Open Access Open Access  Restricted Access Subscription Access

Integrating State-of-the-Art in silico Tools With Molecular Docking to Predict the Impact of the Most Deleterious Amino Acid Substitutions on TRAPPC6A Protein


Affiliations
1 Department of Animal Production, College of Agriculture, Al-Qasim Green University, Al-Qasim, Babil 51001, Iraq
2 Babylon Directorate of Education, Ministry of Education, Babil 51001, Iraq
 

Trafficking Protein Particle Complex subunit 6A (TRAPPC6A) is an important molecule that is mainly involved in the transport of vesicles to the cis-Golgi membrane. Loss of function in this protein leads to a variety of severe disorders. The present study was conducted to prioritize the most deleterious effects of non-synonymous single nucleotide polymorphisms (nsSNPs) on TRAPPC6A protein. Two approaches were employed, sequence-based and structure-based, to predict which nsSNP has the most harmful effects on TRAPPC6A. Docking was performed to compare the ability of normal TRAPPC6A and its most delete-rious mutants to bind with the corresponding recep-tor. All utilized in silico tools indicated highly damaging impacts of three nsSNPs, viz. W74C, G125S and G129D. Docking showed remarkable alterations in the atomic contact energy of TRAPPC6A binding with its receptor. The present finding provides a cost effective method for assessing the damaging effects of nsSNPs on TRAPPC6A, which may help in under-standing the impact of this protein on neurodevelop-mental disorders.

Keywords

Deleterious Mutants, in silico Tools, Molec-ular Docking, Protein Particle Complex, Single Nucleotide Polymorphism.
User
Notifications
Font Size

