DNASCANNER v2: A Web-Based Tool to Analyze the Characteristic Properties of Nucleotide Sequences.

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.

Vaxi-DL: An Artificial Intelligence-Enabled Platform for Vaccine Development.

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/ .

A Data-Driven Approach to Construct a Molecular Map of Trypanosoma cruzi to Identify Drugs and Vaccine Targets.

Chagas disease (CD) is endemic in large parts of Central and South America, as well as in Texas and the southern regions of the United States. Successful parasites, such as the causative agent of CD, Trypanosoma cruzi have adapted to specific hosts during their phylogenesis. In this work, we have assembled an interactive network of the complex relations that occur between molecules within T. cruzi. An expert curation strategy was combined with a text-mining approach to screen 10,234 full-length research articles and over 200,000 abstracts relevant to T. cruzi. We obtained a scale-free network consisting of 1055 nodes and 874 edges, and composed of 838 proteins, 43 genes, 20 complexes, 9 RNAs, 36 simple molecules, 81 phenotypes, and 37 known pharmaceuticals. Further, we deployed an automated docking pipeline to conduct large-scale docking studies involving several thousand drugs and potential targets to identify network-based binding propensities. These experiments have revealed that the existing FDA-approved drugs benznidazole (Bz) and nifurtimox (Nf) show comparatively high binding energies to the T. cruzi network proteins (e.g., PIF1 helicase-like protein, trans-sialidase), when compared with control datasets consisting of proteins from other pathogens. We envisage this work to be of value to those interested in finding new vaccines for CD, as well as drugs against the T. cruzi parasite.

Identification of vaccine targets & design of vaccine against SARS-CoV-2 coronavirus using computational and deep learning-based approaches.

An unusual pneumonia infection, named COVID-19, was reported on December 2019 in China. It was reported to be caused by a novel coronavirus which has infected approximately 220 million people worldwide with a death toll of 4.5 million as of September 2021. This study is focused on finding potential vaccine candidates and designing an in-silico subunit multi-epitope vaccine candidates using a unique computational pipeline, integrating reverse vaccinology, molecular docking and simulation methods. A protein named spike protein of SARS-CoV-2 with the GenBank ID QHD43416.1 was shortlisted as a potential vaccine candidate and was examined for presence of B-cell and T-cell epitopes. We also investigated antigenicity and interaction with distinct polymorphic alleles of the epitopes. High ranking epitopes such as DLCFTNVY (B cell epitope), KIADYNKL (MHC Class-I) and VKNKCVNFN (MHC class-II) were shortlisted for subsequent analysis. Digestion analysis verified the safety and stability of the shortlisted peptides. Docking study reported a strong binding of proposed peptides with HLA-A*02 and HLA-B7 alleles. We used standard methods to construct vaccine model and this construct was evaluated further for its antigenicity, physicochemical properties, 2D and 3D structure prediction and validation. Further, molecular docking followed by molecular dynamics simulation was performed to evaluate the binding affinity and stability of TLR-4 and vaccine complex. Finally, the vaccine construct was reverse transcribed and adapted for E. coli strain K 12 prior to the insertion within the pET-28-a (+) vector for determining translational and microbial expression followed by conservancy analysis. Also, six multi-epitope subunit vaccines were constructed using different strategies containing immunogenic epitopes, appropriate adjuvants and linker sequences. We propose that our vaccine constructs can be used for downstream investigations using in-vitro and in-vivo studies to design effective and safe vaccine against different strains of COVID-19.

Vaxi-DL: A web-based deep learning server to identify potential vaccine candidates.

The development of a new vaccine is a challenging exercise involving several steps including computational studies, experimental work, and animal studies followed by clinical studies. To accelerate the process, in silico screening is frequently used for antigen identification. Here, we present Vaxi-DL, web-based deep learning (DL) software that evaluates the potential of protein sequences to serve as vaccine target antigens. Four different DL pathogen models were trained to predict target antigens in bacteria, protozoa, fungi, and viruses that cause infectious diseases in humans. Datasets containing antigenic and non-antigenic sequences were derived from known vaccine candidates and the Protegen database. Biological and physicochemical properties were computed for the datasets using publicly available bioinformatics tools. For each of the four pathogen models, the datasets were divided into training, validation, and testing subsets and then scaled and normalised. The models were constructed using Fully Connected Layers (FCLs), hyper-tuned, and trained using the training subset. Accuracy, sensitivity, specificity, precision, recall, and AUC (Area under the Curve) were used as metrics to assess the performance of these models. The models were benchmarked using independent datasets of known target antigens against other prediction tools such as VaxiJen and Vaxign-ML. We also tested Vaxi-DL on 219 known potential vaccine candidates (PVC) from 37 different pathogens. Our tool predicted 175 PVCs correctly out of 219 sequences. We also tested Vaxi-DL on different datasets obtained from multiple resources. Our tool has demonstrated an average sensitivity of 93% and will thus be a useful tool for prioritising PVCs for preclinical studies.

