Please wait a minute...
Frontiers in Biology

ISSN 1674-7984

ISSN 1674-7992(Online)

CN 11-5892/Q

Front. Biol.    2014, Vol. 9 Issue (6) : 504-518    https://doi.org/10.1007/s11515-014-1320-4
RESEARCH ARTICLE
Influence of the SNPs on the structural stability of CBS protein: Insight from molecular dynamics simulations
C. GEORGE PRIYA DOSS1,*(),B. RAJITH1,R. MAGESH2,A. ASHISH KUMAR3
1. Medical Biotechnology Division, School of Biosciences and Technology, VIT University, Vellore-14, TamilNadu, India
2. Department of Biotechnology, Faculty of Biomedical Sciences, Technology & Research, Sri Ramachandra University, Chennai-600116, TamilNadu, India
3. Bioinformatics Division, School of Biosciences and Technology, VIT University, Vellore-14, TamilNadu, India
 Download: PDF(3654 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

Cystathionine β-synthase is an essential enzyme of the trans-sulfuration pathway that condenses serine with homocysteine to form cystathionine. Missense mutations in CBS are the major cause of inherited homocystinuria, and the detailed effect of disease associated amino acid substitutions on the structure and stability of human CBS is yet unknown. Here, we apply a unique approach in combining in silico tools and molecular dynamics simulation to provide structural and functional insight into the effect of SNP on the stability and activity of mutant CBS. In addition, principal component analysis and free energy landscape were used to predict the collective motions, thermodynamic stabilities and essential subspace relevant to CBS function. The obtained results indicate that C109R, E176K and D376N mutations have the diverse effect on dynamic behavior of CBS protein. We found that highly conserved D376N mutation, which is present in the active pocket, affects the protein folding mechanism. Our strategy may provide a way in near future to understand and study effects of functional nsSNPs and their role in causing homocystinuria.

Keywords CBS      in silico      molecular dynamics simulation      SNPs     
Corresponding Author(s): C. GEORGE PRIYA DOSS   
Issue Date: 13 January 2015
 Cite this article:   
C. GEORGE PRIYA DOSS,B. RAJITH,R. MAGESH, et al. Influence of the SNPs on the structural stability of CBS protein: Insight from molecular dynamics simulations[J]. Front. Biol., 2014, 9(6): 504-518.
 URL:  
https://academic.hep.com.cn/fib/EN/10.1007/s11515-014-1320-4
https://academic.hep.com.cn/fib/EN/Y2014/V9/I6/504
Variants Amino Acid position SIFT Polyphen 2.0 I Mutant 3
VAR_046921 R18C 0.18 0.453
rs201827340 R18C 0.18 0.453
rs201372812 R45W 0.01 0.998 -1.27
rs148865119 P49L 0.01 0.977 -0.08
VAR_008049 P49L 0.01 0.977 -0.08
VAR_008050 R58W 0.01 0.982 ??0.16
rs199507134 E62K 0.98 0.012 -1.04
VAR_021790 H65R 0 0.982 ??0.05
rs17849313 A69P 0.94 0.00 ??0.32
rs185581633 P70L 0.68 0 ??0.6
rs192232907 K72I 0.16 0.001 ??0.12
VAR_002171 P78R 0.05 0.968 -0.21
VAR_008051 G85R 0 1 -0.3
VAR_002172 P88S 0 1 -1.37
rs71322503 R91K 0.37 0.008 -1.14
rs112029370 F99Y 1 0 -0.81
