International Journal of Bioinformatics Research and Applications (8 papers in press)

Regular Issues

• CoSec  
  by Ankur Chaurasia, Jyotilipsa Mohanty, Laxman Kumar Lukkani, Ayaluru Murali 
  Abstract: A common problem with in-silico protein modelling is to choose the best model out of a cluster of protein models suggested by the online servers. Besides identifying a right model based on torsion angles and potentials, lot of researchers look at the model that retains most of its predicted secondary structures. The comparison of the secondary structure elements at residue level becomes more tedious as the size of the protein increases. So, we have developed two tools PreSSM (Predicted Secondary Structure Matching) and CompASS (Compare Assigned Secondary Structure) under one umbrella CoSec. PreSSM compares the secondary structure elements of a modelled protein from a PDB to the secondary structure predicted, while CompASS compares the secondary structures between two PDBs of the same protein (typically the models before and after simulation/docking with a ligand/mutation). Both tools use STRIDE algorithm to assign secondary structure confirmation to residues in the given protein's structure.
  Keywords: Bioinformatics; Computational Biology; Secondary Structure Analysis; Sequence Analysis; Protein structure; Molecular Dynamic Simulation.
  DOI: 10.1504/IJBRA.2023.10054448
   
  
• Prediction of essential genes using single nucleotide compositional features in genomes of bacteria: a machine learning-based analysis  
  by ANNUSHREE KURMI, Piyali Sen, MADHUSMITA DASH, ASWINI KUMAR PATRA, Suvendra Ray, Siddhartha Satapathy 
  Abstract: Essential genes are crucial for understanding the cellular processes of an organism. In this article, we have done an extensive machine learning-based analysis of single nucleotide composition in thirty-five bacterial genomes across several phylogenetic groups. With an objective of classifying essential genes from the remaining genes, we have used seven machine learning-based classifiers- Logistic Regression, Gaussian Na
  Keywords: essential genes; single nucleotide composition; bacterial genome; machine learning.
  DOI: 10.1504/IJBRA.2023.10054584
   
  
• Fetal Weight Estimation with Descriptive Statistics and Correlation Analysis of Significant Ultrasonographic Parameter and Fuzzy Artmap Classifier  
  by Saba Izadi, Somayeh Saraf Esmili 
  Abstract: High and low weight of the foetus at birth can be associated with an increased risk of neonatal complications. So far, various techniques have been proposed for estimating birth weight. In the proposed method a powerful fuzzy-neural classifier is used. The method is evaluated on a set of 40 ultrasonographic foetal data, in which foetuses are at 37 and 38 weeks of gestation. The features used for classification training and testing are superior features that have been used by experts in the field for many years, including the length of the femur, the bicuspid diameter, and the circumference of the foetal head. The results of the implementation of the proposed method on the dataset indicate the achievement of 98.96% accuracy, which will be evidence of its good performance on the new data. The new method can be used to provide accurate estimates of foetal birth weight.
  Keywords: foetal weight estimation; ultrasonographic parameter; statistics and correlation analysis; fuzzy artmap – ANFIS classifier.
  DOI: 10.1504/IJBRA.2023.10054807
   
  
• Identifying Breast Cancer Molecular class using integrated feature selection and deep learning model.  
  by Monika Lamba, Geetika Munjal, Yogita Gigras 
  Abstract: The extraction of molecular subcategory is one such valuable evidence concerning breast cancer in determining its cure and prognosis. This manuscript has framed a model for molecular subtype-based feature selection known as CFS-BFS followed by classification using deep learning. The proposed model captures significant genes by utilising pre-processing ladder along with the combination of filter and wrapper-based technique CFS-BFS. The obtained genes are assessed via numerous machine learning methodologies where it is remarked that carefully chosen significant genes are more profitable in explaining this molecular problem using deep learning. The study has attained the maximum precision and beats brilliantly in terms of recall, F-score, TP_Rate, fallout, and MCC. Hence, proposed paradigm is recognised as one of the best effective technique determining the outstanding recital with all the chosen micro-array gene expression datasets for significant obtained genes. The genes identified by integrated model are also validated using Kaplan-Meier survival graph to show their credibility in breast cancer prognosis. Survival analysis show that selected genes using integrated approach can separate luminal, non-luminal subcategory utilising various factors including age, disease free survival, and relapse free survival.
  Keywords: feature selection; deep learning; breast cancer; molecular subtype; SMOTE; best first search; CFS-BFS.
  DOI: 10.1504/IJBRA.2023.10054946
   
