1. INTRODUCTION:

“Diabetes” is an interminable illness set apart by elevated levels of the “concentration” of glucose in the “blood”. Two “Categories” of diabetes are there – i) Type 1 “Diabetes” ii) “Type” two Diabetes. Type one “diabetes” is a chronic disease which occurs when enough insulin is not produced by the pancreas to appropriately control glucose levels. Cause of Type one diabetes - The body's own immune framework strikes and knockdown beta cells in the pancreas which are liable for making the hormone insulin. Type two diabetes earlier known as non-insulin- dependent “diabetes” is a functional abnormality which is distinguished by high levels of glucose in blood with regards to relative deficiency and resistance of insulin. Cause of type two diabetes - It happens when there is not sufficient insulin made out by pancreas or the cells of the body become impervious to insulin.

Third primary system of diabetes is Gestational diabetes, and “happens” when “pregnant females” without a past history of diabetes progress “high glucose levels”¹. Glucose toxicity means an increase in insulin secretion and a decrease in insulin resistance due to chronic hyperglycemia which worse the diabetes.“Diabetes” supervenes all through the world yet is progressively normal particularly in type 2². Over 80% of diabetic deaths been seen in middle- and low-earning nations which is the maximum rise in rates³. The quickest pervasiveness rise is anticipated to happen in 2030 in the regions of Africa-Asia where many people with diabetes will presumably live⁴.Homology modelling is an exemplification of the correspondence of environmental residues at “topologically analogous positions” in the “reference”proteins¹. “In” early 1960’s the first homology modelling was studied using bonds and atoms made up of plastic models and wire. With the help of the coordinates of a known protein structure the models were constructed and altered for those “amino acids” that didn't coordinate the structure. Regarding homology modelling the first paper published in 1969 by David Phillips, Brown and associates. Depend on the arrangement and framework of hen egg white lysozyme they modelledlactalbumin.39% was the similarity of two sequences between these two proteins⁵.

The software can be used which organizes the foundation of your sequence identically to the template when a pragmatically firm 3D structure is obtainable for an adequately comparable protein^6(p2). This is "homology modelling". 3D structure prediction of a certain protein sequence for example objective protein from the “amino acid” sequence of a “homologous (template) protein” aimed at which an NMR or “X-Ray” structure is presented centred on an alignment to one or further ac knowledged protein structures. In the event that likeness between the “target sequence and the template sequence” is distinguished, “structural similarity” can be anticipated. All things considered, 30% sequence identity is requisite to create a “valuable model” however with half or better sequence identity would be abundant¹. Homologous proteins- Portion of 2 proteins with similar amino acids: conserved region and highly similar proteins may have same basic function. The element of the “homology model” is reliant on the nature of the “sequence alignment” and “template” structure⁶. Homology Modelling-in view of two significant perceptions: The assembly of a protein is controlled by its “amino acid sequence”. During advancement, the structure is substantially more rationed than sequence. A “homology modelling” repetitive essentials three things of information: The “target sequence”, the sequence of the protein with anonymous 3D “structure”. A “3D template” is chosen by quality of having the maximum sequence identity thru “the” objective sequence. “Crystallography” or “NMR” methods used to determine the 3D “structure of” the template and are “archetypally” anissued atomic “coordinate” "PDB" file from the “Protein Data Bank”. An procedure between the objective sequence and the “template sequence”⁷.

Fig 1: Process of homology modelling

Note: a) “Template acknowledgment” and preliminary alignment; b) Alignment “correction”; c) “Backbone” generation; d) Loop modelling; e) Side-chain modelling; f) Model “optimization”; g) Model Confirmation

2. MATERIALS AND METHODS:

2.1. Genes involved in diabetes:

The genes which are involved in diabetes were found out through literature survey and comprised of KCNJ11, ABCC8, GLUT2, GLUT4, GCGR, HHEX, JAZF1, RBM51, HIT-T15, TNF1, CAPN10, TCF7L2, PTPN22, CTLA4, IL2LA, IF1H1, SLC30A8, PPARG-183, KCNQ1, IGF2BP2, from which these genes namely GLUT-2, HHEX, TNF, CAPN10 and IFIH1 were chosen as they did not have structures and were seen to be involved in diabetes.

