Designing multi-epitope based peptide vaccine targeting spike protein SARS-CoV-2 B1.1.529 (Omicron) variant using computational approaches.

Since the SARS-CoV-2 outbreak in 2019, millions of people have been infected with the virus, and due to its high human-to-human transmission rate, there is a need for a vaccine to protect people. Although some vaccines are in use, due to the high mutation rate in the SARS-CoV-2 multiple variants, the current vaccines may not be sufficient to immunize people against new variant threats. One of the emerging variants of concern is B1.1.529 (Omicron), which carries ~30 mutations in the Spike protein of SARS-CoV-2 is predicted to evade antibodies recognition even from vaccinated people. We used a structure-based approach along with an epitope prediction server to develop a Multi-Epitope based Subunit Vaccine (MESV) involving SARS-CoV-2 B1.1.529 variant spike glycoprotein. The predicted epitope with better antigenicity and non-toxicity were used for designing and predicting vaccine construct features and structure models. The MESV construct In-silico cloning in pET28a expression vector predicted the construct to be highly translational. The proposed MESV vaccine construct was also subjected to immune simulation prediction and was found to be highly antigenic and elicit a cell-mediated immune response. The proposed MESV in the present study has the potential to be evaluated further for vaccine production against the newly identified B1.1.529 (Omicron) variant of concern.

The structure-based cancer-related single amino acid variation prediction

Single amino acid variation (SAV) is an amino acid substitution of the protein sequence that can potentially influence the entire protein structure or function, as well as its binding affinity. Protein destabilization is related to diseases, including several cancers, although using traditional experiments to clarify the relationship between SAVs and cancer uses much time and resources. Some SAV prediction methods use computational approaches, with most predicting SAV-induced changes in protein stability. In this investigation, all SAV characteristics generated from protein sequences, structures and the microenvironment were converted into feature vectors and fed into an integrated predicting system using a support vector machine and genetic algorithm. Critical features were used to estimate the relationship between their properties and cancers caused by SAVs. We describe how we developed a prediction system based on protein sequences and structure that is capable of distinguishing if the SAV is related to cancer or not. The five-fold cross-validation performance of our system is 89.73% for the accuracy, 0.74 for the Matthews correlation coefficient, and 0.81 for the F1 score. We have built an online prediction server, CanSavPre (http://bioinfo.cmu.edu.tw/CanSavPre/), which is expected to become a useful, practical tool for cancer research and precision medicine.

In Silico Activity Identification of Cyclo Peptide Alkaloids from Zizyphus Spina-Christi Species Against Sars-Cov-2 Main Protease

The COVID-19 has spread worldwide and become an international pandemic. The promising target for drug discovery of COVID-19 was SARS-CoV-2 Main Protease (Mpro), that has been successfully crystallized along with its inhibitor. The discovery of peptide-based inhibitors may present better options than small molecules for inhibitor SARS-CoV-2 Mpro. Natural compounds have such a wide potential and still few explored, Zizyphus spina-christi is one of the medicinal plants that have many pharmacological activities and contains a peptide compound from alkaloids class, i.e. cyclopeptide alkaloids, that is interesting to explore as SARS-CoV-2 Mpro inhibitor. The compound structure was drawn and optimized using density functional theory 3-21G method. The protein chosen was the high resolution of SARS-CoV-2 MPro receptor (1.45 Å) with PDB ID: 6WNP, in complex with boceprevir. Molecular docking simulation was performed using Autodock4 with 100 numbers of GA run, the validation methods assessed by RMSD calculation. Furthermore, the prediction of pharmacological activity spectra was carried out using the PASS Prediction server. The results showed RMSD value was 1.98 Å, this docking method was valid. The binding energy of all compounds showed better results than the native ligand (Boceprevir). The in silico PASS prediction results indicated that all compounds showed antiviral activity. Some compounds showed protease inhibitory activity, i.e Ambiphibine-H, Franganine, and Mauritine-A, and the highest Pa (Predicted activity) value showed by Mauritine-A compounds. It can be concluded that the cyclopeptide compounds of Zizyphus spina-christi were indicated to have a potential as COVID-19 therapy targeting SARS-CoV-2 Mpro.

Potential Mortalin-p53 complex abrogation of ent-kaurane diterpenoids from Croton tonkinensis revealed by homology modeling and docking simulation

Seven ent-kaurane diterpenoids from Croton tonkinensis were tested for cytotoxicity against human HCC HepG2 cell line. The abrogation of mortalin-p53 interaction represent an original anticancer therapeutic approach. Teritary structure of protein mortalin was constructed using Protein Structure Prediction Server and crystal structure of p53 was selected from Protein Data Bank involving mortalin-p53 binding domain. Molecular docking studies revealed that the interaction with protein mortalin is more prominent than p53 compound 5 and 1 as the most two potential mortalin-p53 binding inhibitors based on binding free energy and interacting residues analysis.

