Computational screening of disease associated mutations on NPC1 gene and its structural consequence in Niemann-Pick type-C1

doi:10.1007/s11515-014-1314-2

Front. Biol.

2014, Vol. 9

Issue (5) : 410-421 https://doi.org/10.1007/s11515-014-1314-2

RESEARCH ARTICLE

Computational screening of disease associated mutations on NPC1 gene and its structural consequence in Niemann-Pick type-C1

Naresh KANDAKATLA^1,^*(

),Geetha RAMAKRISHNAN¹,Rajasekhar CHEKKARA¹,Namachivayam BALAKRISHNAN²

1. Department of Chemistry, Sathayabama University, Jeppiaar Nagar, Chennai-600119, India
2. St. Joseph’s College, Bharathidasan University, Tiruchirappalli, 620002, India

Download: PDF(368 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Niemann-Pick disease type C1 (NPC1), caused by mutations of NPC1 gene, is an inherited lysosomal lipid storage disorder. Loss of functional NPC1 causes the accumulation of free cholesterol (FC) in endocytic organelles that comprised the characteristics of late endosomes and/or lysosomes. In this study we analyzed the pathogenic effect of 103 nsSNPs reported in NPC1 using computational methods. R1186C, S940L, R958Q and I1061T mutations were predicted as most deleterious and disease associated with NPC1 using SIFT, Polyphen 2.0, PANTHER, PhD-SNP, Pmut and MUTPred tools which were also endorsed with previous in vivo experimental studies. To understand the atomic arrangement in 3D space, the native and disease associated mutant (R1186C, S940L, R958Q and I1061T) structures were modeled. Quantitative structural and flexibility analysis was conceded to observe the structural consequence of prioritized disease associated mutations (R1186C, S940L, R958Q and I1061T). Accessible surface area (ASA), free folding energy (FFE) and hydrogen bond (NH bond) showed more flexibility in 3D space in mutant structures. Based on the quantitative assessment and flexibility analysis of NPC1 variants, I1061T showed the most deleterious effect. Our analysis provides a clear clue to wet laboratory scientists to understand the structural and functional effect of NPC1 gene upon mutation.

Keywords Niemann-Pick disease type C1 SNPs gene mutation

Corresponding Author(s): Naresh KANDAKATLA

Online First Date: 11 August 2014 Issue Date: 11 October 2014

Cite this article:

Naresh KANDAKATLA,Geetha RAMAKRISHNAN,Rajasekhar CHEKKARA, et al. 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.

URL:

https://academic.hep.com.cn/fib/EN/10.1007/s11515-014-1314-2
https://academic.hep.com.cn/fib/EN/Y2014/V9/I5/410