2.2. Protein modelling through i-TASSER:

The FASTA sequence of the unknown protein models were retrieved from UniProt. Then the FASTA sequence was submitted to i-TASSER online server. The modelled proteins were retrieved from the i-TASSER.

2.3. Quality check of modelled protein:

The quality of the modelled protein were analysed using the RAMPAGE software. The quality which is above 85% were used for further analysis.

2.4. Docking analysis:

The receptors chosen for docking analysis comprised of our modelled proteins namely GLUT-2, HHEX, TNF, CAPN10 and IFIH1,whereas the ligands chosen included the active compounds present in the seeds of Murraya Koenigii namely 8-Methoxy-11-Methyl-11h-Indolo[3,2-C] “Quinoline”, 5-O, Benzo[B]Naphtho[2,3-D] “Thiophene”, 9,10-“Dihydro”-6,7,8-, Estra-1,3,5,7,9,15-Hexaen-17-One,3-Methoxy,1,2:7,8-Dibenzophenanthrene,1 Diethylamino)-3-Methylpyrido [1,2-A] Benzimidazole, 2-(3-Methoxy-5-Methylbenzylidene)-7-Methylindan-1-, Pyridine-3-Carbonitrile, 2-Oxo-4-Phenyl-6-Thiophen. The docking experiment was performed using Patch Dock online server by providing the email id along with the selected PDB files of the receptor and ligand respectively. The result was obtained and analysed through PyMOL software. The details such as score, area and ACE were noted for the docked complex having the highest score.

3. RESULTS:

3.1. Analysis of modelled protein:

We retrieved the modelled protein throughi-TASSER server for the structure of protein and its function. Modelled proteins are computational protein structures which are utilised to predict three-dimensional structure of proteins or genes. Protein modelling provide a platform for drug design based on structure and protein function, interaction and antigenic behaviour analysis is done. It also provides increase in stability or novel function to the rational designs of proteins. The quality of the gene was checked through Rampage server. The best modelled proteins are represented in Fig.2. There were 5 model in each receptor with C-Score and percentage respectively. The C-Score and percentage of GLUT-2 is-0.59 (86.7%), -0.23 (88.1%), -2.00 (89.7%), -1.20 (90.3%) and -2.64 (89.1%) and its ligand binding residue are 29, 33, 176,179, 180,183, 297, 298,303,332,394, 395,403,430. The C-Score and percentage of HHEX is -4.26 (64.9%), -4.03 (61.9%), -4.42 (63.4%), and -4.05 (65.3%) and its ligand binding residue are 142,162,168,183,187,190,191,194. The C-Score and percentage of TNF is -2.71 (65.3%), -3.25 (61.2%), -3.57 (67.3%), -3.75 (68.2%), and -3.91 (85.3%) and its ligand binding residue are 164,165,166,167,168, 179, 181, 184, 185,186, 226,228. The C-Score and percentage of CAPN10 is -0.27 (68.8%), -0.28 (71.9%), and -0.01 (79.9%) and its ligand binding residue are 62,66,67,68,69,161,162,163, 212,232,233,234,260, 185. The C-Score and percentage of IFIH1 is -2.22(69.9%),-2.51(74.2%),-2.68(75.4%),-2.9(65.9%), and -3.20(69.9%) and its ligand binding residue are365,366,367,392,414,416,417,420,729,730,731,758,759,760,771,794 (Fig 2).

Table 1: Docking analysis with TNF

LIGAND	SCORE	AREA	ACE	BONDS
				Residue	Atom	Length
8-Methoxy-11-“Methyl”-11h-Indolo[3,2-C]Quinoline, 5-O	4078	483.20	-132.09	ASN-88	O4	2.8
Estra-1,3,5,7,9,15-Hexaen-17-One, 3-Methoxy-	4416	521.00	-148.68	LYS-25	O20	2.3
Pyridine-3-Carbonitrile, 2-Oxo-4-Phenyl-6-(Thiophen)	4196	512.10	-198.20	ASN-88	O9	0.9
Pyridine-3-Carbonitrile, 2-Oxo-4-Phenyl-6-(Thiophen)	4196	512.10	-198.20	LYS-26	O10	2.4

Table 2: Docking analysis with GLUT-2

LIGAND	SCORE	AREA	ACE	BONDS
				Residue	Atom	Length
8-Methoxy-11-Methyl-11h-Indolo[3,2-C]Quinoline, 5-O	4242	539.40	-108.20	LYS-247	O8	2.3
Pyridine-3-Carbonitrile, 2-Oxo-4-Phenyl-6-(Thiophen)	4530	519.70	-116.42	LYS-165	O10	2.1

Table 3: Docking analysis with HHEX

LIGAND	SCORE	AREA	ACE	BONDS
				Residue	Atom	Length
8-Methoxy-11-Methyl-11h-Indolo[3,2-C]Quinoline, 5-O	4366	481.80	-181.06	TYR-111	O4	1.9
Pyridine-3-Carbonitrile, 2-Oxo-4-Phenyl-6-(Thiophen	4856	526.00	-352.82	PRO-32	H26	2.2

Table 4: Docking analysis with CAPN10

LIGAND	SCORE	AREA	ACE	BONDS
				Residue	Atom	Length
8-Methoxy-11-Methyl-11h-Indolo[3,2-C]Quinoline, 5-O	4100	518.90	-137.56	GLN 582	O8	0.9
8-Methoxy-11-Methyl-11h-Indolo[3,2-C]Quinoline, 5-O	4100	518.90	-137.56	GLN 522	O10	2.2
Estra-1,3,5,7,9,15-Hexaen-17-One, 3-Methoxy-	4296	527.40	-141.35	SER 581	O11	1.9
1-(Diethylamino)-3-Methylpyrido[1,2-A]Benzimidazole	4518	515.20	-65.02	VAL 165	N20	2.2
2-(3-Methoxy-5-Methylbenzylidene)-7-Methylindan-1-	4584	593.70	-188.12	PRO 537	O9	2.4
Pyridine-3-Carbonitrile, 2-Oxo-4-Phenyl-6-(Thiophen	4500	478.40	-207.01	TYR 616	N2O	2.6
				ARG 561	O10	2.2
				GLU 564	O10	2.3

Table 5: Docking analysis with IFIH1

LIGAND	SCORE	AREA	ACE	BONDS
				Residue	Atom	Length
8-Methoxy-11-Methyl-11h-Indolo[3,2-C]Quinoline, 5-O	4104	477.10	-27.75	LYS 336	O4	1.8
1-(Diethylamino)-3-Methylpyrido[1,2-A]Benzimidazole	4518	515.20	-65.02	ARG-951	N20	2.7
Pyridine-3-Carbonitrile, 2-Oxo-4-Phenyl-6-(Thiophen	4722	503.30	-197.43	ALA- 490	O10	2.8
Pyridine-3-Carbonitrile, 2-Oxo-4-Phenyl-6-(Thiophen	4722	503.30	-197.43	SER-491	O10	2.4

Fig 4: Docked complex with CAPN10 and HHEX

Notes: Bond (yellow), receptor (thin), ligand (thick)

3.2.5. Compounds docked with CAPN10

The docked complex of CAPN10 with 8-methoxy-11-methyl-11h-indolo[3,2-c]quinoline, Estra-1,3,5,7,9,15-hexaen-17-one, 3-methoxy-,1- (diethylamino)-3-methylpyrido[1,2-a] benzimidazole, 2-(3-methoxy-5-methylbenzylidene)-7-methylindan-1- and pyridine-3-carbonitrile, 2-oxo-4-phenyl-6-(thiophenhas showed binding affinity and the interacting bond details of CAPN10 with 8-methoxy-11-methyl-11h-indolo[3,2-c]quinoline are GLN-582:-0.9 and GLN-522:-2.2 and the interacting bond details of CAPN10 with Estra-1,3,5,7,9,15-hexaen-17-one, 3-methoxy- are SER-581:-1.9 and with 1-(Diethylamino)-3-Methylpyrido[1,2-A] Benzimidazole are VAL-165:-2.2 . The interacting bond details with CAPN10and 2-(3-methoxy-5-methylbenzylidene)-7-methylindan-1-are PRO-537: -2.4. The interacting bond details with CAPN10 and pyridine-3-carbonitrile, 2-oxo-4-phenyl-6-(thiophenare) TYR-616: -2.6, ARG-561: -2.2 and GLU-564: -2.3.

Fig 5: Docked complex with CAPN10 and HHEX

Notes: Bond (yellow), receptor (thin), ligand (thick)

4. DISCUSSION:

Haematopoietically-communicated “homeobox protein”“HHEX” in people is Glucose transporter 2 (GLUT2) is also called facilitated glucose transporter, comprising a “transmembrane” based protein carrier that helps movement of glucose across the plasma membranes which is facilated by proteins. It is also the major transporting system for displacement of glucose mainly between the blood and liver⁸. In humans, SLC2A2 gene encodes this protein. GLUT2 has increased retention for glucose but low accord and therefore works as portion of the "glucose sensor" in the “β-cells of pancreas” in rodents, although in β-cells of human the part of GLUT2 looks like a minor one ⁹. It is a very effective “carrier for glucose”¹⁰. GLUT2 even transport “glucosamine”^11(p). GLUT2 is mainly essential to regulation of osmosis, and stopping stroke induced by “edema, coma” or ischemic attack , in particular when concentration of “blood glucose” is more than “average”¹². GLUT2 can justifiably be called as the "diabetic glucose transporter" or even "stress hyperglycemia glucose transporter."

HHEX stands for “Hematopoietically”-“expressed” homeobox protein is a protein in humans coded by the gene HHEX¹³. The gene is responsible for encoding an individual from “homeobox” group of “translation factors”, a significant number of which some are engaged with formative procedures. Articulation in explicit hematopoietic genealogies proposes that the protein might assume a job in “hematopoietic differentiation” (17) The HHEX translation factor goes about as an advertiser in certain cases and an inhibitor other. It cooperates with various other flagging particles to assume a significant job in the advancement of numerous organs¹⁴. HHEX seems to cooperate with different atom, β-catenin, aiming at improvement of the front organizer. It likewise adds to formative rebuilding and adjustment of “endothelial cells” in a yet to be born organism. The significance of this interpretation factor is represented by the failure of “HHEX” gene comprised knockout mice having foetuses to endure incubation. Not having the outflow of “HHEX”, those specific mice incipient organisms bite the dust in “utero between” Day 13 to Day 16. “HHEX”“knockout” mice show scope of variations from the norm incorporating forebrain irregularities in different degrees of seriousness, just as various different imperfections involving heart, vasculature, liver, monocyte, and thyroid abnormalities¹⁵. HHEX shows some interaction with a protein known as Promyelocyticleukemia protein.

“Tumor necrosis factors” (also known as TNFs) fall under pleiotropic cytokines involved in “apoptosis”, cell multiplication, inflammation and generally stimulate the immune system. Tumor necrosis factors (TNFs) are known to possess strong potential inflammatory cytokines which get produced during the process of “inflammation and development” of lymphoid organs, playing a characterized role specific against the infections caused by bacteria, virus and parasites. In the mammals, “TNF-α, LT-βand TNF-β” are distinct members of a huge number of the “TNF ligand” and “receptor family”¹⁶.The TNF is known as a significant pro‐inflammatory cytokine which is involved in a role in osteolysis. The TNF coding gene is situated on the chromosome 6p21.3 present in the “major histocompatibility complex’s” class III portion¹⁷ . The cytokine is observed to be discharged by the macrophages which can attach, and therefore associate functional role via its receptors namely “TNFRSF1A/TNFR1” and “TNFRSF1B/TNFBR”. Also known to be indulged in the regulation of a vast range of biologically related procedures involving the “proliferation of cells”, “lipid metabolism”, coagulation” and also apoptosis. This cytokine is seen to be associated with different types of diseases related to immune system, cancer and even insulin resistance. Recent studies involving the knockout mice suggests the neuroprotection based role of the cytokine¹⁸.Human TNF has approximately “30% homology” situated in its amino acid sequence with lymphotoxin, a lymphokine which has biologically similar characteristics.

CAPN10 (Calpain 10) is a Protein Coding gene. Maladies related with CAPN10 incorporate Diabetes Mellitus, Noninsulin-Dependent, 1 and Polycystic Ovary Syndrome. Among its related pathways are Focal Adhesion and Integrin Pathway. Quality Ontology (GO) comments identified with this quality incorporate cytoskeletal protein authoritative and calcium-subordinate cysteine- type endopeptidase movement. A significant paralog of this quality is CAPN1(25). Calpains represent a ubiquitous, well-monitored group of calcium-subordinate cysteine proteases. The calpain proteins are heterodimers comprising of an invariant little subunit and variable huge subunits. The huge synergist subunit has four spaces: area I, the N-terminal administrative space that is prepared upon calpain initiation; space II, the protease space; space III, a linker area of obscure capacity; and area IV, the calmodulin-like calcium-restricting domain. This gene encodes a huge subunit. It is a typical calpain in that it comes up short on the calmodulin-like calcium-restricting space and rather has a different C-terminal area. It is comparable in association to calpains 5 and 6. This quality is related with sort 2 or non-insulin-subordinate diabetes mellitus (NIDDM), and is situated inside the NIDDM1 locale (26).

The IFIH1 gene encodes a cytoplasmic viral RNA receptor that enacts type I interferon motioning through the MAVS connector particle (27). IFIH1 is a Protein Coding gene. Ailments associated to IFIH1 incorporate Aicardi-Goutieres Syndrome 7 and Singleton-Merten Syndrome 1. Out of its associated pathways are “RIG-I/MDA5” intervened acceptance of IFN-alpha/beta pathways and Innate Immune System. Quality Ontology (GO) explanations identified with this quality incorporate nucleic corrosive authoritative and hydrolase movement. A significant paralog of this quality is DDX58. Can likewise identify different infections, for example, dengue infection (DENV), west Nile infection (WNV), and reovirus (28) Likewise engaged with antiviral motioning in light of infections containing a dsDNA genome, for example, vaccinia infection. Assumes a significant job in intensifying intrinsic safe motioning through acknowledgment of RNA metabolites that are delivered in the period of infection disease by RNase L. May assume a significant job in upgrading characteristic executioner cell work and might be engaged with development restraint and apoptosis in a few tumor cell lines (29).

5. CONCLUSION:

Modelled proteins were retrieved through i-TASSER server for the structure of protein and its function. Among many proteins involved in Diabetes 5 unstructured proteins were chosen. The quality check of the taken genes are done through RAMPAGE server. The model protein which has high percentage is taken as receptor for docking with bioactive compounds of Murrayakoenigii seed extract which acts as a ligand here for docking analysis. The ligands like 8-Methoxy-11-“Methyl”-11H-“Indolo”[3,2-C] Quinoline, Estra- 1,3,5,7,9,15- Hexaen-17-one, 3-Methoxy-, 1,2:7,8-Dibenzophenanthrene,1-(Diethylamino)-3-Methylpyrido [1,2-A]Benzimidazole 2-(3-Methoxy-5-Methylbenzylidene)-7- Methylindan-1- Pyridine-3-Carbonitrile, 2-Oxo-4-Phenyl-6-(Thiophen has showed better affinity with the chosen receptors like TNF, GLUT-2, HHEX, CAPN10 and IFIH1.

6. ACKNOWLEDGMENT:

The authors are thankful to VIT-SEED GRANT for providing the necessary facilities to carry out this research project.

7. CONFLICTS OF INTEREST:

The authors declare that there is no conflict of interest.

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Received on 29.11.2019 Modified on 11.02.2020

Research J. Pharm. and Tech. 2020; 13(12):6163-6169.

DOI: 10.5958/0974-360X.2020.01075.6