الفهرس 
Title : A computational based approach for studying synaptic cell Adhesion
AbstractOnly 14 pages are availabe for public view
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Abstract
Central Nervous System is responsible for passing signals from all the body to the brain to process it. Brain consists of neurons which are cells that convey information about the world around us, help us make sense of the world and send commands to our muscles to act. Synaptic cell adhesion is connecting neurons in order to establish a network. Neurexins and Neuroligins proteins are two binding partners which represent the core of the synaptic cell adhesion. Neurexin1 gene is playing an important role in synaptic formation, plasticity and maturity. Studies have reported non-synonymous SNPs in Neurexin1 in some diseases. The current thesis is aiming to apply a computational approach for studying the effects of non-synonymous Single Nucleotide Polymorphisms (SNPs) recoded Neurexin1 in diseases patients. The methodology has been implemented in two steps. The first step aims to identify deleterious SNPs, determine damaged protein features (function, stability) and recognize potential protein regions for future research. The effect on protein function is predicted by PROVEAN, SIFT and PolyPhen-2 tools while protein stability is predicted by MUpro and I-Mutant2.0 tools. Prediction results have identified 2 SNPs to be deleterious by all tools and two additional SNPs have agreement between protein function prediction tools and MUpro from stability tools. Higher deleterious prediction results in the stability tools with the percentages of 72%, 78% than the function tools with 25%, 41% and 47%. Agreement percentage of deleterious prediction between stability tools was 56% while 12.5% in the function tools. The identified regions of Neurexin1 for future research are SP and LNS4