SNP ID	Mutation	SIFT	PolyPhen 2.0	PANTHER
Toleranceindex	Prediction	PSIC	prediction	subPSEC	Prediction
rs202140203	Y634C	0.00	Deleterious	1.000	Damaging	-6.31284	Deleterious
rs202046984	I787V	0.71	Tolerated	0.000	Benign	-3.66577	Deleterious
rs201956601	D232G	0.46	Tolerated	0.000	Benign	NA	NA
rs201791992	V517I	0.52	Tolerated	0.003	Benign	-3.72368	Deleterious
rs201351377	N490T	0.04	Deleterious	0.866	Damaging	-5.18241	Deleterious
rs201236716	N598S	0.16	Tolerated	0.877	Damaging	-3.59379	Deleterious
rs201226297	P471L	0.00	Deleterious	1.000	Damaging	-8.04132	Deleterious
rs201156397	A558S	0.18	Tolerated	0.990	Damaging	-5.01162	Deleterious
rs201100763	L737V	0.01	Deleterious	0.995	Damaging	-3.39472	Deleterious
rs201021988	D182N	0.22	Tolerated	0.000	Benign	NA	NA
rs200444084	R1186H	0.00	Deleterious	1.000	Damaging	-4.25642	Deleterious
rs200323346	V148L	0.34	Tolerated	0.000	Benign	NA	NA
rs200291759	N70S	0.45	Tolerated	0.001	Benign	-2.68708	Tolerated
rs200264267	R1272C	0.04	Deleterious	0.999	Damaging	-6.61915	Deleterious
rs200243024	S365L	0.06	Tolerated	0.334	Benign	-3.67158	Deleterious
rs199963560	A181T	0.07	Tolerated	0.500	Damaging	NA	NA
rs199812609	V494M	0.11	Tolerated	0.008	Benign	-4.24951	Deleterious
rs199693280	C334S	0.36	Tolerated	0.000	Benign	-3.37473	Deleterious
rs193182840	V780M	0.01	Deleterious	0.961	Damaging	-6.53794	Deleterious
rs192963719	A183V	0.28	Tolerated	0.000	Benign	NA	NA
rs191876836	K1010Q	0.36	Tolerated	0.007	Benign	-3.84354	Deleterious
rs191537721	D502E	1.00	Tolerated	0.000	Benign	-3.52121	Deleterious
rs190298665	F842L	0.00	Deleterious	1.000	Damaging	-4.95077	Deleterious
rs151305963	R1274Q	0.20	Tolerated	0.003	Benign	-4.17639	Deleterious
rs150334966	S1004L	0.23	Tolerated	0.837	Damaging	-5.30274	Deleterious
rs150154006	E301K	0.50	Tolerated	0.109	Benign	NA	NA
rs150053420	R372Q	0.57	Tolerated	0.001	Benign	-3.57866	Deleterious
rs149074243	M156V	0.42	Tolerated	0.019	Benign	NA	NA
rs149020783	C352F	0.46	Tolerated	0.000	Benign	-4.21514	Deleterious
rs148571882	W1122R	0.46	Tolerated	0.013	Benign	-4.31283	Deleterious
rs148237665	Y932S	0.42	Tolerated	0.077	Benign	-3.74345	Deleterious
rs148035987	R1183H	0.00	Deleterious	0.976	Damaging	-6.7934	Deleterious
rs147615070	M156T	0.67	Tolerated	0.014	Benign	NA	NA
rs147021046	N589S	0.28	Tolerated	0.001	Benign	-4.34832	Deleterious
rs146874573	V753M	0.07	Tolerated	0.643	Damaging	-5.2153	Deleterious
rs146666146	A1018T	0.03	Deleterious	0.883	Damaging	-4.6953	Deleterious
rs145666943	Q60H	0.09	Tolerated	0.656	Damaging	-4.24272	Deleterious
rs145362908	V810L	0.30	Tolerated	0.000	Benign	-3.88266	Deleterious
rs145297180	R1186C	0.00	Deleterious	1.000	Damaging	-6.79621	Deleterious
rs144725473	V1141G	0.00	Deleterious	0.984	Damaging	-4.77998	Deleterious
rs144687654	S652R	0.00	Deleterious	0.999	Damaging	-5.59673	Deleterious
rs143908453	T759A	0.62	Tolerated	0.340	Benign	-3.43971	Deleterious
rs143797098	P424A	0.98	Tolerated	0.014	Benign	-4.17737	Deleterious
rs143205855	G149R	0.33	Tolerated	0.248	Benign	NA	NA
rs143124972	S940L	0.01	Deleterious	0.999	Damaging	-5.68616	Deleterious
rs141892620	I450V	0.45	Tolerated	0.007	Benign	-3.9303	Deleterious
rs141440861	G1073S	0.77	Tolerated	0.069	Benign	-2.81269	Tolerated
rs141361998	V327I	0.13	Tolerated	0.002	Benign	NA	NA
rs141243713	R161W	0.00	Deleterious	0.982	Damaging	NA	NA
rs140952850	R116Q	0.16	Tolerated	0.000	Benign	-3.01094	Deleterious
rs140827681	F1207L	0.47	Tolerated	0.715	Damaging	-3.80895	Deleterious