  • Sacher, M., Shahrzad, N., Kamel, H. and Milev, M. P., TRAPPopathies, an emerging set of disorders linked to variations in the genes encoding transport protein particle (TRAPP)-associated proteins. Traffic, 2018, 20, 5–26.
  • Aridor, M. and Hannan, L. A., Traffic jams II: an update of diseases of intracellular transport. Traffic, 2002, 3, 781–790.
  • Kümmel, D., Oeckinghaus, A., Wang, C., Krappmann, D. and Heinemann, U., Distinct isocomplexes of the TRAPP trafficking factor coexist inside human cells. FEBS Lett., 2008, 582, 3729–3733.
  • Kim, J. J., Lipatova, Z. and Segev, N., TRAPP complexes in secretion and autophagy. Front. Cell Dev. Biol., 2016, 30, 20.
  • Scrivens, P. J., Shahrzad, N., Moores, A., Morin, A., Brunet, S. and Sacher, M., TRAPPC2L is a novel, highly conserved TRAPP-interacting protein. Traffic, 2009, 10, 724–736.
  • Hamilton, G. et al., Alzheimer’s disease genes are associated with measures of cognitive ageing in the Lothian Birth Cohorts of 1921 and 1936. Int. J. Alzheimer’s Dis., 2011, 2011, 505984.
  • Shaw, M. A. et al., Identification of three novel SEDL mutations, including mutation in the rare, non-canonical splice site of exon 4. Clin. Genet., 2003, 64, 235–242.
  • Bögershausen, N. et al., Recessive TRAPPC11 mutations cause a disease spectrum of limb girdle muscular dystrophy and myopathy with movement disorder and intellectual disability. Am. J. Hum. Genet., 2013, 93, 181–190.
  • Koehler, K. et al., A novel TRAPPC11 mutation in two Turkish families associated with cerebral atrophy, global retardation, scoliosis, achalasia and alacrima. J. Med. Genet., 2017, 54, 176–185.
  • Brunet, S. and Sacher, M., In sickness and in health: the role of TRAPP and associated proteins in disease. Traffic, 2014, 15, 803–818.
  • Mohamoud, H. S. et al., A missense mutation in TRAPPC6A leads to build-up of the protein, in patients with a neurodevelopmental syndrome and dysmorphic features. Sci. Rep., 2018, 8, 2053.
  • Doss, C. G. P., Chakraborty, C., Chen, L. and Zhu, H., Integrating in silico prediction methods, molecular docking, and molecular dynamics simulation to predict the impact of ALK missense mutations in structural perspective. Biomed. Res. Int., 2014, 2014, 895831.
  • Al-Shuhaib, M. B. S., D76V, L161R, and C117S are the most pathogenic amino acid substitutions with several dangerous con-sequences on leptin structure, function, and stability. Egypt. J. Med. Hum. Genet., 2019, 20, 32.
  • Ng, P. C. and Henikoff, S., Predicting the effects of amino acid substitutions on protein function. Annu. Rev. Genomics. Hum. Genet., 2006, 22, 61–80.
  • Adzhubei, I. A. et al., A method and server for predicting damag-ing missense mutations. Nature Methods, 2016, 7, 248–249.
  • Ioannidis, N. M. et al., REVEL: an ensemble method for predict-ing the pathogenicity of rare missense variants. Am. J. Hum. Genet., 2016, 99, 877–885.
  • Dong, C. et al., Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum. Mol. Genet., 2015, 24, 2125–2137.
  • Choi, Y., Sims, G. E., Murphy, S., Miller, J. R. and Chan, A. P., Predicting the functional effect of amino acid substitutions and indels. PLoS ONE, 2012, 7, e46688.
  • Tang, H. and Thomas, P. D., PANTHER-PSEP: predicting dis-ease-causing genetic variants using position-specific evolutionary preservation. Bioinformatics, 2016, 32, 2230–2232.
  • Smigielski, E. M., Sirotkin, K., Ward, M. and Sherry, S., T2000 dbSNP: a database of single nucleotide polymorphisms. Nucleic Acids Res., 2000, 28, 352–355.
  • Yates, C. M., Filippis, I., Kelley, L. A. and Sternberg, M. J., SuSPect: enhanced prediction of single amino acid variant (SAV) phenotype using network features. J. Mol. Biol., 2014, 426, 2692–2701.
  • Reva, B., Antipin, Y. and Sander, C., Predicting the functional impact of protein mutations: application to cancer genomics. Nu-cleic Acids Res., 2011, 39, e118.
  • Capriotti, E., Calabrese, R. and Casadio, R., Predicting the insur-gence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary infor-mation. Bioinformatics, 2006, 22, 2729–2734.
  • Conchuir, S. O. et al., A web resource for standardized benchmark datasets, metrics, and Rosetta protocols for macromolecular mod-eling and design. PLoS ONE, 2015, 10, e0130433.
  • Waterhouse, A. et al., SWISS MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res., 2018, 46, W296–W303.
  • Laskowski, R. A., MacArthur, M. W. and Thornton, J. M., PROCHECK: validation of protein structure coordinates. In Inter-national Tables of Crystallography, Vol. F, Crystallography of Biological Macromolecules (eds Rossmann, M. G. and Arnold, E.), Kluwer, Dordrecht, The Netherlands, 2001, pp. 722–725.
  • Capriotti, E., Fariselli, P. and Casadio, R., I-Mutant 2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res., 2005, 33, W306–W310.
  • Chen, C. W., Lin, J. and Chu, Y. W., iStable: off-the-shelf predic-tor integration for predicting protein stability changes. BMC Bio-inform., 2013, 14, S5.
  • Pires, D. E. V., Ascher, D. B. and Blundell, T. L., mCSM: predict-ing the effects of mutations in proteins using graph-based signa-tures. Bioinformatics, 2012, 30, 335–342.
  • Worth, C. L., Preissner, R. and Blundell, T. L., SDM – a server for predicting effects of mutations on protein stability and malfunc-tion. Nucleic Acids Res., 2012, 39, W215–W222.
  • Pires, D. E., Ascher, D. B. and Blundell, T. L., DUET: a server for predicting effects of mutations on protein stability using an inte-grated computational approach. Nucleic Acids Res., 2014, 42, W314–W319.
  • Laimer, J., Hofer, H., Fritz, M., Wegenkittl, S. and Lackner, P., MAESTRO – multi agent stability prediction upon point muta-tions. BMC Bioinform., 2015, 16, 116.
  • Quan, L., Lv, Q. and Zhang, Y., STRUM: structure-based predic-tion of protein stability changes upon single-point mutation. Bioin-formatics, 2016, 32, 2936–2946.
  • Cheng, J., Randall, A. and Baldi, P., Prediction of protein stability changes for single site mutations using support vector machines. Proteins, 2006, 62, 1125–1132.
  • Capriotti, E., Calabrese, R., Fariselli, P., Martelli, P. L., Altman, R. B. and Casadio, R., WSSNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation. BMC Genomics, 2013, 14(Suppl. 3), S6.
  • Rodrigues, C. H., Pires, D. E. and Ascher, D. B., DynaMut: pre-dicting the impact of mutations on protein conformation, flexibi-lity and stability. Nucleic Acids Res., 2018, 46(W1), W350–W355.
  • Wu, Q., Peng, Z., Zhang, Y. and Yang, J., COACH-D: improved protein-ligand binding sites prediction with refined ligand-binding poses through molecular docking. Nucleic Acids Res., 2018, 46, W438–W442.
  • Ashkenazy, H., Erez, E., Martz, E., Pupko, T. and Ben-Tal, N., ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids. Nucleic Acids Res., 2010, 38, W529–W533.
  • Johansson, M. U., Zoete, V., Michielin, O. and Guex, N., Defining and searching for structural motifs using DeepView/Swiss-PdbViewer. BMC Bioinform., 2012, 13, 173.
  • Van Gunsteren, W. F., Billeter, S. R., Eising, A. A., Hunenberger, P. H. and Kruger, P., Biomolecular Simulation: The GROMOS96 Manual and User Guide, vdf Hochschulverlag AG an der ETH Zurich and BIOMOS b.v, Zurich, Switzerland, 1996.
  • Schneidman-Duhovny, D., Inbar, Y., Nussinov, R. and Wolfson, H. J., PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Res., 2005, 33, W363–W367.
  • Al-Shuhaib, M. B. S., Deleterious amino acid substitutions with a series of putative damaging effects on egg components are revealed in the ovalbumin gene family; an in silico approach. Nova Biotechnol. Chim., 2019, 18(2), 1–9.
  • Anand, P. P., Computational modelling of human sarcomeric tele-thonin protein and predicting the functional effect of missense single nucleotide polymorphism. Curr. Sci., 2019, 117, 638–648.
  • Al-Shuhaib, M. B. S., Al-Kafajy, F. R. and Al-Jashami, G. S., A computational approach for explaining the effect of the prl gene polymorphism on prolactin structure and biological activity in Japanese quails. Anim. Biotechnol., 2019, 29, 1–9.
  • Sarhan, R. S., Hashim, H. O. and Al-Shuhaib, M. B. S., The Gly152Val mutation possibly confers resistance to beta-lactam antibiotics in ovine Staphylococcus aureus isolates. Open Vet. J., 2019, 9(4), 339–348.
  • Mustafa, K. M., Ewadh, M. J., Al-Shuhaib, M. B. S. and Hasan, H. G., The in silico prediction of the chloroplast maturase K gene polymorphism in several barley varieties. Agriculture (Pol’nohospodárstvo), 2019, 64(1), 3–16.

