ABSTRACT
Throughout the process of evolution, DNA undergoes the accumulation of distinct mutations, which can often result in highly organized patterns that serve various essential biological functions. These patterns encompass various genomic elements and provide valuable insights into the regulatory and functional aspects of DNA. The physicochemical, mechanical, thermodynamic, and structural properties of DNA sequences play a crucial role in the formation of specific patterns. These properties contribute to the three-dimensional structure of DNA and influence their interactions with proteins, regulatory elements, and other molecules. In this study, we introduce DNASCANNER v2, an advanced version of our previously published algorithm DNASCANNER for analyzing DNA properties. The current tool is built using the FLASK framework in Python language. Featuring a user-friendly interface tailored for nonspecialized researchers, it offers an extensive analysis of 158 DNA properties, including mono/di/trinucleotide frequencies, structural, physicochemical, thermodynamics, and mechanical properties of DNA sequences. The tool provides downloadable results and offers interactive plots for easy interpretation and comparison between different features. We also demonstrate the utility of DNASCANNER v2 in analyzing splice-site junctions, casposon insertion sequences, and transposon insertion sites (TIS) within the bacterial and human genomes, respectively. We also developed a deep learning module for the prediction of potential TIS in a given nucleotide sequence. In the future, we aim to optimize the performance of this prediction model through extensive training on larger data sets.
ABSTRACT
Most dyes present in wastewater from the textile industry exhibit toxicity and are resistant to biodegradation. Hence, the imperative arises for the environmentally significant elimination of textile dye by utilising agricultural waste. The achievement of this objective can be facilitated through the utilisation of the adsorption mechanism, which entails the passive absorption of pollutants using biochar. In this study, we compare the efficacy of the response surface methodology (RSM), the artificial neural network (ANN), the k-nearest neighbour (kNN), and adaptive neuro-fuzzy inference system (ANFIS) in removing crystal violet (CV) from wastewater. The characterisation of biochar is carried out by scanning electron microscope (SEM) and Fourier transform infrared (FTIR). The impacts of the solution pH, adsorbent dosage, initial dye concentration, and temperature were investigated using a variety of models (RSM, ANN, kNN, and ANFIS). The statistical analysis of errors was conducted, resulting in a maximum removal effectiveness of 97.46% under optimised settings. These conditions included an adsorbent dose of 0.4 mg, a pH of 5, a CV concentration of 40.1 mg/L, and a temperature of 20 °C. The ANN, RSM, kNN, and ANFIS models all achieved R2 0.9685, 0.9618, 0.9421, and 0.8823, respectively. Even though all models showed accuracy in predicting the removal of CV dye, it was observed that the ANN model exhibited greater accuracy compared to the other models.
ABSTRACT
Vaccine development is a complex and long process. It involves several steps, including computational studies, experimental analyses, animal model system studies, and clinical trials. This process can be accelerated by using in silico antigen screening to identify potential vaccine candidates. In this chapter, we describe a deep learning-based technique which utilizes 18 biological and 9154 physicochemical properties of proteins for finding potential vaccine candidates. Using this technique, a new web-based system, named Vaxi-DL, was developed which helped in finding new vaccine candidates from bacteria, protozoa, viruses, and fungi. Vaxi-DL is available at: https://vac.kamalrawal.in/vaxidl/ .
Subject(s)
Artificial Intelligence , Vaccines , Animals , Proteins , Antigens , Vaccine DevelopmentABSTRACT
Vitiligo is a chronic asymptomatic disorder affecting melanocytes from the basal layer of the epidermis which leads to a patchy loss of skin color. Even though it is one of the neglected disease conditions, people suffering from vitiligo are more prone to psychological disorders. As of now, various studies have been done in order to project auto-immune implications as the root cause. To understand the complexity of vitiligo, we propose the Vitiligo Information Resource (VIRdb) that integrates both the drug-target and systems approach to produce a comprehensive repository entirely devoted to vitiligo, along with curated information at both protein level and gene level along with potential therapeutics leads. These 25,041 natural compounds are curated from Natural Product Activity and Species Source Database. VIRdb is an attempt to accelerate the drug discovery process and laboratory trials for vitiligo through the computationally derived potential drugs. It is an exhaustive resource consisting of 129 differentially expressed genes, which are validated through gene ontology and pathway enrichment analysis. We also report 22 genes through enrichment analysis which are involved in the regulation of epithelial cell differentiation. At the protein level, 40 curated protein target molecules along with their natural hits that are derived through virtual screening. We also demonstrate the utility of the VIRdb by exploring the Protein-Protein Interaction Network and Gene-Gene Interaction Network of the target proteins and differentially expressed genes. For maintaining the quality and standard of the data in the VIRdb, the gold standard in bioinformatics toolkits like Cytoscape, Schrödinger's GLIDE, along with the server installation of MATLAB, are used for generating results. VIRdb can be accessed through "http://www.vitiligoinfores.com/".