rs140786703	A659V	0.41	Tolerated	0.033	Benign	-4.31882	Deleterious
rs140527006	E1271Q	0.24	Tolerated	0.604	Damaging	-4.43776	Deleterious
rs140211089	H1016R	0.23	Tolerated	0.243	Benign	-2.86351	Tolerated
rs140149624	S425L	0.14	Tolerated	0.006	Benign	-3.56317	Deleterious
rs139751448	R404Q	0.00	Deleterious	1.000	Damaging	-5.56223	Deleterious
rs139612110	S1169I	0.00	Deleterious	1.000	Damaging	-3.97501	Deleterious
rs139485263	N185D	0.17	Tolerated	0.005	Benign	NA	NA
rs139297968	A851T	0.02	Deleterious	0.994	Damaging	-5.57919	Deleterious
rs138277307	E43K	0.93	Tolerated	0.001	Benign	-2.86264	Tolerated
rs138184115	A521S	0.52	Tolerated	0.003	Benign	-3.62631	Deleterious
rs138151007	W291C	0.17	Tolerated	0.957	Damaging	NA	NA
rs138079168	A321V	0.29	Tolerated	0.001	Benign	NA	NA
rs120074136	C113R	0.00	Deleterious	1.000	Damaging	-3.53647	Deleterious
rs120074135	V950M	0.28	Tolerated	0.007	Benign	-4.28583	Deleterious
rs120074134	V378A	0.09	Tolerated	0.969	Damaging	-5.35292	Deleterious
rs120074132	R958Q	0.05	Deleterious	1.000	Damaging	-8.09327	Deleterious
rs120074131	L1213F	0	Deleterious	1.000	Damaging	-3.49078	Deleterious
rs120074130	V889M	0.12	Tolerated	0.995	Damaging	-4.2027	Deleterious
rs113371321	A1187V	0.12	Tolerated	0.999	Damaging	-3.41161	Deleterious
rs112387560	R646H	0.28	Tolerated	0.010	Benign	-5.13502	Deleterious
rs112101747	V706A	0.03	Deleterious	0.994	Damaging	-4.37216	Deleterious
rs111256741	A183T	0.36	Tolerated	0.000	Benign	NA	NA
rs80358259	I1061T	0	Deleterious	1.000	Damaging	-8.53429	Deleterious
rs80358258	A1054T	0.01	Deleterious	0.875	Damaging	-5.70035	Deleterious
rs80358257	P1007A	0.01	Deleterious	0.995	Damaging	-5.56021	Deleterious
rs80358254	G992R	0.50	Tolerated	0.789	Damaging	-3.84824	Deleterious
rs80358253	Q775P	0.00	Deleterious	1.000	Damaging	-7.73353	Deleterious
rs80358252	C177Y	0.02	Deleterious	0.999	Damaging	NA	NA
rs80358251	P237S	0.38	Tolerated	0.003	Benign	NA	NA
rs77815278	Y420S	0.17	Tolerated	0.742	Damaging	-3.46767	Deleterious
rs77080672	R411Q	0.58	Tolerated	0.000	Benign	-4.66248	Deleterious
rs76615690	V624I	1.00	Tolerated	0.005	Benign	-3.83876	Deleterious
rs61731969	M1179V	0.48	Tolerated	0.000	Benign	-4.50621	Deleterious
rs61731962	P434S	0.02	Deleterious	0.001	Benign	-4.94008	Deleterious
rs55680026	N222S	0.74	Tolerated	0.003	Benign	NA	NA
rs35248744	S1200G	0.00	Deleterious	0.998	Damaging	-6.50452	Deleterious
rs34302553	G911S	0.66	Tolerated	0.904	Damaging	-4.16641	Deleterious
rs34226296	V1115F	0.07	Tolerated	0.001	Benign	-4.57463	Deleterious
rs34084984	N961S	0.78	Tolerated	0.034	Benign	-3.56842	Deleterious
rs28942108	R978C	0.05	Deleterious	0.955	Damaging	-3.84198	Deleterious
rs28942107	A1035V	0.01	Deleterious	0.996	Damaging	-5.95932	Deleterious
rs28942106	Y1088C	0.00	Deleterious	1.000	Damaging	-7.52745	Deleterious
rs28942105	N1156S	0.00	Deleterious	1.000	Damaging	-4.42918	Deleterious
rs28942104	T1036M	0.00	Deleterious	1.000	Damaging	-4.29841	Deleterious
rs28940897	Q928P	0.09	Tolerated	0.082	Benign	NA	NA
rs17855819	S151G	0.12	Tolerated	0.091	Benign	NA	NA
rs13381670	T511M	0.01	Deleterious	0.978	Damaging	-4.23833	Deleterious
rs1805084	R1266Q	0.54	Tolerated	0.003	Benign	-3.3839	Deleterious
rs1805082	I858V	0.58	Tolerated	0.047	Benign	-3.71112	Deleterious
rs1805081	H215R	0.53	Tolerated	0.000	Benign	NA	NA
rs1788799	M642I	1.00	Tolerated	0.000	Benign	-3.60971	Deleterious
rs1140458	N931K	0.12	Tolerated	0.034	Benign	-3.89891	Deleterious

