CURRENT ISSUE (In progress)
International Journal of Computational Bioinformatics and In Silico Modeling
ABSTRACT: Bionformatics in agriculture is viewed as impending thrust areas that have opened new avenues for agri-bioinformatics developments. ‘Omics’ technologies have produced huge amount of sequence data from several crops, animals and microorganisms. Efficient computational tools comprising intelligent data query, retrieval analysis and visualization tools have been developed for data mining and accelerating the process of gene discovery. This paper highlights the frontier research work in Agri-Bioinformatics.
How to cite: Ravish Chatrath et. al. Emerging Trends in Agri-Bioinformatics – A meeting report. Int J Comput Bioinfo In Silico Model. 3(6) 2014: 514-516
ABSTRACT: The objective of the study is to identify novel lead compounds and analyze the effect of mutations that result in the disruption of the salt bridge between Lys39 on ICAM1 (Intercellular Adhesion Molecule 1) and Glu241 on LFA1 (Lymphocyte function Associated Antigen 1), which could reduce the effect of Rheumatoid Arthritis. Work is done on the ICAM1-LFA1 (PDB ID: 1MQ8) protein. Mutations were induced into ICAM1 and LFA1 using SIFT and PredictSNP. Homology modeling was done using Discovery Studio. Protein-Protein docking was done using ZDOCK to get ICAM1M-LFA1 and ICAM1M-LFA1M structures. Secondary structure analysis and non-bonded interaction studies were done. Then active site prediction was carried out on ICAM1-LFA1, ICAM1M-LFA1 and ICAM1M-LFA1M structures. Compounds from plant and animal sources having immunosuppressant, anti-TNF and anti-inflammatory properties as well as standard drugs active against Rheumatoid Arthritis were identified. ADMET studies were done. Molecular docking was done using Lead IT. The best results were chosen based on their e-values. The In silico analysis of the current work proves that mutations in ICAM1 lead to disruption of the salt bridge between Lys39 and Glu241, which could reduce the effect of Rheumatoid Arthritis. Probable drugs effective against Rheumatoid Arthritis were identified from various plant and animal sources based on lowest e-value. The standard mutations in ICAM1 and use of various natural compounds as probable drugs could reduce the effects of Rheumatoid Arthritis. QSAR and molecular dynamics can be done on the best compounds chosen as probably drugs and further experimental analysis can be carried out.
KeyWords: Rheumatoid Arthritis; ICAM1; LFA1; Lys39; Glu241; docking
How to cite: Niels Alvaro Menezes et al. Identification of novel lead compounds and mutational analysis of intercellular adhesion molecule 1 in combination with lymphocyte function-associated antigen 1 protein involved in Rheumatoid Arthritis using in silico approach. Int J Comput Bioinfo In Silico Model. 3(6) 2014: 517-524
ABSTRACT: This is a sequel to the previous study in homology modeling and structural analysis of human N-acetyl-alpha-neuraminidase 3 (NEU3). Further analysis was performed with a software package the Molecular Operating Environment. A human NEU2 (PDB code: 1VCU) was selected as a template for the 3D structure modeling of NEU3. The superimposition and root mean square deviation analysis indicated that the modeled NEU3 showed significant 2D and 3D similarities to NEU2. The molecular electrostatic potential (MEP) map of the NEU3 model exhibited that the model was different from the NEU2 model electrostatically at the LBS. Further, docking simulations revealed the similarity of the ligand-receptor bound location between the NEU2 and 3 models. The different binding orientations between the N-acetyl-2,3-dehydro-2-deoxyneuraminic acid (DANA)-NEU2 and DANA-NEU3 complexes reflected the different MEP maps at the LBSs between the NEU2 and 3 models. The docking simulation revealed that DANA possibly inhibits functions of NEU3 interfering with Arg25 and Asn88. These results indicate that the NEU3 model was successfully modeled and analyzed. Our data verify that the model can be utilized for application to target NEU3 for the development of anticancer drugs.
