Protein stability; Single nucleotide polymorphism (SNP)

Indexed keywords

ADENOSINE TRIPHOSPHATE; HISTONE DEACETYLASE 1; MESSENGER RNA; METHYL CPG BINDING PROTEIN 2; MICRORNA; NUCLEASE; TRANSCRIPTION FACTOR SIN3A; DNA;

COMPUTER MODEL; EVOLUTION; GENE; GENE EDITING; GENE SEQUENCE; GENOTYPE; HIGH THROUGHPUT SCREENING; HUMAN; LYSINE SPECIFIC DEMETHYLASE 5C GENE; MACHINE LEARNING; METHYL CPG BINDING PROTEIN 2 GENE; MISSENSE MUTATION; MOLECULAR DIAGNOSIS; MOLECULAR DYNAMICS; NONHUMAN; PROTEIN AGGREGATION; PROTEIN INTERACTION; PROTEIN LOCALIZATION; RANDOM AMPLIFIED POLYMORPHIC DNA; RESTRICTION FRAGMENT LENGTH POLYMORPHISM; REVIEW; RNA EDITING; SINGLE NUCLEOTIDE POLYMORPHISM; ZEBRA FISH; ANIMAL; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DNA SEQUENCE; GENETIC PREDISPOSITION; GENETIC SCREENING; GENETICS; GENOTYPING TECHNIQUE; MUTATION; PROCEDURES; PROTEIN STABILITY;

ANIMALS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DNA; GENETIC PREDISPOSITION TO DISEASE; GENETIC TESTING; GENOTYPING TECHNIQUES; HUMANS; MUTATION; POLYMORPHISM, SINGLE NUCLEOTIDE; PROTEIN STABILITY; SEQUENCE ANALYSIS, DNA;

EID: 84901771117     PISSN: 16616596     EISSN: 14220067     Source Type: Journal    
DOI: 10.3390/ijms15069670     Document Type: Review

References (268)

1. Potapov, V.; Cohen, M.; Schreiber, G. Assessing computational methods for predicting protein stability upon mutation: good on average but not in the details. Protein Eng. Des. Sel. 2009, 22, 553-560.
2. Thusberg, J.; Vihinen, M. Pathogenic or Not? And if so, then how? Studying the effects of missense mutations using bioinformatics methods. Hum. Mutat. 2009, 30, 703-714.
3. Gonzalez-Castejon, M.; Marin, F.; Soler-Rivas, C.; Reglero, G.; Visioli, F.; Rodriguez-Casado, A. Functional non-synonymous polymorphisms prediction methods: Current approaches and future developments. Curr. Med. Chem. 2011, 18, 5095-5103.
4. Thiltgen, G.; Goldstein, R.A. Assessing predictors of changes in protein stability upon mutation using self-consistency. PLoS One 2012, 7, e46084.
5. Zhang, Z.; Miteva, M.A.; Wang, L.; Alexov, E. Analyzing effects of naturally occurring missense mutations. Comput. Math. Methods Med. 2012, 2012, 805827:1-805827:15.
6. Stefl, S.; Nishi, H.; Petukh, M.; Panchenko, A.R.; Alexov, E. Molecular mechanisms of disease-causing missense mutations. J. Mol. Biol. 2013, 425, 3919-3936.
7. Peterson, T.A.; Doughty, E.; Kann, M.G. Towards Precision Medicine: Advances in computational approaches for the analysis of human variants. J. Mol. Biol. 2013, 425, 4047-4063.
8. Chang, C.C.H.; Tey, B.T.; Song, J.; Ramanan, R.N. Towards more accurate prediction of protein folding rates: A review of the existing web-based bioinformatics approaches. Brief. Bioinform. 2014, doi:10.1093/bib/bbu007.
9. Sander, C.; Schneider, R. Database of homology-derived protein structures and the structural meaning of sequence alignment. Proteins 1991, 9, 56-68.
10. Rost, B. Twilight zone of protein sequence alignments. Protein Eng. 1999, 12, 85-94.
11. Ng, P.C.; Henikoff, S. Predicting deleterious amino acid substitutions. Genome Res. 2001, 11, 863-874.
12. Ng, P.C.; Henikoff, S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003, 31, 3812-3814.
13. De Baets, G.; van Durme, J.; Reumers, J.; Maurer-Stroh, S.; Vanhee, P.; Dopazo, J.; Schymkowitz, J.; Rousseau, F. SNPeffect 4.0: On-line prediction of molecular and structural effects of protein-coding variants. Nucleic Acids Res. 2012, 40, D935-D939.
14. Yue, P.; Moult, J. Identification and analysis of deleterious human SNPs. J. Mol. Biol. 2006, 356, 1263-1274.
15. Tavtigian, S.V.; Deffenbaugh, A.M.; Yin, L.; Judkins, T.; Scholl, T.; Samollow, P.B.; de Silva, D.; Zharkikh, A.; Thomas, A. Comprehensive statistical study of 452 BRCA1 missense substitutions with classification of eight recurrent substitutions as neutral. J. Med. Genet. 2006, 43, 295-305.
16. Reva, B.; Antipin, Y.; Sander, C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 2011, 39, E118.
17. Stone, E.A.; Sidow, A. Physicochemical constraint violation by missense substitutions mediates impairment of protein function and disease severity. Genome Res. 2005, 15, 978-986.
18. Adzhubei, I.A.; Schmidt, S.; Peshkin, L.; Ramensky, V.E.; Gerasimova, A.; Bork, P.; Kondrashov, A.S.; Sunyaev, S.R. A method and server for predicting damaging missense mutations. Nat. Methods 2010, 7, 248-249.
19. Schwarz, J.M.; Roedelsperger, C.; Schuelke, M.; Seelow, D. MutationTaster evaluates disease-causing potential of sequence alterations. Nat. Methods 2010, 7, 575-576.
20. Ryan, M.; Diekhans, M.; Lien, S.; Liu, Y.; Karchin, R. LS-SNP/PDB: annotated non-synonymous SNPs mapped to Protein Data Bank structures. Bioinformatics 2009, 25, 1431-1432.
21. Teng, S.; Srivastava, A.K.; Wang, L. Sequence feature-based prediction of protein stability changes upon amino acid substitutions. BMC Genomics 2010, 11, S5.
22. Teng, S.; Srivastava, A.K.; Wang, L. Biological features for sequence-based prediction of protein stability changes upon amino acid substitutions. In Proceedings of the 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, Zhang, J., Li, G.Z., Yang, J.Y., Eds.; IEEE Computer Society: Washington, DC, USA, 2009; pp. 201-206.
23. Cheng, J.L.; Randall, A.; Baldi, P. Prediction of protein stability changes for single-site mutations using support vector machines. Proteins 2006, 62, 1125-1132.
24. Li, B.; Krishnan, V.G.; Mort, M.E.; Xin, F.; Kamati, K.K.; Cooper, D.N.; Mooney, S.D.; Radivojac, P. Automated inference of molecular mechanisms of disease from amino acid substitutions. Bioinformatics 2009, 25, 2744-2750.
25. Schaefer, C.; Meier, A.; Rost, B.; Bromberg, Y. SNPdbe: Constructing an nsSNP functional impacts database. Bioinformatics 2012, 28, 601-602.
26. Johansen, M.B.; Izarzugaza, J.M.G.; Brunak, S.; Petersen, T.N.; Gupta, R. Prediction of disease causing non-synonymous SNPs by the Artificial Neural Network Predictor NetDiseaseSNP. PLoS One 2013, 8, e68370.
27. Venselaar, H.; Te Beek, T.A.; Kuipers, R.K.; Hekkelman, M.L.; Vriend, G. Protein structure analysis of mutations causing inheritable diseases. An e-Science approach with life scientist friendly interfaces. BMC Bioinform. 2010, 11, 548.