Tab.1 Summary of nsSNPs that were analyzed by three computational methods SIFT (Tolerated/Deleterious), PolyPhen 2.0 (Benign/Damaging) and PANTHER (Tolerated/Deleterious).

SNP IDs	Amino acid change	PHDsnp results	Pmut				MUTPred
Score	Prediction	g score	P score	Molecular variation	Prediction reliability
rs202140203	Y634C	Disease	0.6874	Pathological	0.910	0.1836	Gain of catalytic residue	Low confidence
rs201351377	N490T	Disease	0.2170	Neutral	0.669	0.132	Gain of helix	Low confidence
rs201226297	P471L	Disease	0.2581	Neutral	0.572	0.062	Gain of helix	Low confidence
rs201100763	L737V	Neutral	0.2533	Neutral	0.464	0.2612	Loss of stability	Low confidence
rs200444084	R1186H	Disease	0.5891	Pathological	0.900	0.0647	Loss of MoRF binding	Low confidence
rs200264267	R1272C	Disease	0.7847	Pathological	0.448	0.0125	Gain of sheet	Low confidence
rs193182840	V780M	Neutral	0.2847	Neutral	0.706	0.3997	Loss of catalytic residue	Low confidence
rs190298665	F842L	Disease	0.3521	Neutral	0.745	0.1532	Loss of stability	Low confidence
rs148035987	R1183H	Disease	0.5679	Pathological	0.443	0.0269	Loss of MoRF binding	Low confidence
rs146666146	A1018T	Disease	0.6817	Pathological	0.777	0.0737	Gain of phosphorylation	Low confidence
rs145297180	R1186C	Disease	0.6215	Pathological	0.804	0.0127	Loss of disorder	Actionable hypothesis
rs144725473	V1141G	Disease	0.8750	Pathological	0.664	0.0428	Loss of stability	Actionable hyphothesis
rs144687654	S652R	Disease	0.8608	Pathological	0.888	0.0516	Gain of MoRF binding	Low confidence
rs143124972	S940L	Disease	0.7018	Pathological	0.874	0.0284	Gain of catalytic residue	Actionable hypothesis
rs139751448	R404Q	Disease	0.4922	Neutral	0.969	0.0457	Loss of sheet	Actionable hypothesis
rs139612110	S1169I	Disease	0.7754	Pathological	0.787	0.0827	Gain of sheet	Low confidence
rs139297968	A851T	Neutral	0.5439	Pathological	0.661	0.079	Loss of helix	Low confidence
rs120074136	C113R	Neutral	0.7311	Pathological	0.974	0.0396	Gain of disorder	Actionable hypothesis
rs120074132	R958Q	Disease	0.8995	Pathological	0.815	0.0281	Loss of catalytic residue	Confident hypothesis
rs120074131	L1213F	Disease	0.9486	Pathological	0.968	0.0777	Loss of ubiquitination	Low confidence
rs112101747	V706A	Neutral	0.2515	Neutral	0.643	0.0375	Gain of catalytic residue	Actionable hypothesis
rs80358259	I1061T	Disease	0.7847	Pathological	0.950	6e-04	Gain of disorder	Very Confident hypothesis
rs80358258	A1054T	Disease	0.6808	Pathological	0.863	0.141	Loss of ubiquitination	Low confidence
rs80358257	P1007A	Disease	0.3212	Neutral	0.907	0.0151	Loss of stability	Actionable hypothesis
rs80358253	Q775P	Disease	0.7092	Pathological	0.898	0.1853	Gain of catalytic residue	Low confidence
rs35248744	S1200G	Neutral	0.6112	Pathological	0.671	0.0144	Loss of glycosylation	Actionable hypothesis
rs28942108	R978C	Neutral	0.8950	Pathological	0.850	0.0374	Loss of loop	Actionable hypothesis
rs28942107	A1035V	Disease	0.4889	Neutral	0.873	0.039	Gain of sheet	Actionable hypothesis
rs28942106	Y1088C	Disease	0.8843	Pathological	0.897	0.0558	Loss of helix	Low confidence
rs28942105	N1156S	Disease	0.4500	Neutral	0.936	0.2294	Gain of helix	Low confidence
rs28942104	T1036M	Disease	0.6654	Pathological	0.842	0.0986	Loss of loop	Low confidence

