International Journal of Computational Biology and Drug Design (7 papers in press)

Regular Issues

• Importance of safety maintenance of the survived with recent former infection experience during a pandemic syndrome episode: A Study by Difference Equation Approach  
  by Subhasis Bhattacharya, Suman Paul, Sudip Mukherjee 
  Abstract: During the outbreak of a highly infectious disease conceded by a virus, handling of healthcare catastrophe is the most momentous part. Any type of known or unknown relaxation may generate enormous loss in terms of population. Present study consider the concern that survived one who has some fresh former infection history can be fingered with appropriate care throughout the syndrome period otherwise a huge harm can be advent by the state. The study follow difference equation modelling considering two aspects where the survived with former infection history handled with care and not reckoned as a part of sustained population and the other is they encompassed with the general population category. The study considers an example of a hypothetical state with some give infection rate, death rate and quarantine rate. By using R- programme language the study observes that proper care for such group of population is very significant to reduce the situation like human loss.
  Keywords: Infectious disease; SARS-CoV-2; 2019-nCov; Difference Equation; Survived from the infected; Quarantine rate; Death Rate.
  
  
• Virtual screening of plant phytochemicals to discover potent Janus Kinase-1 inhibitors against severe COVID-19 and sepsis.  
  by Shradheya R. R. Gupta, Kavita Joshi, Subham Verma, Rakesh Sharma, Sameer Qureshi, Mansoor Ali Syed, Vandana Nunia 
  Abstract: Janus kinases (JAK) are intracellular tyrosine kinases that transduce cytokine-mediated signals. They play a major role in sepsis and SARS-COVID-19 virus-induced MODS (Multiple Organ Dysfunction Syndrome) progression. Therefore, inhibition of these kinases might be an efficient option for the treatment of sepsis and MODS (like acute respiratory distress syndrome, acute liver injury, etc.). In the absence of notable success for the treatment of these diseases, the current study was focused on finding the potential phytochemicals to inhibit JAK1. We prepared and screened a library of 5229 diverse phytochemicals. On the basis of drug likeness properties (Rule of 5) and ADMET, 2081 phytochemicals were filtered out. These compounds were docked with the JAK1 kinase domain and arranged in their descending binding energy. Upadacitinib, a FDA approved JAK-1 inhibitor was set as a reference in the current study. To further shortlist the compounds from the list, the energy cut-off was set to -11 Kcal/mol, which was higher than Upadacitinib. The top four compounds Kudzuisoflavone B, Taiwaniaflavone 7-O-methyl ether, Formosanatin D, and Withaphysalin A, showed binding energy -12 Kcal/mol, higher than cut-off value were further piped for dynamic simulation. From these four compounds, Kudzuisoflavone B was selected based on the RMSD, RMSF, number of H-bond, hydrophobic interactions, MMPBSA and GROMACS total energy.
  Keywords: Janus kinase; SARS-CoV-2 virus; Sepsis; Virtual drug screening; Phytochemical inhibitors.
  
  
• Advanced DEEPCNN Breast Cancer Mammogram Image Detection and Classification with Butterfly Optimization Algorithm  
  by M.suriya Priyadharsini, J.G.R. Sathiaseelan 
  Abstract: A major aspect influencing human health is breast cancer. Mammography, fine needle aspiration, and surgical biopsy are some of the evolving diagnosis methods for this problem. Pathology images are used to diagnose breast cancer. Breast tumour surgery allows doctors to microscopically study breast tissue. Traditional methods use a cuckoo-optimized radial basis neural network. Earlier RBN algorithms handled feature extraction and reduction differently. To reduce unneeded complexity, outperform CNN for feature extraction and classification. The Butterfly optimization technique suggests a convolutional neural network. Zernike moments' scale, interpretation, and rotation similarity lets us bypass numerous pre-processing steps. The picture dataset was created from tumour treatment archives. The Butterfly optimisation method feeds the DCNN training data. DCNN removes, reduces, and classifies features. By determining the number of historical periods and training images for Deep CNN, optimization improves efficiency and reduces error rates. This approach projects normal, benign, and malignant. The model achieves sensitivity,
  Keywords: Convolutional neural network (CNN); Butterfly optimisation algorithm; Zernike moment-based shape feature extraction; classification; accuracy.
  DOI: 10.1504/IJCBDD.2023.10054022
   
