Computational evaluation of anticipated PE_PGRS39 protein involvement in host-pathogen interplay and its integration into vaccine development

Decoding the structural integrity and multifunctional role of Era protein in the survival of Mycobacterium tuberculosis H37Rv

Era, a widely known GTP binding protein found in many organisms including prokaryotes and eukaryotes and plays a significant role in many fundamental cellular processes like cell growth, differentiation and signaling. In Mycobacterium tuberculosis (Mtb) H37Rv, Era protein had been proved as a GTPase protein but its structural and functional insights are still lacking. Through comparative analysis, structural modeling, docking and using various bioinformatic tools, a detailed investigation of Era was carried out to deduce the structure, function and residues involved in the activity of the protein. Intriguingly, docking results revealed high binding affinity of Era not only with GTP but also with ATP. Myristoylation modifications and phosphorylations on Era were predicted to possibly aid in regulating Era activity and localization; and also the role of Era in translation regulation was foreseen by showing its association with 16s rRNA. Moreover, point mutation of Era residues revealed the effect of W288G and K19G in highly destabilizing the protein structure and activity. Additionally, Era protein was docked with 25 GTPase/ATPase inhibitors, where, Dynasore inhibitor showed the highest affinity for the protein's GTP binding sites and can be used for further drug trials to inhibit growth of mycobacteria.Communicated by Ramaswamy H. Sarma.

Enhancement of novel Endo-polygalacturonase expression in Rhodotorula mucilaginosa PY18: insights from mutagenesis and molecular docking

Pectinase is a particular type of enzyme that can break down pectin compounds and is extensively utilised in the agricultural field. In this study, twenty yeast isolates were isolated and assayed for pectinase activity. Molecular identification by PCR amplification and sequencing of internal transcribed spacer (ITS) regions of isolate no. 18 had the highest pectinase activity of 46.35 U/mg, was identified as Rhodotorula mucilaginosa PY18, and was submitted under accession no. (OM275426) in NCBI. Rhodotorula mucilaginosa PY18 was further enhanced through sequential mutagenesis, resulting in a mutant designated as Rhodotorula mucilaginosa E54 with a specific activity of 114.2 U/mg. Using Response Surface Methodology (RSM), the best culture conditions for the pectinase-producing yeast mutant Rhodotorula mucilaginosa E54 were pH 5, 72-h incubation, 2.5% xylose, and 2.5% malt extract, with a pectinase-specific activity of 156.55 U/mg. Then, the obtained sequences of the endo-polygalacturonase PGI gene from Rhodotorula mucilaginosa PY18 and mutant Rhodotorula mucilaginosa E54 were isolated for the first time, sequenced, and submitted to NCBI accession numbers OQ283005 and OQ283006, respectively. The modelled 3D structure of the endo-PGI enzyme (485 residues) was validated using Ramachandran's plot, which showed 87.71, 85.56, and 91.57% in the most favourable region for template Rhodotorula mucilaginosa KR, strain Rhodotorula mucilaginosa PY18, and mutant Rhodotorula mucilaginosa E54, respectively. In molecular docking studies, the results of template Rhodotorula mucilaginosa KR endo-PG1 showed an interaction with an affinity score of - 6.0, - 5.9, and - 5.6 kcal/mol for active sites 1, 2, and 3, respectively. Rhodotorula mucilaginosa PY18 endo-PG1 showed an interaction affinity with a score of - 5.8, - 6.0, and - 5.0 kcal/mol for active sites 1, 2, and 3, respectively. Mutant Rhodotorula mucilaginosa E54 endo-PG1 showed an interaction affinity of - 5.6, - 5.5, - 5.5 and - 5.4 kcal/mol for active sites 1, 2, and 3, respectively. The endo-PGI genes of both the yeast strain Rhodotorula mucilaginosa PY18 and mutant Rhodotorula mucilaginosa E54 were successfully cloned and expressed in E. coli DH5α, showing significantly higher endo-PG1 activity, which recorded 94.57 and 153.10 U/mg for recombinant Rhodotorula mucilaginosa pGEM-PGI-PY18 and recombinant mutant Rhotorula pGEM-PGI-E54, respectively.

Structure prediction, docking studies and molecular cloning of novel Pichia kudriavzevii YK46 metalloprotease (MetPr) for improvement of feather waste biodegradation

This study addresses the environmental risks associated with the accumulation of keratin waste from poultry, which is resistant to conventional protein degradation methods. To tackle this issue, microbial keratinases have emerged as promising tools for transforming resilient keratin materials into valuable products. We focus on the Metalloprotease (MetPr) gene isolated from novel Pichia kudriavzevii YK46, sequenced, and deposited in the NCBI GenBank database with the accession number OQ511281. The MetPr gene encodes a protein consisting of 557 amino acids and demonstrates a keratinase activity of 164.04 U/ml. The 3D structure of the protein was validated using Ramachandran's plot, revealing that 93% and 97.26% of the 557 residues were situated within the most favoured region for the MetPr proteins of template Pichia kudriavzevii strain 129 and Pichia kudriavzevii YK46, respectively. Computational analyses were employed to determine the binding affinities between the deduced protein and beta keratin. Molecular docking studies elucidated the optimal binding affinities between the metalloprotease (MetPr) and beta-keratin, yielding values of - 260.75 kcal/mol and - 257.02 kcal/mol for the template strains Pichia kudriavzevii strain 129 and Pichia kudriavzevii YK46, respectively. Subsequent molecular cloning and expression of the MetPr gene in E. coli DH5α led to a significantly higher keratinase activity of 281 ± 12.34 U/ml. These findings provide valuable insights into the potential of the MetPr gene and its encoded protein for keratin waste biotransformation, with implications for addressing environmental concerns related to keratinous waste accumulation.

Development of peptide vaccine candidate using highly antigenic PE-PGRS family proteins to stimulate the host immune response against Mycobacterium tuberculosis H37Rv: an immuno-informatics approach

Tuberculosis (TB) is a fast spreading; transmissible disease caused by the Mycobacterium tuberculosis (M. tuberculosis). M. tuberculosis has a high death rate in its endemic regions due to a lack of appropriate treatment and preventative measures. We have used a vaccinomics strategy to create an effective multi-epitope vaccine against M. tuberculosis. The antigenic proteins with the highest antigenicity were utilised to predict cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), and linear B-lymphocyte (LBL) epitopes. CTL and HTL epitopes were covered in 99.97% of the population. Seven epitopes each of CTL, HTL, and LBL were ultimately selected and utilised to develop a multi-epitope vaccine. A vaccine design was developed by combining these epitopes with suitable linkers and LprG adjuvant. The vaccine chimera was revealed to be highly immunogenic, non-allergenic, and non-toxic. To ensure a better expression within the Escherichia coli K12 (E. coli K12) host system, codon adaptation and in silico cloning were accomplished. Following that, various validation studies were conducted, including molecular docking, molecular dynamics simulation, and immunological simulation, all of which indicated that the designed vaccine would be stable in the biological environment and effective against M. tuberculosis infection. The immune simulation revealed higher levels of T-cell and B-cell activity, which corresponded to the actual immune response. Exposure simulations were repeated several times, resulting in increased clonal selection and faster antigen clearance. These results suggest that, if proposed vaccine chimera would test both in-vitro and in-vivo, it could be a viable treatment and preventive strategy for TB.Communicated by Ramaswamy H. Sarma.

Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach

Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.