Tab.2 The disease-associated SNPs are predicted from PHDSnp server results, pathogenecity index obtained from Pmut server and g score, P score, molecular variations and prediction reliability calculated from MutPred server. Here the most deleterious nsSNPs are displayed in bold.

Tab.3 List of ASA value, Free Folding energy, RMSD and No. of Hydrogen bonds in native and mutant NPC1 proteins

Fig.1 (A)–(D) Superimposition of native and mutant (R1186C, S940L, R958Q and I1061T) modeled structures of NPC1.

Fig.2 Normal mode analysis of c-alpha carbon atom of native and mutant NPC1. (A) Native, (B) R1186C, (C) S940L, (D) R958Q, (E) I1061T.

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 pmid: 20354512
2	Amberger J, Bocchini C A, Scott A F, Hamosh A (2009). McKusick's online Mendelian inheritance in man (OMIM?). Nucleic Acids Res, 37(Database issue suppl 1): D793–D796 https://doi.org/10.1093/nar/gkn665 pmid: 18842627
3	Balu K, Purohit R (2013). Mutational analysis of TYR gene and its structural consequences in OCA1A. Gene, 513(1): 184–195 https://doi.org/10.1016/j.gene.2012.09.128 pmid: 23085273
4	Balu K, Rajendran V, Sethumadhavan R, PurohitR (2013). Investigation of binding phenomenon of NSP3 and p130Cas mutants and their effect on cell signalling. Cell Biochem Biophys, 67(2): 623–633 https://doi.org/10.1007/s12013-013-9551-6 pmid: 23494262
5	Bauer P, Knoblich R, Bauer C, Finckh U, Hufen A, Kropp J, Braun S, Kustermann-Kuhn B, Schmidt D, Harzer K, Rolfs A (2002). NPC1: Complete genomic sequence, mutation analysis, and characterization of haplotypes. Hum Mutat, 19(1): 30–38 https://doi.org/10.1002/humu.10016 pmid: 11754101
6	Beltroy E P, Richardson J A, Horton J D, Turley S D, Dietschy J M (2005). Cholesterol accumulation and liver cell death in mice with Niemann-Pick type C disease. Hepatology, 42(4): 886–893 https://doi.org/10.1002/hep.20868 pmid: 16175610
7	Capriotti E, Calabrese R, Casadio R (2006). Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. Bioinformatics, 22(22): 2729–2734 https://doi.org/10.1093/bioinformatics/btl423 pmid: 16895930
8	Carstea E D, Morris J A, Coleman K G, Loftus S K, Zhang D, Cummings C, Gu J, Rosenfeld M A, Pavan W J, Krizman D B, Nagle J, Polymeropoulos M H, Sturley S L, Ioannou Y A, Higgins M E, Comly M, Cooney A, Brown A, Kaneski C R, Blanchette-Mackie E J, Dwyer N K, Neufeld E B, Chang T Y, Liscum L, Strauss J F 3rd, Ohno K, Zeigler M, Carmi R, Sokol J, Markie D, O’Neill R R, van Diggelen O P, Elleder M, Patterson M C, Brady R O, Vanier M T, Pentchev P G, Tagle D A (1997). Niemann-Pick C1 disease gene: homology to mediators of cholesterol homeostasis. Science, 277(5323): 228–231 https://doi.org/10.1126/science.277.5323.228 pmid: 9211849
9	Carvalho M A, Marsillac S M, Karchin R, Manoukian S, Grist S, Swaby R F, Urmenyi T P, Rondinelli E, Silva R, Gayol L, Baumbach L, Sutphen R, Pickard-Brzosowicz J L, Nathanson K L, Sali A, Goldgar D, Couch F J, Radice P, Monteiro A N A (2007). Determination of cancer risk associated with germ line BRCA1 missense variants by functional analysis. Cancer Res, 67(4): 1494–1501 https://doi.org/10.1158/0008-5472.CAN-06-3297 pmid: 17308087
10	Carvalho M, Pino M A, Karchin R, Beddor J, Godinho-Netto M, Mesquita R D, Rodarte R S, Vaz D C, Monteiro V A, Manoukian S, Colombo M, Ripamonti C B, Graeme SuthersR R, Borg A, Radice P, Grist S A, Monteiro A N A, Billack B (2009). Analysis of a set of missense, frameshift, and in-frame deletion variants of BRCA1. Mutat Res, 660(1): 1–11
11	Chasman D, Adams R M (2001). Predicting the functional consequences of non-synonymous single nucleotide polymorphisms: structure-based assessment of amino acid variation. J Mol Biol, 307(2): 683–706 https://doi.org/10.1006/jmbi.2001.4510 pmid: 11254390
12	Fernandez-Valero E M, Ballart A, Iturriaga C, Lluch M, Macias J, Vanier M T, Pineda M, Coll M J (2005). Identification of 25 new mutations in 40 unrelated Spanish Niemann-Pick type C patients: genotype-phenotype correlations. Clin Genet, 68(3): 245–254 https://doi.org/10.1111/j.1399-0004.2005.00490.x pmid: 16098014
13	Ferrer-Costa C, Gelpí J L, Zamakola L, Parraga I, de la Cruz X, Orozco M (2005). PMUT: a web-based tool for the annotation of pathological mutations on proteins. Bioinformatics, 21(14): 3176–3178 https://doi.org/10.1093/bioinformatics/bti486 pmid: 15879453
14	Garver W S, Francis G A, Jelinek D, Shepherd G, Flynn J, Castro G, Walsh Vockley C, Coppock D L, Pettit K M, Heidenreich R A, Meaney F J (2007). The National Niemann-Pick C1 disease database: report of clinical features and health problems. Am J Med Genet A, 143A(11): 1204–1211 https://doi.org/10.1002/ajmg.a.31735 pmid: 17497724
15	Garver W S, Heidenreich R A (2002). The Niemann-Pick C proteins and trafficking of cholesterol through the late endosomal/lysosomal system. Curr Mol Med, 2(5): 485–505 https://doi.org/10.2174/1566524023362375 pmid: 12125814
