Forthcoming articles

International Journal of Computational Biology and Drug Design

International Journal of Computational Biology and Drug Design (IJCBDD)

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International Journal of Computational Biology and Drug Design (25 papers in press)

Regular Issues

  • Data Acquisition and Electrical Instrumentation Engineering Modelling for Intelligent Learning and Recognition
    by Jun Qin, Yuhao Jiang 

  • Development of interactive computer learning program for genetics and molecular biology applications
    by Xiaoli Yang, Bin Chen, Yifan Cai, Charles Tseng 

    by Kalirajan Rajagopal, Pandiselvi A, Gowramma B 
    Abstract: 9-aminoacridines play an important role in the field of antitumor DNA-intercalating agents, due to their antiproliferative properties. Several anticancer agents with 9-anilinoacridines such as amascrine, and nitracrine have been developed. To get insight of intermolecular interactions, the molecular docking studies are performed at active site of HER2. Aim: In the present study, for identification of potential ligands of isoxazole substituted 9-amino acridines as selective HER2 inhibitors (PDB id- 3PP0) targeting breast cancer by using Schrodinger suit-2016-2, Maestro 9.6 version. Molecular docking targeted against HER2 by Glide module, insilco ADMET screening also performed by qikprop module and free binding energy of compounds was calculated by Prime-MMGBSA module. The binding affinity of the designed molecules towards HER2 (PDB id- 3PP0) was selected on the basis of GLIDE score and interaction patterns. Many compounds showed strong hydrophobic interactions and hydrogen bonding interactions and other parameters with amino acid residues and also explain their potency to inhibit HER2 (3PP0). The isoxazole substituted 9-amino acridine derivatives 1a- 1x have good binding affinity with Glide score in the range of -6.6 to -9.7 when compared with the standard ledacrine (-6.3) and tamoxifen (-3.7). The ADMET screening of the designed molecules have almost all the ADMET properties of the compounds are within the recommended values. MM-GBSA binding results of most potent inhibitor displayed stable and favourable. The results reveals that, this study provides evidence for consideration of valuable ligands in isoxazole substituted 9-amino acridine derivatives as potential HER2 inhibitor and the compounds, 1o,f,n,d,m,w with good Glide score may produce significant anti breast cancer activity for further in vitro and in vivo investigations may prove their therapeutic potential.
    Keywords: Acridine; Isoxazole; docking studies; Insilico ADMET screening; MM-GBSA.

  • Boosting Gene Expression Clustering with System-Wide Biological Information: A Robust Autoencoder Approach   Order a copy of this article
    by Hongzhu Cui, Chong Zhou, Xinyu Dai, Yuting Liang, Randy Paffenroth, Dmitry Korkin 
    Abstract: Gene expression analysis provides genome-wide insights into the transcriptional activity of a cell. One of the first computational steps in exploration and analysis of the gene expression data is clustering. With a number of standard clustering methods routinely used, most of the methods do not take prior biological information into account. Here, we propose a new approach for gene expression clustering analysis. The approach benefits from a new deep learning architecture, Robust Autoencoder, which provides a more accurate high-level representation of the feature sets, and from incorporating prior system-wide biological information into the clustering process. We tested our approach on two gene expression datasets and compared the performance with two widely used clustering methods, hierarchical clustering and k-means, and with a recent deep learning clustering approach. Our approach outperformed all other clustering methods on the labeled yeast gene expression dataset. Furthermore, we showed that it is better in identifying the functionally common clusters than k-means on the unlabeled human gene expression dataset. The results demonstrate that our new deep learning architecture can generalize well the specific properties of gene expression profiles. Furthermore, the results confirm our hypothesis that the prior biological network knowledge is helpful in the gene expression clustering.
    Keywords: gene expression; protein-protein interactions; clustering; deep learning.