VAR_021791 L101P 0 1 -0.12
rs34040148 K102Q 0.07 0.301 -1.36
VAR_002173 K102N 0 0.994 -1.61
VAR_021792 C109R 0 1 -2.32
rs121964964 A114V 0.05 0.988 -1.55
VAR_002174 A114V 0.05 0.988 -1.55
VAR_008053 G116R 0 1 ??0.03
VAR_008054 R121C 0 1 -1.95
VAR_008055 R121H 0 1 -2.18
VAR_008056 R121L 0 1 -1.11
VAR_046923 R125P 0 1 -2.2
VAR_002175 R125Q 0.02 1 -2.19
VAR_008057 R125W 0 1 -1.64
VAR_008058 M126V 0 0.997 -0.11
VAR_008059 E128D 0.04 0.396 -1.11
VAR_002176 E131D 0 0.964 -0.5
rs140002610 R132C 0.01 0.998 ??0.6
rs147474549 G134R 0 0.999 -0.08
rs144832032 T135M 0.23 0.057 -0.48
VAR_008060 G139R 0 1 -1.44
VAR_021793 I143M 0 1 ??0.12
VAR_002177 E144K 0 1 -0.75
VAR_002178 P145L 0 1 -0.64
VAR_008061 G148R 0 1 -0.83
VAR_008062 G151R 0 1 -0.41
VAR_008064 I152M 0 0.985 ??0.78
VAR_046924 L154Q 0 1 -0.82
VAR_008065 A155T 0 0.999 -2.06
VAR_046925 A155V 0 1 -0.7
rs199817801 A157T 0.12 0.106 -2.28
VAR_002179 C165Y 0.07 1 -0.63
VAR_046926 V168A 0.01 0.947 -0.75
rs121964970 V168M 0.01 1 -0.21
VAR_002180 V168M 0.01 1 -0.21
VAR_046927 M173V 0 0.651 -1.1
VAR_008066 E176K 0 1 -2.43
VAR_008067 V180A 0.25 0.014 -0.49
rs149649130 R182W 0 1 -0.02
rs138314784 R182Q 0.01 0.919 -0.55
VAR_008068 T191M 0 1 -1.72
VAR_008069 D198V 0 0.997 -0.27
VAR_066099 P200L 0 0.993 -1.31
rs201118737 K211R 0.2 0 -1.22
rs139456571 R224C 0.02 0.872 -0.79
VAR_002181 R224H 0.03 0.782 -0.72
VAR_008070 A226T 0.11 0.353 -0.94
VAR_021794 N228K 0 1 -0.55
VAR_046928 N228S 0 1 -0.66
VAR_046929 A231P 0 0.97 ??0.07
VAR_008071 D234N 0.01 0.998 -0.99
VAR_002182 E239K 0 1 -0.7
rs148257986 M250I 0.01 0.667 ??0.54
VAR_002183 T257M 0 1 ??0.21
rs143124288 G259S 0 1 -1.16
VAR_008072 T262M 0 1 -0.31
VAR_021795 T262R 0 1 -0.51
rs121964969 R266K 0.09 0.59 -0.88
VAR_008073 R266G 0 0.995
VAR_021796 C275Y 0 0.998 -0.2
VAR_066100 I278S 0 0.99 -0.64
rs117019516 I278T 0 0.967 -0.12
VAR_066101 D281N 0.02 0.998 -1.9
rs147040567 I286V 0.16 0.002 ??0.19
VAR_046932 A288P 0 0.98 -0.3
VAR_046933 A288T 0 0.986 -0.28
rs141502207 A288S 0.03 0.593
VAR_002185 P290L 0 0.997
rs201155833 E291D 0.3 0 -0.67
VAR_008076 E302K 0.17 0.616 -0.15
VAR_008077 G305R 0 1 ??0.27
VAR_002186 G307S 0 1 -0.22
VAR_008078 V320A 0.07 0.977 -1.12
VAR_066102 D321V 0 1 -0.9
VAR_008079 A331E 0 0.711 -0.33
VAR_002187 A331V 0.01 0.845 -0.16
VAR_002188 R336C 0 1 -0.62
VAR_008080 R336H 0 1 -1.32
VAR_021797 L338P 0 1 -0.5
VAR_021798 G347S 0 0.999 -1.93
VAR_021799 S349N 0 0.986 -0.72
VAR_008081 S352N 0.01 0.566 -0.21
VAR_008082 T353M 0 0.102 -0.17
rs121964972 T353M 0 0.102 -0.17
VAR_008083 V354M 1 0.016 -0.42
VAR_021800 A355P 0.19 0.66 -0.03
rs148589243 V358M 0.09 0.412 -0.16
VAR_046934 A361T 0.01 0.482 -0.83
VAR_008084 R369C 0 1 -0.44
rs11700812 R369P 0 1 -0.09
rs11700812 R369H 0 1 -0.72
VAR_008085 C370Y 0 1 -0.54
VAR_002190 V371M 0 1 ??0.06
VAR_046935 D376N 0 1 -2.07
VAR_021801 R379Q 0.01 0.847 -0.99
VAR_046936 R379W 0 1 -0.31
VAR_002191 K384E 0.01 0.996 -1.2
rs121964967 K384E 0.01 0.996 -1.2
VAR_008086 K384N 0 0.999 -1.69
VAR_008087 M391I 0.01 0.414 ??0.13
rs28934892 P422L 0.11 0.9