  
• In silico studies on Acalypha indica, Catharanthus roseus and Coleus aromaticus derivative compounds against Omicron  
  by S. Radha Thirumalaiarasu  
  Abstract: The omicron, a variant of corona virus has high rate of transmissibility, lower vaccine efficiency and exhibit increase risk of reinfection. There is an urgent need for suitable medicine replacing synthetic drug to avoid side effects. Molecular docking tool was used to analyse the binding ability of natural and synthetic compounds with the target Omicron protein (7TL9). ADME and bioactivity score performed to evaluate drug likeness properties. The phytochemicals like mannoside from Catharanthus roseus which has the score value (
  Keywords: Omicron; medicinal plant; molecular docking; Acaindinin; Acalypha indica.
  DOI: 10.1504/IJBRA.2023.10055660
   
  
• Insights into CFTR Structural Properties and Dynamic Nature of Regulatory Domain using Computational Approach  
  by Vasavi Garisetti, Arthikasree Anandamurthy, Roslin Elsa Varughese, Gayathri Dasararaju 
  Abstract: The cystic fibrosis transmembrane conductance regulator (CFTR) protein is a multi-domain ion channel which is a part of the ATP-binding cassette (ABC) superfamily. The ion channel is regulated by phosphorylation of its regulatory domain (R) and binding of adenosine triphosphate (ATP) to the nucleotide binding domains. We have predicted the model of the complete CFTR including the R domain using AlphaFold2 and have studied the behaviour of different domains of the predicted model in a simulated dynamic environment with POPC lipid bilayers for 200 ns. A combination of free energy landscapes, principal component and comparative structural analyses were used to gain a broad perspective of the protein’s conformational properties. The CFTR R domain, which is disordered, lacks a suitable 3D structure; this is the basis for the current study. The process of utilising new tools will provide a fresh viewpoint on how to comprehend the dynamic nature of the R domain and also provide insights into the structural characteristics of CFTR.
  Keywords: cystic fibrosis; cystic fibrosis transmembrane conductance regulator; CFTR; R domain; intrinsically disordered proteins; molecular dynamics simulation; transmembrane protein.
  DOI: 10.1504/IJBRA.2023.10055908
   
  
• Goat Casin Peptides and their Potential through an IN SILICO Approach  
  by Joanderson Pereira Cândido Da Silva, Paula Perazzo De Souza Barbosa, Joellington Marinho De Almeida, Nathália Miranda De Medeiros, Emmanuel Silva Marinho, Tatiane Santi Gadelha 
  Abstract: The caprine caseins s1, s2, , and from UniProtKB were characterised for amino acid profile, and hydrolysed in silico with pepsin or trypsin. The generated peptides were characterised for physical-defined properties, bioactive potential, Boman index, toxicity and allergenicity. The peptides generated are bioactive, predominating dipeptidyl peptidase IV and ACE inhibitors activities. Only two peptides are toxic, and most have been proposed to be allergenic by Allergen Online or SDAP, because they have < 50% identity with other confirmed allergenic proteins, including bovine milk caseins, however, they were considered non-allergenic by the methods MEME/MAST and BLAST in Algpred, requiring more studies for better evaluation. Despite of variations, bioinformatics tools are useful before bench tests, because allows time and cost savings, and provides studies projection for use by pharmaceutical and food industries, besides to identify sequences that may need other tests to evaluate the safety before being introduced in use.
  Keywords: proteolysis; bioactive peptides; bioinformatics; prediction; eating disorders; industrial applications; biopotential; allergenicity; toxicity; physical-defined properties.
  DOI: 10.1504/IJBRA.2023.10055913
   
  
• Prediction of ncRNA from RNA-Seq Data using Machine Learning Techniques  
  by Faroza Shamsheem, T. Arundhathi Tunga, Khaleda Afroaz 
  Abstract: The cystic fibrosis transmembrane conductance regulator (CFTR) protein is a multi-domain ion channel which is a part of the ATP-binding cassette (ABC) superfamily. The ion channel is regulated by phosphorylation of its regulatory domain (R) and binding of adenosine triphosphate (ATP) to the nucleotide binding domains. We have predicted the model of the complete CFTR including the R domain using AlphaFold2 and have studied the behaviour of different domains of the predicted model in a simulated dynamic environment with POPC lipid bilayers for 200 ns. A combination of free energy landscapes, principal component and comparative structural analyses were used to gain a broad perspective of the protein's conformational properties. The CFTR R domain, which is disordered, lacks a suitable 3D structure; this is the basis for the current study. The process of utilising new tools will provide a fresh viewpoint on how to comprehend the dynamic nature of the R domain and also provide insights into the structural characteristics of CFTR.
  Keywords: cystic fibrosis; cystic fibrosis transmembrane conductance regulator; CFTR; R domain; intrinsically disordered proteins; molecular dynamics simulation; transmembrane protein.
  DOI: 10.1504/IJBRA.2023.10056423