  • Mathematical modelling of hepatitis C virus dynamics response to therapeutic effects of interferon and ribavirin   Order a copy of this article
    by Jean Marie Ntaganda 
    Abstract: This paper aims at designing a two compartmental mathematical model for determining the response of protein (Interferon) and drug (Ribarivin) for a patient who is suffering from hepatitis C virus (HVC). The stability of developed mathematical model is established. Using inverse techniques, model parameters and functions are identified. To test efficiency and response to interferon and ribavirin as HCV treatment, the validation of the mathematical model is achieved by considering a patient on treatment during 12 months. The results obtained are rather satisfactory since model parameters vary around their corresponding value that is equilibrium values for healthy subjects. Furthermore, the reaction of the disease to treatment can be modeled and a feedback can be approximated by the solution of an optimal control problem. The increasing necessity to interpret the meaning of measurable variables such as interferon and ribavirin under both physiological and pathological conditions for a patient has imposed the need for relatively simple models that should be able to describe as accurately as possible the mechanical behavior of the disease.
    Keywords: HVC; Treatment; Interferon; Ribavirin; Parameters identification; Stability; Equilibrium value; Healthy subjects; Numerical simulation.

  • Polypharmacological Potential of Natural Compounds against Prostate Cancer Explored Using Molecular Docking and Molecular Dynamics Simulations   Order a copy of this article
    by Priya Antony, Bincy Baby, Zahrah Al Homedi, Walaa Al Halabi, Amanat Ali, Ranjit Vijayan 
    Abstract: Prostate cancer is one of the most frequently diagnosed forms of cancer with high global incidence and mortality rate. Overexpression of several non-receptor tyrosine kinases (NRTKs) have been widely observed in prostate cancer and are attractive therapeutic targets. Virtual Screening of the InterBioScreen natural compounds library was employed to identify potential anticancer molecules with polypharmacological properties. Using molecular docking and simulation analysis the interaction and stability of these molecules in the active site of three kinases - Bruton's tyrosine kinase (BTK), focal adhesion kinase (FAK) and Src kinase was studied. The study revealed that compounds STOCK1N 32236, STOCK1N 30449, STOCK1N 24193 and STOCK1N 23077, which are structurally similar, possessed polypharmacological properties by interacting with all the three NRTKs in a similar manner by orienting one naphthalene group towards the hinge region and the other towards the activation loop. Binding score and interactions of these natural compounds were better than currently available kinase inhibitors. 100 ns molecular dynamics simulation showed that these molecules were bound stably in the active site. The screened natural molecules could be a framework for developing novel kinase inhibitors for the treatment of prostate cancer.
    Keywords: polypharmacology; Bruton's tyrosine kinase; focal adhesion kinase; Src kinase; molecular docking; molecular dynamics.

  • Interactions of Leptospiral PI-PLC with the membrane phosphoinositides; an insight of the protein-phospholipid association in the pathogenesis   Order a copy of this article
    by Samaha T.H 
    Abstract: Protein-phospholipid interactions encompass the basis of many biological processes including host-pathogen recognition and disease transmission. So, as an attempt to characterise the protein-lipid associations in the pathogenesis of Leptospirosis, this study reports the interactions of PI-PLC (LA_1375) of Leptospira interrogans serovar Lai strain 56601 with PIP2 and PIP3. Molecular docking analysis was carried out to unveil the interactions of the protein with the plasma membrane phosphoinositides. The binding affinities of the interactions, most favourable protein-phospholipid binding poses, and the binding site residues were revealed. The binding energies were found to be -14.04 and -11.64 kcal/mol respectively for PIP2 and PIP3 interaction. Also, the analysis explored the high-affinity binding of the protein with the substrate PIP2. PIP2 is the precursor of intracellular second messengers DAG and IP3; responsible for the activation of pro-inflammatory mediators and other signalling cascades. Moreover, both PIP2 and PIP3 have been implicated in various cellular processes through the activation of lipid signalling cascades. Hence, further in-vitro confirmation of the annotated interactions may unravel the basis of membrane degradation, invasion, and inflammation processes related to the pathogenesis of Leptospirosis.
    Keywords: Leptospirosis; phosphatidylinositol-specific phospholipase C; LA_1375; molecular docking; membrane phosphoinositides; pathogenesis.

