International Journal of Computational Biology and Drug Design (19 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
Identification of dual target anti-inflammatory inhibitors using merged structure based pharmacophore modelling and docking approach
by Manikandan Selvaraj, Muhd Hanis MD Idris, Siti Norhidayu Mohd Amin, Mohd Zaki Salleh, Teh Lay Kek
Abstract: Merged structure-based pharmacophore modelling followed by 3-D database search and molecular docking were the sequential protocol applied in order to identify selective novel COX-2 and PDE4D as dual target anti-inflammatory inhibitors. Utilization of the key interaction features of crystal structures of COX-2 (pdb: 1CX2) and PDE4D (pdb: N0YN) was exploited as critical component in the selection of dual target inhibitors. Through this approach, nine chalcone and flavones scaffold like compounds were selected as putative dual target anti-inflammatory inhibitors from Asinex database. In general understanding such approach could provide valuable insights into discovery of novel anti-inflammatory inhibitors as therapeutic agents.
Keywords: Cyclooxygenase; phosphodiesterase; dual target; merged pharmacophore; docking.
Adaptive-Fuzzy clustering based texture analysis for classifying liver cancer in abdominal CT images
by Amita Das, Priti Das, S.S. Panda, Sukanta Sabut
Abstract: Segmentation of diseased liver in abdominal CT images is a challenging task due to variations in shapes, tissue similarity between adjoining organs like kidney, heart and also pathologies caused by diseases. The computer aided diagnosis (CAD) systems is very useful in automatic analysis of tumor position and finding a region of interest (ROI) from images. We propose a technique that integrates the fuzzy clustering with adaptive thresholding for segmenting the liver and the finding tumor region in abdominal CT images. Various features like texture features, morphological features and statistical features have been extracted from the output images and used as input to the classifier. The proposed method was evaluated in a series of 45 images taken from MICCAI datasets and open sources. Neural network classifier has been used to classify the malignant and benign tumor of the liver. The efficiency of the proposed algorithm is tested in terms of sensitivity, specificity, and accuracy. The accuracy of 97.82%, 95.74% is achieved in BPN and LVQ and a higher accuracy of 98.82% is achieved with PNN in detecting tumors which are comparable to published results. This method could be an effective solution for identifying the tumor region of liver on CT abdominal images.rnrn
Keywords: CT image; liver; tumor; segmentation; region of interest; neural network classifier.
Comparative analysis of machine learning based QSAR Models and molecular docking studies to screen potential anti-tubercular inhibitors against InhA of Mycobacterium tuberculosis
by Madhulata Kumari
Abstract: Machine learning techniques are advanced computational techniques which can be used to build quantitative structureactivity relationship (QSAR) model of compounds data set to find out important descriptors which are able to predict a specific biological activity from unknown compounds to discover better drugs. In the present study, by optimizing descriptors using correlation-based feature selection, principal component analysis, and genetic programing technique, several machine learning techniques were used to build QSAR models on three different experimental datasets of InhA inhibitors. The best QSAR models were deployed on a data set of 1450 approved drug from drug bank to screen new InhA inhibitors. Amoxicillin was found to show highest predicted activity pIC50=6.54, and Itraconazole was the second compound with highest predicted activity 6.4 (pIC50 ) that was calculated based on the best Random Forest (RF) model using CFS-GS-FW descriptor set in the dataset of ChEMBL997779 of InhA of Mtb. Additionally, screening by molecular docking identified top-ranked ten approved drugs as anti-tubercular hits showing G-scores -8.23 to -6.95 (in kcal/mol) as compared with control compounds(known InhA Mtb inhibitors) G-scores -7.86 to -6.68 (in kcal/mol). Thus results indicate these potent compounds may have the better binding affinity for InhA of Mtb. From ourstudies, we conclude that machine learning based QSAR models can be useful for the development of novel target specific anti-tubercular compounds.
