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Combinatorial Chemistry & High Throughput Screening

Editor-in-Chief

ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

Research Article

Alz-Disc: A Tool to Discriminate Disease-causing and Neutral Mutations in Alzheimer's Disease

Author(s): A. Kulandaisamy, S. Akila Parvathy Dharshini and M. Michael Gromiha*

Volume 26, Issue 4, 2023

Published on: 05 August, 2022

Page: [769 - 777] Pages: 9

DOI: 10.2174/1386207325666220520102316

Price: $65

Abstract

Background: Alzheimer's disease (AD) is the most common neurodegenerative disorder that affects the neuronal system and leads to memory loss. Many coding gene variants are associated with this disease and it is important to characterize their annotations.

Methods: We collected the Alzheimer's disease-causing and neutral mutations from different databases. For each mutation, we computed the different features from protein sequence. Further, these features were used to build a Bayes network-based machine-learning algorithm to discriminate between the disease-causing and neutral mutations in AD.

Results: We have constructed a comprehensive dataset of 314 Alzheimer's disease-causing and 370 neutral mutations and explored their characteristic features such as conservation scores, positionspecific scoring matrix (PSSM) profile, and the change in hydrophobicity, different amino acid residue substitution matrices and neighboring residue information for identifying the disease-causing mutations. Utilizing these features, we have developed a disease-specific tool named Alz-disc, for discriminating the disease-causing and neutral mutations using sequence information alone. The performance of the present method showed an accuracy of 89% for independent test set, which is 13% higher than available generic methods. This method is freely available as a web server at https://web.iitm.ac.in/bioinfo2/alzdisc/.

Conclusions: This study is useful to annotate the effect of new variants and develop mutation specific drug design strategies for Alzheimer’s disease.

Keywords: Mutations, Alzheimer, neurodegenerative disorder, disease-causing, discrimination, web server.