  • Correlation among Hydrophobic Aromatic and Aliphatic Residues in the Six Enzyme Classes   Order a copy of this article
    by ANINDITA ROY CHOWDHURY, Nagendra H. G., Alpana Seal 
    Abstract: Hydrophobic force as one of the fundamental forces contributes in folding of the primary sequence of amino acids into a functional three dimensional protein structure. Hydrophobic interactions of side-chains provide maximum stability to correctly folded proteins. Earlier, the authors identified the aromatic and aliphatic residues contributing maximum and minimum hydrophobicity in all the six enzyme classes. The present investigation examines the relative contributions towards hydrophobicity of the different hydrophobic amino acids in both aromatic and aliphatic categories. Notably in a sequence, inverse relationship between residues of similar hydrophobic strength both in aromatic and aliphatic categories seems to exist. This analysis is likely to provide insight for finer analysis of the enzyme molecule.
    Keywords: hydrophobicity scale; enzymes; correlation between hydrophobic residues; residual plot; inverse relation.

  • Modeling and Simulation of Transmission Lines in a Biological Neuron   Order a copy of this article
    by Charaf Eddine Bailoul, Nour Eddine Alaa 
    Abstract: In this paper, we present a new mathematical model that explains the transmissionrnalong a biological neuron. We also present a numerical scheme based on the four order beta-method to simulate numerically the transmission. The idea is to couple the beta-method of high order with Runge Kutta method in order to get high order schemes without oscillations. Furthermore, various numerical experiments are presented to show the power and efficiency of our proposed model.
    Keywords: Finite-Difference Time-Domain (FDTD); Beta Method; Transmission Lines; Fourth Order; Electromagnetic Propagation.

    by Kalirajan Rajagopal, Iniyavan K, Rathika G, Pandiselvi A 
    Abstract: Novel chalcone substituted 9-anilinoacridines(1a-z) were designed by insilico method for their Topoisomerase-II(Topo-II) inhibitory activity due to DNA-intercalating properties. Docking studies of compounds 1a-z as selective TOPO-II (id-1ZXM) inhibitors by using Schrodinger suit2016-2. Docking study for the molecules were performed by Glide module, insilco ADMET screening by qikprop module and free binding energy by Prime-MMGBSA module. The binding affinity of molecules towards TOPO-II was selected on the basis of GLIDE score. Many compounds showed strong hydrophobic interactions and hydrogen bonding interactions to inhibit TOPO-II. The compounds 1a-z, except 1k have good binding affinity with Glide scores in the range of -5.52 to -7.27 when compared with the standard Ethacridine(-4.23). The ADMET properties are within the recommended values. MM-GBSA binding results of the most potent inhibitor are favourable. The compounds, 1x,z,m,f,r,i with significant Glide scores may produce significant anti-microbial and anti-cancer activities for further investigations may prove their therapeutic potential.
    Keywords: Acridine; Chalcone; docking studies; In-silico ADMET screening; MM-GBSA.

Special Issue on: ICIBM 2018 Intelligent Biology and Medicine

  • Drug-Drug Interaction Prediction based on Co-Medication Patterns and Graph Matching   Order a copy of this article
    by Wen-Hao Chiang, Li Shen, Lang Li, Xia Ning 
    Abstract: High-order Drug-Drug Interactions (DDIs) and associated Adverse Drug Reactions (ADRs) are common, particularly for elderly people, and therefore represent a significant public health problem. Currently, high-order DDI detection primarily relies on the spontaneous reporting of ADR events. However, proactive prediction of unknown DDIs and their ADRs has indispensable benefit for protective health care. In this manuscript, the problem of predicting whether a drug combination of arbitrary orders is likely to induce adverse drug reactions is considered. The prediction problem becomes highly non-trivial when arbitrary orders of drug combinations have to been accommodated by the prospective computational methods. To solve this problem, novel kernels over drug combinations of arbitrary orders are developed within support vector machines for the prediction. Graph matching methods are used in the novel kernels to measure the similarities among drug combinations, in which drug co-medication patterns are leveraged to measure single drug similarities. The experimental results on a real-world dataset demonstrated that the new kernels achieve an area under the curve (AUC) value 0.912 for the prediction problem. The new methods with drug co-medication based single drug similarities can accurately predict whether a drug combination is likely to induce adverse drug reactions of interest.
    Keywords: drug-drug interaction prediction; drug combination similarity; co-medication; graph matching; arbitrary order; adverse drug reaction; myopathy; single drug similarity; support vector machines; binary classification problem.