16	Garver W S, Jelinek D, Meaney F J, Flynn J, Pettit K M, Shepherd G, Heidenreich R A, Vockley C M W, Castro G, Francis G A (2010). The National Niemann-Pick Type C1 Disease Database: correlation of lipid profiles, mutations, and biochemical phenotypes. J Lipid Res, 51(2): 406–415 https://doi.org/10.1194/jlr.P000331 pmid: 19744920
17	Goldgar D E, Easton D F, Deffenbaugh A M, Monteiro A N, Tavtigian S V, Couch F J, the Breast Cancer Information Core (BIC) Steering Committee (2004). Integrated evaluation of DNA sequence variants of unknown clinical significance: application to BRCA1 and BRCA2. Am J Hum Genet, 75(4): 535–544 https://doi.org/10.1086/424388 pmid: 15290653
18	Goldstein J L, Brown M S (1992). Lipoprotein receptors and the control of plasma LDL cholesterol levels. Eur Heart J, 13(Suppl B): 34–36 https://doi.org/10.1093/eurheartj/13.suppl_B.34 pmid: 1644100
19	Greer W L, Dobson M J, Girouard G S, Byers D M, Riddell D C, Neumann P E (1999). Mutations in NPC1 highlight a conserved NPC1-specific cysteine-rich domain, Am J Hum Genet, 65(5): 1252–1260
20	Harpaz Y, Gerstein M, Chothia C (1994). Volume changes on protein folding. Structure, 2(7): 641–649 https://doi.org/10.1016/S0969-2126(00)00065-4 pmid: 7922041
21	Hess B, Kutzner C, Van Der Spoel D, Lindahl E (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
22	Hollup S M, Salensminde G, Reuter N (2005). WEBnm@: a web application for normal mode analyses of proteins. BMC Bioinformatics, 6(1): 52 https://doi.org/10.1186/1471-2105-6-52 pmid: 15762993
23	Ioannou Y A (2000). The structure and function of the Niemann-Pick C1 protein. Mol Genet Metab, 71(1-2): 175–181 https://doi.org/10.1006/mgme.2000.3061 pmid: 11001808
24	Kamaraj B, Purohit R (2014). Computational screening of disease-associated mutations in OCA2 gene. Cell Biochem Biophys, 68(1): 97–109 https://doi.org/10.1007/s12013-013-9697-2 pmid: 23824587
25	Kamaraj B, Purohit R (2013). In silico screening and molecular dynamics simulation of disease-associated nssnp in tyrp1 gene and its structural consequences in OCA3. BioMed Res Int, 2013: Article ID 697051 https://doi.org/10.1155/2013/697051.
26	Kaplan W, Littlejohn T G (2001). Swiss-PDB viewer (deep view). Brief Bioinform, 2(2): 195–197 https://doi.org/10.1093/bib/2.2.195 pmid: 11465736
27	Karchin R (2009). Next generation tools for the annotation of human SNPs. Brief Bioinform, 10(1): 35–52 https://doi.org/10.1093/bib/bbn047 pmid: 19181721
28	Kobayashi T, Beuchat M H, Lindsay M, Frias S, Palmiter R D, Sakuraba H, Parton R G, Gruenberg J (1999). Late endosomal membranes rich in lysobisphosphatidic acid regulate cholesterol transport. Nat Cell Biol, 1(2): 113–118 https://doi.org/10.1038/15666 pmid: 10559883
29	Kumar A, Purohit R (2012a). Computational centrosomics: an approach to understand the dynamic behaviour of centrosome. Gene, 511(1): 125–126 https://doi.org/10.1016/j.gene.2012.09.040 pmid: 23000070
30	Kumar A, Purohit R (2012c). Computational investigation of pathogenic nsSNPs in CEP63 protein. Gene, 503(1): 75–82 https://doi.org/10.1016/j.gene.2012.04.032 pmid: 22555018
31	Kumar A, Purohit R (2013). Cancer associated E17K mutation causes rapid conformational drift in AKT1 pleckstrin homology (PH) domain. PLoS One, 8(5): e64364 https://doi.org/10.1371/journal.pone.0064364 pmid: 23741320
32	Kumar A, Purohit R (2014). Use of Long Term Molecular Dynamics Simulation in Predicting Cancer Associated SNPs. PLoS Comput Biol, 10(4): e1003318 https://doi.org/10.1371/journal.pcbi.1003318 pmid: 24722014
33	Kumar A, Rajendran V, Sethumadhavan R, Purohit R (2013). Molecular dynamic simulation reveals damaging impact of RAC1 F28L mutation in the switch I region. PLoS One, 8(10): e77453 https://doi.org/10.1371/journal.pone.0077453 pmid: 24146998
34	Kumar A, Rajendran V, Sethumadhavan R, Purohit R (2013a). Computational investigation of cancer-associated molecular mechanism in Aurora A (S155R) mutation. Cell Biochem Biophys, 66(3): 787–796 https://doi.org/10.1007/s12013-013-9524-9 pmid: 23412841
35	Kumar A, Rajendran V, Sethumadhavan R, Purohit R (2013b). Evidence of colorectal cancer-associated mutation in MCAK: a computational report. Cell Biochem Biophys, 67(3): 837–851 https://doi.org/10.1007/s12013-013-9572-1 pmid: 23564489
36	Kumar A, Rajendran V, Sethumadhavan R, Purohit R (2014). Relationship between a point mutation S97C in CK1δ protein and its affect on ATP-binding affinity. J Biomol Struct Dyn, 32(3): 394–405 https://doi.org/10.1080/07391102.2013.770373 pmid: 23527964
37	Kumar A, Rajendran V, Sethumadhavan R, Shukla P, Tiwari S, Purohit R (2014). Computational SNP analysis: current approaches and future prospects. Cell Biochem Biophys, 68(2): 233–239 https://doi.org/10.1007/s12013-013-9705-6 pmid: 23852834