Graphical Abstract
[1]
Goate, A.; Chartier-Harlin, M.C.; Mullan, M.; Brown, J.; Crawford, F.; Fidani, L.; Giuffra, L.; Haynes, A.; Irving, N.; James, L.; Mant, R. Segregation of a missense mutation in the amyloid precursor protein gene with familial Alzheimer’s disease. Nature, 1991, 349(6311), 704-706.
[http://dx.doi.org/10.1038/349704a0] [PMID: 1671712]
[2]
van der Kant, R.; Goldstein, L.S. Cellular functions of the amyloid precursor protein from development to dementia. Dev. Cell, 2015, 32(4), 502-515.
[http://dx.doi.org/10.1016/j.devcel.2015.01.022] [PMID: 25710536]
[3]
Siman, R.; Reaume, A.G.; Savage, M.J.; Trusko, S.; Lin, Y.G.; Scott, R.W.; Flood, D.G. Presenilin-1 P264L knock-in mutation: Differential effects on abeta production, amyloid deposition, and neuronal vulnerability. J. Neurosci., 2000, 20(23), 8717-8726.
[http://dx.doi.org/10.1523/JNEUROSCI.20-23-08717.2000] [PMID: 11102478]
[4]
Takasugi, N.; Tomita, T.; Hayashi, I.; Tsuruoka, M.; Niimura, M.; Takahashi, Y.; Thinakaran, G.; Iwatsubo, T. The role of presenilin cofactors in the γ-secretase complex. Nature, 2003, 422(6930), 438-441.
[http://dx.doi.org/10.1038/nature01506] [PMID: 12660785]
[5]
Selkoe, D.J. The cell biology of β-amyloid precursor protein and presenilin in Alzheimer’s disease. Trends Cell Biol., 1998, 8(11), 447-453.
[http://dx.doi.org/10.1016/S0962-8924(98)01363-4] [PMID: 9854312]
[6]
Pitas, R.E.; Boyles, J.K.; Lee, S.H.; Foss, D.; Mahley, R.W. Astrocytes synthesize apolipoprotein E and metabolize apolipoprotein E-containing lipoproteins. Biochim. Biophys. Acta, 1987, 917(1), 148-161.
[http://dx.doi.org/10.1016/0005-2760(87)90295-5] [PMID: 3539206]
[7]
Kanekiyo, T.; Xu, H.; Bu, G. ApoE and Aβ in Alzheimer’s disease: Accidental encounters or partners? Neuron, 2014, 81(4), 740-754.
[http://dx.doi.org/10.1016/j.neuron.2014.01.045] [PMID: 24559670]
[8]
Castellana, S.; Mazza, T. Congruency in the prediction of pathogenic missense mutations: State-of-the-art web-based tools. Brief. Bioinform., 2013, 14(4), 448-459.
[http://dx.doi.org/10.1093/bib/bbt013] [PMID: 23505257]
[9]
Tang, H.; Thomas, P.D. Tools for predicting the functional impact of nonsynonymous genetic variation. Genetics, 2016, 203(2), 635-647.
[http://dx.doi.org/10.1534/genetics.116.190033] [PMID: 27270698]
[10]
Brown, D.K.; Tastan Bishop, Ö. Role of structural bioinformatics in drug discovery by computational SNP analysis: Analyzing variation at the protein level. Glob. Heart, 2017, 12(2), 151-161.
[http://dx.doi.org/10.1016/j.gheart.2017.01.009] [PMID: 28302551]
[11]
Ng, P.C.; Henikoff, S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res., 2003, 31(13), 3812-3814.
[http://dx.doi.org/10.1093/nar/gkg509] [PMID: 12824425]
[12]
Reva, B.; Antipin, Y.; Sander, C. Predicting the functional impact of protein mutations: Application to cancer genomics. Nucleic Acids Res., 2011, 39(17), e118.
[http://dx.doi.org/10.1093/nar/gkr407] [PMID: 21727090]
[13]
Adzhubei, I.; Jordan, D.M.; Sunyaev, S.R. Predicting functional effect of human missense mutations using PolyPhen-2. Curr. Protoc. Hum. Genet., 2013, 7, 20.
[http://dx.doi.org/10.1002/0471142905.hg0720s76] [PMID: 23315928]
[14]
Shihab, H.A.; Gough, J.; Cooper, D.N.; Day, I.N.; Gaunt, T.R. Predicting the functional consequences of cancer-associated amino acid substitutions. Bioinformatics, 2013, 29(12), 1504-1510.
[http://dx.doi.org/10.1093/bioinformatics/btt182] [PMID: 23620363]
[15]
Schwarz, J.M.; Cooper, D.N.; Schuelke, M.; Seelow, D. MutationTaster2: Mutation prediction for the deep-sequencing age. Nat. Methods, 2014, 11(4), 361-362.
[http://dx.doi.org/10.1038/nmeth.2890] [PMID: 24681721]
[16]
Choi, Y.; Chan, A.P. PROVEAN web server: A tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics, 2015, 31(16), 2745-2747.
[http://dx.doi.org/10.1093/bioinformatics/btv195] [PMID: 25851949]
[17]