  • Pessimistic Optimization For Modeling Microbial Communities With Uncertainty   Order a copy of this article
    by Meltem Apaydin, Liang Xu, Bo Zeng, Xiaoning Qian 
    Abstract: It is important to understand the complicated interactions of microbial communities who play critical roles in the ecological system, human health and diseases. Optimization-based mathematical models provide ways to analyze and obtain predictions on microbial communities. However, there are inherent model and data uncertainties from the existing knowledge and experiments about different microbial communities so that the imposed models may not exactly reflect the reality in nature. Here, addressing these challenges and aiming to have a flexible framework to model microbial communities with uncertainty, we introduce P-OptCom, an extension of an existing method OptCom, based on the ideas from the pessimistic bilevel optimization literature. This framework relies on the coordinated decision making between the single upper-(communitylevel) and multiple lower-level (multiple microorganisms or guilds) decision makers to support robust solutions to better approximate microbial community steady states even when the individual microorganisms behavior deviate from the optimum in terms of their cellular fitness criteria. We formulate the problem by considering suboptimal behavior of the individual members, and relaxing the constraints denoting the interactions within communities to obtain a model flexible enough to deal with potential uncertainties. Our study demonstrates that without experimental knowledge in advance, we are able to analyze the tradeoffs among the members of microbial communities and closely approximate the actual experimental measurements.
    Keywords: Microbial communities; Pessimistic bilevel optimization; Stoichiometric-based genome-scale metabolic modeling.

  • TopQA: A Topological Representation for Single-Model Protein Quality Assessment with Machine Learning   Order a copy of this article
    by John Smith, Matthew Conover, Natalie Stephenson, Jesse Eickholt, Dong Si, Miao Sun, Renzhi Cao 
    Abstract: Correctly predicting the complex three-dimensional structure of a protein from its sequence would allow for a superior understanding of the function of specific proteins. Thus, advancements could be made in drug discovery, nanotechnology, and many other biological fields. We propose a novel method aimed to tackle a crucial step in the protein prediction problem, assessing the quality of generated predictions. Previously, some research has focused on qualities of proteins, such as the distance between amino acids or energy functions. Our method, to the best of our knowledge, is the first to analyze the topology of the predicted structure. We confirmed our representation with a widely used visualization tool, Chimera, and found that it provided accurate information regarding the location of the protein\'s backbone. Using this information, we implemented a novel algorithm to process this information based on Convolutional Neural Network (CNN) to predict the GDT\\_TS score (a metric for assessing the quality of a model) for given protein models. Our method has shown promising results, achieving an overall correlation of 0.41 on testing dataset of CASP12. Future work will aim to implement additional features into our representation.The software is freely available at GitHub:
    Keywords: Convolutional Neural Network; protein single-model quality assessment; topological representation.

  • High scoring segment selection for pairwise whole genome sequence alignment with the maximum scoring subsequence and GPUs   Order a copy of this article
    by Abdulrhman Aljouie, Ling Zhong, Usman Roshan 
    Abstract: Whole genome alignment programs use exact string matching with hash tables to quickly identify high scoring fragments between a query and target sequence around which a full alignment is then built. In a recent large-scale comparison of alignment programs called Alignathon it was discovered that while evolutionary similar genomes were easy to align, divergent genomes still posed a challenge to existing methods. As a first step to fill this gap we explore the use of more exact methods to identify high scoring fragments which we then pass on to a standard pipeline. We identify such segments between two whole genome sequences with the maximum scoring subsequence instead of hash tables. This is computationally extremely expensive and so we employ the parallelism of a Graphics Processing Unit to speed it up. We split the query genome into several fragments and determine its best match to the target with a previously published GPU algorithm for aligning short reads to a genome sequence. We then pass such high scoring fragments on to the LASTZ program which extends the fragment to obtain a more complete alignment. Upon evaluation on simulated data, where the true alignment is known, we see that this method gives an average of at least 20% higher accuracy than the alignment given by LASTZ at the expense of a few hours of additional runtime. We make our source code freely available at url{}.rn
    Keywords: genome alignment; anchor selection; LASTZ; GPU.

