Biology subjects

Periwal, N.

Publications and source records attributed to Periwal, N..

5 recordsLinked to original sources

bioRxiv2025

Effective prediction of IL-17 inducing peptides using hybrid approach: iIL17pred

BackgroundInterleukin 17 (IL-17) plays a crucial role in regulating the immune system and is associated with numerous diseases. Modulating IL-17 levels has demonstrated potential in mitigating disease symptoms, positioning it as a compelling target for drug development. Therefore, identifying and characterizing novel drug molecules capable of influencing IL-17 levels is critical. Recent advances in therapeutic peptides underscore their promise as attractive drug candidates, inspiring the development of IL-17 modulating peptides. ResultsThis study aimed to enhance existing methods for efficiently classifying IL-17 inducing peptides. Positive and negative datasets were obtained from the Immune Epitope Database, and peptide features were extracted using the pfeature algorithm. A three-stage hybrid approach combining BLAST, MERCI, and machine learning techniques was employed to accurately classify IL-17-inducing peptides. ConclusionsExtensive benchmarking experiments revealed that our proposed method outperforms existing techniques i.e IL17eScan, across key performance metrics, including sensitivity, accuracy, and area under the curve-receiver operating characteristics (AUC-ROC). Our algorithm achieved an accuracy of 88.08% and a Matthews Correlation Coefficient (MCC) of 0.68 on an external dataset, significantly surpassing the accuracy of 78.57% and MCC of 0.57 achieved by existing methods under comparable conditions. This demonstrates a substantial improvement in IL-17 peptide classification. The results are accessible via a user-friendly web server (http://www.soodlab.com/iil17pred/). These findings hold significant potential for predicting IL-17-inducing peptides, which can be further validated experimentally.

bioinformatics↗

bioRxiv2022

Human blood plasma proteins modeling and binding affinities with Δ9-tetrahydrocannabinol active metabolites: In silico approach

Tetrahydrocannabinol (THC) is a key psychotropic constituent of cannabis sativa. It is also known as {Delta}9-tetrahydrocannabinol ({Delta}9-THC). Previous study suggested that owing to its high lipophilicity, it piles up in adipose tissue and it is disseminated into blood stream for prolonged time. Research suggests that numerous diseases such as multiple sclerosis, neurodegenerative disorders, epilepsy, schizophrenia, osteoporosis, cancer, glaucoma and cardiovascular disorders can be treated using this substance. However, apart from having therapeutic potential, many studies have reported detrimental outcomes along with addiction of {Delta}9-THC for short-term and long-term consumption. Thus, in this study, we determined the binding affinities of {Delta}9-THC and its two active metabolites, 11-Hydroxy-{Delta}9-tetrahydrocannabinol (11-OH-{Delta}9-THC) and 8beta,11-dihydroxy-{Delta}9-tetrahydrocannabinol (8{beta},11-diOH-{Delta}9-THC) with 401 human blood plasma proteins using molecular docking analysis. Results show that {Delta}9-THC has greater binding potential with plasma proteins as compared to other two metabolites. Overall, ADGRE5, ALB, APOA5, APOD, CP, PON1 and PON3 proteins showed the highest binding affinities with three cannabis metabolites.

bioinformatics↗

bioRxiv2022

Time series analysis of SARS-CoV-2 genomes and correlations among highly prevalent mutations