28. Yue, P.; Melamud, E.; Moult, J. SNPs3D: Candidate gene and SNP selection for association studies. BMC Bioinform. 2006, 7, 166.
29. Noble, W.S. What is a support vector machine? Nat. Biotechnol. 2006, 24, 1565-1567.
30. Joachim, T. Making large-scale SVM learning practical. In Advances in Kernel Methods-Support Vector Learning; Schölkopf, B., Burges, C., Smola, A., Eds.; MIT Press: Cambridge, MA, USA, 1999.
31. Capriotti, E.; Fariselli, P.; Casadio, R. A neural-network-based method for predicting protein stability changes upon single point mutations. Bioinformatics 2004, 20, 63-68.
32. Capriotti, E.; Fariselli, P.; Casadio, R. I-Mutant2.0: Predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res. 2005, 33, W306-W310.
33. 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, W382-W388.
34. Schymkowitz, J.W.H.; Rousseau, F.; Martins, I.C.; Ferkinghoff-Borg, J.; Stricher, F.; Serrano, L. Prediction of water and metal binding sites and their affinities by using the Fold-X force field. Proc. Natl. Acad. Sci. USA 2005, 102, 10147-10152.
35. Yang, Y.; Zhou, Y. Specific interactions for ab initio folding of protein terminal regions with secondary structures. Proteins 2008, 72, 793-803.
36. Yang, Y.; Zhou, Y. Ab initio folding of terminal segments with secondary structures reveals the fine difference between two closely related all-atom statistical energy functions. Protein Sci. 2008, 17, 1212-1219.
37. Dehouck, Y.; Grosfils, A.; Folch, B.; Gilis, D.; Bogaerts, P.; Rooman, M. Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: PoPMuSiC-2.0. Bioinformatics 2009, 25, 2537-2543.
38. Dehouck, Y.; Kwasigroch, J.M.; Gilis, D.; Rooman, M. PoPMuSiC 2.1: A web server for the estimation of protein stability changes upon mutation and sequence optimality. BMC Bioinform. 2011, 12, 151.
39. Ozen, A.; Gonen, M.; Alpaydin, E.; Haliloglu, T. Machine learning integration for predicting the effect of single amino acid substitutions on protein stability. BMC Struct. Biol. 2009, 9, 66.
40. Folkman, L.; Stantic, B.; Sattar, A. Sequence-only evolutionary and predicted structural features for the prediction of stability changes in protein mutants. BMC Bioinform. 2013, 14, S6.
41. Folkman, L.; Stantic, B.; Sattar, A. Towards sequence-based prediction of mutation-induced stability changes in unseen non-homologous proteins. BMC Genomics 2014, 15, S4.
42. Bhasin, M.; Raghava, G.P.S. ESLpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSI-BLAST. Nucleic Acids Res. 2004, 32, W414-W419.
43. Cai, C.Z.; Han, L.Y.; Ji, Z.L.; Chen, X.; Chen, Y.Z. SVM-Prot: Web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Res. 2003, 31, 3692-3697.
44. Rangwala, H.; Kauffman, C.; Karypis, G. svmPRAT: SVM-based protein residue annotation toolkit. BMC Bioinform. 2009, 10, 439.
45. Rangwala, H.; Kauffman, C.; Karypis, G. A kernel framework for protein residue annotation. In Advances in Knowledge Discovery and Data Mining, Proceedings; Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.B., Eds.; Springer-Verlag: Berlin/Heidelberg, Germany, 2009; Volume 5476, pp. 439-451.
46. Masso, M.; Vaisman, I.I. AUTO-MUTE: Web-based tools for predicting stability changes in proteins due to single amino acid replacements. Protein Eng. Des. Sel. 2010, 23, 683-687.
47. Masso, M.; Vaisman, I.I. Accurate prediction of stability changes in protein mutants by combining machine learning with structure based computational mutagenesis. Bioinformatics 2008, 24, 2002-2009.
48. Tangrot, J.; Wang, L.X.; Kagstrom, B.; Sauer, U.H. FISH-Family identification of sequence homologues using structure anchored hidden Markov models. Nucleic Acids Res. 2006, 34, W10-W14.
49. Tangrot, J.; Wang, L.; Kagstrom, B.; Sauer, U.H. Design, construction and use of the FISH server. In Applied Parallel Computing: State of the Art in Scientific Computing; Kagstrom, B., Elmroth, E., Dongarra, J., Wasniewski, J., Eds.; Springer-Verlag: Berlin/Heidelberg, Germany, 2007; Volume 4699, pp. 647-657.
50. Wang, L.X.; Sauer, U.H. OnD-CRF: Predicting order and disorder in proteins conditional random fields. Bioinformatics 2008, 24, 1401-1402.
51. Huang, L.T.; Gromiha, M.M.; Ho, S.Y. iPTREE-STAB: Interpretable decision tree based method for predicting protein stability changes upon mutations. Bioinformatics 2007, 23, 1292-1293.
52. Dosztanyi, Z.; Fiser, A.; Simon, I. Stabilization centers in proteins: Identification, characterization and predictions. J. Mol. Biol. 1997, 272, 597-612.
53. Dosztanyi, Z.; Magyar, C.; Tusnady, G.E.; Simon, I. SCide: Identification of stabilization centers in proteins. Bioinformatics 2003, 19, 899-900.
54. Ferrer-Costa, C.; Orozco, M.; de la Cruz, X. Characterization of disease-associated single amino acid polymorphisms in terms of sequence and structure properties. J. Mol. Biol. 2002, 315, 771-786.
55. Ferrer-Costa, C.; Orozco, M.; de la Cruz, X. Sequence-based prediction of pathological mutations. Proteins 2004, 57, 811-819.
56. Ferrer-Costa, C.; Orozco, M.; de la Cruz, X. Use of bioinformatics tools for the annotation of disease-associated mutations in animal models. Proteins 2005, 61, 878-887.
57. Bromberg, Y.; Rost, B. SNAP: Predict effect of non-synonymous polymorphisms on function. Nucleic Acids Res. 2007, 35, 3823-3835.
58. Calabrese, R.; Capriotti, E.; Fariselli, P.; Martelli, P.L.; Casadio, R. Functional annotations improve the predictive score of human disease-related mutations in proteins. Hum. Mutat. 2009, 30, 1237-1244.
59. Tian, J.; Wu, N.; Guo, X.; Guo, J.; Zhang, J.; Fan, Y. Predicting the phenotypic effects of non-synonymous single nucleotide polymorphisms based on support vector machines. BMC Bioinform. 2007, 8, 450.
60. Kaminker, J.S.; Zhang, Y.; Watanabe, C.; Zhang, Z. CanPredict: A computational tool for predicting cancer-associated missense mutations. Nucleic Acids Res. 2007, 35, W595-W598.
61. Saunders, C.T.; Baker, D. Evaluation of structural and evolutionary contributions to deleterious mutation prediction. J. Mol. Biol. 2002, 322, 891-901.
62. Bowie, J.U.; Luthy, R.; Eisenberg, D. A method to identify protein sequences that fold into a known 3-dimensional structure. Science 1991, 253, 164-170.
63. Acharya, V.; Nagarajaram, H.A. Hansa: An automated method for discriminating disease and neutral human nsSNPs. Hum. Mutat. 2012, 33, 332-337.
64. Acharya, V.; Nagarajaram, H.A. Response to: Statistical analysis of missense mutation classifiers. Hum. Mutat. 2013, 34, 407.
65. Dehouck, Y.; Kwasigroch, J.M.; Rooman, M.; Gilis, D. BeAtMuSiC: Prediction of changes in protein-protein binding affinity on mutations. Nucleic Acids Res. 2013, 41, W333-W339.