Keywords: Machine learning algorithms; Quantitative structure-activity relationships; Support vector machine; Random forest; Multilayer Perceptron; Genetic Algorithm; Genetic Programming; Regression; Mycobacterium tuberculosis; Gaussian Process; Correlation-based Feature Selection; InhA.
A genetic programming-based approach and machine learning approaches to the classification of multiclass anti-malarial datasets
by Madhulata Kumari, Neeraj Tiwari
Abstract: Feature selection approaches have been widely applied to deal with the various sample size problem in the classification of activity of datasets. The present work focuses on the understanding system of descriptors of anti-malarial inhibitors by Genetic programming (GP) to understand the impact of descriptors on inhibitory effects. The experimental dataset of inhibitors of anti-malarial was to derive the optimized system by GP. Additionally, we have developed machine learning models using Random Forest, Decision Tree, Support Vector Machine and Naive Bayes on an antimalarial dataset obtained from ChEMBL database and evaluated for their predictive capability. Based on the statistical evaluation, Random Forest model showed the higher area under the curve (AUC), better accuracy, sensitivity, and specificity in the cross-validation tests as compared to others. The statistical results indicated that the RF model was the best predictive model with 82.51% accuracy, 89.7% ROC. We deployed the RF classifier model on three datasets; phytochemical compound dataset, NCI natural product dataset IV and approved drugs dataset containing 918, 423 and 1554 compounds resulting 153, 81 and 250 compounds respectively as anti-malarial compounds. Further, to prioritize drug-like compounds, Lipinskis rule was applied on active phytochemicals which resulted in 13 hit anti-malarial molecules. Thus, such predictive models are useful to find out novel hit anti-malarial compounds and could also be used to discover novel drugs for other diseases.
Keywords: Machine learning approaches; Data mining; Random Forest; SVM; Naïve Bayes; Decision Tree; Malaria; Phytochemical; Natural product.
Putative Inhibitors of Homology-modelled Chorismate Synthase of Shigella flexneri
by CATHERINE L
Abstract: Shigellosis is an infection of the intestinal epithelium. We focus on the multidrug resistant S. flexneri (MDRSf) pathogen. We chose as our target chorismate synthase (SfCS), a key enzyme in the biosynthesis of aromatic amino acids in the shikimate-chorismate pathway. The SfCS crystal structure is unknown, so we built a homology model using the SfCS Serotype 2a sequence. Using the model, clusters of protein-protein interaction anchor residue hotspots were obtained, upon which a pharmacophore model was built. Virtual screening on 22,723,923 compounds resulted in seven hits. Of these, one was admissible against checks for ADME-Tox pharmacokinetics and cytochrome P450 toxicities. A scaffold-hopping procedure resulted in two other candidates. All three were docked to pockets determined using a new measure of residue depth associated with the Voronoi procedure. Remarkably, the three putative inhibitors have high pIC50 values that exceed those of many common antibiotics now in use.
Keywords: Shigellosis; diseases of the poor; pharmacophore; virtual screening; scaffold-hopping.
Designing of suitable linkers for the chimeric proteins to achieve the desired flexibility and extended conformation
by Manoj Patidar, Naveen Yadav, Sarat Dalai
Abstract: The designing and production of therapeutic chimeric proteins is the central focus area of many industrial R & D and research institutes. The efficient productions of chimeric proteins not only require suitable domains or partner proteins and intact receptor binding sites, but the selection of linkers is equally important. The linkers are essential to provide space between two domains, prevent from steric hindrance and most importantly to make the chimeric proteins flexible. In this in silico study, we have systematically designed various linkers, tested their feasibility and evaluated their essentiality. Here, we fused cytokine of interest (i.e., IL-2) with IgG1 Fc via various linkers. We designed linkers of various lengths and amino acid composition and tested their ability to provide the extended conformation and desired flexibility and also to minimize the conformational changes. Additionally, we have evaluated the role of linker in dimerization of chimeric proteins. We next tested the influence of linkers on stability and functionality of chimeric proteins.
