International Journal of Computational Biology and Drug Design (16 papers in press)
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
MOLECULAR DOCKING STUDIES, IN-SILICO ADMET SCREENING, MM-GBSA BINDING FREE ENERGYOF SOME NOVEL CHALCONE SUBSTITUTED 9-ANILINOACRIDINES AS TOPOISOMERASE II INHIBITORS
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.
Identification of novel neuraminidase inhibitors through e-pharmacophore based virtual screening
by Rohini Kanagavelu, Shanthi Veerappapillai
Abstract: The surface protein of Influenza virus, Neuraminidase (NA), is believed to play a critical role in the release of new viral particle and thus spreads infection. Hence it has been considered as a possible drug target for influenza A virus infection. Despite the number of available drugs for the treatment of influenza infection, the emergence of mutants with novel mutations has embellished more resistance to potent NA inhibitor. Considering the same, in the present study an attempt has been made to discover potent inhibitors from ASINEX library of 467802 molecules through e-pharmacophore based virtual screening strategy. The results from our analysis along with available experimental evidences comprehend that the lead molecule BAS 04358434 could be used as a promising candidate for NA inhibition. Moreover, the hit compound showed potent inhibitory activity against all the mutant structures considered in our analysis. In summary, we speculate that the outcomes of this research are of substantial prominence in the rational designing of novel and efficacious NA inhibitors.
Keywords: Neuraminidase; e-Pharmacophore Model; Enrichment Analysis; Virtual Screening; ASINEX database; Qikprop.
In-silico drug target identification and pharmacophore mapping for Leishmania donovani based on metabolic pathways
by Nikita Chordia, Deepak Bhayal, Priyesh Hardia
Abstract: A wide variety of human population is infected with Leishmania donovani. It is a protozoan parasite which causes very lethal disease called as visceral leishmaniasis. It is the second killer parasitic disease after malaria. It is transmitted by female sandfly and infects both children and adults. It is very prevalent disease and reported to be spread in 88 countries causes 20000-30000 death each year. Till now, there is no specific vaccine or drug for visceral leishmaniasis. It is the most neglected tropical disease in terms of drug discovery and development. Here, we analyzed the metabolic pathway of this parasite for identifying potential drug target. The essential node (gene) which is non- homologous to human in the metabolic pathway were considered for network reconstruction. Reconstructed network is analyzed which results in identification of five drug targets namely: threonine aldolase, Acetyl-CoA acyltransferase pyruvate orthophosphate dikinase, ATP-binding cassette and P-glycoprotein. These targets are efficient and specific for treating Leishmania donovani parasite. For these identified drug targets, pharmacophore is designed that can be used as drug to treat visceral leishmaniasis. Further, docking studies reveals the action of pharmacophore on these drug targets.
Keywords: Leishmania donovani; leishmaniasis; sand fly; parasite; pathways; human; drug target; pharmacophore; docking; node; protein; gene.
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
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.
EXPLORING THE ANTINEOPLASTIC EFFECT OF PHYTOCHEMICALS FROM IPOMEA SEPIARIA AGAINST MATRIX METALLOPEPTIDASES A PHARMACOINFORMATICS APPROACH
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
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
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.
PPI NETWORK ANALYSIS OF DIABETIC RETINOPATHY GENES
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
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
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.
Special Issue on: ICIBM 2019 State-of-the-art Computational Methods and Tools for Analysis of High-dimensional Biological and Biomedical Datasets
Skyhawk: An Artificial Neural Network-based discriminator for reviewing clinically significant genomic variants
by Ruibang Luo, Tak-Wah Lam, Michael Schatz
Abstract: Motivation: Many rare diseases and cancers are fundamentally diseases of the genome. In the past several years, genome sequencing has become one of the most important tools in clinical practice for rare disease diagnosis and targeted cancer therapy. However, variant interpretation remains the bottleneck as is not yet automated and may take a specialist several hours of work per patient. On average, one-fifth of this time is spent on visually confirming the authenticity of the candidate variants.rnResults: We developed Skyhawk, an artificial neural network-based discriminator that mimics the process of expert review on clinically significant genomics variants. Skyhawk runs in less than one minute to review ten thousand variants, and about 30 minutes to review all variants in a typical whole-genome sequencing sample. Among the false positive singletons identified by GATK HaplotypeCaller, UnifiedGenotyper and 16GT in the HG005 GIAB sample, 79.7% were rejected by Skyhawk. Worked on the Variants with Unknown Significance (VUS), Skyhawk marked most of the false positive variants for manual review and most of the true positive variants no need for review.rn
Keywords: Clinical decision support; Variant validation; Artificial neural network; Third-generation sequencing; Variant calling.