66. Gardy, J.L.; Brinkman, F.S.L. Methods for predicting bacterial protein subcellular localization. Nat. Rev. Microbiol. 2006, 4, 741-751.
67. Chou, K.C.; Shen, H.B. Recent progress in protein subcellular location prediction. Anal. Biochem. 2007, 370, 1-16.
68. Imai, K.; Nakai, K. Prediction of subcellular locations of proteins: Where to proceed? Proteomics 2010, 10, 3970-3983.
69. Nakai, K.; Horton, P. PSORT: A program for detecting sorting signals in proteins and predicting their subcellular localization. Trends Biochem. Sci. 1999, 24, 34-35.
70. Nielsen, H.; Engelbrecht, J.; Brunak, S.; vonHeijne, G. Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Protein Eng. 1997, 10, 1-6.
71. Nielsen, H.; Krogh, A. Prediction of signal peptides and signal anchors by a hidden Markov model. Proc. Int. Conf. Intell. Syst. Mol. Biol. 1998, 6, 122-130.
72. Emanuelsson, O.; Brunak, S.; von Heijne, G.; Nielsen, H. Locating proteins in the cell using TargetP, SignalP and related tools. Nat. Protoc. 2007, 2, 953-971.
73. Shen, H.-B.; Chou, K.-C. Predicting protein fold pattern with functional domain and sequential evolution information. J. Theor. Biol. 2009, 256, 441-446.
74. Shen, H.-B.; Chou, K.-C. Ensemble classifier for protein fold pattern recognition. Bioinformatics 2006, 22, 1717-1722.
75. Chou, K.C. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 2005, 21, 10-19.
76. Ashburner, M.; Ball, C.A.; Blake, J.A.; Botstein, D.; Butler, H.; Cherry, J.M.; Davis, A.P.; Dolinski, K.; Dwight, S.S.; Eppig, J.T.; et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 2000, 25, 25-29.
77. Chou, K.C. Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins 2001, 43, 246-255.
78. Wan, S.B.; Mak, M.W.; Kung, S.Y. GOASVM: A subcellular location predictor by incorporating term-frequency gene ontology into the general form of Chou's pseudo-amino acid composition. J. Theor. Biol. 2013, 323, 40-48.
79. Goldberg, T.; Hamp, T.; Rost, B. LocTree2 predicts localization for all domains of life. Bioinformatics 2012, 28, I458-I465.
80. Nair, R.; Rost, B. Mimicking cellular sorting improves prediction of subcellular localization. J. Mol. Biol. 2005, 348, 85-100.
81. Mika, S.; Rost, B. UniqueProt: Creating representative protein sequence sets. Nucleic Acids Res. 2003, 31, 3789-3791.
82. Yu, C.S.; Lin, C.J.; Hwang, J.K. Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions. Protein Sci. 2004, 13, 1402-1406.
83. Yu, C.-S.; Chen, Y.-C.; Lu, C.-H.; Hwang, J.-K. Prediction of protein subcellular localization. Proteins 2006, 64, 643-651.
84. Horton, P.; Park, K.-J.; Obayashi, T.; Fujita, N.; Harada, H.; Adams-Collier, C.J.; Nakai, K. WoLF PSORT: Protein localization predictor. Nucleic Acids Res. 2007, 35, W585-W587.
85. King, B.R.; Vural, S.; Pandey, S.; Barteau, A.; Guda, C. ngLOC: Software and web server for predicting protein subcellular localization in prokaryotes and eukaryotes. BMC Res. Notes 2012, 5, 1-7.
86. Briesemeister, S.; Blum, T.; Brady, S.; Lam, Y.; Kohlbacher, O.; Shatkay, H. SherLoc2: A high-accuracy hybrid method for predicting subcellular localization of proteins. J. Proteome Res. 2009, 8, 5363-5366.
87. Chi, S.M.; Nam, D. WegoLoc: Accurate prediction of protein subcellular localization using weighted Gene Ontology terms. Bioinformatics 2012, 28, 1028-1030.
88. Guda, C.; Subramaniam, S. pTARGET: A new method for predicting protein subcellular localization in eukaryotes. Bioinformatics 2005, 21, 3963-3969.
89. Guda, C. pTARGET: A web server for predicting protein subcellular localization. Nucleic Acids Res. 2006, 34, W210-W213.
90. Mer, A.S.; Andrade-Navarro, M.A. A novel approach for protein subcellular location prediction using amino acid exposure. BMC Bioinform. 2013, 14, 342.
91. Briesemeister, S.; Rahnenführer, J.; Kohlbacher, O. YLoc-An interpretable web server for predicting subcellular localization. Nucleic Acids Res. 2010, 38, W497-W502.
92. Briesemeister, S.; Rahnenführer, J.; Kohlbacher, O. Going from where to why-Interpretable prediction of protein subcellular localization. Bioinformatics 2010, 26, 1232-1238.
93. Binder, J.; Pletscher-Frankild, S.; Tsafou, K.; Stolte, C.; O'Donoghue, S.; Schneider, R.; Jensen, L. COMPARTMENTS: Unification and visualization of protein subcellular localization evidence. Database 2014, 2014, bau012.
94. Song, J.; Takemoto, K.; Shen, H.; Tan, H.; Gromiha, M.M.; Akutsu, T. Prediction of protein folding rates from structural topology and complex network properties. IPSJ Trans. Bioinform. 2010, 3, 40-53.
95. Capriotti, E.; Casadio, R. K-Fold: A tool for the prediction of the protein folding kinetic order and rate. Bioinformatics 2007, 23, 385-386.
96. Gromiha, M.M.; Selvaraj, S. Comparison between long-range interactions and contact order in determining the folding rate of two-state proteins: application of long-range order to folding rate prediction. J. Mol. Biol. 2001, 310, 27-32.
97. Gromiha, M.M. Importance of native-state topology for determining the folding rate of two-state proteins. J. Chem. Inf. Comput. Sci. 2003, 43, 1481-1485.
98. Gromiha, M.M. A statistical model for predicting protein folding rates from amino acid sequence with structural class information. J. Chem. Inf. Model. 2005, 45, 494-501.
99. Punta, M.; Rost, B. PROFcon: Novel prediction of long-range contacts. Bioinformatics 2005, 21, 2960-2968.
100. Wishart, D.S.; Arndt, D.; Berjanskii, M.; Guo, A.C.; Shi, Y.; Shrivastava, S.; Zhou, J.; Zhou, Y.; Lin, G. PPT-DB: The protein property prediction and testing database. Nucleic Acids Res. 2008, 36, D222-D229.
101. Fang, Y.; Gao, S.; Tai, D.; Middaugh, C.R.; Fang, J. Identification of properties important to protein aggregation using feature selection. BMC Bioinform. 2013, 14, 314.
102. Oliveberg, M. Waltz, an exciting new move in amyloid prediction. Nat. Methods 2010, 7, 187-188.
103. Garbuzynskiy, S.O.; Lobanov, M.Y.; Galzitskaya, O.V. FoldAmyloid: A method of prediction of amyloidogenic regions from protein sequence. Bioinformatics 2010, 26, 326-332.
104. Tartaglia, G.G.; Vendruscolo, M. The Zyggregator method for predicting protein aggregation propensities. Chem. Soc. Rev. 2008, 37, 1395-1401.
105. Thomas, P.D.; Dill, K.A. Statistical potentials extracted from protein structures: How accurate are they? J. Mol. Biol. 1996, 257, 457-469.
106. Thomas, P.D.; Dill, K.A. An iterative method for extracting energy-like quantities from protein structures. Proc. Natl. Acad. Sci. USA 1996, 93, 11628-11633.