38	Kumar P, Henikoff S, Ng P C (2009). Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc, 4(7): 1073–1081 https://doi.org/10.1038/nprot.2009.86 pmid: 19561590
39	Kumar A, Purohit R (2012b). Computational screening and molecular dynamics simulation of disease associated nsSNPs in CENP-E. Mutat Res, 738–739: 28–37
40	Kumar A, Rajendran V, Sethumadhavan R, Purohit R (2012). In silico prediction of a disease-associated STIL mutant and its affect on the recruitment of centromere protein J (CENPJ). FEBS Open Bio, 2: 285–293
41	Laskowski R A, Rullmannn J A, MacArthur M W, Kaptein R, Thornton J M (1996). AQUA and PROCHECK-NMR: programs for checking the quality of protein structures solved by NMR. J Biomol NMR, 8(4): 477–486 https://doi.org/10.1007/BF00228148 pmid: 9008363
42	Li B, Krishnan V G, Mort M E, Xin F, Kamati K K, Cooper D N, Mooney S D, Radivojac P (2009). Automated inference of molecular mechanisms of disease from amino acid substitutions. Bioinformatics, 25(21): 2744–2750 https://doi.org/10.1093/bioinformatics/btp528 pmid: 19734154
43	Liscum L (2000). Niemann-Pick type C mutations cause lipid traffic jam. Traffic, 1(3): 218–225 https://doi.org/10.1034/j.1600-0854.2000.010304.x pmid: 11208105
44	Millat G, Ba?lo N, Molinero S, Rodriguez C, Chikh K, Vanier M T (2005). Niemann-Pick C disease: use of denaturing high performance liquid chromatography for the detection of NPC1 and NPC2 genetic variations and impact on management of patients and families. Mol Genet Metab, 86(1-2): 220–232 https://doi.org/10.1016/j.ymgme.2005.07.007 pmid: 16126423
45	Millat G, Mar?ais C, Rafi M A, Yamamoto T, Morris J A, Pentchev P G, Ohno K, Wenger D A, Vanier M T (1999). Niemann-Pick C1 disease: the I1061T substitution is a frequent mutant allele in patients of Western European descent and correlates with a classic juvenile phenotype. Am J Hum Genet, 65(5): 1321–1329 https://doi.org/10.1086/302626 pmid: 10521297
46	Millat G, Mar?ais C, Tomasetto C, Chikh K, Fensom A H, Harzer K, Wenger D A, Ohno K, Vanier M T (2001). Niemann-Pick C1 disease: correlations between NPC1 mutations, levels of NPC1 protein, and phenotypes emphasize the functional significance of the putative sterol-sensing domain and of the cysteine-rich luminal loop. Am J Hum Genet, 68(6): 1373–1385 https://doi.org/10.1086/320606 pmid: 11333381
47	Mooney S (2005). Bioinformatics approaches and resources for single nucleotide polymorphism functional analysis. Brief Bioinform, 6(1): 44–56 https://doi.org/10.1093/bib/6.1.44 pmid: 15826356
48	Morris J A, Zhang D, Coleman K G, Nagle J, Pentchev P G, Carstea E D (1999). The genomic organization and polymorphism analysis of the human Niemann-Pick C1 gene. Biochem Biophys Res Commun, 261(2): 493–498 https://doi.org/10.1006/bbrc.1999.1070 pmid: 10425213
49	Ng P C, Henikoff S (2006). Predicting the effects of amino acid substitutions on protein function. Annu Rev Genomics Hum Genet, 7(1): 61–80 https://doi.org/10.1146/annurev.genom.7.080505.115630 pmid: 16824020
50	Pipalia N H, Huang A, Ralph H, Rujoi M, Maxfield F R (2006). Automated microscopy screening for compounds that partially revert cholesterol accumulation in Niemann-Pick C cells. J Lipid Res, 47(2): 284–301 https://doi.org/10.1194/jlr.M500388-JLR200 pmid: 16288097
51	Puri V, Watanabe R, Dominguez M, Sun X, Wheatley C L, Marks D L, Pagano R E (1999). Cholesterol modulates membrane traffic along the endocytic pathway in sphingolipid-storage diseases. Nat Cell Biol, 1(6): 386–388 https://doi.org/10.1038/14084 pmid: 10559968
52	Purohit R (2014). Role of ELA region in auto-activation of mutant KIT receptor: a molecular dynamics simulation insight. J Biomol Struct Dyn, 32(7): 1033–1046 https://doi.org/10.1080/07391102.2013.803264 pmid: 23782055
53	Purohit R, Rajendran V, Sethumadhavan R (2011a). Studies on adaptability of binding residues and flap region of TMC-114 resistance HIV-1 protease mutants. J Biomol Struct Dyn, 29(1): 137–152 https://doi.org/10.1080/07391102.2011.10507379 pmid: 21696230
54	Purohit R, Rajendran V, Sethumadhavan R (2011b). Relationship between mutation of serine residue at 315th position in M. tuberculosis catalase-peroxidase enzyme and Isoniazid susceptibility: an in silico analysis. J Mol Model, 17(4): 869–877 https://doi.org/10.1007/s00894-010-0785-6 pmid: 20593210
55	Purohit R, Sethumadhavan R (2009). Structural basis for the resilience of Darunavir (TMC114) resistance major flap mutations of HIV-1 protease. Interdiscip Sci, 1(4): 320–328 https://doi.org/10.1007/s12539-009-0043-8 pmid: 20640812
56	Rajendran V, Purohit R, Sethumadhavan R (2012). In silico investigation of molecular mechanism of laminopathy caused by a point mutation (R482W) in lamin A/C protein. Amino Acids, 43(2): 603–615 https://doi.org/10.1007/s00726-011-1108-7 pmid: 21989830