KeyWords: N-acetyl-alpha-neuraminidase 3 (NEU3), cancer, in silico
How to cite: Hideaki Yamaguchi. Structural insight into the homology modeled human N-acetyl-alpha-neuraminidase 3 (NEU3). Int J Comput Bioinfo In Silico Model. 3(6) 2014: 525-530
ABSTRACT: Colorectal cancer (CRC) is one of the most diffuse cancers worldwide; evidences showed that Adenomatous Polyposis Coli (APC) is a multifunctional tumor suppressor gene that regulates and controls many biological functions; mutations in this gene has been reported in many cases of CRC. SNPs contribute to gene mutations and expression variations justifying phenotypic variations among population and hence such SNPs would be potential biological targets for identification and analysis; therefore this work focused on analysis of SNPs in the coding regions of APC gene found as non-synonymous variants (nsSNP) and those in the 3'un-translated region (3'UTR) affecting miRNA binding using computational methods. 333 nsSNPs were analyzed by tools that concerning structural and functional aberrations and measure degrees and scores of alterations; and then the protein variants were subjected to structural modeling to highlight the impact of amino acid substitution upon protein phenotype. Analysis with Sift and Polyphen resulted in 15 damaging nsSNPs out of a total and marked 5 amino acid substitutions (E142G, R99W, R24N, L680S and W157T) with probably high deleterious scores, while analysis of 51 3'UTR SNPs by its special tool PolymiRTS resulted in no single nucleotide variant at that region could disturb the conserved sites of miRNA. It has been found that the use of such computational analysis tools was highly valuable and critical to highlight life threatening mutations and early sounds for APC gene aberrations causing truncated products with adverse outcomes.
KeyWords: Colorectal Carcinoma; APC nsSNPs; Single nucleotide polymorphism (SNP); Protein modeling, 3'UTR SNPs
How to cite: Shahenaz S. Salih et al. Computational Detection of Deleterious Single Nucleotide Polymorphisms in Human Adenomatous Polyposis Coli Gene the Gate-Keeper of Colorectal Carcinoma. Int J Comput Bioinfo In Silico Model. 3(6) 2014: 531-537
ABSTRACT: Next generation sequencing (NGS) technology is better method for genome and transcriptome sequencing. NGS technologies are relatively easy and error free compared to the Sanger method. With the help of NGS we can identify gene structure as well as transcriptome sequencing. A typical metadata driven management, analysis and visualization of G-Protein coupled receptor (GPCR) dataset in different-different species is reported here. We considered the assignment of GPCR reads to gene families using BLAST for identification of genes and introduced a clustering method which reduces the complexity of metagenome dataset. We report that the clustering method is more accurate than the direct assignment for studies of the Homo sapiens GPCRs and other GPCRs in general. Along with the advent of next-generation sequencing platforms, several high-performance sequence analysis pipelines will be helpful for the detection of type 2 diabetes.
KeyWords: GPCRs, NGS, Dataset, BLAST, Type 2 diabetes
How to cite: Aman Chandra Kaushik et al. Metadata-Driven Management, Analysis, and Visualization of GPCR data using NGS approach. Int J Comput Bioinfo In Silico Model. 3(6) 2014: 538-542
ABSTRACT: Haemophilia is a bleeding disorder in which the clotting mechanism does notwork properly and it slows down the blood clotting. Persons with this condition experience prolonged bleeding followed by an injury. There are basically two genes which are mainly responsible for this disease (F8 and F9).This paper focuses on finding out novel interaction between genes as well as proteins involved in process and constructing the pathway using system biology (Cell Designer http://celldesigner.org/). According to the concentration of the molecules, the reaction protocols were defined for simulation type using mass action kinetics equations V= k* π *Si or irreversible Michaelis-Menten equations V= Vm*S/Km+S. Then the parameter like time span, error tolerance and solver were set and finally the haemophilia pathway was executed for simulation. The following data was generated: simulation graph, Signal Injection: Pulse response graph and Reaction Rate(s) Vs Species graph of Haemophilia pathway. This pathway is useful in identifying novel genes as well as proteins which interact with F8 genes with respect to time.