Anoosha, P.; Huang, L.T.; Sakthivel, R.; Karunagaran, D.; Gromiha, M.M. Discrimination of driver and passenger mutations in epidermal growth factor receptor in cancer. Mutat. Res., 2015, 780, 24-34.
[http://dx.doi.org/10.1016/j.mrfmmm.2015.07.005] [PMID: 26264175]
[18]
Kulandaisamy, A.; Zaucha, J.; Sakthivel, R.; Frishman, D.; Michael Gromiha, M. Pred-MutHTP: Prediction of disease-causing and neutral mutations in human transmembrane proteins. Hum. Mutat., 2020, 41(3), 581-590.
[http://dx.doi.org/10.1002/humu.23961] [PMID: 31821684]
[19]
Capriotti, E.; Altman, R.B. A new disease-specific machine learning approach for the prediction of cancer-causing missense variants. Genomics, 2011, 98(4), 310-317.
[http://dx.doi.org/10.1016/j.ygeno.2011.06.010] [PMID: 21763417]
[20]
Tang, N.; Sandahl, T.D.; Ott, P.; Kepp, K.P. Computing the pathogenicity of Wilson’s disease ATP7B mutations: Implications for disease prevalence. J. Chem. Inf. Model., 2019, 59(12), 5230-5243.
[http://dx.doi.org/10.1021/acs.jcim.9b00852] [PMID: 31751128]
[21]
Dorfman, R.; Nalpathamkalam, T.; Taylor, C.; Gonska, T.; Keenan, K.; Yuan, X.W.; Corey, M.; Tsui, L.C.; Zielenski, J.; Durie, P. Do common in silico tools predict the clinical consequences of amino-acid substitutions in the CFTR gene? Clin. Genet., 2010, 77(5), 464-473.
[http://dx.doi.org/10.1111/j.1399-0004.2009.01351.x] [PMID: 20059485]
[22]
Rangaswamy, U.; Dharshini, S.A.P.; Yesudhas, D.; Gromiha, M.M. VEPAD - Predicting the effect of variants associated with Alzheimer’s disease using machine learning. Comput. Biol. Med., 2020, 124, 103933.
[http://dx.doi.org/10.1016/j.compbiomed.2020.103933] [PMID: 32828070]
[23]
Ferreira, K.C.D.V.; Fialho, L.F.; Franco, O.L.; de Alencar, S.A.; Porto, W.F. Benchmarking analysis of deleterious SNP prediction tools on CYP2D6 enzyme. Chem. Biol. Drug Des., 2020, 96(3), 984-994.
[http://dx.doi.org/10.1111/cbdd.13676] [PMID: 32149466]
[24]
Douville, C.; Carter, H.; Kim, R.; Niknafs, N.; Diekhans, M.; Stenson, P.D.; Cooper, D.N.; Ryan, M.; Karchin, R. CRAVAT: Cancer-related analysis of variants toolkit. Bioinformatics, 2013, 29(5), 647-648.
[http://dx.doi.org/10.1093/bioinformatics/btt017] [PMID: 23325621]
[25]
Mao, Y.; Chen, H.; Liang, H.; Meric-Bernstam, F.; Mills, G.B.; Chen, K. CanDrA: Cancer-specific driver missense mutation annotation with optimized features. PLoS One, 2013, 8(10), e77945.
[http://dx.doi.org/10.1371/journal.pone.0077945] [PMID: 24205039]
[26]
Landhuis, E.; Dance, A. Alzforum news highlights: Caffeine, anesthesia, and twin epigenetics. J. Alzheimers Dis., 2010, 19(1), 211-216.
[http://dx.doi.org/10.3233/JAD-2010-1256]
[27]
Kulandaisamy, A.; Binny Priya, S.; Sakthivel, R.; Tarnovskaya, S.; Bizin, I.; Hönigschmid, P.; Frishman, D.; Gromiha, M.M. MutHTP: Mutations in human transmembrane proteins. Bioinformatics, 2018, 34(13), 2325-2326.
[http://dx.doi.org/10.1093/bioinformatics/bty054] [PMID: 29401218]
[28]
Ganesan, K.; Kulandaisamy, A.; Binny Priya, S.; Gromiha, M.M. HuVarBase: A human variant database with comprehensive information at gene and protein levels. PLoS One, 2019, 14(1), e0210475.
[http://dx.doi.org/10.1371/journal.pone.0210475] [PMID: 30703169]
[29]
Landrum, M.J.; Lee, J.M.; Riley, G.R.; Jang, W.; Rubinstein, W.S.; Church, D.M.; Maglott, D.R. ClinVar: Public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res., 2014, 42(Database issue), D980-D985.
[http://dx.doi.org/10.1093/nar/gkt1113] [PMID: 24234437]
[30]
Sherry, S.T.; Ward, M.H.; Kholodov, M.; Baker, J.; Phan, L.; Smigielski, E.M.; Sirotkin, K. dbSNP: The NCBI database of genetic variation. Nucleic Acids Res., 2001, 29(1), 308-311.
[http://dx.doi.org/10.1093/nar/29.1.308] [PMID: 11125122]
[31]
Kawashima, S.; Kanehisa, M. AAindex: Amino acid index database. Nucleic Acids Res., 2000, 28(1), 374-374.
[http://dx.doi.org/10.1093/nar/28.1.374] [PMID: 10592278]
[32]
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(2), 494-501.
[http://dx.doi.org/10.1021/ci049757q] [PMID: 15807515]
[33]