57	Rajendran V, Sethumadhavan R (2014). Drug resistance mechanism of PncA in Mycobacterium tuberculosis. J Biomol Struct Dyn, 32(2): 209–221
58	Ribeiro I, Marc?o A, Amaral O, Sá Miranda M C, Vanier M T, Millat G (2001). Niemann-Pick type C disease: NPC1 mutations associated with severe and mild cellular cholesterol trafficking alterations. Hum Genet, 109(1): 24–32 https://doi.org/10.1007/s004390100531 pmid: 11479732
59	Runz H, Dolle D, Schlitter A M, Zschocke J (2008). NPC-db, a Niemann-Pick type C disease gene variation database. Hum Mutat, 29(3): 345–350 https://doi.org/10.1002/humu.20636 pmid: 18081003
60	Scott C, Ioannou Y A(2004). The NPC1 protein: structure implies function. Biochimica et Biophysica Acta (BBA)-Mol Cell Biol L, 1685(1): 8–13
61	Sherry S T, Ward M H, Kholodov M, Baker J, Phan L, Smigielski E 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 pmid: 11125122
62	Steward R E, MacArthur M W, Laskowski R A, Thornton J M (2003). Molecular basis of inherited diseases: a structural perspective. Trends Genet, 19(9): 505–513 https://doi.org/10.1016/S0168-9525(03)00195-1 pmid: 12957544
63	Sun X, Marks D L, Park W D, Wheatley C L, Puri V, O’Brien J F, Kraft D L, Lundquist P A, Patterson M C, Pagano R E, Snow K (2001). Niemann-Pick C variant detection by altered sphingolipid trafficking and correlation with mutations within a specific domain of NPC1. Am J Hum Genet, 68(6): 1361–1372 https://doi.org/10.1086/320599 pmid: 11349231
64	Sun X, Marks D L, Park W D, Wheatley C L, Puri V, O’Brien J F, Kraft D L, Lundquist P A, Patterson M C, Pagano R E, Snow K (2001). Niemann-Pick C variant detection by altered sphingolipid trafficking and correlation with mutations within a specific domain of NPC1. Am J Hum Genet, 68(6): 1361–1372 https://doi.org/10.1086/320599 pmid: 11349231
65	Sunyaev S, Ramensky V, Bork P (2000). Towards a structural basis of human non-synonymous single nucleotide polymorphisms. Trends Genet, 16(5): 198–200 https://doi.org/10.1016/S0168-9525(00)01988-0 pmid: 10782110
66	Tamura H, Takahashi T, Ban N, Torisu H, Ninomiya H, Takada G, Inagaki N (2006). Niemann-Pick type C disease: novel NPC1 mutations and characterization of the concomitant acid sphingomyelinase deficiency. Mol Genet Metab, 87(2): 113–121 https://doi.org/10.1016/j.ymgme.2005.07.025 pmid: 16143556
67	Tarugi P, Ballarini G, Bembi B, Battisti C, Palmeri S, Panzani F, Di Leo E, Martini C, Federico A, Calandra S (2002). Niemann-Pick type C disease: mutations of NPC1 gene and evidence of abnormal expression of some mutant alleles in fibroblasts. J Lipid Res, 43(11): 1908–1919 https://doi.org/10.1194/jlr.M200203-JLR200 pmid: 12401890
68	Thomas P D, Campbell M J, Kejariwal A, Mi H, Karlak B, Daverman R, Diemer K, Muruganujan A, Narechania A (2003). PANTHER: a library of protein families and subfamilies indexed by function. Genome Res, 13(9): 2129–2141 https://doi.org/10.1101/gr.772403 pmid: 12952881
69	Vanier M T, Suzuki K (1998). Recent advances in elucidating Niemann-Pick C disease. Brain Pathol, 8(1): 163–174 https://doi.org/10.1111/j.1750-3639.1998.tb00143.x pmid: 9458174
70	Wiederstein M, Sippl M J (2007). ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res, 35(Web Server issue suppl 2): W407-10 https://doi.org/10.1093/nar/gkm290 pmid: 17517781
71	Willard L, Ranjan A, Zhang H, Monzavi H, Boyko R F, Sykes B D, Wishart D S (2003). VADAR: a web server for quantitative evaluation of protein structure quality. Nucleic Acids Res, 31(13): 3316–3319 https://doi.org/10.1093/nar/gkg565 pmid: 12824316
72	Xiong H, Higaki K, Wei C J, Bao X H, Zhang Y H, Fu N, Qin J, Adachi K, Kumura Y, Ninomiya H, Nanba E, Wu X R (2012). Genotype/phenotype of 6 Chinese cases with Niemann-Pick disease type C. Gene, 498(2): 332–335 https://doi.org/10.1016/j.gene.2012.01.026 pmid: 22326530
73	Yamamoto T, Ninomiya H, Matsumoto M, Ohta Y, Nanba E, Tsutsumi Y, Yamakawa K, Millat G, Vanieer M, Pentchev P G, Ohno K (2000). Genotype-phenotype relationship of Niemann-Pick disease type C: a possible correlation between clinical onset and levels of NPC1 protein in isolated skin fibroblasts. J Med Genet, 37(9): 707–712
74	Zhang Y (2008). I-TASSER server for protein 3D structure prediction. BMC Bioinformatics, 9(1): 40 https://doi.org/10.1186/1471-2105-9-40 pmid: 18215316

[1]	C. GEORGE PRIYA DOSS,B. RAJITH,R. MAGESH,A. ASHISH KUMAR. Influence of the SNPs on the structural stability of CBS protein: Insight from molecular dynamics simulations[J]. Front. Biol., 2014, 9(6): 504-518.
[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]	GUO Yibin, DU Chuanshu, WANG Jingjing. Detection of a new mutation (T1140C) in a patient with Hunter syndrome from Guangdong, China[J]. Front. Biol., 2007, 2(4): 383-386.

Viewed

Full text

Abstract

Cited

Shared

Discussed