PgenePapers: a novel database and search tools of reported regulatory pseudogenes
by Achal Awasthi, Yan Zhang
Abstract: Pseudogenes arose from duplication or retroduplication of genes, however, accumulation of mutations has disabled their protein-coding ability. Although they have been thought of as genomic fossils, recent studies have shown that a considerable number of pseudogenes are actually transcribed in normal and/or cancerous human tissues, and some of them can even regulate gene expression. Studies have detected pseudogene differential expression in specific cancer subtypes, indicating potential functions of pseudogenes in cancer development and clinical relevance to disease outcomes. All these show that pseudogenes make a new class of modulators of gene expression, however, their roles are still largely unknown. Unlike coding genes which have rich functional annotations, there is still a lack of functional annotations of pseudogenes. There is not yet a database focusing on regulatory roles of pseudogenes, even though functional studies have been published in literature. We extracted information about regulatory pseudogenes by analyzing PubMed literature using natural language processing techniques followed by manual curation. The expression values of genes and pseudogenes for all 31 cancer types studied in TCGA were used to get the correlation between genes and pseudogenes. Based on this information, we reconstructed the regulatory networks involving pseudogenes and regulated genes (pseudogene-gene pairs) with disease and tissue specific annotations. We further extended the pseudogene-gene networks to include information on potential miRNAs and drugs targeting components of the networks, based on expression profiles, miRNA binding predictions and known FDA approved drugs. We developed the first comprehensive database of reported regulatory pseudogenes. In order to facilitate the usage of the database, we also developed a user-friendly app called PgenePapers (https://integrativeomics.shinyapps.io/PgenePapers/) which allows flexible database search and provides network visualization. PgenePapers app can display the pseudogene-gene pairs with their functional categories, all the supporting text from literature, interactive visualization of the pseudogene-gene association networks, and customized gene-pseudogene-miRNA-drug networks.
Keywords: regulatory pseudogene; database; search tools; graph presentation; correlation network; Shiny app.
Generating Simulated SNP array and Sequencing Data to Assess Genomic Segmentation Algorithms
by Mark Zucker, Kevin Coombes
Abstract: In order to validate methods for the analysis of high throughput data, it is necessary to obtain data for which the underlying truth is known, so one can verify the accuracy of inferences made by the method and thus quantify the confidence with which it can make inferences. Knowing the ground truth can be extraordinarily difficult in biology, since one can essentially never knows, even in highly controlled conditions, what proportion of cells have what aberrations in a bulk cell sample, particularly in populations of aberration-prone cancer cells. For this reason, the ability to simulate SNP array and DNA sequencing data that recapitulates the variance structure and population complexity of real biological samples would be very useful in assessing the accuracy of and comparing bioinformatics algorithms. In particular, we discuss here the use of segmentation algorithms to identify breakpoints and copy number variation in SNP array or sequencing data. We developed a tool, implemented in an R package called TACG (True and Accurate Clone Generator), to simulate both ground truth and realistic SNP array and/or SNV data. We present this tool and apply it to the assessment of several different approaches to segmentation of copy number data from SNP arrays, with a particular interest in detecting CNVs in cancer samples. We demonstrate that DNAcopy, an algorithm using circular binary segmentation, generally performs best, which is in agreement with previous research. We further determine the conditions under which it and other methods break down. In particular, we assess how characteristics such as clonal heterogeneity, the presence of nested CNVs, and the type of aberration affect algorithm accuracy. The simulations we generated proved to be useful in determining not just the comparative overall accuracy of different algorithms, but also in determining how their efficacy is affected by the biological characteristics of samples from which the data was generated.
Keywords: SNP Array; copy number alteration; cancer; simulation.
Predicting Re-admission to Hospital for Diabetes Treatment: A Machine Learning Solution
by Satish M. Srinivasan, Yok-Fong Paat, Philmore Halls, Ruth Kalule, Thomas E. Harvey
Background: Predictive analytics embrace an extensive range of techniques including but are not limited to statistical modelling, Machine Learning, Artificial Intelligence and Data Mining. It has profound usefulness in different applications such as business intelligence, public health, disaster management and response, as well as many other fields. This technique is well-known as a practice for identifying patterns within data to predict future outcomes and trends. The objective of this study is to design and implement a predictive analytics system that can be used to forecast the likelihood that a diabetic patient will be readmitted to the hospital.
Results: Upon extensively cleaning the Diabetes 130-US hospitals dataset containing patient records spanning 10 years from 1999 till 2008, we modelled the relationship between the predictors and the response variable using the Random Forest classifier. Upon performing hyperparameter optimization for the Random Forest, we obtained a maximum AUC of 0.684 with a precision and recall of 46% and 60% respectively and an F1 Score of 52.07%. Our study reveals that attributes such as number of inpatient visits, discharge disposition, admission type, and number of laboratory tests are strong predictors for the response variable (i.e. re-admission of patients).
Conclusion: Findings from this study can help hospitals design suitable protocols to ensure that patients with a higher probability of re-admission are recovering well and possibly reduce the risk of future re-admission. In the long run, not only will our study improve the life quality of diabetic patients, it will also help reduce the medical expenses associated with re-admission.
Keywords: Random Forest; Data Cleaning; Predictive Analytics; Hyperparameter tuning; optimization.