Gasteiger, E.; Hoogland, C.; Gattiker, A.; Wilkins, M.R.; Appel, R.D.; Bairoch, A. Protein identification and analysis tools on the ExPASy server. The proteomics protocols handbook; Humana press, 2005, pp. 571-607.
[http://dx.doi.org/10.1385/1-59259-890-0:571]
[34]
Boratyn, G.M.; Camacho, C.; Cooper, P.S.; Coulouris, G.; Fong, A.; Ma, N.; Madden, T.L.; Matten, W.T.; McGinnis, S.D.; Merezhuk, Y.; Raytselis, Y.; Sayers, E.W.; Tao, T.; Ye, J.; Zaretskaya, I. BLAST: A more efficient report with usability improvements. Nucleic Acids Res., 2013, 41(Web Server issue), W29-33.
[http://dx.doi.org/10.1093/nar/gkt282] [PMID: 23609542]
[35]
Katoh, K.; Kuma, K.; Toh, H.; Miyata, T. MAFFT version 5: Improvement in accuracy of multiple sequence alignment. Nucleic Acids Res., 2005, 33(2), 511-518.
[http://dx.doi.org/10.1093/nar/gki198] [PMID: 15661851]
[36]
Madeira, Fábio; Martin, David M. A.; Procter, James B.; Geoffrey, J. AACon: A Fast Amino Acid Conservation Calculation Service. 2018.
[37]
Altschul, S.F.; Koonin, E.V. Iterated profile searches with PSI-BLAST-a tool for discovery in protein databases. Trends Biochem. Sci., 1998, 23(11), 444-447.
[http://dx.doi.org/10.1016/S0968-0004(98)01298-5] [PMID: 9852764]
[38]
Hall, M.; Frank, E.; Holmes, G.; Pfahringer, B.; Reutemann, P.; Witten, I.H. The WEKA data mining software: An update. SIGKDD Explor., 2009, 11, 10-18.
[http://dx.doi.org/10.1145/1656274.1656278]
[39]
Stone, M. Cross‐validatory choice and assessment of statistical predictions. J. R. Stat. Soc. B, 1974, 36(2), 111-133.
[http://dx.doi.org/10.1111/j.2517-6161.1974.tb00994.x]
[40]
Kulandaisamy, A.; Lathi, V.; ViswaPoorani, K.; Yugandhar, K.; Gromiha, M.M. Important amino acid residues involved in folding and binding of protein-protein complexes. Int. J. Biol. Macromol., 2017, 94(Pt A), 438-444.
[http://dx.doi.org/10.1016/j.ijbiomac.2016.10.045] [PMID: 27765571]
[41]
Kulandaisamy, A.; Srivastava, A.; Nagarajan, R.; Gromiha, M.M. Dissecting and analyzing key residues in protein-DNA complexes. J. Mol. Recognit., 2018, 31(4), e2692.
[http://dx.doi.org/10.1002/jmr.2692] [PMID: 29230895]
[42]
Kulandaisamy, A.; Srivastava, A.; Kumar, P.; Nagarajan, R.; Priya, S.B.; Gromiha, M.M. Identification and analysis of key residues in protein–RNA complexes. IEEE/ACM Trans Comput Biol Bioinform, 2018, 15(5), 1436-44.
[http://dx.doi.org/10.1109/TCBB.2018.2834387]
[43]
Kulandaisamy, A.; Priya, S.B.; Sakthivel, R.; Frishman, D.; Gromiha, M.M. Statistical analysis of disease-causing and neutral mutations in human membrane proteins. Proteins, 2019, 87(6), 452-466.
[http://dx.doi.org/10.1002/prot.25667] [PMID: 30714211]
[44]
Pandey, M.; Gromiha, M.M. Predicting potential residues associated with lung cancer using deep neural network. Mutat. Res., 2021, 822, 111737.
[http://dx.doi.org/10.1016/j.mrfmmm.2020.111737] [PMID: 33508631]
[45]
Shihab, H.A.; Gough, J.; Cooper, D.N.; Stenson, P.D.; Barker, G.L.; Edwards, K.J.; Day, I.N.; Gaunt, T.R. Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Hum. Mutat., 2013, 34(1), 57-65.
[http://dx.doi.org/10.1002/humu.22225] [PMID: 23033316]
[46]
Liu, C.; Luo, X. Potential molecular and graphene oxide chelators to dissolve amyloid-β plaques in Alzheimer’s disease: A density functional theory study. J. Mater. Chem. B Mater. Biol. Med., 2021, 9(11), 2736-2746.
[http://dx.doi.org/10.1039/D0TB02985H] [PMID: 33688880]
[47]
Allec, S.I.; Sun, Y.; Sun, J.; Chang, C.A.; Wong, B.M. Heterogeneous CPU+ GPU-enabled simulations for DFTB molecular dynamics of large chemical and biological systems. J. Chem. Theory Comput., 2019, 15(5), 2807-2815.
[http://dx.doi.org/10.1021/acs.jctc.8b01239] [PMID: 30916958]
[48]
Fedorov, D.G.; Li, H.; Mironov, V.; Alexeev, Y. Computational methods for biochemical simulations implemented in GAMESS. Methods Mol. Biol., 2020, 2114, 123-142.
[http://dx.doi.org/10.1007/978-1-0716-0282-9_8] [PMID: 32016890]

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