KeyWords: F8, F9, System Biology, Kinetics, Simulation
How to cite: Aman Chandra Kaushik et al. A comprehensive Hemophilia Interaction Network Model. Int J Comput Bioinfo In Silico Model. 3(6) 2014: 543-548
ABSTRACT: Argonaute proteins have been well characterized for their key role in RNA mediated gene silencing pathways. Association of Argonaute proteins with siRNA to form RISC (RNA induced silencing complex) to catalyze post transcriptional gene silencing by degrading and slicing messenger RNA is established as a significant role of Argonaute protein family. Current work proposes the interaction of Argonaute proteins with DEXD c and Helicase C domain containing protein, RNA helicase LGP2 based on similarity in the phylogenetic distance matrices of the two proteins which is inferred from results obtained by implementation of mirror tree approach. Further verification of this potential interaction has been done by applying different online tools for the prediction of subcellular localization of Argonaute proteins and RNA helicase LGP2. Subcellular localization has shown that like Argonaute proteins, RNA helicase LGP2 is also localized to cytoplasm.
KeyWords: Argonaute, Mirror tree, Phylogenetic profiling, RNA Helicase LGP2, Subcellular localization
How to cite: Pallavi Singh. Computational Analysis of Argonaute Protein Interactions Using Mirror Tree Approach. Int J Comput Bioinfo In Silico Model. 3(6) 2014: 549-552
ABSTRACT: North-Western Himalayan states are treasure trove of Biological diversity which is an important source of various compounds, genes and proteins of industrial and pharmaceutical importance. Thus biodiversity generates economic revenue through bio-prospecting which links biodiversity with industries. However conservation and sustainable use of biological diversity is critically important not only to ensure the continuous supply of food, fibre, fodder but also to maintain ecological balance and promote industrial growth in Himalayan states. Advances in Nanotechnology, Biotechnology and Bioinformatics are of immense importance in preservation and bio-prospecting of important molecules by harnessing biodiversity so as to promote industrial and agricultural growth of these states. During the brainstorming session, participants deliberated on the applications of nanotechnology, biotechnology and information technology to manage the challenges of food and nutritional security in these states and to promote the growth on front of agriculture, biomedical and industrial sector. This report summarizes the nanotechnology, biotechnology and bioinformatics as integrated discipline of frontier science and technologies “Nano-Bio-Information Technology” for the holistic development of hill states of India with very useful recommendations.
KeyWords: Biotechnology, Bioinformatics, Nanotechnology
How to cite: Anil Kumar. Strengthening the Efforts on “Nano-Bio-Information Technology for the Development of North-Western Himalayan States of India” A Brainstorming Session Report. Int J Comput Bioinfo In Silico Model. 3(6) 2014: 553-561
ABSTRACT: HLA genes play important roles in the immune system and have multiple alleles that show extensive variation across human populations. Single-nucleotide polymorphisms (SNPs) play a major role in the understanding of the genetic basis of many complex human diseases. Also, the genetics of human phenotype variation could be understood by knowing the functions of these SNPs. It is still a major challenge to identify the functional SNPs in a disease-related gene. In this work, we explore whether SNPs mutations in HLA complex genes affect Allograft rejection -renal transplantation rejection-, the genetic variation that can alter the function and structure of HLA-DRB1 and HLA-DQB1 genes had been analyzed using computational methods. HLA-DRB1 gene contained a total of 4078 SNPs, of which 367 were nonsynonymous SNPs, likewise HLA-DQB1 gene contain total of 5459 SNPs of which 107 were nsSNPs. It was found through HLA-DRB1 that, 24 nsSNPs were predicted to be damaging by both SIFT and PolyPhen servers, comparing with the only one nsSNP (rs41558214) predicted by SIFT program through HLA-DQB1 gene. This study proposes that the main target mutation for the kidney transplantation rejection could be caused by SNPs located in coding- nonsynonymous regions of HLA-DRB1 gene. HLA typing using SNPs analysis is a suitable, accurate and an easy way to cover all types of HLA genes and could provide beneficial outcomes.
KeyWords: Single-nucleotide polymorphisms (SNPs), renal transplantation rejection, computational or bioinformatics methods, HLA-DRB1 gene. HLA-DQB1 gene, Allograft rejection
How to cite: Mohamed M. Hassan et al. Computational analysis of deleterious nsSNPs within HLA-DRB1 and HLA-DQB1 genes responsible for Allograft rejection. Int J Comput Bioinfo In Silico Model. 3(6) 2014: 562-577