Keywords: Chimeric proteins; linker; Immunoglobulin; protein engineering; dimerization.
Exploring Polypharmacology of Some Natural Products Using Similarity Search Target Fishing Approach
by Ihab Almasri
Abstract: Natural products have long been considered as important sources for drug discovery due to the diversity of their chemical structures and broad range of biological activities attained by modulation of different biological targets. Therefore, the identification of the molecular targets of natural products is a milestone step in rational design of more potent and safer compounds. In this work, we explored the polypharmacology of three natural products having pleiotropic health beneficial effects: resveratrol, curcumin and berberine, using a ligand-based target fishing approach. The fishing protocol was started with the generation of a chemogenomic database that links individual targets with specific target ligands or group of drugs. Targets profile was then generated for each of the natural products via chemical/shape similarity search using ROCS software. The applied method was able not only to retrieve known targets within the top-ranked list for the natural compounds but also identified off-targets which were found by docking simulation to be potential targets and were consistent with recently identified bioactivities of these compounds. ROCS-based target fishing approach (RTFA) was proved to be successful in pharmacological profiling of the selected natural products and in the identification of new off-targets worth further evaluation.
Keywords: natural products; polypharmacology; similarity search; target fishing; docking.
An in silico approach for construction of a chimeric protein, targeting virulence factors of Shigella spp.
by Emad Kordbacheh
Abstract: Shigellosis is still a high burden gastrointestinal disease with increased frequency of antibiotic resistance. Regardless, there were about 50 different serotypes across the four different species of Shigella, the type III secretion apparatus (T3SA) are conserved among them; and IpaD, IpaB, and IcsA proteins participate in its function. recent studies indicate the stx gene has been found in all Shigella spp. and has a fundamental role in hemorrhagic colitis. Prior to chimeric construction design, bioinformatics tools were recruited for aiming this purpose. in the level of nucleosome, sequences choosing and optimizing, and in the phase of transcriptome, some prediction in associate with mRNA form, also in step of proteome, physicochemical parameter, best stability, first to third structures and model validation were some prediction performed in assistance with in-silico servers. Moreover, estimating antigenic and allergenic propensity, subcellular localization and protein functional was accomplished by bioinformatic software. Finally, these results would be beneficial in an animal model purpose for development a pervasive candidate immunogen against Shigella spp.rn
Keywords: Shigella species . Bioinformatics . Subunit vaccine . Virulence factors .
A novel approach for dissimilar gene selection and multi-class classification of neuromuscular disorders: Combining median matrix and radial basis function based support vector machine
by Divya Anand, Babita Pandey, Devendra Pandey
Abstract: Accurate prediction of the kind of neuromuscular disorders is fundamental for choosing the optimal treatment for patients. Analysis of gene expression data through microarrays leads to the proper classification of neuromuscular disorders. Here we intend to select the compact subsets of dissimilar and discriminating genes from thousands of genes that can successfully classify the various kinds of neuromuscular disorders. We propose a new integrated model for gene selection and multi-class classification of neuromuscular disorders. The gene expression matrix is processed to create a median matrix for the selection of compact and different subsets of genes for every class. The classification algorithms use the combination of selected genes for prediction of the kind of neuromuscular disorder samples. The various classification algorithms employed are linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (KNN), linear support vector machine (Linear SVM) and radial basis function based support vector machine (RBF SVM). The classification algorithms use the one-versus-all approach to decompose the multi-class classification problem into binary class classification problem. The accuracy and effectiveness of the proposed model are exhibited through analysis of publicly available microarray data set of 13 neuromuscular disorders. It selects only a few biomarker and dissimilar genes for each class of neuromuscular disorder. It selects a minimum of 4 genes in one class and a maximum of 19 genes in another class. The integration of the proposed method of gene selection with RBF SVM classification algorithm has outperformed in most of the cases. The results confirm the ability of the proposed model for identifying the subsets of most discriminating, non-redundant and dissimilar genes which helps the classifier to give a high classification performance.