rs138211175 V425M 0.01 0.983 -0.8
VAR_008088 T434N 0 0.86 -0.54
VAR_008089 I435T 0.07 0.528 -0.64
VAR_008090 R439Q 0.19 0.212 -0.25
rs28934891 D444N 0.05 0.122 -0.46
VAR_066103 A446S 0 0.336 -1.79
rs201585750 A452V 0.07 0.012
VAR_002193 V454E 1 0
VAR_021803 L456P 0.04 0.999 -0.56
rs141428279 M464T 0 0.999 -2.21
rs121964971 S466L 0.01 0.376 ??0.2
VAR_008091 S466L 0.01 0.376 ??0.2
rs201098477 G471R 0.21 0.752 -1.62
VAR_008092 R491C 0.08 0.002 -0.45
rs200613751 M505I 0.43 0
rs145228319 E514K 0.5 0.012 -1.28
rs201916339 G522R 0.02 0.027 ??0.1
VAR_046937 Q526K 0.98 0
VAR_008093 V534D 0 0.996 -1.37
VAR_002194 L539S 0 0.996 -0.93
rs121964968 L539S 0 0.996 -0.93
rs139651937 A545S 0.65 0.001 -1.91
rs150828989 R548Q 0.7 0.007 -0.67
VAR_046938 R548Q 0.7 0.007 -0.67
Tab.1  Analysis of functional SNPs in CBS gene using SIFT, PolyPhen 2 and I Mutant 3.0 tools
SNP ID Allele (A/a) Regulatory element Type DifferenceP-value1 Adjusted differenceP-value 2 FS score3
rs706209 C/T LM226 TF 1.25E-06 0.012 0.176
rs4987122 C/T TGCGCANK TF 1.95E-05 0.189 0.189
rs1051316 C/T TF, SR 1
rs3788050 G/T hlh-2::hlh-3 TF 3.20E-06 0.032 0.158
rs8127973 C/T TF 0.158
rs2124458 C/T CCCNNNAWT TF 1.93E-05 0.188 0.176
rs2124459 C/T TF 0.176
rs2124460 C/T YGTCCTTGR TF 1.49E-05 0.144 0.176
rs2124461 C/T LM176 TF 2.04E-05 0.199 0.199
rs8132811 C/T usp TF 1.47E-05 0.143 0.176
rs760124 A/G Egr1 TF 1.57E-05 0.153 0.05
rs234701 A/G LM141 TF 1.87E-05 0.182 0.176
rs11700992 A/G PUT3 TF 9.61E-06 0.093 0.176
rs234702 C/G LM145 TF 1.35E-05 0.138 0.176
rs6586282 C/T Tcfcp2I1 TF 9.08E-06 0.088 0.176
rs2895956 C/T opa TF 3.71E-06 0.036 0.176
rs9325622 A/G Tcfe2a_2 TF 1.25E-06 0.012 0.176
rs11203172 C/T TGCGCANK TF 1.95E-05 0.188 0.176
rs234704 A/G SOX2 TF 2.88E-05 0.035 0.176
rs1801181 A/G Pou5f1 TF, SR 1.06E-06 0.189 0.289
rs2014564 A/G REL TF, 1.49E-05 0.006 0.208
rs4920037 C/G Spz1 TF 9.00E-06 0.088 0.176
rs7276378 C/T TEAD1 TF 3.21E-06 0.046 0.176
rs1789953 C/T Ik-2 TF 2.25E-06 0.112 0.05
rs2228298 A/G LM176 SR 2.95E-05 0.198 0.103
rs5742905 C/T LM226 SR 1.99E-05 0.044 0.648
rs9978861 A/G NIT2 TF 2.14E-05 0.899 0.178
rs9978863 A/G SC35 TF 1.77E-05 0.142 0.208
rs2849727 A/G RUNX1 TF 1.57E-05 0.153 0.208
rs234705 C/G HSF TF 1.87E-05 0.182 0.176
rs1788466 C/T Spz TF 9.61E-06 0.093 0.208
rs234706 C/T Egr1 TF 2.04E-05 0.199 0.103
rs2298758 A/G Ik-2 TF 1.47E-05 0.143 0.269
rs2298759 C/T hlh-2::hlh-3 TF 1.57E-05 0.153 0.208
rs2298760 A/G NIT-2 TF 1.87E-05 0.182 0.242
rs2298761 A/G Sp1 TF 9.61E-06 0.093 0.242
rs234707 A/G HSF TF 2.04E-05 0.199 0.242
rs234708 C/G Egr1 TF 1.47E-05 0.143 0.242
Tab.2  Potential cis-acting regulatory elements affected by nsSNPs identified by expression quantitative trait loci analysis
Amino acid change Total energy (kJ/mol)
before minimization After minimization
Native -11063.460 -15831.080
C109R -10127.572 -15343.102
R121H -10711.939 -15217.965
R125P -10153.237 -15604.006
E176K -10078.544 -15217.565
D376N -10009.343 -15118.850
Tab.3  Total energy of native and mutant structures before and after energy minimization.