107. BenNaim, A. Statistical potentials extracted from protein structures: Are these meaningful potentials? J. Chem. Phys. 1997, 107, 3698-3706.
108. Buchete, N.V.; Straub, J.E.; Thirumalai, D. Development of novel statistical potentials for protein fold recognition. Curr. Opin. Struct. Biol. 2004, 14, 225-232.
109. Skolnick, J. In quest of an empirical potential for protein structure prediction. Curr. Opin. Struct. Biol. 2006, 16, 166-171.
110. Worth, C.L.; Preissner, R.; Blundell, T.L. SDM-A server for predicting effects of mutations on protein stability and malfunction. Nucleic Acids Res. 2011, 39, W215-W222.
111. Parthiban, V.; Gromiha, M.M.; Schomburg, D. CUPSAT: Prediction of protein stability upon point mutations. Nucleic Acids Res. 2006, 34, W239-W242.
112. Munson, P.J.; Singh, R.K. Statistical significance of hierarchical multi-body potentials based on Delaunay tessellation and their application in sequence-structure alignment. Protein Sci. 1997, 6, 1467-1481.
113. Zhou, H.Y.; Zhou, Y.Q. Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Protein Sci. 2002, 11, 2714-2726.
114. Shen, M.Y.; Sali, A. Statistical potential for assessment and prediction of protein structures. Protein Sci. 2006, 15, 2507-2524.
115. Mayewski, S. A multibody, whole-residue potential for protein structures, with testing by Monte Carlo simulated annealing. Proteins 2005, 59, 152-169.
116. Zhou, Y.; Zhou, H.Y.; Zhang, C.; Liu, S. What is a desirable statistical energy function for proteins and how can it be obtained? Cell Biochem. Biophys. 2006, 46, 165-174.
117. Zhang, C.; Liu, S.; Zhou, H.Y.; Zhou, Y.Q. The dependence of all-atom statistical potentials on structural training database. Biophys. J. 2004, 86, 3349-3358.
118. Liu, T.; Samudrala, R. The effect of experimental resolution on the performance of knowledge-based discriminatory functions for protein structure selection. Protein Eng. Des. Sel. 2006, 19, 431-437.
119. Liu, Y.; Kuhlman, B. RosettaDesign server for protein design. Nucleic Acids Res. 2006, 34, W235-W238.
120. Kang, S.; Chen, G.; Xiao, G. Robust prediction of mutation-induced protein stability change by property encoding of amino acids. Protein Eng. Des. Sel. 2009, 22, 75-83.
121. Cohen, M.; Potapov, V.; Schreiber, G. Four distances between pairs of amino acids provide a precise description of their interaction. PLoS Comput. Biol. 2009, 5, e1000470.
122. Potapov, V.; Cohen, M.; Inbar, Y.; Schreiber, G. Protein structure modelling and evaluation based on a 4-distance description of side-chain interactions. BMC Bioinform. 2010, 11, 374.
123. Pires, D.E.V.; Ascher, D.B.; Blundell, T.L. mCSM: Predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics 2014, 30, 335-342.
124. Yin, S.; Ding, F.; Dokholyan, N.V. Eris: An automated estimator of protein stability. Nat. Methods 2007, 4, 466-467.
125. Cheng, T.M.K.; Lu, Y.-E.; Vendruscolo, M.; Lio, P.; Blundell, T.L. Prediction by graph theoretic measures of structural effects in proteins arising from non-synonymous single nucleotide polymorphisms. PLoS Comput. Biol. 2008, 4, e1000135.
126. Aqvist, J.; Medina, C.; Samuelsson, J.E. New method for predicting binding-affinity in computer-aided drug design. Protein Eng. 1994, 7, 385-391.
127. Bueno, M.; Camacho, C.J.; Sancho, J. SIMPLE estimate of the free energy change due to aliphatic mutations: Superior predictions based on first principles. Proteins 2007, 68, 850-862.
128. Benedix, A.; Becker, C.M.; de Groot, B.L.; Caflisch, A.; Bockmann, R.A. Predicting free energy changes using structural ensembles. Nat. Methods 2009, 6, 3-4.
129. Wickstrom, L.; Gallicchio, E.; Levy, R.M. The linear interaction energy method for the prediction of protein stability changes upon mutation. Proteins 2012, 80, 111-125.
130. Li, M.; Petukh, M.; Alexov, E.; Panchenko, A.R. Predicting the impact of missense mutations on protein-protein binding affinity. J. Chem. Theor. Comput. 2014, 10, 1770-1780.
131. deGroot, B.L.; vanAalten, D.M.F.; Scheek, R.M.; Amadei, A.; Vriend, G.; Berendsen, H.J.C. Prediction of protein conformational freedom from distance constraints. Proteins 1997, 29, 240-251.
132. Li, L.; Li, C.; Sarkar, S.; Zhang, J.; Witham, S.; Zhang, Z.; Wang, L.; Smith, N.; Petukh, M.; Alexov, E. DelPhi: A comprehensive suite for DelPhi software and associated resources. BMC Biophys. 2012, 5, 1-11.
133. Schlitter, J. Estimation of absolute and relative entropies of macromolecules using the covariance-matrix. Chem. Phys. Lett. 1993, 215, 617-621.
134. Zhang, Z.; Wang, L.; Gao, Y.; Zhang, J.; Zhenirovskyy, M.; Alexov, E. Predicting folding free energy changes upon single point mutations. Bioinformatics 2012, 28, 664-671.
135. Pappu, R.V.; Hart, R.K.; Ponder, J.W. Analysis and application of potential energy smoothing and search methods for global optimization. J. Phys. Chem. B 1998, 102, 9725-9742.
136. Guerois, R.; Nielsen, J.E.; Serrano, L. Predicting changes in the stability of proteins and protein complexes: A study of more than 1000 mutations. J. Mol. Biol. 2002, 320, 369-387.
137. Pokala, N.; Handel, T.M. Energy functions for protein design: Adjustment with protein-protein complex affinities, models for the unfolded state, and negative design of solubility and specificity. J. Mol. Biol. 2005, 347, 203-227.
138. Kumar, A.; Rajendran, V.; Sethumadhavan, R.; Purohit, R. Molecular dynamic simulation reveals damaging impact of RAC1 F28L mutation in the switch I region. PLoS One 2013, 8, e77453.
139. Beveridge, D.L.; Dicapua, F.M. Free-energy via molecular simulation-Applications to chemical and biomolecular systems. Ann. Rev. Biophys. Biophys. Chem. 1989, 18, 431-492.
140. Kirkwood, J.G. Statistical mechanics of fluid mixtures. J. Chem. Phys. 1935, 3, 300-313.
141. Frenkel, D.; Smit, B. Understanding Molecular Simulation: From Algorithms to Applications, 2nd ed.; Academic Press: New York, NY, USA, 2001.
142. Chipot, C. Free Energy Calculations: Theory and Applications in Chemistry and Biology; Springer-Verlag: Berlin/Heidelburg, Germany, 2007; Volume 86.
143. Qin, S.; Pang, X.D.; Zhou, H.X. Automated prediction of protein association rate constants. Structure 2011, 19, 1744-1751.
144. Alsallaq, R.; Zhou, H.X. Electrostatic rate enhancement and transient complex of protein-protein association. Proteins 2008, 71, 320-335.
145. Bai, H.J.; Yang, K.; Yu, D.Q.; Zhang, C.S.; Chen, F.J.; Lai, L.H. Predicting kinetic constants of protein-protein interactions based on structural properties. Proteins 2011, 79, 720-734.
146. Hoeting, J.A.; Madigan, D.; Raftery, A.E.; Volinsky, C.T. Bayesian model averaging: A tutorial. Stat. Sci. 1999, 382-401.