Identification of vaccine targets in pathogens and design of a vaccine using computational approaches.

Antigen identification is an important step in the vaccine development process. Computational approaches including deep learning systems can play an important role in the identification of vaccine targets using genomic and proteomic information. Here, we present a new computational system to discover and analyse novel vaccine targets leading to the design of a multi-epitope subunit vaccine candidate. The system incorporates reverse vaccinology and immuno-informatics tools to screen genomic and proteomic datasets of several pathogens such as Trypanosoma cruzi, Plasmodium falciparum, and Vibrio cholerae to identify potential vaccine candidates (PVC). Further, as a case study, we performed a detailed analysis of the genomic and proteomic dataset of T. cruzi (CL Brenner and Y strain) to shortlist eight proteins as possible vaccine antigen candidates using properties such as secretory/surface-exposed nature, low transmembrane helix (< 2), essentiality, virulence, antigenic, and non-homology with host/gut flora proteins. Subsequently, highly antigenic and immunogenic MHC class I, MHC class II and B cell epitopes were extracted from top-ranking vaccine targets. The designed vaccine construct containing 24 epitopes, 3 adjuvants, and 4 linkers was analysed for its physicochemical properties using different tools, including docking analysis. Immunological simulation studies suggested significant levels of T-helper, T-cytotoxic cells, and IgG1 will be elicited upon administration of such a putative multi-epitope vaccine construct. The vaccine construct is predicted to be soluble, stable, non-allergenic, non-toxic, and to offer cross-protection against related Trypanosoma species and strains. Further, studies are required to validate safety and immunogenicity of the vaccine.

Clinical Characteristics and Remedy Profiles of Patients with COVID-19: A Retrospective Cohort Study.

OBJECTIVE: The aim of the study was to identify indicated homeopathic remedies based on the clinical characteristics of coronavirus disease 2019 (COVID-19) patients in India. METHODS: In this retrospective, cohort study, confirmed COVID-19 patients admitted at a COVID Health Centre in New Delhi between April 29 and June 17, 2020 were given conventional and homeopathic treatment. Patients were grouped into mild, moderate or severe categories of disease. Their symptomatologic profiles were analyzed to identify indicated homeopathic medicines. RESULTS: A total of 196 COVID-19 patients were admitted. One hundred and seventy-eight patients had mild symptoms; eighteen patients had moderate symptoms; no patients with severe symptoms were included as they were referred to tertiary care centers with ventilatory support. The mean age of patients with mild symptoms was significantly lower (38.6 years; standard deviation or SD ± 15.8) compared with patients in the moderate category (66.0 years; SD ± 9.09). The most important symptoms identified were fever (43.4%), cough (47.4%), sore throat (29.6%), headache (18.4%), myalgia (17.9%), fatigue (16.8%), chest discomfort (13.8%), chills (12.6%), shortness of breath (11.2%) and loss of taste (10.2%). Twenty-eight homeopathic medicines were prescribed, the most frequently indicated being Bryonia alba (33.3%), Arsenicum album (18.1%), Pulsatilla nigricans (13.8%), Nux vomica (8%), Rhus toxicodendron (7.2%) and Gelsemium sempervirens (5.8%), in 30C potency. CONCLUSION: Data from the current study reveal that Arsenicum album, Bryonia alba, Pulsatilla nigricans, Nux vomica, Rhus toxicodendron and Gelsemium sempervirens are the most frequently indicated homeopathic medicines. A randomized controlled clinical trial based on this finding is the next step.

Comprehensive Map of Molecules Implicated in Obesity.