Keywords: Neuromuscular disorder classification; gene selection; microarray; median matrix; support vector machine; gene expression data.
DEVELOPMENT OF SPECIFIC DHODH INHIBITORS FOR PLASMODIUM AND HUMAN SPECIES
by Lilly Saleena, Priya Swaminathan
Abstract: Abstract Malaria still remains one of the challenging and dominant public health issue infecting about 300-500 millions of people and killing about three million people. The most serious and fatal malarial infections are caused by Plasmodium falciparum and the parasite has developed resistance to commonly employed therapeutics. Hence the objective is to develop a novel anti malarial drug targeting Dihydroorotate dehydrogenase (DHODH), an enzyme involved in pyrimidine biosynthesis, essential for parasite growth. A decrease in parasite growth correlated with a decrease in levels of DHODH mRNA. Thus targeting this leads to a potential anti malarial drug. DHODH is existing in both humans and Plasmodium falciparum. Targeting the former leads to discovery of anti proliferative and anti inflammatory agents. Sequence analysis and structure comparison of DHODH of both Human and Plasmodium falciparum reveals similarities and variations among them there by providing a chance to design a specific inhibitor. High throughput virtual screening of the existing anti-malarial drugs acting on DHODH is to be performed from pubchem and BindingDB databases. Pharmacophore mapping and searching was done for the top twenty virtual screening compounds using hip hop algorithm. The compounds thus obtained are to be docked with both Human and Plasmodium DHODH. Comparing the results helped in the identification of inhibitors specific to Plasmodium and Human DHODH. Potential anti malarial and anti inflammatory lead compounds that has similar structure to the specific inhibitors can be further developed to cure naturally resistant strains of Plasmodium falciparum
Keywords: Plasmodium falciparum; DHODH; anti malarial; anti inflammatory; Pharmacophore.
Assessment of anti-arthritic potential of traditionally fermented Ayurvedic polyherbal product Chandanasava by molecular modeling, docking and dynamics approaches
by Annadurai Vinothkanna, Bagavathy Shanmugam Karthikeyan, Ramachandran Vijayan, Soundarapandian Sekar
Abstract: Rheumatoid arthritis is triggerred by proteus mirabilis and its virulence factor, urease. We have used molecular modeling, docking, dynamics simulations and experimental approaches to assess the anti-arthritic potential of phytochemicals of Ayurvedic polyherbal formulation Chandanasava by targeting urease subunits of Proteus mirabilis. Chandanasava exhibited antibacterial activity against Proteus mirabilis and Gas Chromatography-Mass Spectroscopy analysis indicated the presence of 42 bioactive phytochemicals. The three dimensional structures of urease subunits (ureA, ureB and ureC) were not available and hence these structures were predicted using homology modeling approach and validated using Ramachandran plot. Molecular docking and dynamics simulations of phytochemicals of Chandanasava against urease subunits showed efficient binding of almost all the compounds. Significantly, lactose, isosorbide, 1,2,3-Benzenetriol, 1,2-Cyclopentanedione and 2-Furancarboxaldehyde, 5-(hydroxymethyl) binds efficiently among other compounds. Thus Chandanasava formulation and some of its bioactive compounds give insights about its therapeutic property against arthritis and further investigations on it can bring out promising therapeutics.
Keywords: Proteus mirabilis; Urease subunits; Urinary Tract Infection; Rheumatoid Arthritis; Molecular docking; Molecular dynamics.