Fig.1  (A) Time evolution of backbone RMSDs are shown as a function of time of the wild and mutant structures at 6000 ps. The symbol coding scheme is as follows: wild (black color), mutant C109R (blue color), E176K (green color) and D376N (red color). (B) RMSF of the backbone carbon alpha over the entire simulation. The ordinate is RMSF (nm), and the abscissa is atom. The symbol coding scheme is as follows: wild (black color), mutant C109R (blue color), E176K (green color) and D376N (red color). (C) Rg of the protein backbone over the entire simulation. The ordinate is Rg (nm), and the abscissa is residue. The symbol coding scheme is as follows: wild (black color), mutant C109R (Blue color), E176K (green color) and D376N (red color). (D) Solvent accessible surface of protein over the entire simulation. The ordinate is SASA (nm2), and the abscissa is atom. The symbol coding scheme is as follows: wild (black color), mutant C109R (blue color), E176K (green color) and D376N (red color). (E) Analysis of Intermolecular NH bond of native and mutant model protein at 6000ps. Average number of intermolecular hydrogen bond in native and mutant versus time. The symbol coding scheme is as follows: wild (black color), mutant C109R (blue color), E176K (green color) and D376N (red color).
Fig.2  2D projection of first two principal eigenvector and comparison of free energy landscape of native and mutant CBS protein at 300K. (A) 2D Projection of native CBS at 300K. (B) Free energy landscape of native CBS. (C) 2D Projection of mutant C109R CBS at 300K. (D) Free energy landscape of Mutant CBS. (E) 2D Projection of mutant E176K CBS at 300K. (F) 2D Free energy landscape of mutant E176K CBS. (G) 2D Projection of mutant D376N CBS at 300K. (H) Free energy landscape of mutant D376N CBS.
Fig.3  The conservation pattern of amino acid sequence in CBS. The location of amino acid residues in CBS based on the evolutionary conservation pattern. Red color box indicate the position of native amino acid position which are predicted to be most functionally significant by various in silico tools. Color intensity increases with the degree of conservation.
1 Adzhubei I A, Schmidt S, Peshkin L, Ramensky V E, Gerasimova A, Bork P, Kondrashov A S, Sunyaev S R (2010). A method and server for predicting damaging missense mutations. Nat Methods, 7(4): 248–249
https://doi.org/10.1038/nmeth0410-248
2 Afman L A, Lievers K J A, Kluijtmans L A J (2003). Gene-gene interaction between the cystathionine b-synthase 31 base pair variable number of tandem repeats and the methylenetetrahydrofolate reductase 677C>T polymorphism on homocysteine levels and risk for neural tube defects. Mol Genet Metab, 78(3): 211–215
https://doi.org/10.1016/S1096-7192(03)00021-0
3 Aly T A, Eller E, Ide A, Gowan K, Babu S R, Erlich H A, Rewers M J, Eisenbarth G S, Fain P R (2006). Multi-SNP analysis of MHC region: remarkable conservation of HLA-A1–B8-DR3 haplotype. Diabetes, 55(5): 1265–1269
https://doi.org/10.2337/db05-1276
4 Amadei A, Linssen A B M, Berendsen H J C (1993). Essential dynamics of proteins. Proteins, 17(4): 412–425
https://doi.org/10.1002/prot.340170408
5 Amberger J, Bocchini C A, Scott A F, Hamosh A (2009). Online Mendelian Inheritance in Man (OMIM). Nucleic Acids Res, 37(Database): D793–D796
https://doi.org/10.1093/nar/gkn665
6 Amos B, Rolf A (1996). The SWISS-PROT protein sequence data bank and its new supplement TREMBL. Nucleic Acids Res, 24(1): 21–25
