Literature DB >> 24596939

Structural studies on AIPL1 and its functional interactions with NUB1 to identify key interacting residues in LCA4.

S Muthukumaran1, V Umashankar1, Meena Revathi Valliappan2.   

Abstract

Leber congenital amaurosis (LCA) is an autosomal recessive disorder that causes visual impairment in children due to fifteen different gene mutations. Of these, mutations in Aryl-Hydrocarbon Receptor Interacting Protein-like 1 (AIPL1) cause the most severe form of LCA (LCA4) leading to the degeneration of photoreceptor cells. NEDD8 Ultimate Buster 1 (NUB1), a protein that regulates cell cycle progression, interacts with AIPL1 to prevent the over expression of NUB1. In the case of over expression, cell cycle progression is disrupted and may lead to LCA. The studies on interactions between these two proteins will aid in identifying potential modulators for this condition. Since no three-dimensional structure is currently available for these two proteins, in this study we predicted the structures of these two proteins by molecular modelling methods. Moreover, we also modelled the three proven significant mutant forms of AIPL1 spanning the tetratricopeptide domain. Finally, both the modelled wild and mutant structures of AIPL1 (A197P, C239R and G262S) were computationally docked to NUB1, so as to map the potential molecular interactions. This is the first study on modelling the structure-function relationship of AIPL1-NUB1 interactions which shall aid in discovery of novel therapeutic agents.

Entities:  

Keywords:  AIPL1; Leber congenital amaurosis; Molecular dynamics; Molecular modelling; Protein–protein docking

Year:  2013        PMID: 24596939      PMCID: PMC3709034          DOI: 10.1007/s12177-013-9102-9

Source DB:  PubMed          Journal:  J Ocul Biol Dis Infor        ISSN: 1936-8437


  21 in total

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Authors:  Lindsay T Kirschman; Saravanan Kolandaivelu; Jeanne M Frederick; Loan Dang; Andrew F X Goldberg; Wolfgang Baehr; Visvanathan Ramamurthy
Journal:  Hum Mol Genet       Date:  2009-12-30       Impact factor: 6.150

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Authors:  Yang Zhang
Journal:  BMC Bioinformatics       Date:  2008-01-23       Impact factor: 3.169

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