In-silico mutational study of ferulic acid decarboxylase for improvement of substrate binding empathy
by Pravin Kumar, Shashwati Ghosh Sachan, Raju Poddar
Abstract: Biotransformation of ferulic acid by microorganisms provides a better alternative for production of flavor and fragrance compounds like 4-vinylguaiacol and vanillin. Ferulic acid is transformed to 4-vinylguaiacol using the non-oxidative decarboxylation pathway by Ferulic Acid Decarboxylase (FADase). Here we report, computational mutational analysis of active site of FADase. Site directed mutations (single nucleotide polymorphisms, SNPs) were commenced using in-silico molecular modeling methods. Energy minimization, dynamic cross-correlation map (DCCM) and principle components analysis (PCA) methods were subsequently applied to validate different conformers (SNPs) of FADase. Substrate ferulic acid was docked with different SNPs. It was observed that, certain amino acids like Tyr21, Trp25, Tyr27 and Glu134 at active sites are responsible for better binding to ferulic acid. Further, mutated form Y27F (Tyr27Phe) of FADase shows a better binding affinity towards ferulic acid than its native form through structure analysis and docking studies.
Keywords: Ferulic Acid Decarboxylase; Enzyme modeling; site directed mutation; DCCM; PCA; docking.
Flexible Molecular Docking: Application of Hybrid Tabu-Simplex Optimization
by Ghania KHENSOUS, Belhadri MESSABIH, Abdallah CHOUARFIA, Bernard MAIGRET
Abstract: In this paper, we present a molecular docking method to predict the optimal binding pose of a flexible ligand in a flexible protein-binding pocket. For this purpose, a Tabu global search optimization algorithm is used, and the best Tabu solutions are then refined using the Nelder-Mead Simplex local search optimization algorithm. Most docking methods use scoring functions to approximate the binding affinity between the two molecular partners. In our application, the intra-molecular and intermolecular energies are calculated explicitly from a classical molecular mechanics model, which includes polarization terms. The variables of our optimization problem are the ligand positions (Euler angles + translation vector), the ligand and the protein side chains dihedral angles instead of the Cartesian coordinates in order to reduce the problem dimensionality. While the GOLD software (GOLD for Genetic Optimization for Ligand Docking) is usually considered as a standard in molecular docking, our docking approach is illustrated on four protein/ligand complexes for which GOLD failed, suggesting that the proposed method is promising.
Keywords: Drug Design; Metaheuristic Optimization; Protein-Ligand Docking; Simplex Algorithm; Tabu Search Algorithm.
Interaction studies of Angelica polymorpha and Beilschmiedia pulverulenta phytochemicals with acetylcholinesterase as anti-Alzheimers disease target
by Tomisin Happy Ogunwa
Abstract: Angelica polymorpha and Beilschmiedia pulverulenta are medicinal plants locally used by people in some parts of Asia and Africa due to their beneficial health effects particularly in the treatment of Alzheimers disease (AD). The phytoconstituents responsible for such bioactivity have recently been identified in the plants. Herein, in silico approach was used to explore the interaction of such phytochemicals with acetylcholinesterase (AChE) as a validated target in the treatment of AD to provide insights into their precise binding pattern and affinity, order of chemical interaction, inhibitory potential and residues that contribute to the enzyme-phytoconstituent complex stability. With binding affinity ranging from -7.0 kcal/mol to -10.2 kcal/mol and tacrine-comparable orientation, the chemical scaffold of the phytochemicals from both plants displayed deep penetration and fit conveniently into the narrow gorge of AChE. Optimization of these ligands scaffold might yield new AChE inhibitors with desirable higher efficacy.
Keywords: Phytoconstituents; Angelica polymorpha; Beilschmiedia pulverulenta; Molecular interaction; Docking.
