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Aspatwar, A.

Publications and source records attributed to Aspatwar, A..

10 recordsLinked to original sources

APV9WR: An Integrated Web Resource of Alpha papillomavirus 9 for Genomics, Proteomics, Phylogenetic and Therapeutic Analysis

PurposeAlphapapillomavirus 9 is a virus belonging to the Papillomaviridae family. It has a close genetic relationship with high-risk HPV-16 and other HPV strains such as HPV-31, HPV-52, HPV-35, HPV-58, HPV-67, and HPV-35. This virus is responsible for causing warts and malignant tumors and is responsible for about 75% of cervical malignancies and pre-cancerous lesions worldwide. As a result, it requires specialized research and attention. Our goal is to create a comprehensive resource that can assist researchers and scientific groups in their work. Material and MethodologyA total of 1230 full genome sequences and 9140 protein sequences were obtained from GenBank and NCBI Virus, respectively. Further Phylogenetic Analysis, Codon usage and context analysis, CpG islands analysis, Glycosylation sites, Diagnostic Primers, B cell Epitopes and MHC I and MHC II binders were identified and analyzed using relevant Bioinformatics tools and Python program ResultsAPV9WR is a web resource that was developed after analysis. Our data indicate that HPV 35 and HPV 38 have the most genomic diversity. From codon usage analysis, it was observed that AAA, AUU, UAU, UGU, and UUU are the most used codons, while ACG, CCG, CGA, CGG, CGU, GCG, and UCG are some of the unusual codons in APV9 nucleotide sequence with accession id - LC626346.1. We found 4714 CpG island locations in 1230 complete nucleotide sequences of APV9, and only 663 CpG island locations were unique. Further N-linked glycosylation, O-linked glycosylation, diagnostic primers, Potential B-cell epitopes and MHC I and MHC II binders were also analyzed and tabulated. ConclusionWe have consolidated basic information about the virus, such as entire genomic sequences and proteins. It primarily comprises a wide range of studies and outcomes, including genome alignment, phylogenetic inferences, codon context and usage bias, and important CpG island statistics. Furthermore, primers for molecular diagnostics were identified, and glycosylation sites were located and investigated. Most significantly, potential therapeutic elements such as vaccine epitopes and obtaining potential information about them were investigated. Our collective effort on this tool is meant to serve the greater good of the research community for therapeutic intervention for Alphapapillomavirus 9. Using this link https://apv9nsut.web.app will take you to the web app.

bioinformatics↗

Physiological modeling and biomechanical insights of cytokine modulation in COVID-19 and pulmonary fibrosis through graph-based approach

Withdrawal StatementThe authors have withdrawn their manuscript owing to the inability to complete the revisions or follow up on the manuscript at this time, due to personal circumstances. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

bioinformatics↗

Elucidating the molecular mechanism of phytochemicals against Parkinson's disease through an integrated systems biology and molecular modeling approach

Withdrawal StatementThe authors have withdrawn their manuscript owing to the inability to complete the revisions or follow up on the manuscript at this time, due to personal circumstances. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

bioinformatics↗

Mechanistic modeling of Mycobacterium tuberculosis β-carbonic anhydrase inhibitors using integrated systems biology and the QSAR approach

Withdrawal StatementThe authors have withdrawn their manuscript owing to the inability to complete the revisions or follow up on the manuscript at this time, due to personal circumstances. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

bioinformatics↗

Elucidating Molecular Mechanism and Chemical Space of Chalcones through Knowledge Graph and Machine Learning: A Combined Bio-Cheminformatic Pipeline

We developed a bio-cheminformatics method, exploring disease inhibition mechanisms using machine learning-enhanced quantitative structure-activity relationship (ML-QSAR) models and knowledge-driven neural networks. ML-QSAR models were developed using molecular fingerprint descriptors and the Random Forest algorithm to explore the chemical spaces of Chalcones inhibitors against diverse disease properties, including antifungal, anti-inflammatory, anticancer, antimicrobial, and antiviral effects. We generated and validated robust machine learning-based bioactivity prediction models ((https://ashspred.streamlit.app/) for the top genes. These models underwent ROC and applicability domain analysis, followed by molecular docking studies to elucidate the molecular mechanisms of the molecules. Through comprehensive neural network analysis, crucial genes such as AKT1, HSP90A1, SRC, and STAT3 were identified. The PubChem fingerprint-based model revealed key descriptors: PubchemFP521 for AKT1, PubchemFP180 for SRC, PubchemFP633 for HSP90, and PubchemFP145 and PubchemFP338 for STAT3, consistently contributing to bioactivity across targets. Notably, chalcone derivatives demonstrated significant bioactivity against target genes, with compound RA1 displaying a predictive pIC50 value of 5.76 against HSP90A and strong binding affinities across other targets. Compounds RA5 to RA7 also exhibited high binding affinity scores comparable to or exceeding existing drugs. These findings emphasize the importance of knowledge-based neural network-based research for developing effective drugs against diverse disease properties. These interactions warrant further in vitro and in vivo investigations to elucidate their potential in rational drug design. The presented models provide valuable insights for inhibitor design and hold promise for drug development. Future research will prioritize investigating these molecules for mycobacterium tuberculosis, enhancing the comprehension of effectiveness in addressing infectious diseases.

bioinformatics↗

Molecular Insights into Zea mays Active Phytochemicals for Diabetes and Inflammation Treatment: A Web App-Based Machine Learning and Network Pharmacology Approach

Withdrawal StatementThe authors have withdrawn their manuscript owing to the inability to complete the revisions or follow up on the manuscript at this time, due to personal circumstances. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

bioinformatics↗

Deciphering the Biochemical and Physiological Mechanisms of Mycobacterium Tuberculosis β-Carbonic Anhydrase 3 and Its Modulators through Mechanistic QSAR Model and Integrated Systems Biology Approaches

Withdrawal StatementThe authors have withdrawn their manuscript owing to the inability to complete the revisions or follow up on the manuscript at this time, due to personal circumstances. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

bioinformatics↗

Multimolecular feature-based machine learning: system biology enhanced RF-QSAR modeling for the efficient prediction of the inhibitory potential of diverse SARS CoV-2 3CL Protease inhibitors

Withdrawal StatementThe authors have withdrawn their manuscript owing to the inability to complete the revisions or follow up on the manuscript at this time, due to personal circumstances. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

bioinformatics↗

Neural Network and Random Forest Algorithms as Catalysts in QSAR/QSAAR Modeling: Targeting β-Carbonic Anhydrase for Antituberculosis Drug Design

Withdrawal StatementThe authors have withdrawn their manuscript owing to the inability to complete the revisions or follow up on the manuscript at this time, due to personal circumstances. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

bioinformatics↗