Browsing by Author "Jaiswal, Mohini"

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Bioinformatics tools for epitope prediction
(Springer Nature Publishing AG, 2020) Jaiswal, Mohini; Zahra, Shafaque; Kumar, Shailesh
Immunological protection is conferred by immune cells, i.e., B and T cells, which can efficiently develop pathogen-specific memory and thus involved in adaptive immunity. More specifically, these immune cells can recognize a specific portion of their respective antigens termed as epitopes which possess their own significant values. There is a noble reason to identify the antigenic region of an antigen as it is having a great empirical cause, which includes exploration of disease etiology, the advancement of diagnosis assays, immune monitoring, and to design epitope-based vaccines. It requires detection and prediction of epitopes which is a considerable concern in the preparation of a peptide-based vaccine that is the centralized issue of immunoinformatics. Experimental screening is involved for large arrays of probable epitope candidates; thereby it is pricey and tedious. There is a requirement of more-advanced immunoinformatics tools as a prodigious amount of information has accumulated because of the onset of next-generation sequencing approaches for collection, analysis, and interpretation of data. Further, development of in silico epitope prediction methods has substantially reduced the difficulties related to epitope mapping by shortening potential epitope candidates list for experimental testing. These software tools have diverse applications in diagnosis of infectious diseases and allergies, understanding immune system function, vaccine designing, and prognosis of cancer. This chapter presents an outlook on how these tools are capable to predict epitopes of various antigens.
PlantPepDB: A manually curated plant peptide database
(Springer Nature Publishing AG, 2020) Das, Durdam; Jaiswal, Mohini; Khan, Fatima Nazish; Ahamad, Shahzaib; Kumar, Shailesh
Plants produce an array of peptides as part of their innate defense mechanism against pathogens. The potential use of these peptides for various therapeutic purposes is increasing per diem. In order to excel in this research, the community requires web repositories that provide reliable and accurate information about these phyto-peptides. This work is an attempt to bridge the gaps in plant-based peptide research. PlantPepDB is a manually curated database that consists of 3848 plant-derived peptides among which 2821 are experimentally validated at the protein level, 458 have experimental evidence at the transcript level, 530 are predicted and only 39 peptides are inferred from homology. Incorporation of physicochemical properties and tertiary structure into PlantPepDB will help the users to study the therapeutic potential of a peptide, thus, debuts as a powerful resource for therapeutic research. Different options like Simple, Advanced, PhysicoChem and AA composition search along with browsing utilities are provided in the database for the users to execute dynamic search and retrieve the desired data. Interestingly, many peptides that were considered to possess only a single property were found to exhibit multiple properties after careful curation and merging the duplicate data that was collected from published literature and already available databases. Overall, PlantPepDB is the first database comprising detailed analysis and comprehensive information of phyto-peptides from a broad functional range which will be useful for peptide-based applied research. PlantPepDB is freely available at http://www.nipgr.ac.in/PlantPepDB/.
PTPAMP: prediction tool for plant-derived antimicrobial peptides
(Springer Nature Publishing AG, 2023) Jaiswal, Mohini; Singh, Ajeet; Kumar, Shailesh
The emergence of antimicrobial peptides (AMPs) as a potential alternative to conventional antibiotics has led to the development of efficient computational methods for predicting AMPs. Among all organisms, the presence of multiple genes encoding AMPs in plants demands the development of a plant-based prediction tool. To this end, we developed models based on multiple peptide features like amino acid composition, dipeptide composition, and physicochemical attributes for predicting plant-derived AMPs. The selected compositional models are integrated into a web server termed PTPAMP. The designed web server is capable of classifying a query peptide sequence into four functional activities, i.e., antimicrobial (AMP), antibacterial (ABP), antifungal (AFP), and antiviral (AVP). Our models achieved an average area under the curve of 0.95, 0.91, 0.85, and 0.88 for AMP, ABP, AFP, and AVP, respectively, on benchmark datasets, which were ~ 6.75% higher than the state-of-the-art methods. Moreover, our analysis indicates the abundance of cysteine residues in plant-derived AMPs and the distribution of other residues like G, S, K, and R, which differ as per the peptide structural family. Finally, we have developed a user-friendly web server, available at the URL: http://www.nipgr.ac.in/PTPAMP/. We expect the substantial input of this predictor for high-throughput identification of plant-derived AMPs followed by additional insights into their functions.
smAMPsTK: a toolkit to unravel the smORFome encoding AMPs of plant species
(Taylor & Francis Group, 2024) Jaiswal, Mohini; Kumar, Shailesh
The pervasive repertoire of plant molecules with the potential to serve as a substitute for conventional antibiotics has led to obtaining better insights into plant-derived antimicrobial peptides (AMPs). The massive distribution of Small Open Reading Frames (smORFs) throughout eukaryotic genomes with proven extensive biological functions reflects their practicality as antimicrobials. Here, we have developed a pipeline named smAMPsTK to unveil the underlying hidden smORFs encoding AMPs for plant species. By applying this pipeline, we have elicited AMPs of various functional activity of lengths ranging from 5 to 100 aa by employing publicly available transcriptome data of five different angiosperms. Later, we studied the coding potential of AMPs-smORFs, the inclusion of diverse translation initiation start codons, and amino acid frequency. Codon usage study signifies no such codon usage biases for smORFs encoding AMPs. Majorly three start codons are prominent in generating AMPs. The evolutionary and conservational study proclaimed the widespread distribution of AMPs encoding genes throughout the plant kingdom. Domain analysis revealed that nearly all AMPs have chitin-binding ability, establishing their role as antifungal agents. The current study includes a developed methodology to characterize smORFs encoding AMPs, and their implications as antimicrobial, antibacterial, antifungal, or antiviral provided by SVM score and prediction status calculated by machine learning-based prediction models. The pipeline, complete package, and the results derived for five angiosperms are freely available at https://github.com/skbinfo/smAMPsTK.