147. Agius, R.; Torchala, M.; Moal, I.H.; Fernandez-Recio, J.; Bates, P.A. Characterizing changes in the rate of protein-protein dissociation upon interface mutation using hotspot energy and organization. PLoS Comput. Biol. 2013, 9, e1003216.
148. Moretti, R.; Fleishman, S.J.; Agius, R.; Torchala, M.; Bates, P.A.; Kastritis, P.L.; Rodrigues, J.P.; Trellet, M.; Bonvin, A.M.; Cui, M.; et al. Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions. Proteins 2013, 81, 1980-1987.
149. Chiti, F.; Stefani, M.; Taddei, N.; Ramponi, G.; Dobson, C.M. Rationalization of the effects of mutations on peptide and protein aggregation rates. Nature 2003, 424, 805-808.
150. Tartaglia, G.G.; Cavalli, A.; Pellarin, R.; Caflisch, A. The role of aromaticity, exposed surface, and dipole moment in determining protein aggregation rates. Protein Sci. 2004, 13, 1939-1941.
151. Fernandez-Escamilla, A.M.; Rousseau, F.; Schymkowitz, J.; Serrano, L. Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins. Nat. Biotechnol. 2004, 22, 1302-1306.
152. Rousseau, F.; Schymkowitz, J.; Serrano, L. Protein aggregation and amyloidosis: confusion of the kinds? Curr. Opin. Struct. Biol. 2006, 16, 118-126.
153. Linding, R.; Schymkowitz, J.; Rousseau, F.; Diella, F.; Serrano, L. A comparative study of the relationship between protein structure and beta-aggregation in globular and intrinsically disordered proteins. J. Mol. Biol. 2004, 342, 345-353.
154. Trovato, A.; Chiti, F.; Maritan, A.; Seno, F. Insight into the structure of amyloid fibrils from the analysis of globular proteins. PLoS Comput. Biol. 2006, 2, 1608-1618.
155. Trovato, A.; Seno, F.; Tosatto, S.C.E. The PASTA server for protein aggregation prediction. Protein Eng. Des. Sel. 2007, 20, 521-523.
156. Conchillo-Sole, O.; de Groot, N.S.; Aviles, F.X.; Vendrell, J.; Daura, X.; Ventura, S. AGGRESCAN: A server for the prediction and evaluation of "hot spots" of aggregation in polypeptides. BMC Bioinform. 2007, 8, 65.
157. Lander, E.S.; Schork, N.J. Genetic dissection of complex traits. Science 1994, 265, 2037-2048.
158. Martin, D.B.; Nelson, P.S. From genomics to proteomics: techniques and applications in cancer research. Trends Cell Biol. 2001, 11, S60-S65.
159. Landegren, U.; Nilsson, M.; Kwok, P.Y. Reading bits of genetic information: methods for single-nucleotide polymorphism analysis. Genome Res. 1998, 8, 769-776.
160. Carlson, C.S.; Eberle, M.A.; Kruglyak, L.; Nickerson, D.A. Mapping complex disease loci in whole-genome association studies. Nature 2004, 429, 446-452.
161. Shendure, J.; Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 2008, 26, 1135-1145.
162. Etter, P.D.; Bassham, S.; Hohenlohe, P.A.; Johnson, E.A.; Cresko, W.A. SNP discovery and genotyping for evolutionary genetics using RAD sequencing. Methods Mol. Biol. 2011, 772, 157-178.
163. Robasky, K.; Lewis, N.E.; Church, G.M. The role of replicates for error mitigation in next-generation sequencing. Nat. Rev. Genet. 2014, 15, 56-62.
164. Donis-Keller, H.; Green, P.; Helms, C.; Cartinhour, S.; Weiffenbach, B.; Stephens, K.; Keith, T.P.; Bowden, D.W.; Smith, D.R.; Lander, E.S.; et al. A genetic linkage map of the human genome. Cell 1987, 51, 319-337.
165. Zhang, C.; Liu, S.; Zhu, Q.Q.; Zhou, Y.Q. A knowledge-based energy function for protein-ligand, protein-protein, and protein-DNA complexes. J. Med. Chem. 2005, 48, 2325-2335.
166. Gurgey, A.; Beksac, S.; Mesci, L.; Cakar, N.; Karakas, U.; Kutlar, A.; Altay, C. Prenatal diagnosis of sickle cell anemia using PCR and restriction enzyme DdeI. Turk. J. Pediatr. 1993, 35, 159-162.
167. Williams, J.G.; Kubelik, A.R.; Livak, K.J.; Rafalski, J.A.; Tingey, S.V. DNA polymorphisms amplified by arbitrary primers are useful as genetic markers. Nucleic Acids Res. 1990, 18, 6531-6535.
168. Shangkuan, Y.H.; Lin, H.C. Application of random amplified polymorphic DNA analysis to differentiate strains of Salmonella typhi and other Salmonella species. J. Appl. Microbiol. 1998, 85, 693-702.
169. Quintaes, B.R.; Leal, N.C.; Reis, E.M.; Hofer, E. Optimization of randomly amplified polymorphic DNA-polymerase chain reaction for molecular typing of Salmonella enterica serovar Typhi. Rev. Soc. Bras. Med. Trop. 2004, 37, 143-147.
170. Konry, T.; Hayman, R.B.; Walt, D.R. Microsphere-based rolling circle amplification microarray for the detection of DNA and proteins in a single assay. Anal. Chem. 2009, 81, 5777-5782.
171. Epstein, J.R.; Leung, A.P.; Lee, K.H.; Walt, D.R. High-density, microsphere-based fiber optic DNA microarrays. Biosens. Bioelectron. 2003, 18, 541-546.
172. Shalon, D.; Smith, S.J.; Brown, P.O. A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization. Genome Res. 1996, 6, 639-645.
173. Truco, M.J.; Ashrafi, H.; Kozik, A.; van Leeuwen, H.; Bowers, J.; Reyes Chin, W.O.S.; Stoffel, K.; Xu, H.; Hill, T.; van Deynze, A.; et al. An ultra high-density, transcript-based, genetic map of lettuce. G3 (Bethesda) 2013, 3, 617-631.
174. Pastinen, T.; Raitio, M.; Lindroos, K.; Tainola, P.; Peltonen, L.; Syvanen, A.C. A system for specific, high-throughput genotyping by allele-specific primer extension on microarrays. Genome Res. 2000, 10, 1031-1042.
175. Koch, W.H. Technology platforms for pharmacogenomic diagnostic assays. Nat. Rev. Drug Discov. 2004, 3, 749-761.
176. Baek, I.C.; Jang, J.P.; Choi, H.B.; Choi, E.J.; Ko, W.Y.; Kim, T.G. Microarrays for high-throughput genotyping of MICA alleles using allele-specific primer extension. Tissue Antigens 2013, 82, 259-268.
177. Shen, R.; Fan, J.B.; Campbell, D.; Chang, W.; Chen, J.; Doucet, D.; Yeakley, J.; Bibikova, M.; Garcia, E.W.; McBride, C.; et al. High-throughput SNP genotyping on universal bead arrays. Mutat. Res. 2005, 573, 70-82.
178. Van Heek, N.T.; Clayton, S.J.; Sturm, P.D.; Walker, J.; Gouma, D.J.; Noorduyn, L.A.; Offerhaus, G.J.; Fox, J.C. Comparison of the novel quantitative ARMS assay and an enriched PCR-ASO assay for K-ras mutations with conventional cytology on endobiliary brush cytology from 312 consecutive extrahepatic biliary stenoses. J. Clin. Pathol. 2005, 58, 1315-1320.
179. Macgregor, S.; Zhao, Z.Z.; Henders, A.; Nicholas, M.G.; Montgomery, G.W.; Visscher, P.M. Highly cost-efficient genome-wide association studies using DNA pools and dense SNP arrays. Nucleic Acids Res. 2008, 36, e35.
