International Journal for Computational Biology (IJCB)
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Editorial Forward
Get PDFWith the availability of next generation sequencing technology, there is a tremendous need for development of novel tools, algorithms and methodologies for extracting useful information and knowledge from exponentially growing data. This need has catalyzed active research in the overlapping fields of Machine Learning (ML) and Artificial Intelligence (AI). First issue of IJCB is bringing some very good research articles with a detailed view of the cutting edge machine learning algorithms
iFace: A Bioinformatics Tool for the Analysis of Protein-Protein Interface
Get PDFDetailed knowledge of protein-protein interaction is essential to understand various biochemical and biological functions. In this paper, we present a bioinformatics tool to analyze the protein-protein interfaces using three-dimensional structural information. iFace identifies protein-protein interaction sites and various interactions that contribute to the specificity and strength of the protein complex
Applications of Support Vector Machines as a Robust tool in High Throughput Virtual Screening
Get PDFChemical space is enormously huge but not all of it is pertinent for the drug designing. Virtual screening methods act as knowledge-based filters to discover the coveted novel lead molecules possessing desired pharmacological properties. Support Vector Machines (SVM) is a reliable virtual screening tool for prioritizing molecules with the required biological activity and minimum toxicity. It has to its credit inherent advantages such as support for noisy data mainly coming from varied high-throughput biological assays, high sensitivity, specificity, prediction accuracy and reduction in false positives. SVM-based classification methods can efficiently discriminate inhibitors from non-inhibitors, actives from inactives, toxic from non-toxic and promiscuous from non-promiscuous molecules. As the principles of drug design are also applicable for agrochemicals, SVM methods are being applied for virtual screening for pesticides too. The current review discusses the basic kernels and models used for binary discrimination and also features used for developing SVM-based scoring functions, which will enhance our understanding of molecular interactions. SVM modeling has also been compared by many researchers with other statistical methods such as Artificial Neural Networks, k-nearest neighbour (kNN), decision trees, partial least squares, etc. Such studies have also been discussed in this review. Moreover, a case study involving the use of SVM method for screening molecules for cancer therapy has been carried out and the preliminary results presented here indicate that the SVM is an excellent classifier for screening the molecules
Molecular Docking study of Catechins compounds from Camellia sinensis against UPPS in Staphylococcus aureus
Get PDFAntibiotics resistant Staphylococcus aureus (S. aureus) is an emerging concern in the medical field. Due to their increasing resistance to numerous antibiotics, there is indeed essential to explore both potential targets and effective antibiotics. Therefore, we considered undecaprenyl diphosphate synthase (UPPS) as a potential target as it is an essential enzyme in cell wall biosynthesis of S. aureus. Earlier reports on these four major compounds from Camellia sinensis plant extract such as catechins (C), epicatechin (EC), epicatechin gallate (ECg) and epigallocatechin gallate (EGCg) suggested that it could be an effective antibacterial agent. Thus, we attempt to validate the antibacterial activity of these compounds against UPPS via molecular docking analysis. Interestingly, we found that epicatechin gallate (ECg) has the highest binding energy with UPPS protein by forming nine hydrogen bonds with the amino acid residues at the binding site of the receptor. Hence, our results infer that ECg from Camellia sinensis poses significant anti-bacterial activities. Thus, the aim of this study was to provide an effective antibacterial molecule and potent target which might be helpful in further modification to increase their sensitivity
Plant Genomic Databases for Oilseeds Crop Improvement
Get PDFoai:ojs.ijcb.in:article/8Plant genomic databases are collections of huge information on plants, germplasm accessions, descriptors, plant genetics, physical and genomic sequence maps, QTLs, loci, sequence information, molecular markers, references etc. At present more than 100 plants genomic databases are available. These are dedicated to generic genome data focusing on specific crops. Some of the important oilseeds plant databases include Castor Bean Genome Database, CGPDB, SoyBase, Legume Information System (LIS), Brassica database, Sinbase etc. Due to availability of number of genomic databases for crop plants before using any of these databases the researcher needs to visit most appropriate database and choose suitable parameters for genomic information mining. The wealth of information available to researchers today can be overwhelming hence; understanding the plant databases for harnessing genomic information is the need of the hour for crop improvement research programmes
Molecular Docking of Flindersine with some targets related to β-cells Protection
Get PDFDiabetes mellitus (DM) is the most widespread metabolic disorder affecting millions worldwide. Molecular docking studies are useful in identifying some useful ligands which could be used to target proteins related to β-cell protection. Flindersine isolated from the plant Toddalia asiatica (L.) Lam. (Rutaceae) has been shown by us to possess antidiabetic property. With a view to identify in silico the possible mode of docking with different target proteins like PPARγ and GLUT4 which play important roles in protecting β-cells from damage. Chemical characteristics of Flindersine were retrieved from pubchem database http://pubchem.ncbi.nlm.nih.gov. The docking analysis in the active sites of 2PRG and Homology modeled protein structure of GLUT4 were performed by the Auto dock program. The docking results showed good binding interactions of the ligand with both the targets at very low energy level. In our in silico analysis, flindersine isolated from Toddalia asiatica clearly demonstrated that it could improve diabetic condition by increasing insulin secretion from remnant or regenerated pancreatic beta cells and could promote insulin sensitization and glucose uptake activities. When compared with standard drug Rosiglitazone that is commercially available flindersine can further diminish the degree of shrinkage and necrosis of beta cells of pancreas. Thus flindersine can be considered for developing into a potent antidiabetic drug