https://doi.org/10.1093/nar/24.1.21
7 Beer H D, Wohlfahrt G, McCarthy J E, Schomburg D, Schmid R D (1996). Analysis of the catalytic mechanism of a fungal lipase using computer-aided design and structural mutants. Protein Eng, 9(6): 507–517
https://doi.org/10.1093/protein/9.6.507
8 Benjamini Y, Hochberg Y (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser A Stat Soc, 57: 289–300
9 Berman H M, Westbrook J, Feng Z, Gilliland G, Bhat T N, Weissig H, Shindyalov I N, Bourne P E (2000). The protein data bank. Nucleic Acids Res, 28(1): 235–242
https://doi.org/10.1093/nar/28.1.235
10 Boyles A L, Billups A V, Deak K L, Siegel D G, Mehltretter L, Slifer S H, Bassuk A G, Kessler J A, Reed M C, Nijhout H F, George T M, Enterline D S, Gilbert J R, Speer M C (2006). NTD Collaborative Group. Neural tube defects and folate pathway genes: family-based association tests of gene-gene and gene-environment interactions. Environ Health Perspect, 114(10): 1547–1552
https://doi.org/10.1289/ehp.9166
11 Boyles A L, Wilcox A J, Taylor J A, Meyer K, Fredriksen A, Ueland P M, Drevon C A, Vollset S E, Lie R T (2008). Folate and one-carbon metabolism gene polymorphisms and their associations with oral facial clefts. Am J Med Genet A, 146A(4): 440–449
https://doi.org/10.1002/ajmg.a.32162
12 Capriotti E, Fariselli P, Rossi I, Casadio R (2008). A three-state prediction of single point mutations on protein stability changes. BMC Bioinformatics, 2(Suppl 2): S6
https://doi.org/10.1186/1471-2105-9-S2-S6
13 Chan P A, Duraisamy S, Miller P J, Newell J A, McBride C, Bond J P, Raevaara T, Ollila S, Nystr?m M, Grimm A J, Christodoulou J, Oetting W S, Greenblatt M S (2007). Interpreting missense variants: comparing computational methods in human disease genes CDKN2A, MLH1, MSH2, MECP2, and tyrosinase (TYR). Hum Mutat, 28(7): 683–693
https://doi.org/10.1002/humu.20492
14 Cheung V G, Nayak R R, Wang I X, Elwyn S, Cousins S M, Morley M, Spielman R S (2010). Polymorphic Cis- and Trans-Regulation of Human Gene Expression. PLoS Biol, 8(9): e1000480
https://doi.org/10.1371/journal.pbio.1000480
15 Chun S, Fay J C (2009). Identification of deleterious mutations within three human genomes. Genome Res, 19(9): 1553–1561
https://doi.org/10.1101/gr.092619.109
16 Doniger S W, Kim H S, Swain D, Corcuera D, Williams M, Yang S P, Fay J C (2008). Catalog of neutral and deleterious polymorphism in yeast. PLoS Genet, 4(8): e1000183
https://doi.org/10.1371/journal.pgen.1000183
17 Essmann U, Perera L, Berkowitz M L, Darden T, Lee H, Pedersen L G (1995). A smooth particle meshes Ewald method. J Chem Phys, 103(19): 8577–8593
https://doi.org/10.1063/1.470117
18 Fan B J, Chen T, Grosskreutz C, Pasquale L, Rhee D, DelBono E, Haines J L, Wiggs J L (2008). Lack of association of polymorphisms in homocysteine metabolism genes with pseudoexfoliation syndrome and glaucoma. Mol Vis, 14: 2484–2491
19 Gaustadnes M, Wilcken B, Oliveriusova J, McGill J, Fletcher J, Kraus J P, Wilcken D E (2002). The molecular basis of cystathionine beta-synthase deficiency in Australian patients: genotype-phenotype correlations and response to treatment. Hum Mutat, 20(2): 117–126
https://doi.org/10.1002/humu.10104
20 George Priya Doss C (2012). In Silico Profiling of deleterious Amino Acid Substitutions of Potential Pathological Importance in Hemophilia A and Hemophilia B. BMC J Biomed Sci, 19: 30