Abstract Views: 214

PDF Views: 74




  • Integrating State-of-the-Art in silico Tools With Molecular Docking to Predict the Impact of the Most Deleterious Amino Acid Substitutions on TRAPPC6A Protein

Abstract Views: 214  |  PDF Views: 74

Authors

Mohammed Baqur S. Al-Shuhaib
Department of Animal Production, College of Agriculture, Al-Qasim Green University, Al-Qasim, Babil 51001, Iraq
Jafar M. B. Al-Shuhaib
Babylon Directorate of Education, Ministry of Education, Babil 51001, Iraq

Abstract


Trafficking Protein Particle Complex subunit 6A (TRAPPC6A) is an important molecule that is mainly involved in the transport of vesicles to the cis-Golgi membrane. Loss of function in this protein leads to a variety of severe disorders. The present study was conducted to prioritize the most deleterious effects of non-synonymous single nucleotide polymorphisms (nsSNPs) on TRAPPC6A protein. Two approaches were employed, sequence-based and structure-based, to predict which nsSNP has the most harmful effects on TRAPPC6A. Docking was performed to compare the ability of normal TRAPPC6A and its most delete-rious mutants to bind with the corresponding recep-tor. All utilized in silico tools indicated highly damaging impacts of three nsSNPs, viz. W74C, G125S and G129D. Docking showed remarkable alterations in the atomic contact energy of TRAPPC6A binding with its receptor. The present finding provides a cost effective method for assessing the damaging effects of nsSNPs on TRAPPC6A, which may help in under-standing the impact of this protein on neurodevelop-mental disorders.

Keywords


Deleterious Mutants, in silico Tools, Molec-ular Docking, Protein Particle Complex, Single Nucleotide Polymorphism.

References





DOI: https://doi.org/10.18520/cs%2Fv120%2Fi2%2F398-405