Abstract
Misfolded proteins result when a protein follows the wrong folding pathway. Accumulation of misfolded proteins can cause disorders, known as amyloid diseases. Unfortunately, some of them are very common. The most prevalent one is Alzheimer’s disease. Alzheimer’s disease is a neurodegenerative disorder and the commonest form of dementia. The current study aims to assess the impact of somatic mutations in PSEN1 gene. The said mutations are the most common cause of familial Alzheimer’s disease. As protein functionality can be affected by mutations, the study of possible alterations in the tertiary structure of proteins may reveal new insights related to the relationship between mutations and protein functions. To examine the effect of mutations, the primary structures and their related mutations were retrieved from public databases. Each structure (mutated and unmutated) was predicted based on effective structure prediction methodologies. A benchmarking of the structural predictive tools was accomplished. Comparative analyses of mutated and unmutated proteins were performed based on classic bioinformatics methods (TM-Score, RMSD, etc.) as well as on established shape-based descriptors retrieved from object recognition methodologies. Unsupervised methodologies were applied to the structures, in order to identify groups of mutation with similar mutational impact. Our results provide an essential knowledge toward protein’s functionality in structure-based drug design.
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References
Andreeva A et al (2008) Data growth and its impact on the SCOP database: new developments. Nucleic Acids Res 36(Database issue):D419–D425
Borchelt DR, Thinakaran G, Eckman CB, Lee MK, Davenport F, Ratovitsky T, Prada CM, Kim G, Seekins S, Yager D, Slunt HH, Wang R, Seeger M, Levey AI, Gandy SE, Copeland NG, Jenkins NA, Price DL, Younkin SG, Sisodia SS (1996) Familial Alzheimer’s disease–linked Presenilin 1 variants elevate Aβ1–42/1–40 ratio in vitro and in vivo. Neuron 17:1005–1013
Cacquevel M, Aeschbach L, Houacine J, Fraering PC (2012) Alzheimer’s disease- linked mutations in Presenilin-1 result in a drastic loss of activity in purified γ- secretase complexes. PLoS One 7:e35133
Carmali S et al (2017) Tertiary structure-based prediction of how ATRP initiators react with proteins. ACS Biomater Sci Eng 3(9):2086–2097
Cruts M, Theuns J, Van Broeckhoven C (2012) Locus-specific mutation databases for neurodegenerative brain diseases. Hum Mutat 33:1340–1344
Finn RD et al (2016) The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res 44(D1):D279–D285
Greene LH et al (2007) The CATH domain structure database: new protocols and classification levels give a more comprehensive resource for exploring evolution. Nucleic Acids Res 35(Database issue):D291–D297
Haas J et al (2018) Continuous automated model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12. Proteins 86:387–398
Hardy J (2006) Alzheimer’s disease: the amyloid hypothesis – an update and reappraisal. J Alzheimers Dis 9:151–153
Hitomi H, Holm L (2009) Advances and pitfalls of protein structural alignment. Curr Opin Struct Biol 19(3):341–348
Holm L, Sander C (1994) The FSSP database of structurally aligned protein fold families. Nucleic Acids Res 22(17):3600–3609
Jamal S, Goyal S, Shanker A, Grover A (2017) Computational screening and exploration of disease-associated genes in Alzheimer’s disease. J Cell Biochem 118:1471–1479
Katzman R (1976) The prevalence and malignancy of Alzheimer disease. A major killer [editorial]. Arch Neurol 33:217–218
Murayama O, Tomita T, Nihonmatsu N, Murayama M, Sun X, Honda T, Iwatsubo T, Takashima A (1999) Enhancement of amyloid Beta 42 secretion by 28 different Presenilin 1 mutations of familial Alzheimer’s disease. Neurosci Lett 265:61–63
Pauwels K, Williams TL, Morris KL, Jonckheere W, Vandersteen A, Kelly G, Schymkowitz J, Rousseau F, Pastore A, Serpell LC, Broersen K (2012) Structural basis for increased toxicity of pathological Aβ42:Aβ40 ratios in Alzheimer disease. J Biol Chem 287:5650–5660
Polychronidou El et al (2018) Automated shape-based clustering of 3D immunoglobulin protein structures in chronic lymphocytic leukemia. BMC Bioinformatics 19(14):414
Roy Α, Kucukural Α, Zhang Υ (2010) I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc 5:725–738
Selkoe DJ, Hardy J (2016) The amyloid hypothesis of Alzheimer’s disease at 25 years. EMBO Mol Med 8(6):595–608
Sievers F, Higgins DG (2018) Clustal omega for making accurate alignments of many protein sequences. Protein Sci 27(1):135–145
Skwark MJ et al (2014) Improved contact predictions using the recognition of protein like contact patterns. PLoS Comput Biol 10(11):e1003889
Somavarapu AK, Kepp KP (2016) Loss of stability and hydrophobicity of Presenilin 1 mutations causing Alzheimer’s disease. J Neurochem 137:101–111
Sun L, Zhou R, Yang G, Shi Y (2017) Analysis of 138 pathogenic mutations in Presenilin-1 on the in vitro production of Aβ42 and Aβ40 peptides by γ-secretase. Proc Natl Acad Sci U S A 114:E476–E485
Tang N, Kepp KP (2018) Aβ42/Aβ40 ratios of Presenilin 1 mutations correlate with clinical onset of Alzheimer’s disease. J Alzheimers Dis 66:939–945
Tellechea P, Pujol N, Esteve-Belloch P, Echeveste B, García-Eulate MR, Arbizu J et al (2018) Early- and late-onset Alzheimer disease: are they the same entity? Neurología (Engl Ed) 33(4):244–253
Vieira RT, Caixeta L, Machado S, Silva AC, Nardi AE, Arias-Carrión O et al (2013) Epidemiology of early-onset dementia: a review of the literature. Clin Pract Epidemiol Ment Health 9:88–95
Webb B, Sali Α (2002) Comparative protein structure modeling using MODELLER. Curr Protoc Bioinformatics 54:5.6.1–5.6.37. John Wiley & Sons, Inc
Woodruff G, Young JE, Martinez FJ, Buen F, Gore A, Kinaga J, Li Z, Yuan SH, Zhang K, Goldstein LSB (2013) The Presenilin-1 ΔE9 mutation results in reduced γ-secretase activity, but not Total loss of PS1 function, in isogenic human stem cells. Cell Rep 5:974–985
Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y (2015) The I-TASSER suite: protein structure and function prediction. Nat Methods 12:7–8
Zhang Υ (2008) I-TASSER server for protein 3D structure prediction. BMC Bioinformatics 9:40
Acknowledgments
The authors declare no conflict of interest with regard to this work. Τhe research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology (GSRT), under the HFRI PhD Fellowship grant (GA. no. 2096).
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Polychronidou, E., Avramouli, A., Vlamos, P. (2020). Alzheimer’s Disease: The Role of Mutations in Protein Folding. In: Vlamos, P. (eds) GeNeDis 2018. Advances in Experimental Medicine and Biology, vol 1195. Springer, Cham. https://doi.org/10.1007/978-3-030-32633-3_31
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DOI: https://doi.org/10.1007/978-3-030-32633-3_31
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