Computational formulation of a multiepitope vaccine unveils an exceptional prophylactic candidate against Merkel cell polyomavirus

Merkel cell carcinoma (MCC) is a rare neuroendocrine skin malignancy caused by human Merkel cell polyomavirus (MCV), leading to the most aggressive skin cancer in humans. MCV has been identified in approximately 43%-100% of MCC cases, contributing to the highly aggressive nature of primary cutaneous carcinoma and leading to a notable mortality rate. Currently, no existing vaccines or drug candidates have shown efficacy in addressing the ailment caused by this specific pathogen. Therefore, this study aimed to design a novel multiepitope vaccine candidate against the virus using integrated immunoinformatics and vaccinomics approaches. Initially, the highest antigenic, immunogenic, and non-allergenic epitopes of cytotoxic T lymphocytes, helper T lymphocytes, and linear B lymphocytes corresponding to the virus whole protein sequences were identified and retrieved for vaccine construction. Subsequently, the selected epitopes were linked with appropriate linkers and added an adjuvant in front of the construct to enhance the immunogenicity of the vaccine candidates. Additionally, molecular docking and dynamics simulations identified strong and stable binding interactions between vaccine candidates and human Toll-like receptor 4. Furthermore, computer-aided immune simulation found the real-life-like immune response of vaccine candidates upon administration to the human body. Finally, codon optimization was conducted on the vaccine candidates to facilitate the in silico cloning of the vaccine into the pET28+(a) cloning vector. In conclusion, the vaccine candidate developed in this study is anticipated to augment the immune response in humans and effectively combat the virus. Nevertheless, it is imperative to conduct in vitro and in vivo assays to evaluate the efficacy of these vaccine candidates thoroughly. These evaluations will provide critical insights into the vaccine's effectiveness and potential for further development.

Thermostability modification of β-mannanase from Aspergillus niger via flexibility modification engineering

Introduction

β-Mannanases can hydrolyze mannans, which are widely available in nature. However, the optimum temperature of most β-mannanases is too low to be directly utilized in industry.

Methods

To further improve the thermostability of Anman (mannanase from Aspergillus niger CBS513.88), B-factor and Gibbs unfolding free energy change were used to modify the flexible of Anman, and then combined with multiple sequence alignment and consensus mutation to generate an excellent mutant. At last, we analyzed the intermolecular forces between Anman and the mutant by molecular dynamics simulation.

Results

The thermostability of combined mutant mut5 (E15C/S65P/A84P/A195P/T298P) was increased by 70% than the wild-type Amman at 70°C, and the melting temperature (Tm) and half-life (t1/2) values were increased by 2°C and 7.8-folds, respectively. Molecular dynamics simulation showed reduced flexibility and additional chemical bonds in the region near the mutation site.

Discussion

These results indicate that we obtained a Anman mutant that is more suitable for industrial application, and they also confirm that a combination of rational and semi-rational techniques is helpful for screening mutant sites.

In silico Exploration of Inhibitors for SARS-CoV-2's Papain-Like Protease

Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with very limited treatments so far. Demonstrated with good druggability, two major proteases of SARS-CoV-2, namely main protease (Mpro) and papain-like protease (PLpro) that are essential for viral maturation, have become the targets for many newly designed inhibitors. Unlike Mpro that has been heavily investigated, PLpro is not well-studied so far. Here, we carried out the in silico high-throughput screening of all FDA-approved drugs via the flexible docking simulation for potential inhibitors of PLpro and explored the molecular mechanism of binding between a known inhibitor rac5c and PLpro. Our results, from molecular dynamics simulation, show that the chances of drug repurposing for PLpro might be low. On the other hand, our long (about 450 ns) MD simulation confirms that rac5c can be bound stably inside the substrate-binding site of PLpro and unveils the molecular mechanism of binding for the rac5c-PLpro complex. The latter may help perform further structural optimization and design potent leads for inhibiting PLpro.

Ultrahigh Adhesion Force Between Silica-Binding Peptide SB7 and Glass Substrate Studied by Single-Molecule Force Spectroscopy and Molecular Dynamic Simulation

Many proteins and peptides have been identified to effectively and specifically bind on certain surfaces such as silica, polystyrene and titanium dioxide. It is of great interest, in many areas such as enzyme immobilization, surface functionalization and nanotechnology, to understand how these proteins/peptides bind to solid surfaces. Here we use single-molecule force spectroscopy (SMFS) based on atomic force microscopy to directly measure the adhesion force between a silica-binding peptide SB7 and glass surface at single molecule level. SMFS results show that the adhesion force of a single SB7 detaching from the glass surface distributes in two populations at ~220 pN and 610 pN, which is higher than the unfolding forces of most mechanically stable proteins and the unbinding forces of most stable protein-protein interactions. Molecular dynamics simulation reveals that the electrostatic interactions between positively charged arginine residues and the silica surface dominates the binding of SB7 on silica. Our study provides experimental evidence and molecular mechanism at the single-molecule level for the SB7-based immobilization of proteins on silica-based surface, which is able to withstand high mechanical forces, making it an ideal fusion tag for silica surface immobilization or peptide-base adhesive materials.