Human Caveolin-1 a potent inhibitor for prostate cancer therapy: a computational approach
by Uzma Khanam, Balwant Kishan Malik, Puniti Mathur, Bhawna Rathi
Abstract: Caveolin-1 (Cav-1) is 22 kDa caveolae protein, acts as a scaffold within caveolar membranes. It interacts with alpha subunits of G-protein and thereby regulates their activity. Earlier studies reported elevated or up-regulated levels of caveolin-1 in the serum of prostate cancer patients. Secreted Cav-1 promotes angiogenesis, cell proliferation and anti-apoptotic activities in prostate cancer patients. Cav-1 upregulation is mainly related to prostate cancer metastasis. Keeping above facts in view, the present study was designed to explore Cav-1 as a target for prostate cancer therapy using computational approach. Molecular docking, structural base molecular modelling and molecular dynamics simulations were performed to investigate Cav-1 inhibitors. A predictive model was generated and validated to establish a stable structure. ZINC database of biogenic compounds was used for induced fit docking (IFD) and high throughput virtual screening. The H-bond interactions of the compounds with active site residues of Cav-1 was estimated by IFD and 100 ns long molecular dynamic simulations. The reported compounds showed significant binding and thus can be considered as potent therapeutic inhibitors of Cav-1. This study provides a valuable insight into biochemical interactions of Cav-1 for therapeutic applications and warrants for experimental validation of the predicted active(s).
Keywords: Molecular dynamics simulation; virtual screening; molecular docking; prostate cancer; caveolin-1; induced fit docking; protein-protein interaction network.
An in silico approach to design a potential drug for Haemophilia A
by Srishti Munjal, Gaurav Jaisawal, Navodit Goel, Udai Pratap Singh, Ajay Vishwakrma, Abhinav Srivastava
Abstract: Haemophilia A has been known as a disease since the late 20th century but till date, there has not been developed a cure for it. Treatments that temporarily relieve bleeding episodes include new factor replacement therapies with longer half-lives delaying the frequency of blood transfusions. There is a need to devise a new drug for the same. in silico drug designing comes as a powerful tool in designing a molecule to be used as drug in comparatively less time. In this study a new drug molecule was designed using Bioinformatic tools. The causative gene was found out to be X-linked F8 and the corresponding protein as coagulation factor VIII. Material and Methods: Target proteins were identified from protein databases and their structures were observed. Cavities in the protein were determined using SPDBV (Swiss PDB Viewer). Ligands and its isomers, following the Lipinski’s rule of five, were prepared through Molinspiration. Docking between the ligands and target proteins were performed using Molegro Virtual Docker. Results: Thirteen proteins were selected and twelve ligands were prepared. Docking studies were performed and two criteria were compared MolDock score and hydrogen bond score. The most appropriate values as -836.722 for MolDock score and -55.02 for H-Bond score were obtained with 1SDD and ligand 1.
Keywords: Haemophilia A, Factor VIII, X-linked disease, drug designing.
De novo Drug Design, Pharmacophore Search and Molecular Docking for Inhibitors to treat TB and HIV co-infection
by Satheeshkumar Sellamuthu, Ashok Kumar, Sushil Singh
Abstract: Novel molecules were designed as possible inhibitors of ATP synthase through de novo drug design, but were not drug-like molecules. Hence, ZINC database was searched for drug-like molecules from the common pharmacophore of the designed molecules. A total of 472 hits were obtained, among them, ZINC39552534, ZINC39371747, and ZINC38959526 produced better docking than the standard drug Bedaquiline. The vulnerability of TB and HIV co-infection has necessitated the search for inhibitors effective against both the diseases. Hence, the hits obtained were further screened for possible interaction with HIV reverse transcriptase. ZINC63941671, ZINC05858010, and ZINC05857787 were found better over the standard drug Rilpivirine, but their interaction was least against ATP synthase. Further, ZINC38959526 (lead against ATP synthase) and ZINC05858010 (lead against reverse transcriptase) share some common chemical features and based on this, new hybrid molecules were designed to inhibit both the targets. The possibility of hERG toxicity was also checked to eliminate unwanted cardiotoxicity.
Keywords: ATP synthase inhibitors; De novo drug design; HIV; hERG toxicity; Molecular docking; Pharmacophore search; Reverse transcriptase; Tuberculosis; ZINC database