  • Brain-wide structural connectivity alterations under the control of Alzheimer risk genes   Order a copy of this article
    by Jingwen Yan, Vinesh Raja V, Zhi Huang, Enrico Amico, Kwangsik Nho, Shiaofen Fang, Olaf Sporns, Yu-chien Wu, Andrew Saykin, Joaquin Goni, Li Shen 
    Abstract: Background: Alzheimer's disease is the most common form of brain dementiarncharacterized by gradual loss of memory followed by further deterioration of otherrncognitive function. Large-scale genome-wide association studies have identi edrnand validated more than 20 AD risk genes. However, how these genes are relatedrnto the brain-wide breakdown of structural connectivity in AD patients remainsrnunknown.rnMethods: We used the genotype and di usion tensor imaging (DTI) data in thernAlzheimer's Disease Neuroimaging Initiative (ADNI) database. After constructingrnthe brain network for each subject, we extracted three types of link measures,rnincluding ber anisotropy, ber length and density. We then performed a targetedrngenetic association analysis of brain-wide connectivity measures using generalrnlinear regression models. Age at scan and gender were included in the regressionrnmodel as covariates. For fair comparison of the genetic e ect on di erentrnmeasures, ber anisotropy, ber length and density were all normalized withrnmean as 0 and standard deviation as one.We aim to discover the abnormalrnbrain-wide network alterations under the control of 34 AD risk SNPs identi ed inrnprevious large-scale genome-wide association studies.rnResults: After enforcing the stringent Bonferroni correction, rs10498633 inrnSLC24A4 were found to signi cantly associated with anisotropy, total numberrnand length of bers, including some connecting brain hemispheres. rs429358 inrntop AD risk gene APOE shows nominal signi cance of association with therndensity of fibers between Subcortical and Cerebellum (p=2.71e-6).
    Keywords: brain connectivity; imaging genetics association; Alzheimer's disease.

  • A De-Novo drug design and ADMET study to design small molecule stabilizers targeting mutant (V210I) human prion protein against familial Creutzfeldt-Jakob disease (fCJD).   Order a copy of this article
    by Rafat Alam, G.M. Sayedur Rahman, Nahid Hasan, Abu Sayeed Chowdhury 
    Abstract: The purpose of our project was to computationally design small molecule stabilizers targeting mutant (V210I) human prion protein (HuPrP) using combined De-novo, pharmacophore, molecular docking and ADMET study to cure familial Creutzfeldt-Jakob disease (fCJD). Successful Development of drugs against familial CJD might provide valuable insight for design and development of new antiprion drugs and understand their mechanisms. We collected the target protein structure from Protein Data Bank (RCSB PDB). After that, we minimized the energy using Yasara energy minimization webserver and validated the structure using RAMPAGE webserver. We used KV Finder, a plug-in of Pymol to identify the drug binding pockets in the target protein. The pocket information was used for de-novo ligand design using the e-LEA3D webserver. Those ligands were used to generate a pharmacophore using LigandScout for the selected pocket. The designed pharmacophore was implied to the webserver Pharmit for virtual screening of small molecules from Pubchem database and the screened small molecules were docked into the target pocket of the protein using the software Autodock Vina. Best 5 molecules were identified with binding affinities of 7.7, 7.2, 7.2, 7.1 and 7.1 kcal mol-1 respectively. Finally, we analyzed the ADMET properties of the best five ligands using the webserver SwissADME. All the five small molecules were proven to be the ideal candidates for further drug development.
    Keywords: ADMET; de-novo drug design; Docking; Prion; PDB; Pharmacophore.