Obesity is a global epidemic affecting over 1.5 billion people and is one of the risk factors for several diseases such as type 2 diabetes mellitus and hypertension. We have constructed a comprehensive map of the molecules reported to be implicated in obesity. A deep curation strategy was complemented by a novel semi-automated text mining system in order to screen 1,000 full-length research articles and over 90,000 abstracts that are relevant to obesity. We obtain a scale free network of 804 nodes and 971 edges, composed of 510 proteins, 115 genes, 62 complexes, 23 RNA molecules, 83 simple molecules, 3 phenotype and 3 drugs in "bow-tie" architecture. We classify this network into 5 modules and identify new links between the recently discovered fat mass and obesity associated FTO gene with well studied examples such as insulin and leptin. We further built an automated docking pipeline to dock orlistat as well as other drugs against the 24,000 proteins in the human structural proteome to explain the therapeutics and side effects at a network level. Based upon our experiments, we propose that therapeutic effect comes through the binding of one drug with several molecules in target network, and the binding propensity is both statistically significant and different in comparison with any other part of human structural proteome.

Comparative Characterization of Cardiac Development Specific microRNAs: Fetal Regulators for Future.

MicroRNAs (miRNAs) are small, conserved RNAs known to regulate several biological processes by influencing gene expression in eukaryotes. The implication of miRNAs as another player of regulatory layers during heart development and diseases has recently been explored. However, there is no study which elucidates the profiling of miRNAs during development of heart till date. Very limited miRNAs have been reported to date in cardiac context. In addition, integration of large scale experimental data with computational and comparative approaches remains an unsolved challenge.The present study was designed to identify the microRNAs implicated in heart development using next generation sequencing, bioinformatics and experimental approaches. We sequenced six small RNA libraries prepared from different developmental stages of the heart using chicken as a model system to produce millions of short sequence reads. We detected 353 known and 703 novel miRNAs involved in heart development. Out of total 1056 microRNAs identified, 32.7% of total dataset of known microRNAs displayed differential expression whereas seven well studied microRNAs namely let-7, miR-140, miR-181, miR-30, miR-205, miR-103 and miR-22 were found to be conserved throughout the heart development. The 3'UTR sequences of genes were screened from Gallus gallus genome for potential microRNA targets. The target mRNAs were appeared to be enriched with genes related to cell cycle, apoptosis, signaling pathways, extracellular remodeling, metabolism, chromatin remodeling and transcriptional regulators. Our study presents the first comprehensive overview of microRNA profiling during heart development and prediction of possible cardiac specific targets and has a big potential in future to develop microRNA based therapeutics against cardiac pathologies where fetal gene re-expression is witnessed in adult heart.

Identification and characterization of MGEs and their insertion sites in the gorilla genome.

Recently published gorilla genome has offered an opportunity to study human evolution through variety of approaches. Mobile genetic elements (MGEs) insert non randomly in genome through mechanisms such as retrotransposition and may cause gene inactivation, transduction, regulation of gene expression and genome expansion. Here we report that majority of gorilla genome is occupied with MGEs (> 36%) with presence of LTRs and Non-LTRs such as Alus and L1s. Other types of MGEs such as MIRs, retrovirus like elements ERVs and DNA transposons are also found using repeatmasker and ELAN pipeline. The distribution is similar to Humans and Macaca genome. Using DNA Scanner we also scanned preinsertion loci for number of different properties such as DNA denaturation, energy measures, potential for protein interactions and sequence based features. We also predicted preinsertion loci with > 70% accuracy using a machine learning tool called insertion site finder (ISF) based upon support vector machines.

Detecting motifs and patterns at mobile genetic element insertion site.

Mobile genetic elements (MGEs) occupy major proportion of eukaryotic genomes and are present in significant numbers in prokaryote genomes also. Here we report a new method which extracts a motif at the site of insertion of MGE using tools such as DNA SCANNER. The flanking region of the insertion site is extracted and is analyzed in DNA Scanner for physiochemical properties like protein-interaction measures, energy profiles as well as structural parameters. In case significant signals are observed, the most frequently occurring n-mer (5

Distribution of MGEs and their insertion sites in the Macaca mulatta genome.

Mobile genetic elements (MGEs) are fragments of DNA that can move around within the genome through retrotransposition. These are responsible for various important events such as gene inactivation, transduction, regulation of gene expression and genome expansion. The present work involves the identification and study of the distribution of Alu and L1 retrotransposons in the genome of Macaca mulatta, an extensively used organism in biomedical studies. We also make comparisons with MGE distributions in other primate genomes and study the physicochemical properties of the local DNA structure around the transposon insertion site using ELAN. The present work also includes computational testing of the pre-insertion loci in order to detect unique features based on DNA structure, thermodynamic considerations and protein interaction measures. Although there is significant sequence divergence between the elements of M. mulatta and H. sapiens, their genome wide distribution is very similar; comparing the distribution of L1's in all available X chromosome sequences suggests a common mechanism behind the spread of MGE's in primate genomes.

