Computational exploration of novel antimicrobial modalities targeting fucose-binding lectins and ribosomes in Mycobacterium smegmatis using tRNA-encoded peptides.

tRNA-Encoded Peptides (tREPs), encoded by small open reading frames (smORFs) within tRNA genes, have recently emerged as a new class of functional peptides exhibiting antiparasitic activity. The discovery of tREPs has led to a re-evaluation of the role of tRNAs in biology and has expanded our understanding of the genetic code. This presents an immense, unexplored potential in the realm of tRNA-peptide interactions, paving the way for groundbreaking discoveries and innovative applications in various biological functions. This study explores the antimicrobial potential of tREPs against protein targets by employing a computational method that uses verified data sources and highly recognized predictive algorithms to provide a sorted list of likely antimicrobial peptides, which were then filtered for toxicity, cell permeability, allergenicity and half-life. These peptides were then docked with screened protein targets and computationally validated using molecular dynamics (MD) simulations for 150 ns and the binding free energy was estimated. The peptides Pep2 (VVLWRKPRVRKTG) and Pep6 (HRLRLRRRKPWW) exhibited good binding affinities of -110.5 +/- 2.5 and -129.0 +/- 3.9, respectively, with RMSD values of 0.4 and 0.25 nm against the fucose-binding lectin (7NEF) and the 30S ribosome of Mycobacterium smegmatis (5O5J) protein targets. The 7NEF-Pep2 and 5O5J-Pep6 complexes indicated higher negative binding free energies of -52.55 kcal/mol and -55.52 kcal/mol respectively, as calculated by Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA). Thus, the tREPs derived peptides designed as a part of this study, provide novel approaches for potential anti-bacterial therapeutic modalities.Communicated by Ramaswamy H. Sarma.

Computational Prediction of Potential Vaccine Candidates From tRNA Encoded peptides (tREP) Using a Bioinformatic Workflow and Molecular Dynamics Validations.

Transfer RNAs (tRNA) are non-coding RNAs. Encouraged by biological applications discovered for peptides derived from other non-coding genomic regions, we explore the possibility of deriving epitope-based vaccines from tRNA encoded peptides (tREP) in this study. Epitope-based vaccines have been identified as an effective strategy to mitigate safety and specificity concerns observed in vaccine development. In this study, we explore the potential of tREP as a source for epitope-based vaccines for virus pathogens. We present a computational workflow that uses verified data sources and community-validated predictive tools to produce a ranked list of plausible epitope-based vaccines starting from tRNA sequences. The top epitope, bound to the predicted HLA molecule, for the virus pathogen is computationally validated through 200 ns molecular dynamics (MD) simulations followed by binding free energy calculations. The simulation results indicate that two tRNA encoded epitope-based vaccines, RRHIDIVV and IMVRFSAE for Mamastrovirus 3 and Norovirus GII, respectively, are likely candidates. Peptides originating from tRNAs provide unexplored opportunities for vaccine design. Encouraged by our previous experimental study, which established the inhibitory properties of tREPs against infectious parasites, we have proposed a computationally validated set of peptides derived from tREPs as vaccines for viral pathogens.

In silico investigations and molecular insights for designing tRNA-encoded peptides as potential therapeutics for targeting over-expressed receptors in breast cancer.

tRNA- Encoded Peptides (tREPs) have recently been discovered as new functional peptides and hold promise as therapeutics for anti-parasitic applications. In this study, in silico investigations were conducted to design tRNA-encoded peptides with the potential to target over-expressed receptors in breast cancer cells. tRNA genes were translated into corresponding peptides (tREPs) using computational tools. The tREPs, which were predicted as anticancer peptides, were then screened for various ADMET properties. Molecular docking studies were conducted for three cancer target receptors, the Estrogen Receptor (ER), Peroxisome Proliferator-Activated Receptor (PPAR) and the Epidermal Growth Factor Receptor (EGFR). Based on the docking results, specific tREPs were screened and molecular dynamics simulations were performed, and the binding energies were further explored using MMPBSA calculations. The peptide Pep1 (DWIAWRHHNDIVSWLTCGPRFKSWS) and Pep2 (GFIAWWSRHLELAQTRFKSWWS) exhibited a good binding affinity against the Estrogen Receptor (ER) and the Peroxisome Proliferator-Activated Receptor Alpha (PPAR) cancer target. The Pep1-ER and Pep1-PPAR complex maintained an average of two hydrogen bonds throughout the simulation and demonstrated a higher negative binding free energy of -72.27 kcal/mol and -65.16 kcal/mol respectively, as calculated by MMPBSA. Therefore, the tREPs designed as anticancer peptides in this study provide novel approaches for potential anticancer therapeutic modalities.Communicated by Ramaswamy H. Sarma.