  • A Hidden Markov Model-based approach to reconstructing double minute chromosome amplicons   Order a copy of this article
    by Ruslan Mardugalliamov, Kamal Al Nasr, Matthew Hayes 
    Abstract: Double minute chromosomes (DMs) are circular fragments of extrachromosomal DNA. They are a mechanism for extreme gene amplification in the cells of some malignant tumors. Their existence strongly correlates with malignant tumor cell behavior and drug resistance. Locating DMs is important for informing precision therapy to cancer treatment. Furthermore, accurate detection of double minutes requires precise reconstruction of their amplicons, which are the highly-amplified gene-carrying contiguous segments that adjoin to form DMs. This work presents AmpliconFinder -- a Hidden-Markov Model-based approach to detect DM amplicons. To assess its efficacy, AmpliconFinder was used to augment an earlier framework for DM detection (DMFinder), thus improving its robustness to noisy sequence data, and thus improving its sensitivity to detect DMs. Experiments on simulated genomic data have shown that augmenting DMFinder with AmpliconFinder significantly increased the sensitivity of DMFinder on these data. Moreover, DMFinder with AmpliconFinder found all previously reported DMs in three pediatric medulloblastoma datasets, whereas the original DMFinder framework found none.
    Keywords: next generation sequencing; double minute chromosome; double minute; structural variation; amplicon; tumor genome reconstruction;.

  • Modelling of hypoxia gene expression for three different cancer cell lines   Order a copy of this article
    by Babak Soltanalizadeh, Erika Gonzalez Rodriguez, Vahed Maroufy, W. Jim Zheng, Hulin Wu 
    Abstract: Gene dynamic analysis is essential in identifying target genes involved in the pathogenesis of various diseases, including cancer. Hypoxia often influences cancer prognosis. We applied a multi-step pipeline to study dynamic gene expressions in response to hypoxia in three cancer cell lines: prostate (DU145), colon (HT29), and breast (MCF7). We identified 26 distinct temporal expression patterns in DU145 and 29 patterns in HT29 and MCF7. Module-based dynamic networks were developed for each cell line. Because our analyses exploited the time-dependent nature of gene expression for identifying significant genes novel significant genes and transcription factors were identified. Our gene network returned significant information regarding biologically important modules of genes. In particular, results suggest that changes expression of BMP6 and ARSJ might play a key role in the time-dependent response to hypoxia in breast cancer. Furthermore, the network can potentially learn the regulatory path between transcription factors and the downstream genes.
    Keywords: gene expression; hypoxia; colon cancer; breast cancer; prostate cancer; significant genes; BMP6; ARSJ.

Special Issue on: CMBH 18 Computational Approaches in Biology And Medicine

  • Comparative In-Silico Parmacokinetics and Molecular Docking Study on Gedunin Isolated from Azadirachta indica, its Modified Derivatives and Selected Antifolate Drugs as Potential Dihydrofolate Reductase Inhibitors of Plasmodium falciparum   Order a copy of this article
    by Samuel Cosmas, Olanrewaju Durojaye, Parker Joshua, Joyce Ogidigo, Collins Difa, Justus Nwachukwu 
    Abstract: Introduction: Malaria is one of the most common diseases that threaten many of the subtropical and tropical regions. Countries where the risk of transmission of malaria is at the high rate are over a hundred currently and these counties are being visited by over 125, 000, 000 international travelers on yearly basis. Plasmodium, a protozoan parasite is the cause of malaria. The Plasmodium parasites that cause the human malaria are of four different species: P. falciparum, P. malariae, P. ovale and P. vivax. The folate metabolism of the malaria parasite which leads to the synthesis and the use up of folate cofactors is inhibited by antifolate drugs hence, the reason behind their use as antimalarials. Gedunin, a bioactive product from a natural origin such as the Azadirachta indica possess potential antimalarial activities and as such can be developed into drugs to target the folate synthesis pathway of the Plasmodium parasite. Materials and Methods: Chemical structures of ligands were drawn with the MarvinSketch software and converted into SMILES strings for the calculation of pharmacokinetic parameters. This was achieved by utilizing the SwissADME server. Ligand chemical structures were minimized and viewed using the Chimera and Pymol software respectively. Minimized ligand structures were saved as Mol2 files in preparation for docking while the binding energies between each experimental ligands and the Plasmodium falciparum DHFR enzyme was predicted using the AutoDock Vina software. Polar interactions were also viewed through the Pymol and this was used in the prediction of the bind pockets. Sequence alignment between the human and Plasmodium falciparum DHFR was performed using the Clustal Omega alignment software. Results: The predicted binding energies between the three selected antifolate drugs (cycloguanil, proguanil, pyrimethamine), gedunin, its derivatives (C=O, C2H5, C3H6O2, C4H8O2, CONH2, NH2, OCH3, OH) and the Plasmodium falciparium DHFR enzyme were -8.0, -7.5, -8.0, -9.5, -9.0, -8.4, -8.9, -8.2, -8.9, -8.7, -8.3, -8.4Kcal/mol respectively. The experimental ligands were viewed to form weak interactions with a total of 15 amino acids, leading to the prediction of 2 binding pockets while the sequence alignment result showed 32% identity between the human and P. falciparum DHFR enzyme. Conclusion: The results from the experiment showed that gedunin and its modified derivatives might be better antimalarial agents than the antifolate drugs as revealed by the predicted binding energies between the target enzyme and the ligands.
    Keywords: Plasmodium; Azadirachta indica; Antifolate drugs; Parasite; Dihydrofolate Reductase.