Genome-wide analysis of mobile genetic element insertion sites.

Mobile genetic elements (MGEs) account for a significant fraction of eukaryotic genomes and are implicated in altered gene expression and disease. We present an efficient computational protocol for MGE insertion site analysis. ELAN, the suite of tools described here uses standard techniques to identify different MGEs and their distribution on the genome. One component, DNASCANNER analyses known insertion sites of MGEs for the presence of signals that are based on a combination of local physical and chemical properties. ISF (insertion site finder) is a machine-learning tool that incorporates information derived from DNASCANNER. ISF permits classification of a given DNA sequence as a potential insertion site or not, using a support vector machine. We have studied the genomes of Homo sapiens, Mus musculus, Drosophila melanogaster and Entamoeba histolytica via a protocol whereby DNASCANNER is used to identify a common set of statistically important signals flanking the insertion sites in the various genomes. These are used in ISF for insertion site prediction, and the current accuracy of the tool is over 65%. We find similar signals at gene boundaries and splice sites. Together, these data are suggestive of a common insertion mechanism that operates in a variety of eukaryotes.

High AU content: a signature of upregulated miRNA in cardiac diseases.

MicroRNAs have been implicated for the regulation of gene expression. These miRNA are a class of single stranded non coding RNAs, formed from endogenous transcripts and measure typically about 19-25 nucleotides in length. They are important regulators of the various biological and metabolic functions taking place in humans. Many miRNAs show tissue specific expression. Human heart is a complex organ which during various diseased and developed conditions shows differential expression of miRNA. Here, we overview the recent findings on miRNA in cardiac diseases and report the presence of high AU content in differentially expressed miRNA in developed and diseased condition of heart as compared to all the miRNA present in the human. A total of 905 human miRNA sequences taken from miRBase were computationally analyzed. Trend analysis was performed to study the influence of positional frequency of the nucleotides. This study will help us in understanding the significance of AU rich elements in miRNA during the development of cardiac diseases.

Identification of insertion hot spots for non-LTR retrotransposons: computational and biochemical application to Entamoeba histolytica.

The genome of the human pathogen Entamoeba histolytica contains non-long terminal repeat (LTR) retrotransposons, the EhLINEs and EhSINEs, which lack targeted insertion. We investigated the importance of local DNA structure, and sequence preference of the element-encoded endonuclease (EN) in selecting target sites for retrotransposon insertion. Pre-insertion loci were tested computationally to detect unique features based on DNA structure, thermodynamic considerations and protein interaction measures. Target sites could readily be distinguished from other genomic sites based on these criteria. The contribution of the EhLINE1-encoded EN in target site selection was investigated biochemically. The sequence-specificity of the EN was tested in vitro with a variety of mutated substrates. It was possible to assign a consensus sequence, 5'-GCATT-3', which was efficiently nicked between A-T and T-T. The upstream G residue enhanced EN activity, possibly serving to limit retrotransposition in the A+T-rich E.histolytica genome. Mutated substrates with poor EN activity showed structural differences compared with normal substrates. Analysis of retrotransposon insertion sites from a variety of organisms showed that, in general, regions of favorable DNA structure were recognized for retrotransposition. A combination of favorable DNA structure and preferred EN nicking sequence in the vicinity of this structure may determine the genomic hotspots for retrotransposition.

The LINEs and SINEs of Entamoeba histolytica: comparative analysis and genomic distribution.

Autonomous non-long terminal repeat retrotransposons are commonly referred to as long interspersed elements (LINEs). Short non-autonomous elements that borrow the LINE machinery are called SINES. The Entamoeba histolytica genome contains three classes of LINEs and SINEs. Together the EhLINEs/SINEs account for about 6% of the genome. The recognizable functional domains in all three EhLINEs included reverse transcriptase and endonuclease. A novel feature was the presence of two types of members-some with a single long ORF (less frequent) and some with two ORFs (more frequent) in both EhLINE1 and 2. The two ORFs were generated by conserved changes leading to stop codon. Computational analysis of the immediate flanking sequences for each element showed that they inserted in AT-rich sequences, with a preponderance of Ts in the upstream site. The elements were very frequently located close to protein-coding genes and other EhLINEs/SINEs. The possible influence of these elements on expression of neighboring genes needs to be determined.