In silico based multi-epitope vaccine design against norovirus.

Norovirus (NoV) belongs to the Calciviridae family that causes diarrhoea, vomiting, and stomach pain in people who have acute gastroenteritis (AGE). Identifying multi-epitope dependent vaccines for single stranded positive sense viruses such as NoV has been a long due. Although efforts have been in place to look into the candidate epitopes, understanding molecular mimicry and finding new epitopes for inducing immune responses against the T/B-cells which play an important role for the cell-mediated and humoral immunity was not dealt with in great detail. The current study focuses on identifying new epitopes from various databases that were filtered for antigenicity, allergenicity, and toxicity. The adjuvant ß-defensin along with different linkers were used for vaccine construction. Further, the binding relationship between the vaccine construct and toll-like immune receptor (TLR3) complex was determined using a molecular docking analysis, followed by molecular dynamics simulation of 100 ns. The vaccine candidate developed expresses good solubility with a score of 0.530, Z-score of -4.39 and molecular docking score of -140.4 ± 12.1. The MD trajectories reveal that there is a stability between TLR3 and the developed vaccine candidate with an average of 0.91 nm RMSD value and also the system highest occupancy H-bond formed between GLU127 of TLR3 and TYR10 of vaccine candidate (61.55%). Four more H-bonds exist with an occupancy of more than 32% between TLR3 and the vaccine candidates which makes it stable. Thus, the multi-epitope based vaccine developed in the present study forms the basis for further experimental investigations to develop a potentially good vaccine against NoV.Communicated by Ramaswamy H. Sarma.

ProAll-D: protein allergen detection using long short term memory - a deep learning approach.

Background: An allergic reaction is the immune system's overreacting to a previously encountered, typically benign molecule, frequently a protein. Allergy reactions can result in rashes, itching, mucous membrane swelling, asthma, coughing, and other bizarre symptoms. To anticipate allergies, a wide range of principles and methods have been applied in bioinformatics. The sequence similarity approach's positive predictive value is very low and ineffective for methods based on FAO/WHO criteria, making it difficult to predict possible allergens. Method: This work advocated the use of a deep learning model LSTM (Long Short-Term Memory) to overcome the limitations of traditional approaches and machine learning lower performance models in predicting the allergenicity of dietary proteins. A total of 2,427 allergens and 2,427 non-allergens, from a variety of sources, including the Central Science Laboratory and the NCBI are used. The data was divided 80:20 for training and testing purposes. These techniques have all been implemented in Python. To describe the protein sequences of allergens and non-allergens, five E-descriptors were used. E1 (hydrophilic character of peptides), E2 (length), E3(propensity to form helices), E4(abundance and dispersion), and E5 (propensity of beta strands) are used to make the variable-length protein sequence to uniform length using ACC transformation. A total of eight machine learning techniques have been taken into consideration. Results: The Gaussian Naive Bayes as accuracy of 64.14 %, Radius Neighbour's Classifier with 49.2 %, Bagging Classifier was 85.8 %, ADA Boost was 76.9 %, Linear Discriminant Analysis has 76.13 %, Quadratic Discriminant Analysis was 84.2 %, Extra Tree Classifier was 90%, and LSTM is 91.5 %. Conclusion: As the LSTM, has an AUC value of 91.5 % is regarded best in predicting allergens. A web server called ProAll-D has been created that successfully identifies novel allergens using the LSTM approach. Users can use the link https://doi.org/10.17632/tjmt97xpjf.1 to access the ProAll-D server and data.