Galectin from homosepians sequence was retrieved from NCBI lam pept (GI 413862) Acumou name in humgalbin binding protein mRNA. The gene name is Mac2. The gene sequence was then translated in to six forms translation method and generated possible forms. Further the sequence was combined and aligned for conservative identity. The translated sequence was subjected to predict structure using homology modeling.
Modeling was done using PRIME module from schodringer by taking two template as structure. The modeled structure was protected for quality check by procheck (Fig. 4.1.2). The quality of the protein was observed as 90% more than its library. So the protein is technically qualified to be an Histidine in biological form. Further the protein was considered to do energy mutation to get high energy constraints. The hierarchical portability to have the protein mass to energy should be minimum.
6.2 Translated Sequence and Protein Sequence are as follows
Translated Sequence:
QPPSGKWQTIFRSMMRYLGLETQTLKDGLAHGGTSLLGQGATQGLPILGPTPGRHPQGLILDRHLQAPTMEHLELIPEHLHLESTQGHPAALGPTHLLDSQVPPEPTLPLAPMAPLLGHLCLITCLCLGEWCLACQFWARSPMQTELLISKEGMMLPSTLTHASMRTTGESLFAIQSWIITGEGKKDSRFSHLKVGNHSKYMYWLNLTTSRLQMMLTCCSTIIGLKNSMKSENWEFLVTTSPVLHIPYNLKGADKKKKRITLHVRFHVHCRENFYIHQYPP
GGILMNVKIFSTVNMKPLHMGLDSFFFFLICPFQIISWYMKHWGLCHQKFPVFFHVFPDDCTATSEHHSLQPSGQVQPVHVFMVSHFQMGKPTVFLPFPSYYPALYCKQLSCCSHSVGSGRQHHSLFGNLKQFCLHWASPCPELLSACEAPLPQAKAGYKAQSVAQQGRHRGQWQGRLRGHLAVQKMGRPQGRWVALGRLQVQVLRDKLQVLHGRRLEVPVQDKPLGVPARGRPQDRKPLGSPLPQQAGSPMRQAILEGLGFQTQITHHGAKNCLPFSARWLLF
SSHRAENGRQFFAPCVIWVWKPKPSRMAWRMGEPACWGRGLPRGFLSWGLPRAGTPRGLSWTGTSRRLPWSTWSLSRSTCTWSLPRATQRPWGLPIFWTAKCPRSLPCHWPLWRPCWATDCALPAFAWGSGASHADNNSGHGEAQCKQNCFRFPKRECCLPLPTLQEQQESHCLQYKAGLGKGRKTVGFPIKWETIQNTCTGTPLQGCSECSLVAVQSSGKTQNQKTGNFWHRPHQCFIYHDIIKGQIKKKKKESKPYMCKGFMFTVEKIFTFINIP
GGYMKFSLQTNLYTCKVILFFFFSAPFRLYHGISTGEVYVTRNSQFSDFIEFFNPMIVLQQVSIIHCNLEVVRFNQYMYFEWFPTFKWENRLSFFPSPVIIQLCIANNDSPVVLIEAWVKVEGNIIPSLEISNSVCIGLHRAQNCYQHARHHSPRQRQVIRHNQWPSRGAIGASGRVGSGGTWLSRRWVGPRAAGWPWVDSRCRCSGISSRCSMVGAWRCLSRISPWGCLPGVGPRIGSPWVAPCPSRLVPPCARPSLRVWVSRPRRIMERKIVCHFPLGGCSS
AATERKMADNFSLHDALSGSGNPNPQGWPGAWGNQPAGAGGYPGASYPGAYPGQAPPGAYPGQAPPGAYHGAPGAYPGAPAPGVYPGPPSGPGAYPSSGQPSAPGAYPATGPYGAPAGPLIVPYNLPLPGGVVPRMLITILGTVKPNANRIALDFQRGNDVAFHFNPRFNENNRRVIVCNTKLDNNWGREERQSVFPFESGKPFKIHVLVEPDHFKVAVNDAHLLQYNHRVKKLNEIRKLGISGDIDLTSASYTMISERGRLKKKKKNLNLTCVKVSCSLRKFLHSSISP
GDIDECKNFLYSEHETFTHVRFRFFFFFFNLPLSDYIMVYEALVRSMSPEIPSFLISLSFLTRLYCNKASFTATLKWSGSTSTCILNGFPLSNGKTDCLSSLPQLLSSFVLQTMTLLLFSLKRGLKWKATSFPLWKSKAILFALGFTVPRIVISMRGTTPPGKGRLGTISGPAGAPGPVAGAPGALGCPEDGAPGPLGGPGTPGAGAPGAPGAPWAPGGACPGAPGGACPGAPGEAPGPPAPAGWFPHAPGHPGFGFPDPDNASWSEKLSAIFRSVAAVS
Protien Sequence:
> 1KJL:A|PDBID|CHAIN|SEQUENCE
GPYGAPAGPLIVPYNLPLPGGVVPRMLITILGTVKPNANRIALDFQRGNDVAFHFNPRFNENNRRVIVCNTKLDNNWGREERQSVFPFESGKPFKIQVLVEPDHFKVAVNDAHLLQYNHRVKKLNEISKLGISGDIDLTSASYTMI
Mycolic acid structure was taken to moderate with galectin protein by slide method. The structure was analyzed to proceed functional annotation. It shows the occupancy has more number of hydroxyl and ethyl group.
Docking of Mycolic acid derivative with galectin protein was studied. The differential g score was identified from the galectin Mycolic acid complex − 8.06, -7.23, -5.85, -4.22, -4.56, -4.89, -5.99, -5.21, -4.28, -4.34 E(kj/mol). Finally α1 derivative has − 8.06 g score having higher binding affinity towards galectin protein.
The initialization of NN is being done with the help of Matlab. Already trained templates would be available in the software, hence, easy access might be possible.
Type can be chosen by which the training of NN is done. Since this project used the fitting tool to train the NN.
The fitting tool is selected to study about the number of hidden layers and the output layer is made.
The formation of the hidden layers is obviously seen and the simulation is enhanced. The whole architecture is formed by fixing of hidden layers.
Once when the input gets loaded into the network, the process starts. The performance and various training states can be seen.
Thus the Output is obtained in the form of graph and various ranges of values are plotted successfully. The trained targets and test outputs are also displayed. This when practically implemented in a kit can be synthesized easily.