The efforts of the scientific community to tame the recent SARS-CoV-2 pandemic seems to have been diluted by the emergence of new viral strains. Therefore, it becomes imperative to study and understand the effect of mutations on viral evolution, fitness and pathogenesis. In this regard, we performed a time-series analysis on 59541 SARS-CoV-2 genomic sequences from around the world. These 59541 genomes were grouped according to the months (January 2020-March 2021) based on the collection date. Meta-analysis of this data led us to identify highly significant mutations in viral genomes. Correlation and Hierarchical Clustering of the highly significant mutations led us to the identification of sixteen mutation pairs that were correlated with each other and were present in >30% of the genomes under study. Among these mutation pairs, some of the mutations have been shown to contribute towards the viral replication and fitness suggesting the possible role of other unexplored mutations in viral evolution and pathogenesis. Additionally, we employed various computational tools to investigate the effects of T85I, P323L, and Q57H mutations in Non-structural protein 2 (Nsp2), RNA-dependent RNA polymerase (RdRp) and Open reading frame 3a (ORF3a) respectively. Results show that T85I in Nsp2 and Q57H in ORF3a mutations are deleterious and destabilize the parent protein whereas P323L in RdRp is neutral and has a stabilizing effect. The normalized linear mutual information (nLMI) calculations revealed the significant residue correlation in Nsp2 and ORF3a in contrast to reduce correlation in RdRp protein.

bioinformatics↗

bioRxiv2021

A novel binary k-mer approach for classification of coding and non-coding RNAs across diverse species

Classification among coding (CDS) and non-coding RNA (ncRNA) sequences is a challenge and several machine learning models have been developed for the same. Since the frequency of curated coding sequences is many-folds as compared to that of the ncRNAs, we devised a novel approach to work with the complete datasets from fifteen diverse species. In our proposed novel binary approach, we replaced all the As and Ts with 0s and Gs and Cs with 1s to obtain a binary form of coding and ncRNAs. The k-mer analysis of these binary sequences revealed that the frequency of binary patterns among the coding and ncRNAs can be used as features to distinguish among them. Using insights from these distinguishing frequencies, we used k-nearest neighbour classifier to classify among them. Our strategy is not only time-efficient but leads to significantly increased performance metrics including Matthews correlation coefficient (MCC) for some species like P. paniscus, M. mulatta, M. lucifugus, G. gallus, C. japonica, C. abingdonii, A. carolinensis, D. melanogaster and C. elegans when compared with the conventional ATGC approach. Additionally, we also show that the values of MCC obtained for diverse species tested on the model based on H. sapiens correlated with the geological evolutionary timeline thereby further strengthening our approach. Therefore, we propose that CDS and ncRNAs can be efficiently classified using "2-character" frequency as compared to "4-character" frequency of ATGC approach. Thus, our highly efficient binary approach can replace the more complex ATGC approach successfully.

genomics↗

bioRxiv2020

In-silico analysis of SARS-CoV-2 genomes: Insights from SARS encoded non-coding RNAs

Recently a novel coronavirus (SARS-CoV-2) emerged from Wuhan, China and has infected more than 571000 people leading to more than 26000 deaths. Since SARS-CoV-2 genome sequences show similarity with those of SARS, we sought to analyze all the available SARS-CoV-2 genomes based on the insights obtained from SARS genome specifically focusing on non-coding RNAs. Here, results are presented from the dual approach i.e identifying host encoded miRNAs that might regulate viral pathogenesis as well as identifying viral encoded miRNAs that might regulate host cell signaling pathways and aid in viral pathogenesis. Analysis utilizing first approach resulted in the identification of 10 host encoded miRNAs that could target the genome of both the viruses (SARS-CoV-2 and SARS reference genome). Interestingly our analysis revealed that there is significantly higher number of host miRNAs that could target SARS-CoV-2 genome as compared to the SARS reference genome. Results from second approach involving SARS-CoV-2 and SARS reference genome identified a set of virus encoded miRNAs which might regulate host signaling pathways. Our analysis further identified a similar "GA" rich motif in SARS-CoV-2 genome that was shown to play a vital role in lung pathogenesis during severe SARS infections. Hence, we successfully identified human and virus encoded miRNAs that might regulate pathogenesis of both these coronaviruses and the fact that more number of host miRNAs could target SARS-CoV-2 genomes possibly reveal as to why this virus follows mild pathogenesis in healthy individuals. We identified non-coding sequences in SARS-CoV-2 genomes that were earlier reported to contribute towards SARS pathology. The study provides insights into the overlapping sequences among these viruses for their effective inhibition as well as identifying new drug targets that could be used for development of new antivirals.

microbiology↗