21 George Priya Doss C, Chakraborty C, Syed Haneef S A, NagaSundaram N, Chen, Zhu H (2014). Evolution and structure-based computational design to reveal the impact of deleterious missense mutations in type 2 maturity-onset diabetes of the young. Theranostics, 4: 366–385
https://doi.org/10.7150/thno.7473
22 Hao D C, Feng Y, Xiao R, Xiao P G (2011). Non-neutral nonsynonymous single nucleotide polymorphisms in human ABC transporters: the first comparison of six prediction methods. Pharmacol Rep, 63(4): 924–934
https://doi.org/10.1016/S1734-1140(11)70608-9
23 Hegger R, Altis A, Nguyen P H, Stock G (2007). How complex is the dynamics of peptide folding? Phys Rev Lett, 98(2): 028102
https://doi.org/10.1103/PhysRevLett.98.028102
24 Hess B, Kutzner D, Spoel D (2008). GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. J Chem Theory Comput, 4(3): 435–447
https://doi.org/10.1021/ct700301q
25 Hicks S, Wheeler D A, Plon S E, Kimmel M (2011). Prediction of missense mutation functionality depends on both the algorithm and sequence alignment employed. Hum Mutat, 32(6): 661–668
https://doi.org/10.1002/humu.21490
26 Hnízda A, Majtan T, Liu L, Pey A L, Carpenter J F, Kodí?ek M, Ko?ich V, Kraus J P (2012). Conformational properties of nine purified cystathionine β-synthase mutants. Biochemistry, 51(23): 4755–4763
https://doi.org/10.1021/bi300435e
27 Janosík M, Oliveriusová J, Janosíková B, Sokolová J, Kraus E, Kraus J P, Kozich V (2001). Impaired heme binding and aggregation of mutant cystathionine betasynthase subunits in homocystinuria. Am J Hum Genet, 51(6): 1506–1513
https://doi.org/10.1086/320597
28 Jolliffe I T (2002). Principal Component Analysis. New York: Springer
29 Jorgensen W L, Chandrasekhar J, Madura J D, Impey R W, Klein M L (1983). Comparison of simple potential functions for simulating liquid water. J Chem Phys, 79(2): 926
https://doi.org/10.1063/1.445869
30 Katsushima F, Oliveriusova J, Sakamoto O, Ohura T, Kondo Y, Iinuma K, Kraus E, Stouracova R, Kraus J P (2006). Expression study of mutant cystathionine beta-synthase found in Japanese patients with homocystinuria. Mol Genet Metab, 87(4): 323–328
https://doi.org/10.1016/j.ymgme.2005.09.013
31 Khan S, Vihinen M (2010). Performance of protein stability predictors. Hum Mutat, 31(6): 675–678
https://doi.org/10.1002/humu.21242
32 Kim C E, Gallagher P M, Guttormsen A B, Refsum H, Ueland P M, Ose L, Folling I, Whitehead A S, Tsai M Y, Kruger W (1997). Functional modeling of vitamin responsiveness in yeast: a common pyridoxine-responsive cystathionine b synthase mutation in homocystinuria. Hum Mol Genet, 6(13): 2213–2221
https://doi.org/10.1093/hmg/6.13.2213
33 Kraus J P, Janosík M, Kozich V, Mandell R, Shih V, Sperandeo M P, Sebastio G, de Franchis R, Andria G, Kluijtmans L A, Blom H, Boers G H, Gordon R B, Kamoun P, Tsai M Y, Kruger W D, Koch H G, Ohura T, Gaustadnes M(1999). Cystathionine beta-synthase mutations in homocystinuria. Hum Mutat, 13: 362–375
https://doi.org/10.1002/(SICI)1098-1004(1999)13:5<362::AID-HUMU4>3.0.CO;2-K
34 Kruger W D, Wang L, Jhee K H, Singh R H, Elsas L J2nd (2003). Cystathionine beta-synthase deficiency in Georgia (USA): correlation of clinical and biochemical phenotype with genotype. Hum Mutat, 22(6): 434–441
https://doi.org/10.1002/humu.10290
35 Lee P H, Shatkay H (2008). F-SNP: computationally predicted functional SNPs for disease association studies. Nucleic Acids Res, 36(Database): D820–D824
https://doi.org/10.1093/nar/gkm904
36 Lee P H, Shatkay H (2009). An integrative scoring system for ranking SNPs by their potential deleterious effects. Bioinformatics, 25(8): 1048–1055