  
• A Unique Noise Detector Developed for the Filtering of X-Ray Images of Bone Fractures  
  by A. Selin Vironicka, J.G.R. Sathiaseelan 
  Abstract: In various fields, especially in the health division, rapidly developing technologies emerge daily. However, some old techniques are still very popular, efficient, and effective. X-Rays are one of these bone fracture detection techniques. However, the size of fractures is sometimes insignificant and cannot be easily detected. Efficient and smart systems should therefore be developed. Image processing is one of the mainly hopeful and extensive medical imaging research fields. Medical imagery is most important because different medical images are diagnosed at different recovery stages. Images may be distorted by noise during diagnosis, or X-ray images may contain noise. Filters are generally used to remove noise from certain image acquisition errors. For image enhancement, different filtering techniques are used. Because the filtering system presentation thrives on noise detection system accuracy, we present a new noise detection scheme using two decision levels in this paper. Here, we recognize the infected pixels grossly in the first step and finally decide, in the second stage, whether or not the given pixel is corrupt. Comprehensive simulations also confirmed the efficacy of the proposed filter.
  Keywords: Mean Filter; Gaussian Filter; Mean Square Error (MSE); Switching Based Median Filter (SBMF); Structural Similarity Index Metric (SSIM); Peak Signal to Noise Ratio (PSNR).
  DOI: 10.1504/IJCBDD.2023.10054768
   
  
• Triclustering of Gene Expression Microarray Data using a Hybrid Bio-Inspired Approach  
  by Balamurugan Rengeswaran, Pushkar Nahar, Suyash Agarwal, S.P. Raja 
  Abstract: Clustering is a segment of unsupervised learning that measures the similarity of samples’ feature data into a metric called the estimate of similarity. Assembling these unlabelled samples using the estimate of similarity is called clustering. The Triclustering of microarray genes contains a subset of genes that carry information about the behaviour of these genes under specific conditions over specific periods. In this paper, we present a meta-heuristic technique that is a hybrid cuckoo search algorithm used to solve the computationally intensive problem. In general, cuckoo search approach relies upon the fact that the cuckoo lays its eggs in the nest of host birds; however, if the egg is not identified and knocked down, the cuckoo's eggs are hatched. In this study, we propose cuckoo search with harmony search for triclustering genes. This algorithm was evaluated over certain parameters and the experimental results outperformed other existing algorithms
  Keywords: Triclustering; microarray; cuckoo search; optimization; ontology.
  DOI: 10.1504/IJCBDD.2023.10055230
   
  
• Residue Interaction Network analysis and Molecular dynamics simulation of 6K Viroporin: Chikungunya Virus Channel Proteins  
  by Chaitra Mallasandra Krishnappa, Pratishtha Rai, Ayesha Zeba, Anjali Ganjiwale 
  Abstract: 6K of Chikungunya virus are small hydrophobic proteins composed of 61 amino acids classified as a class IIA viroporins. The molecular insights on the structure and membrane interaction of 6K protein are not known. Elucidation of structure and role of 6K protein is an important step to understand and combat Chikungunya infections. The aim of this study is to characterise stable higher order oligomers forming the functional unit of 6K viroporin. Molecular dynamics simulation in dipalmitoylphosphatidylcholine (DPPC) bilayer of different oligomeric states of 6K model shows hexamer to be the most stable form for 6K protein. The residue centrality analysis for hexamer shows higher Z-score for Tyr 9, Phe 18, Trp 19 and Ile 24 as the hub residues for the residue interaction network (RIN). The models obtained point aromatic pocket formed by Tyr 3, Tyr 9 and Trp 11 towards the N and C-terminal facing endoplasmic reticulum lumen. Our study provides the first step towards understanding the structure and function of 6K viroporins of the Chikungunya virus.
  Keywords: Viroporin; Chikungunya; Channel protein; 6K; ab initio model; ion channels; Graph theory; Oligomers; Ab initio modeling.
  DOI: 10.1504/IJCBDD.2023.10056106
   
  
• Genetic Variability, Correlation, Diversity, Path Coefficients and Principal Component Analysis in Indian Mustard  
  by Sumanta Prasad Chand, Sandip Debnath, Mehdi Rahimi, Shampa Purakayastha, Sanghamitra Rout 
  Abstract: After soybean and palm, Brassica species are the third-most significant oilseed crops in the world. Globally, Indian mustard (Brassica juncea L.) is used as an oilseed, a vegetable, and a condiment. The seventy different genotypes of Indian mustard were grown at the farm of Visva-Bharati University's Institute of Agriculture using an RCBD with three replications in 20172018 and 20182019 to investigate genetic variability, cause and effect relationship and diversity. The findings demonstrated that environmental factors contribute to the development of characteristics because the PCV values were higher than the GCV values. The direct impact of seed yield per plant on oil production per plant was highly positive (0.551). Using Tochers technique, the 70 genotypes were divided into eight groups. The mustard accessions PCA revealed a varied pattern of grouping. The main genetic factors that caused genetic divergence were the oil yield per plant, seed yield per plant, number of siliqua on branches and number of siliqua per plant.
  Keywords: Brassica Juncea; Cluster; Variance; Path; Tocher's Method; Mahalanobis D2 statistic; Principal Component Analysis.
  DOI: 10.1504/IJCBDD.2023.10056599