180. Goelet, P.; Knapp, M.; Anderson, S.U.S. Methord for Dethermining Nucleotide identity through Primer Extension. U.S. Patent 5,888,819, 30 March 1999.
181. Mandoiu, I.I.; Prajescu, C. High-throughput SNP genotyping by SBE/SBH. IEEE Trans. Nanobiosci. 2007, 6, 28-35.
182. Hirschhorn, J.N.; Sklar, P.; Lindblad-Toh, K.; Lim, Y.M.; Ruiz-Gutierrez, M.; Bolk, S.; Langhorst, B.; Schaffner, S.; Winchester, E.; Lander, E.S. SBE-TAGS: an array-based method for efficient single-nucleotide polymorphism genotyping. Proc. Natl. Acad. Sci. USA 2000, 97, 12164-12169.
183. Bell, P.A.; Chaturvedi, S.; Gelfand, C.A.; Huang, C.Y.; Kochersperger, M.; Kopla, R.; Modica, F.; Pohl, M.; Varde, S.; Zhao, R.; et al. SNPstream UHT: Ultra-high throughput SNP genotyping for pharmacogenomics and drug discovery. Biotechniques 2002, 74, 76-77.
184. Liu, H.; Wang, H.; Shi, Z.; Wang, H.; Silke, C.Y.S.; Tan, W.; Lu, Z. TaqMan probe array for quantitative detection of DNA targets. Nucleic Acids Res. 2006, 34, e4.
185. Shen, G.Q.; Abdullah, K.G.; Wang, Q.K. The TaqMan method for SNP genotyping. Methods Mol. Biol. 2009, 578, 293-306.
186. Cao, W. Recent developments in ligase-mediated amplification and detection. Trends Biotechnol. 2004, 22, 38-44.
187. Baner, J.; Isaksson, A.; Waldenstrom, E.; Jarvius, J.; Landegren, U.; Nilsson, M. Parallel gene analysis with allele-specific padlock probes and tag microarrays. Nucleic Acids Res. 2003, 31 e103.
188. Hardenbol, P.; Baner, J.; Jain, M.; Nilsson, M.; Namsaraev, E.A.; Karlin-Neumann, G.A.; Fakhrai-Rad, H.; Ronaghi, M.; Willis, T.D.; Landegren, U.; et al. Multiplexed genotyping with sequence-tagged molecular inversion probes. Nat. Biotechnol. 2003, 21, 673-678.
189. Lamy, P.; Andersen, C.L.; Wikman, F.P.; Wiuf, C. Genotyping and annotation of Affymetrix SNP arrays. Nucleic Acids Res. 2006, 34, doi:10.1093/nar/gkl475.
190. Orom, U.A.; Nielsen, F.C.; Lund, A.H. MicroRNA-10a binds the 5'UTR of ribosomal protein mRNAs and enhances their translation. Mol. Cell. 2008, 30, 460-471.
191. Bartel, D.P. MicroRNAs: Genomics, biogenesis, mechanism, and function. Cell 2004, 116, 281-297.
192. Saunders, M.A.; Liang, H.; Li, W.H. Human polymorphism at microRNAs and microRNA target sites. Proc. Natl. Acad. Sci. USA 2007, 104, 3300-3305.
193. Landi, D.; Gemignani, F.; Naccarati, A.; Pardini, B.; Vodicka, P.; Vodickova, L.; Novotny, J.; Forsti, A.; Hemminki, K.; Canzian, F.; et al. Polymorphisms within micro-RNA-binding sites and risk of sporadic colorectal cancer. Carcinogenesis 2008, 29, 579-584.
194. Lewis, J.D.; Meehan, R.R.; Henzel, W.J.; Maurer-Fogy, I.; Jeppesen, P.; Klein, F.; Bird, A. Purification, sequence, and cellular localization of a novel chromosomal protein that binds to methylated DNA. Cell 1992, 69, 905-914.
195. Nan, X.; Campoy, F.J.; Bird, A. MeCP2 is a transcriptional repressor with abundant binding sites in genomic chromatin. Cell 1997, 88, 471-481.
196. Amir, R.E.; van den, V.E.I.; Wan, M.; Tran, C.Q.; Francke, U.; Zoghbi, H.Y. Rett syndrome is caused by mutations in X-linked MECP2, encoding methyl-CpG-binding protein 2. Nat. Genet. 1999, 23, 185-188.
197. Yusufzai, T.M.; Wolffe, A.P. Functional consequences of Rett syndrome mutations on human MeCP2. Nucleic Acids Res. 2000, 28, 4172-4179.
198. Chen, W.G.; Chang, Q.; Lin, Y.; Meissner, A.; West, A.E.; Griffith, E.C.; Jaenisch, R.; Greenberg, M.E. Derepression of BDNF transcription involves calcium-dependent phosphorylation of MeCP2. Science 2003, 302, 885-889.
199. Georgel, P.T.; Horowitz-Scherer, R.A.; Adkins, N.; Woodcock, C.L.; Wade, P.A.; Hansen, J.C. Chromatin compaction by human MeCP2. Assembly of novel secondary chromatin structures in the absence of DNA methylation. J. Biol. Chem. 2003, 278, 32181-32188.
200. Galvao, T.C.; Thomas, J.O. Structure-specific binding of MeCP2 to four-way junction DNA through its methyl CpG-binding domain. Nucleic Acids Res. 2005, 33, 6603-6609.
201. Ghosh, R.P.; Horowitz-Scherer, R.A.; Nikitina, T.; Gierasch, L.M.; Woodcock, C.L. Rett syndrome-causing mutations in human MeCP2 result in diverse structural changes that impact folding and DNA interactions. J. Biol. Chem. 2008, 283, 20523-20534.
202. Mellen, M.; Ayata, P.; Dewell, S.; Kriaucionis, S.; Heintz, N. MeCP2 binds to 5hmC enriched within active genes and accessible chromatin in the nervous system. Cell 2012, 151, 1417-1430.
203. Lyst, M.J.; Ekiert, R.; Ebert, D.H.; Merusi, C.; Nowak, J.; Selfridge, J.; Guy, J.; Kastan, N.R.; Robinson, N.D.; de Lima Alves, F.; et al. Rett syndrome mutations abolish the interaction of MeCP2 with the NCoR/SMRT co-repressor. Nat. Neurosci. 2013, 16, 898-902.
204. Baker, S.A.; Chen, L.; Wilkins, A.D.; Yu, P.; Lichtarge, O.; Zoghbi, H.Y. An AT-hook domain in MeCP2 determines the clinical course of Rett syndrome and related disorders. Cell 2013, 152, 984-996.
205. Erika Hawkins, M.S.; Michael Beck, M.S.; Braeden Butler, B.S.; Keith Wood, P.D. Dual-luciferase reporter assay: An advanced co-reporter technology integrating firefly and renilla luciferase assays. Promega Notes Mag. 1996, 2-8.
206. McNabb, D.S.; Reed, R.; Marciniak, R.A. Dual luciferase assay system for rapid assessment of gene expression in Saccharomyces cerevisiae. Eukaryot. Cell 2005, 4, 1539-1549.
207. Nicoloso, M.S.; Sun, H.; Spizzo, R.; Kim, H.; Wickramasinghe, P.; Shimizu, M.; Wojcik, S.E.; Ferdin, J.; Kunej, T.; Xiao, L.; et al. Single-nucleotide polymorphisms inside microRNA target sites influence tumor susceptibility. Cancer Res. 2010, 70, 2789-2798.
208. Avner, P.; Heard, E. X-chromosome inactivation: counting, choice and initiation. Nat. Rev. Genet. 2001, 2, 59-67.
209. Tycko, B.; Morison, I.M. Physiological functions of imprinted genes. J. Cell. Physiol. 2002, 192, 245-258.
210. Kim, J.; Bartel, D.P. Allelic imbalance sequencing reveals that single-nucleotide polymorphisms frequently alter microRNA-directed repression. Nat. Biotechnol. 2009, 27, 472-477.
211. Bannantine, J.P.; Stabel, J.R.; Lamont, E.A.; Briggs, R.E.; Sreevatsan, S. Monoclonal antibodies bind a SNP-sensitive epitope that is present uniquely in mycobacterium avium subspecies paratuberculosis. Front. Microbiol. 2011, 2, 163.
212. Wuthrich, K. The way to NMR structures of proteins. Nat. Struct. Biol. 2001, 8, 923-925.
213. Nakanishi, K.; Berova, N.; Woody, R. Circular Dichroism: Principles and Applications; Jogn Wiley and Sons: New York, NY, USA 1994; p. 473.
214. Dubois, A.; Deuve, J.L.; Navarro, P.; Merzouk, S.; Pichard, S.; Commere, P.H.; Louise, A.; Arnaud, D.; Avner, P.; Morey, C. Spontaneous reactivation of clusters of X-linked genes is associated with the plasticity of X-inactivation in mouse trophoblast stem cells. Stem Cells 2014, 32, 377-390.
215. Yang, Y.; Dou, S.X.; Ren, H.; Wang, P.Y.; Zhang, X.D.; Qian, M.; Pan, B.Y.; Xi, X.G. Evidence for a functional dimeric form of the PcrA helicase in DNA unwinding. Nucleic Acids Res. 2008, 36, 1976-1989.
216. Ren, H.; Dou, S.X.; Zhang, X.D.; Wang, P.Y.; Kanagaraj, R.; Liu, J.L.; Janscak, P.; Hu, J.S.; Xi, X.G. The zinc-binding motif of human RECQ5beta suppresses the intrinsic strand-annealing activity of its DExH helicase domain and is essential for the helicase activity of the enzyme. Biochem. J. 2008, 412, 425-433.
217. Sammond, D.W.; Eletr, Z.M.; Purbeck, C.; Kimple, R.J.; Siderovski, D.P.; Kuhlman, B. Structure-based protocol for identifying mutations that enhance protein-protein binding affinities. J. Mol. Biol. 2007, 371, 1392-1404.
218. Ciucci, A.; Palma, C.; Manzini, S.; Werge, T.M. Point mutation increases a form of the NK1 receptor with high affinity for neurokinin A and B and septide. Br. J. Pharmacol. 1998, 125, 393-401.
219. Ren, H.; Dou, S.X.; Rigolet, P.; Yang, Y.; Wang, P.Y.; Amor-Gueret, M.; Xi, X.G. The arginine finger of the Bloom syndrome protein: its structural organization and its role in energy coupling. Nucleic Acids Res. 2007, 35, 6029-6041.
220. Gentile, S.; Martin, N.; Scappini, E.; Williams, J.; Erxleben, C.; Armstrong, D.L. The human ERG1 channel polymorphism, K897T, creates a phosphorylation site that inhibits channel activity. Proc. Natl. Acad. Sci. USA 2008, 105, 14704-14708.
221. Ebert, D.H.; Gabel, H.W.; Robinson, N.D.; Kastan, N.R.; Hu, L.S.; Cohen, S.; Navarro, A.J.; Lyst, M.J.; Ekiert, R.; Bird, A.P.; et al. Activity-dependent phosphorylation of MeCP2 threonine 308 regulates interaction with NCoR. Nature 2013, 499, 341-345.
222. Josephy, P.D.; Pan, D.; Ianni, M.D.; Mannervik, B. Functional studies of single-nucleotide polymorphic variants of human glutathione transferase T1-1 involving residues in the dimer interface. Arch. Biochem. Biophys. 2011, 513, 87-93.
223. Hsu, C.H.; Wen, Z.H.; Lin, C.S.; Chakraborty, C. The zebrafish model: use in studying cellular mechanisms for a spectrum of clinical disease entities. Curr. Neurovasc. Res. 2007, 4, 111-120.
224. Best, J.D.; Alderton, W.K. Zebrafish: An in vivo model for the study of neurological diseases. Neuropsychiatr. Dis. Treat. 2008, 4, 567-576.
225. Lieschke, G.J.; Currie, P.D. Animal models of human disease: zebrafish swim into view. Nat. Rev. Genet. 2007, 8, 353-367.
226. Sager, J.J.; Bai, Q.; Burton, E.A. Transgenic zebrafish models of neurodegenerative diseases. Brain Struct. Funct. 2010, 214, 285-302.
227. Gupta, A.; Meng, X.; Zhu, L.J.; Lawson, N.D.; Wolfe, S.A. Zinc finger protein-dependent and-independent contributions to the in vivo off-target activity of zinc finger nucleases. Nucleic Acids Res. 2011, 39, 381-392.
228. Gerety, S.S.; Breau, M.A.; Sasai, N.; Xu, Q.; Briscoe, J.; Wilkinson, D.G. An inducible transgene expression system for zebrafish and chick. Development 2013, 140, 2235-2243.
229. Kok, F.O.; Gupta, A.; Lawson, N.D.; Wolfe, S.A. Construction and application of site-specific artificial nucleases for targeted gene editing. Methods Mol. Biol. 2014, 1101, 267-303.
230. Gupta, A.; Hall, V.L.; Kok, F.O.; Shin, M.; McNulty, J.C.; Lawson, N.D.; Wolfe, S.A. Targeted chromosomal deletions and inversions in zebrafish. Genome Res. 2013, 23, 1008-1017.
231. Sun, N.; Zhao, H. Transcription activator-like effector nucleases (TALENs): A highly efficient and versatile tool for genome editing. Biotechnol. Bioeng. 2013, 110, 1811-1821.
232. Hwang, W.Y.; Fu, Y.; Reyon, D.; Maeder, M.L.; Kaini, P.; Sander, J.D.; Joung, J.K.; Peterson, R.T.; Yeh, J.R. Heritable and precise zebrafish genome editing using a CRISPR-Cas system. PLoS One 2013, 8, e68708.
233. Sander, J.D.; Joung, J.K. CRISPR-Cas systems for editing, regulating and targeting genomes. Nat. Biotechnol. 2014, 32, 347-355.
234. Shalem, O.; Sanjana, N.E.; Hartenian, E.; Shi, X.; Scott, D.A.; Mikkelsen, T.S.; Heckl, D.; Ebert, B.L.; Root, D.E.; Doench, J.G.; et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 2014, 343, 84-87.
235. Sashital, D.G.; Wiedenheft, B.; Doudna, J.A. Mechanism of foreign DNA selection in a bacterial adaptive immune system. Mol. Cell. 2012, 46, 606-615.
236. Bhaya, D.; Davison, M.; Barrangou, R. CRISPR-Cas systems in bacteria and archaea: Versatile small RNAs for adaptive defense and regulation. Ann. Rev. Genet. 2011, 45, 273-297.
237. Jinek, M.; Chylinski, K.; Fonfara, I.; Hauer, M.; Doudna, J.A.; Charpentier, E. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 2012, 337, 816-821.
238. Blackburn, P.R.; Campbell, J.M.; Clark, K.J.; Ekker, S.C. The CRISPR system-Keeping zebrafish gene targeting fresh. Zebrafish 2013, 10, 116-118.
239. Huang, P.; Zhu, Z.; Lin, S.; Zhang, B. Reverse genetic approaches in zebrafish. J. Genet. Genomics 2012, 39, 421-433.