https://doi.org/10.1093/bioinformatics/btp103
37 Lino Cardenas C L, Renault N, Farce A, Cauffiez C, Allorge D, Lo-Guidice J M, Lhermitte M, Chavatte P, Broly F, Chevalier D (2011). Genetic polymorphism of CYP4A11 and CYP4A22 genes and in silico insights from comparative 3D modelling in a French population. Gene, 487(1): 10–20
https://doi.org/10.1016/j.gene.2011.07.015
38 Macintyre G, Bailey J, Haviv I, Kowalczyk A (2010). is-rSNP: a novel technique for in silico regulatory SNP detection. Bioinformatics, 26(18): i524–i530
https://doi.org/10.1093/bioinformatics/btq378
39 Magesh R, George Priya Doss C (2012). Computational methods to work as first-pass filter in deleterious SNP analysis of alkaptonuria. ScientificWorldJournal, 2012: 738423
40 Martinez CA, Northrup H, Lin J I, Morrison A C, Fletcher J M, Tyerman G H, Au K S (2009). Genetic association study of putative functional single nucleotide polymorphisms of genes in folate metabolism and spina bifida. Am J Obstet Gynecol201: 394e1–11
41 Meier M, Janosik M, Kery V, Kraus J P, Burkhard P (2001). Structure of human cystathionine beta-synthase: a unique pyridoxal 5′-phosphate-dependent heme protein. EMBO J, 20(15): 3910–3916
https://doi.org/10.1093/emboj/20.15.3910
42 Meier M, Oliveriusova J, Kraus J P, Burkhard P (2003). Structural insights into mutations of cystathionine beta-synthase. Biochim Biophys Acta, 1647(1–2): 206–213
https://doi.org/10.1016/S1570-9639(03)00048-7
43 Metayer C, Scélo G, Chokkalingam A P, Barcellos L F, Aldrich M C, Chang J S, Guha N, Urayama K Y, Hansen H M, Block G, Kiley V, Wiencke J K, Wiemels J L, Buffler P A (2011). Genetic variants in the folate pathway and risk of childhood acute lymphoblastic leukemia. Cancer Causes Control, 22(9): 1243–1258
https://doi.org/10.1007/s10552-011-9795-7
44 Mudd S H, Levy H, Kraus J P (2001). Disorders in transsulfuration. In: The Metabolic and Molecular Bases of Inherited Disease. McGraw-Hill, NY, pp. 2007–2056
45 Ng P C, Henikoff S (2003). SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res, 31(13): 3812–3814
https://doi.org/10.1093/nar/gkg509
46 Paré G, Chasman D I, Parker A N, Zee R R, M?larstig A, Seedorf U, Collins R, Watkins H, Hamsten A, Miletich J P, Ridker P M (2009). Novel associations of CPS1, MUT, NOX4, and DPEP1 with plasma homocysteine in a healthy population: a genome-wide evaluation of 13 974 participants in the Women's Genome Health Study. Circ Cardiovasc Genet, 2(2): 142–150
https://doi.org/10.1161/CIRCGENETICS.108.829804
47 Rabbani B, Mahdieh N, Haghi Ashtiani M T, Setoodeh A, Rabbani A (2012). In silico structural, functional and pathogenicity evaluation of a novel mutation: an overview of HSD3B2 gene mutations. Gene, 503(2): 215–221
https://doi.org/10.1016/j.gene.2012.04.080
48 Sandberg R, Neilson J R, Sarma A, Sharp P A, Burge C B (2008). Proliferating cells express mrnas with shortened 39 untranslated regions and fewer microRNA target sites. Science, 320(5883): 1643–1647
https://doi.org/10.1126/science.1155390
49 Sandelin A, Alkema W, Engstr?m P, Wasserman W W, Lenhard B (2004). JASPAR: an open-access database for eukaryotic transcription factor binding profiles. Nucleic Acids Res, 32(90001): D91–D94
https://doi.org/10.1093/nar/gkh012
50 Schwarz J M, R?delsperger C, Schuelke M, Seelow D (2010). MutationTaster evaluates disease-causing potential of sequence alterations. Nat Methods, 7(8): 575–576