    by Ariya S S, Baby Joseph, Jemmy Christy H 
    Abstract: Cancer is one of the leading causes of death worldwide. Though advanced treatment options are available, a cure for this disease is still unidentified. This work is aimed at the identification of drugs to target the matrix metallopeptidase, a major protein overexpressed in this disease. 247 active phytochemicals from the medicinal plant Ipomea sepiaria were taken as the lead drugs and molecular docking analysis as well as interaction studies were carried out. The binding affinity for each ligand with the target was determined along with Molecular dynamic simulation of the complex to determine the stability of the complex in the system. Thus eight drugs namely Tetradecanoic acid, Nerolidol, Ipomeanine, Dibutyl phthalate, Cis-Caffeic acid, Caffeic acid, Moupinamide and N-Cis-Feruloyltyramine were found to be the most promising drugs for treating cancer. They outperformed the scores of four different drugs available in the market.
    Keywords: Cancer; docking; simulation; phytochemicals; ADMET; matrix metallopeptidase; receptor; ligands.

  • Inter k-shell Connectivity: A Novel Computational Approach to Identify Drug Targets   Order a copy of this article
    by Praveen K. Singh 
    Abstract: Central lethality rule suggests that hub proteins are the most important and their deletion leads to more damage to biological networks as compared to non-hub proteins. Hub proteins present towards the core of Protein-Protein Interaction (PPI) network are considered to be more important in comparison to proteins at the periphery. The k-shell decomposition method generates k-shell index which indicates the local and global importance of a protein in PPI network. But there is no method till now reported which can differentiate among the proteins with same k-shell index. In this work attempt has been made to add parameter Inter k-shell connectivity to differentiate proteins with same k-shell index and exploring their biological importance.
    Keywords: Centrality lethality rule; Protein networks; k-shell decomposition; Mycobacterium.

  • In- silico analysis of peptidoglycan hydrolases from Serratia marcescens and other Serratia species   Order a copy of this article
    by Aditi Rathee, Kanika Gupta, Seema Kumari, Sanjay Chhibber, Ashok Kumar 
    Abstract: Bacteria possess a protective extracytoplasmic glycopeptide polymer i.e. peptidoglycan. In case of Gram-positive bacteria, it acts as scaffolds to many virulence factors whereas in Gram-negative bacteria, it serves as an anchor to outer membrane. Many antibiotics act on bacteria by inhibiting the activity of enzymes involved in the synthesis of peptidoglycan. However during the years, overexposure of antibiotics has led to modification of peptidoglycan chain by bacteria viz. N-deacetylation, N-glycolylation, O-acetylation etc. Peptidoglycan hydrolases are known to play an important role in the suppression of bacterial infections as a component of the innate immune system as well as disintegrating peptidoglycan which is an important factor in the pathogenesis of various organisms. Present study explicates computational analysis of a peptidoglycan hydrolase enzyme from a total of 41 fully sequenced genomes of Serratia marcescenes and other Serratia species.75 unique motifs were identified among the protein sequences of peptidoglycan hydrolase.
    Keywords: Antibiotic resistance; Peptidoglycan hydrolase; Sequence analysis; multiple sequence alignment; conserved motif.

