COMPUTATIONAL TOOLS TO DETECT SINGLE NUCLEOTIDE POLYMORPHISM (SNP) IN NUCLEOTIDE SEQUENCES: A REVIEW

ABSTRACT Single nucleotide polymorphisms (SNPs) are basically single base pair alterations present in the genomic DNA. SNPs is usually treated as one of the most common genetic markers in case of plants, animals as well as the human genome to study the complex genetic traits and evolutionary status of the genome. SNPs are widely used as popular markers due to their continuous presence in the genome, highly reproducible, relatively easy to score. In addition to this, SNPs in coding sequences are used to directly examine the genetics of expressing genes and to study various polymorphic functional traits. Specifically the non-synonymous SNPs are more attractive because they alter the amino acid that ultimately affecting the protein functions. The direct application of SNP exists with pharmacogenomics study and crop improvement. Various strategies have been used for SNP discovery that comes from both observational and computational techniques. SNPs can be detected by laboratory based experimental methods, which are time consuming and expensive also the development costs are high. The implementations of Bioinformatics approach reduce the development cost of SNPs as it uses publicly available sequences from databases like expressed sequence tags (ESTS) that cause the development of SNP markers rapid and less expensive

COVID-19: An Updated Insight of the Pandemic

Novel coronavirus (SARS-CoV-2) out-broke in the city of Wuhan in China and widely spread across the globe in a pandemic manner, causing societal and economic disruptions. Though the origin of the novel virus is still a debating topic, it is certain that SARS-CoV-2 acquired human to human transmission capacity. Regardless of aggressive containment and quarantine approaches, the number of confirmed cases continues to rise and being reported due to its highly infectious nature. As of the time, there is a little scope for the antiviral drugs or vaccines for the treatment of coronavirus infection; due to the vigorous mutation rate in the viral genome. However, existing anti-parasite drugs like ivermectin and chloroquine could effectively inhibit the virus has been reported. Few of the vaccines have come up with certain degree of efficacy and many are under the clinical trial phase. The research on novel coronavirus is still in the preliminary stage. In this chapter, we systematically summarize the origin, transmission route, molecular characterization, pathogenic mechanism, contagious nature, clinical symptoms, diagnosis, treatment, mutation and infection as well as prevention strategy of coronavirus disease based on the recently available literature. In addition to this, this chapter presents updated insights of the current state of knowledge pertaining to novel coronavirus and can be referred for potential future studies

Bioinspired Algorithms in Solving Three-Dimensional Protein Structure Prediction Problems

Proteins play a vital molecular role in all living organisms. Experimentally, it is difficult to predict the protein structure, however alternatively theoretical prediction method holds good for it. The 3D structure prediction of proteins is very much important in biology and this leads to the discovery of different useful drugs, enzymes, and currently this is considered as an important research domain. The prediction of proteins is related to identification of its tertiary structure. From the computational point of view, different models (protein representations) have been developed along with certain efficient optimization methods to predict the protein structure. The bio-inspired computation is used mostly for optimization process during solving protein structure. These algorithms now a days has received great interests and attention in the literature. This chapter aim basically for discussing the key features of recently developed five different types of bio-inspired computational algorithms, applied in protein structure prediction problems. </jats:p

In silico Prediction of Anti–SARS-CoV-2 Effect of Dermaseptin Peptides from Amphibian Origin: Prediction of anti–SARS-CoV-2 effect of the dermaseptin peptides

The novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has now been declared as a global pandemic by the World Health Organization (WHO). Several drug molecules have been proposed that can be used against the virus. As an alternative to effective drug molecules, the antiviral peptides have the potential for effective application to control the infectious disease. In this work, the anti- SARS-CoV-2 effect of dermaseptin peptide molecules produced by the skin of the frog was evaluated by using the computational method. Three numbers of antiviral dermaseptin peptides were obtained by searching the antimicrobial peptide database (APD). First, the structure prediction of peptides was done by Pep Fold 2.0 server followed by structure validation by PROCHECK program. Then, the protein-peptide docking simulations were performed using the COVID-19 docking server. The peptides' binding affinity with the SARS-CoV-2 spike protein macromolecule was evaluated along with eight negative control peptides and human angiotensin converting enzymes 2 (ACE2). The protein-peptide docking and interaction analysis resulted in finding that dermaseptin-S9 peptide molecule was the most efficient molecule among the selected peptides with a binding energy of -331.54 KJ/mole. Hence, as a follow-up study, the dermaseptin-S9 peptide molecule can be further designed to enhance its specificity and binding affinity for its better use against the SARSCoV-2 disease. HIGHLIGHTS Three numbers of antiviral dermaseptin peptides from APD database were in silico evaluated against SARS-CoV-2 spike protein. The antiviral dermaseptin -S9 peptide showed the highest binding affinity towards the SARS-CoV-2 spike protein macromolecule. The hydrophobic property of the distributed amino acids of the derrmaseptin-9 molecule might be related to the binding affinity

Microbial Bioreactor Systems for Dehalogenation of Organic Pollutants

Halogenated organic compounds having many beneficial applications, both in industries and agriculture sectors. Basically, the uses are as pesticides, solvents, surfactants, and plastics. However, their large, widespread uses throughout the world have resulted the negative impact on the environment. Considering their treatment process are widely accepted by using the bioreactor systems. The large variety of microorganisms present in the bioreactor and their interaction is the key to the effective treatment and removal of these compounds. Usually the microbes produce the enzymes known as dehalogenase to remove the halogen form the compounds to make it non-toxic. Many of the different steps and about the microbial groups in degradation process of halogenated compounds are well understood, but more details concerning the microbial community are yet to be discovered. This chapter describes about the different dehalogenation systems available in microbes and their ultimate application in different bioreactor systems for the degradation analysis of several harmful halogenated compounds. </jats:p

Application of Bioinformatics Techniques to Screen and Characterize the Plant-Based Anti-Cancer Compounds

Plant-based natural products provide a strong background to evaluate, predict the novel class of compounds having anti-cancer properties, as well as to explore their potential mechanism mechanisms of action. Due to the huge cost and time utilization in the traditional drug development approaches, bioinformatics plays a major role to facilitate drug discovery with less cost and time strategies. Several bioinformatics-based approaches being used recently to screen as well as to characterize the potential plant-based compounds can be used to treat several types of cancer. Some of the computational approaches are target identification, screening of compounds molecular docking, molecular dynamics simulations, QSAR analysis, pharmacophore modeling, and ADMET (absorption, distribution, metabolism, excretion, and toxicity). This chapter describes specific computational methods being used currently to screen and characterize different plant-based anti-cancer molecules by taking examples from the recent literature and discussing their advantages and limitations. </jats:p