https://doi.org/10.1038/nmeth0810-575
51 Sherry S T, Ward M, Sirotkin K (2001). dbSNP: the NCBI database of genetic variation. Nucleic Acids Res, 29(1): 308–311
https://doi.org/10.1093/nar/29.1.308
52 Singh L R, Chen X, Kozich V, Kruger W D (2007). Chemical chaperone rescue of mutant human cystathionine beta-synthase. Mol Genet Metab, 91(4): 335–342
https://doi.org/10.1016/j.ymgme.2007.04.011
53 Steck P A, Pershouse M A, Jasser S A, Yung W K, Lin H, Ligon A H, Langford L A, Baumgard M L, Hattier T, Davis T, Frye C, Hu R, Swedlund B, Teng D H, Tavtigian S V (1997). Identification of a candidate tumour suppressor gene, MMAC1, at chromosome 10q23.3 that is mutated in multiple advanced cancers. Nat Genet, 15(4): 356–362
https://doi.org/10.1038/ng0497-356
54 Steinmaus C, Yuan Y, Kalman D, Rey O A, Skibola C F, Dauphine D, Basu A, Porter K E, Hubbard A, Bates M N, Smith M T, Smith A H (2010). Individual differences in arsenic metabolism and lung cancer in a case-control study in Cordoba, Argentina. Toxicol Appl Pharmacol, 247(2): 138–145
https://doi.org/10.1016/j.taap.2010.06.006
55 Thusberg J, Olatubosun A, Vihinen M (2011). Performance of mutation pathogenicity prediction methods on missense variants. Hum Mutat, 32(4): 358–368
https://doi.org/10.1002/humu.21445
56 Thusberg J, Vihinen M (2009). Pathogenic or not? And if so, then how? Studying the effects of missense mutations using bioinformatics methods. Hum Mutat, 30(5): 703–714
https://doi.org/10.1002/humu.20938
57 Tilley M M, Northrup H, Au K S (2012). Genetic studies of the cystathionine beta-synthase gene and myelomeningocele. Birth Defects Res A Clin Mol Teratol, 94(1): 52–56
https://doi.org/10.1002/bdra.22855
58 van Aalten D M, Amadei A, Linssen A B, Eijsink V G, Vriend G, Berendsen H J (1995). The essential dynamics of thermolysin: confirmation of the hinge-bending motion and comparison of simulation in vacuum and water. Proteins, 22(1): 45–54
https://doi.org/10.1002/prot.340220107
59 Wang G, Guo X, Floros J (2005). Differences in the translation efficiency and mRNA stability mediated by 59-UTR splice variants of human SP-A1 and SPA2 genes. AJP- Lung Physiol, 289: L497–L508
60 Wei Q, Wang L, Wang Q, Kruger W D, Dunbrack R L (2010). Testing computational prediction of missense mutation phenotypes: functional characterization of 204 mutations of human cystathionine beta synthase. Proteins, 78: 2058–2074
61 Wernimont S M, Clark A G, Stover P J, Wells M T, Litonjua A A, Weiss S T, Gaziano J M, Tucker K L, Baccarelli A, Schwartz J, Bollati V, Cassano P A (2011). Folate network genetic variation, plasma homocysteine, and global genomic methylation content: a genetic association study. BMC Med Genet, 12(1): 150
https://doi.org/10.1186/1471-2350-12-150
[1] Naresh KANDAKATLA,Geetha RAMAKRISHNAN,Rajasekhar CHEKKARA,Namachivayam BALAKRISHNAN. Computational screening of disease associated mutations on NPC1 gene and its structural consequence in Niemann-Pick type-C1[J]. Front. Biol., 2014, 9(5): 410-421.
[2] Juan WANG, Wenqin SONG. Analysis of genotype polymorphism of tumor-related genes harbored in chromosome arm 1p and 8p in hepatocellular carcinoma patients by cSNP chip[J]. Front Biol Chin, 2009, 4(1): 82-88.
[3] LIU Kai, WANG Yan, TAN Hongwei, CHEN Guangju, TONG Zhenhe. Computational simulations on the fish-type-II antifreeze protein-ice-solvent system[J]. Front. Biol., 2007, 2(2): 180-183.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed