Analysis of protein modeling for envelope glycoprotein GP120 for HIV via bioinformatics approaches

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1 International Research Journal of Virology Vol. 1(1), pp , March, ISSN: x IRJV Research Article Analysis of protein modeling for envelope glycoprotein GP120 for HIV via bioinformatics approaches *Alka Dubey 1, Neelesh Yadav 2, Swinderjeet Singh Kalra 3 * 1 Visiting Research Associate, in Bioinformatics Infrastructure Facility (Funded by Dept. of Biotechnology, Govt. of India) of Forest Research Institute, Dehradun, India 2 Scientist and Coordinator, Bioinformatics Centre, Forest Research Institute, Dehradun, India 3 Department of Chemistry, D.A-V College Kanpur, India Basically in this topic we discussed about the 3D structure of gp120 protein and this is also known as envelope glycoprotein and this glycoprotein exposed on the surface of the HIV envelope. Gp120 is essential for virus entry into cell because its plays a vital role in attachment to specific cell surface receptors. This is also play a very important role to enter a HIV virus in a living cell with the help of cell external receptors and gp120 also plays a vital role in the ability of HIV-1 to enter CD4+ cells so that s why in this topic we study about envelope glycoprotein and its computational predicted models via bioinformatics approaches and the method of homology modelling is based on the observations because that s protein tertiary structure is better conserved than amino acid sequence. Keywords: GP120, 3D structure, HIV, protein modelling, envelope proteins INTRODUCTION Human immunodeficiency virus is a lentivirus (slowly replicating virus) that causes Acquired immuno deficiency syndrome (AIDS). HIV attached to the human CD4 receptor and leads to the infection. (dubey and kalra.(2013) Acquired immunodeficiency syndrome (AIDS)was first reported by the us centre of disease (CDC),a few years later it was found that s a retrovirus called human immune deficiency virus (HIV) and this causative agent in AIDS, the study of HIV protease is one of the most important approaches for the therapeutic intervention in HIV infection and their development is regarded as major success of design, HIV attacks on the CD4+ (T helper cells in human) lymphocyte and these are key component of the body s immune system. The Present anti retroviral HIV drugs targets based on three protein reverse transcriptase, protease, integrase.(dwivedi and saxena.(2013) Human immunodeficiency virus (HIV) and simian (SIV) immunodeficiency virus entry is mediated by binding of the viral envelope glycoprotein (Env) to CD4 and chemokine receptors, CCR5 and/or CXCR4. CD4 induces extensive conformational changes that expose and induce formation of a chemokine receptor binding site on gp120. CD4-independent Env's of HIV type 1 (HIV-1), HIV-2, and SIV have been identified that exhibit exposed chemokine receptor binding sites and can bind directly to CCR5 or CXCR4 in the absence of CD4. While many studies have examined determinants for gp120- CCR5 binding, analysis of gp120-cxcr4 binding has been hindered by the apparently lower affinity of this interaction for X4-tropic HIV-1 isolates. *Corresponding author: Alka Dubey, Bioinformatics Infrastructure Facility, alkabioinfo964@gmail.com, Tel: ,

2 Dubey 002 Table 1. Model residues in the following four regions with model s energy values Model number Core Allowed Generously Disallowed Models energy (kcal/mol) Model :1 74.2% 23.8% 0.8% 1.2% Model :2 70.0% 24.6% 2.9% 2.5% Model :3 75.0% 21.2% 2.9% 0.8% Model :4 71.0% 23.3% 3.8% 1.7% Model :5 70.8% 27.1% 0.8% 1.2% Model :6 70.8% 27.5% 0.4% 1.2% Model :7 73.8% 24.2% 0.4% 1.7% Model :8 73.3% 22.5% 2.5% 1.7% Model :9 73.8% 24.2% 0.8% 1.2% Model : % 25.4% 1.7% 1.7% Name of models with statical scores for the best model selection like, core,allowed,generously,disallowed,models energy values and the model number three is best model this analysis basis on their favorable statical selection These gp120 proteins will be useful in dissecting determinants for CXCR4 binding and Env triggering and in evaluating pharmacologic inhibitors of the gp120- CXCR4 interaction.(lin et al. (2013)The entry of human immunodeficiency virus (HIV) into cells requires the sequential interaction of the viral exteriorenvelope glycoprotein, gp120, with the CD4 glycoprotein and a chemokine receptor on the cell surface. These interactions initiate a fusion of the viral and cellular membranes. Although gpl20 can elicit virus-neutralizing antibodies, HIV eludes the immune system. We have solved the X-ray crystal structure at 2.5A resolution of an HIV-1gp120 core complexed with a two-domain fragment of human CD4 and an antigen-binding fragment of a neutralizing antibody that blocks chemokine-receptor binding. The structure reveals a cavity-laden CD4 gp120 interface, a conserved binding site for the chemokine receptor, evidence fora conformational change uponcd4binding, the nature of a CD4-induced antibody epitope, and specific mechanisms for immune evasion. Our results provide a framework for understanding the complex biology of HIV entry into cells and should guide efforts to intervene (peter et al., 1998). MATERIALS AND METHODS For this analysis we used different type of online and offline computational tools as software s and servers for this scientific analysis and also for protein sequence of GP120 for HIV and then desired sequence Retrieves from NCBI and then for go for model prediction via online software GENO3D, were use to find out potential 3D structure of GP120 protein basis on their statistical values and some other essential calculations for CADD and vaccine designing then go for validate the potential models via the SAVES SERVER and the structure visualize via Discovery studio then go for analyze the predicted results from online validating software s. RESULTS The results analysis base on Sequence of GP120 [>gi gb AAF AF236859_1gp120 [Human immunodeficiency virus 1] Protein sequence to 3Dmodel prediction In this project homology modelling completed with Geno3D it s a type of an automatic web server for protein molecular starting with a query protein sequence. And it s based on Lipinski rules. Models energy (kcal/mol): model 3: (Table1, 2, 3) (Fig. 1, 2, 3, 4). PARAMETERS IN TABLES Ramachandran plot quality This property is measured by the percentage of the protein's residues that are in the most favoured, or core, regions of the Ramachandran plot. For a good model structure, obtained at high resolution, one would expect this percentage to be over 90%. However, as the resolution gets poorer, so this figure decreases - as might be expected. The shaded region reflects this expected decrease with worsening resolution. Peptide bond planarity This property is measured by calculating the standard deviation of the protein structure's omega torsion angles. The smaller the value the tighter the clustering around the ideal of 180 degrees (which represents a perfectly planar peptide bond).

3 Int. Res. J. Virol. 003 Figure 1. white colours bolls explains hydrogen bond and blue colour expose for residue greater than25.00% max SAS and green colour buried for residue less then 10.00%masx SAS and red boll presenting a movement. Figure 2. Wire frame structure of model: 3 and this structure presenting a amino acids and arrow presenting a hydrophobic movement. Bad non-bonded interactions. This property is measured by the number of bad contacts per 100 residues. These are defined as contacts where the distance of closest approach is less than or equal to 2.6Å. Calpha tetrahedral distortion This property is measured by calculating the standard deviation of the zeta torsion angle. This is a notional torsion angle in that it is not defined about any actual bond in the structure. Rather, it is defined by the following four atoms within a given residue: Calpha, N, C, and Cbeta. Main-chain hydrogen bond energy This property is measured by the standard deviation of the hydrogen bond energies for main-chain hydrogen bonds. Overall G-factor The overall G-factor is a measure of the overall normality of the structure. The overall value is

4 Dubey 004 Table 2. Presenting a selected model with Ramchandran plot and plot of residues Ramchandran plot Ramchandran plots for all residue types Ramachandran plot can be used in two somewhat different ways. One is to show in theory which values, or conformations, of the ψ and φ angles are possible for an amino-acid residue in a protein. The plot shows separate Ramachandran plots for each of the 20 different amino acid types.the darker the shaded area on each plot, the more favourable the region. The data on which the shading is based has come from a data set of 163 non-homologous, high-resolution protein chains chosen from structures solved by X-ray crystallography to a resolution of 2.0Å or better and an R-factor no greater than 20%.The numbers in brackets, following each residue name, show the total number of data points on that graph. (Nishikawa and Ooi, 1986) Figure 3. Model 3 verify from the save server with warning. obtained from an average of all the different G-factors for each residue in the structure (Kabsch et al., (1983) The various graphs and diagrams on this plot show how the protein's geometrical properties vary along its sequence. This gives a visualization of which regions appear to have consistently poor or unusual geometry (perhaps because they are poorly defined) and which have more normal geometry predictions from SAVE server via model 3: This Meta server runs 6 programs for checking and validating protein structures during and after model refinement. A user can run all 6 programs to get a collective view of the input structure, or individual

5 Int. Res. J. Virol. 005 Table 3. Model 3 structural plots with main-chain parameters with residue properties Main chain parameters Residue properties The six graphs on the main-chain parameters plot show how the structure (represented by the solid square) compares with well-refined structures at a similar resolution. The dark band in each graph represents the results from the well-refined structures; the central line is a least-squares fit to the mean trend as a function of resolution, while the width of the band on either side of it corresponds to a variation of one standard deviation about the mean. In some cases, the trend is dependent on the resolution, and in other cases it is not. For structures solved by NMR a nominal resolution of 2.0Å is used. Figure 4. Model: 3 secondary structures with estimated accessibility with triangle as favourable region in protein. programs can be selected. The evaluations the server makes of the outputs of each program are labeled by a simple 3 color scheme. DISCUSSION This study was initiated with the objective to find out who is a potential candidate for stop the HIV infection and the candidate related to drug and vaccines and we know that Vaccines stimulate the body s immune system to provide protection against infection or disease. Drugs may be used for a limited duration, or on a regular basis for chronic disorders but our outcome theory to support vaccine is potential candidate for HIV because it s easy to memorize our cells. CONCLUSION The study revealed that basic motif for this study is we want to know who is potential candidate for stop the HIV infection and the candidate means DRUG /VACCINES in

6 Dubey 006 this project we analyze drug is not potential candidate to control the HIV infection but vaccine is potential candidate to control the HIV infection. But this is a virtual coordination according computational biology its may be possible or not with some favorable statical values. As we know that a vaccine is a medical product designed to stimulate your body's immune system in order to prevent or control an infection. An effective vaccine strains your immune system to fight a particular microorganism so that it can't make you sick. Although there are currently no vaccines to prevent or treat HIV, researchers are developing and testing potential HIV vaccines. HIV vaccines designed to prevent HIV infection in HIV negative peoples are called preventive vaccines. According to these predictions analysis is better to design a vaccine for this infection because this is easy to memorize our cells for upcoming possibility of this infection. ACKNOWLEGEDMENT The corresponding author wish to thanks Dr. Neelesh Yadav, Scientist and Coordinator, Bioinformatics Centre, Forest Research Institute, Dehradun, India, for valuable scientific platform and support for scientific favourable rights. And we also thank full DBT-India for giving us this plate form for scientific computational Work. REFERENCE Dubey A, Swinder JSK (2013).Computational comparative modelling and visualization for HIV1 and HIV2 proteins via the software SYBYL-X 2.0. Int. J. Sci. Res. Pub., 3: 10 Alka D, Vijay LS (2013). In Silico Drug Designing of Protease Inhibitors to Find the Potential Drug Candidate for HIV1. Computational Biology and Bioinformatics, (1) 3: George L, Fre de ric B, Josephine R, Robert WD, James AH (2013). Identification of gp120 Binding Sites on CXCR4 by UsingCD4Independent Human Immunodeficiency VirusType 2 Env Proteins. J. Virol., 77: Kabsch W, Sander C (1983). Dictionary of protein secondary structure: pattern recognition of hydrogenbonded and geometrical feature. Biopolymer. 22: Nishikawa K, Ooi T (1986). Radial locations of aminoacid residues in a globular protein - correlation with the sequence. J. Biochem., 100: Peter DK, Richard W, James R, Raymond WS, Joseph S, Wayne AH (1998). Structure of an HIVgp120 envelope glycoprotein in complex with thecd4 receptor and a neutralizing human antibody. Nature Accepted 12 April, Citation: Alka D, Yadav N, Kalra SS (2014). Analysis of protein modeling for envelope glycoprotein GP120 for HIV via bioinformatics approaches. International Research Journal of Virology 1(1): Copyright: 2014 Dubey et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.