240. Ansai, S.; Inohaya, K.; Yoshiura, Y.; Schartl, M.; Uemura, N.; Takahashi, R.; Kinoshita, M. Design, evaluation, and screening methods for efficient targeted mutagenesis with transcription activator-like effector nucleases in medaka. Dev. Growth Differ. 2014, 56, 98-107.
241. Edelheit, O.; Hanukoglu, A.; Hanukoglu, I. Simple and efficient site-directed mutagenesis using two single-primer reactions in parallel to generate mutants for protein structure-function studies. BMC Biotechnol. 2009, 9, 61.
242. Agulnik, A.I.; Mitchell, M.J.; Mattei, M.G.; Borsani, G.; Avner, P.A.; Lerner, J.L.; Bishop, C.E. A novel X gene with a widely transcribed Y-linked homologue escapes X-inactivation in mouse and human. Hum. Mol. Genet. 1994, 3, 879-884.
243. Takeuchi, T.; Yamazaki, Y.; Katoh-Fukui, Y.; Tsuchiya, R.; Kondo, S.; Motoyama, J.; Higashinakagawa, T. Gene trap capture of a novel mouse gene, jumonji, required for neural tube formation. Genes Dev. 1995, 9, 1211-1222.
244. Jensen, L.R.; Amende, M.; Gurok, U.; Moser, B.; Gimmel, V.; Tzschach, A.; Janecke, A.R.; Tariverdian, G.; Chelly, J.; Fryns, J.P.; et al. Mutations in the JARID1C gene, which is involved in transcriptional regulation and chromatin remodeling, cause X-linked mental retardation. Am. J. Hum. Genet. 2005, 76, 227-236.
245. Santos, C.; Rodriguez-Revenga, L.; Madrigal, I.; Badenas, C.; Pineda, M.; Mila, M. A novel mutation in JARID1C gene associated with mental retardation. Eur. J. Hum. Genet. 2006, 14, 583-586.
246. Harvey, C.G.; Menon, S.D.; Stachowiak, B.; Noor, A.; Proctor, A.; Mensah, A.K.; Mnatzakanian, G.N.; Alfred, S.E.; Guo, R.; Scherer, S.W.; et al. Sequence variants within exon 1 of MECP2 occur in females with mental retardation. Am. J. Med. Genet. B 2007, 144B, 355-360.
247. Christodoulou, J.; Ho, G. MECP2-Related Disorders. GeneReviews. 1993. Available online: http://www.ncbi.nlm.nih.gov/books/NBK1497/ (accessed on 12 March 2014).
248. Chandler, S.P.; Guschin, D.; Landsberger, N.; Wolffe, A.P. The methyl-CpG binding transcriptional repressor MeCP2 stably associates with nucleosomal DNA. Biochemistry 1999, 38, 7008-7018.
249. Nan, X.; Ng, H.H.; Johnson, C.A.; Laherty, C.D.; Turner, B.M.; Eisenman, R.N.; Bird, A. Transcriptional repression by the methyl-CpG-binding protein MeCP2 involves a histone deacetylase complex. Nature 1998, 393, 386-389.
250. Smrt, R.D.; Eaves-Egenes, J.; Barkho, B.Z.; Santistevan, N.J.; Zhao, C.; Aimone, J.B.; Gage, F.H.; Zhao, X. Mecp2 deficiency leads to delayed maturation and altered gene expression in hippocampal neurons. Neurobiol. Dis. 2007, 27, 77-89.
251. Cohen, S.; Gabel, H.W.; Hemberg, M.; Hutchinson, A.N.; Sadacca, L.A.; Ebert, D.H.; Harmin, D.A.; Greenberg, R.S.; Verdine, V.K.; Zhou, Z.; et al. Genome-wide activity-dependent MeCP2 phosphorylation regulates nervous system development and function. Neuron 2011, 72, 72-85.
252. Xiang, Z.; Honig, B. Extending the accuracy limits of prediction for side-chain conformations. J. Mol. Biol. 2001, 311, 421-430.
253. Xiang, Z.; Soto, C.S.; Honig, B. Evaluating conformational free energies: The colony energy and its application to the problem of loop prediction. Proc. Natl. Acad. Sci. USA 2002, 99, 7432-7437.
254. Humphrey, W.; Dalke, A.; Schulten, K. VMD: Visual molecular dynamics. J. Mol. Graph. 1996, 14, 33-38.
255. Phillips, J.C.; Braun, R.; Wang, W.; Gumbart, J.; Tajkhorshid, E.; Villa, E.; Chipot, C.; Skeel, R.D.; Kalé, L.; Schulten, K. Scalable molecular dynamics with NAMD. J. Comput. Chem. 2005, 26, 1781-1802.
256. NAMD was developed by the Theoretical and Computational Biophysics Group in the Beckman Institute for Advanced Science and Technology at the University of Illinois at Urbana-Champaign.
257. Patton, E.E.; Zon, L.I. The art and design of genetic screens: zebrafish. Nat. Rev. Genet. 2001, 2, 956-966.
258. Pietri, T.; Roman, A.C.; Guyon, N.; Romano, S.A.; Washbourne, P.; Moens, C.B.; de Polavieja, G.G.; Sumbre, G. The first mecp2-null zebrafish model shows altered motor behaviors. Front. Neural. Circuits 2013, 7, 118.
259. Gibbs, E.M.; Horstick, E.J.; Dowling, J.J. Swimming into prominence: The zebrafish as a valuable tool for studying human myopathies and muscular dystrophies. FEBS J. 2013, 280, 4187-4197.
260. De Bona, C.; Zappella, M.; Hayek, G.; Meloni, I.; Vitelli, F.; Bruttini, M.; Cusano, R.; Loffredo, P.; Longo, I.; Renieri, A. Preserved speech variant is allelic of classic Rett syndrome. Eur. J. Hum. Genet. 2000, 8, 325-330.
261. Bebbington, A.; Anderson, A.; Ravine, D.; Fyfe, S.; Pineda, M.; de Klerk, N.; Ben-Zeev, B.; Yatawara, N.; Percy, A.; Kaufmann, W.E.; et al. Investigating genotype-phenotype relationships in Rett syndrome using an international data set. Neurology 2008, 70, 868-875.
262. Renieri, A.; Mari, F.; Mencarelli, M.A.; Scala, E.; Ariani, F.; Longo, I.; Meloni, I.; Cevenini, G.; Pini, G.; Hayek, G.; et al. Diagnostic criteria for the Zappella variant of Rett syndrome (the preserved speech variant). Brain Dev. 2009, 31, 208-216.
263. Thisse, B.; Heyer, V.; Lux, A.; Alunni, V.; Degrave, A.; Seiliez, I.; Kirchner, J.; Parkhill, J.P.; Thisse, C. Spatial and temporal expression of the zebrafish genome by large-scale in situ hybridization screening. Methods Cell Biol. 2004, 77, 505-519.
264. Thisse, C.; Thisse, B. High-resolution in situ hybridization to whole-mount zebrafish embryos. Nat. Protoc. 2008, 3, 59-69.
265. Clarke, L.; Zheng-Bradley, X.; Smith, R.; Kulesha, E.; Xiao, C.; Toneva, I.; Vaughan, B.; Preuss, D.; Leinonen, R.; Shumway, M.; et al. The 1000 Genomes Project: data management and community access. Nat. Meth. 2012, 9, 459-462.
266. A map of human genome variation from population-scale sequencing. Nature 2010, 467, 1061-1073.
267. Seth, A.; Stemple, D.L.; Barroso, I. The emerging use of zebrafish to model metabolic disease. Dis. Model Mech. 2013, 6, 1080-1088.
268. Wager, K.; Mahmood, F.; Russell, C. Modelling inborn errors of metabolism in zebrafish. J. Inherit. Metab. Dis. 2014, 1-13.