    by VIDHYA GOPALAKRISHNAN, Arvind Nambiar, Sukanya Basu, Madhuvanthi G 
    Abstract: Diabetic retinopathy is the leading causes of blindness in many countries. PDR is more severe than NPDR. To characterize the pathogenesis of PDR, proteomic studies have discovered a set of genes involved in the disease. In this study, we analyzed the PPIN of DR proteins to identify the hub nodes. Since protein interaction network analysis is the best method for molecular assessment, a PPI network related to diabetic retinopathy genes was generated using Cytoscape software. The constructed protein network was analyzed using ClusterONE, ClueGO and cytohubba plugins. Among 497 candidate proteins which were identified, 482 were included in the main connected component. The topology and associated functionality between the proteins were studied based on centrality parameters such as degree, betweenness and closeness. Two significant clusters were determined which contained the experimentally proven five seed proteins. The Gene ontology results revealed various biological pathways associated with diabetic retinopathy. These findings identified important hub proteins as well as their direct interacting partners that can be considered as therapeutic biomarkers for drug design and treating disease.
    Keywords: Diabetic retinopathy; PPIN; Cytoscape.

  • A review on iris recognition system for person identification   Order a copy of this article
    by B. Bharathi Varadharajulu, P. Bindhu Shamily 
    Abstract: Iris is an evenly highlighted radial membrane with complex patterns that are perceptible upon near inspection, which exist behind the cornea of the eye with a changeable circular opening i.e. pupil. In reality the texture of iris is completely unique and complex for everyone, even two iris of a person are unalike. Iris Recognition System is a technique of identifying people using those complex distinctive features in patterns. Generally, Iris recognition system is used in security allied applications such as, authenticating PCs, Network and Mobile devices, Physical and Logical Premises Access Control, National Border Control, Secure banking and financial transactions, National Identity like AADHAAR in India etc. This paper reviews the state-of-art design and implementation of various Iris recognition Systems. The contributions of the paper include (1) Conferring the importance, applications and deployment of Iris Recognition system related to human identification (2) Providing an analysis on Iris recognition methods in effect (3) Discussing the present research defies and (4) providing commendations for the future research on Iris recognition system.
    Keywords: Iris Recognition; Pattern recognition; Segmentation; Normalisation; Feature Extraction; Visually impaired people; Alzheimer’s disease; Acquisition; Smart wearable; Person identification.

  • Insilico approach for the prediction of functional nsSNPs in WIF 1 gene of WNT pathway   Order a copy of this article
    by Swetha Sunkar, Aravind Madineni, Surya Chandan Reddy Sanepalli, Neeharika Desam 
    Abstract: Cancer, a deadly disease in the current living is caused by many factors, the most common involving certain changes in genes that control cell growth and division. WNT pathway is generally involved in controlling gene expression and cell behavior. The studies revealed that mutations in genes of the WNT pathway lead to different types of cancer. In our study, cancer-related novel gene candidate, WNT inhibitory factor 1 (WIF1) that involves in controlling the WNT pathway was focused on to analyze the potentially deleterious non-synonymous single nucleotide polymorphisms. From the dbSNP database, the SNPs of the WIF1 gene were retrieved. These SNPs were analyzed using various computational tools viz., Poly-Phen-2, I-mutant3.0, FATHMM, Panther, SIFT for their pathogenicity and stability analysis to determine whether they alter the protein structure and function. The pathogenicity analysis re-vealed that
    Keywords: In silico analysis; WIF 1; WNT Pathway; nsSNPs; Homology modeling.