Identification of Novel Anticancer Agent by in silico Methods for Inhibition of KLK-12 Protein

A critical route for cancer metastases is pathological angiogenesis. The protein Kallikrein-12 (KLK-12) is a serine protease reported to be involved in a variety of biochemical processes that have a functional role in angiogenesis. The KLK-12 protein hydrolyzes the cysteine rich angiogenic inducer 61 (CYR61) protein and controls the bioavailability of angiogenesis-inducing growth factors. The work proposed involves the homology modeling of the KLK-12 protein, identify essential residues to be putatively linked to the natural substrate. Protein-protein docking is done to characterize Trp35, Gln36, Gly38, Trp82 and His107 residues of the active site, in addition to active site servers (active site prediction server and CASTp). Using Auto Dock Vina software, virtual screening studies were carried out to identify the substituted carboxamide scaffolds as a pharmacophore binding at the active site. Based on binding energy, ADME and visual inspection, an isochromene carboxamide moiety is identified as antiangiogenic and cancer antagonists.

Identification of CD8+ T cell epitopes through proteasome cleavage site predictions

Background We previously introduced PCPS (Proteasome Cleavage Prediction Server), a web-based tool to predict proteasome cleavage sites using n-grams. Here, we evaluated the ability of PCPS immunoproteasome cleavage model to discriminate CD8+ T cell epitopes. Results We first assembled an epitope dataset consisting of 844 unique virus-specific CD8+ T cell epitopes and their source proteins. We then analyzed cleavage predictions by PCPS immunoproteasome cleavage model on this dataset and compared them with those provided by a related method implemented by NetChop web server. PCPS was clearly superior to NetChop in term of sensitivity (0.89 vs. 0.79) but somewhat inferior with regard to specificity (0.55 vs. 0.60). Judging by the Mathew’s Correlation Coefficient, PCPS predictions were overall superior to those provided by NetChop (0.46 vs. 0.39). We next analyzed the power of C-terminal cleavage predictions provided by the same PCPS model to discriminate CD8+ T cell epitopes, finding that they could be discriminated from random peptides with an accuracy of 0.74. Following these results, we tuned the PCPS web server to predict CD8+ T cell epitopes and predicted the entire SARS-CoV-2 epitope space. Conclusions We report an improved version of PCPS named iPCPS for predicting proteasome cleavage sites and peptides with CD8+ T cell epitope features. iPCPS is available for free public use at https://imed.med.ucm.es/Tools/pcps/.

StreptomeDB 3.0: an updated compendium of streptomycetes natural products

Antimicrobial resistance is an emerging global health threat necessitating the rapid development of novel antimicrobials. Remarkably, the vast majority of currently available antibiotics are natural products (NPs) isolated from streptomycetes, soil-dwelling bacteria of the genus Streptomyces. However, there is still a huge reservoir of streptomycetes NPs which remains pharmaceutically untapped and a compendium thereof could serve as a source of inspiration for the rational design of novel antibiotics. Initially released in 2012, StreptomeDB (http://www.pharmbioinf.uni-freiburg.de/streptomedb) is the first and only public online database that enables the interactive phylogenetic exploration of streptomycetes and their isolated or mutasynthesized NPs. In this third release, there are substantial improvements over its forerunners, especially in terms of data content. For instance, about 2500 unique NPs were newly annotated through manual curation of about 1300 PubMed-indexed articles, published in the last five years since the second release. To increase interoperability, StreptomeDB entries were hyperlinked to several spectral, (bio)chemical and chemical vendor databases, and also to a genome-based NP prediction server. Moreover, predicted pharmacokinetic and toxicity profiles were added. Lastly, some recent real-world use cases of StreptomeDB are highlighted, to illustrate its applicability in life sciences.

The structural basis of the oncogenic mutant K-Ras4B homodimers

Ras proteins activate their effectors through physical interactions in response to the various extracellular stimuli at the plasma membrane. Oncogenic Ras forms dimer and nanoclusters at the plasma membrane, boosting the downstream MAPK signal. It was reported that K-Ras4B can dimerize through two major interfaces: (i) the effector lobe interface, mapped to Switch I and effector binding regions; (ii) the allosteric lobe interface involving α3 and α4 helices. Recent experiments showed that constitutively active, oncogenic mutant K-Ras4BG12D dimers are enriched in the plasma membrane. Here, we perform molecular dynamics simulations of K-Ras4BG12D homodimers aiming to quantify the two major interfaces in atomic level. To examine the effect of mutations on dimerization, two double mutations, K101D/R102E on the allosteric lobe and R41E/K42D on the effector lobe interfaces were added to the K-Ras4BG12D dimer simulations. We observed that the effector lobe K-Ras4BG12D dimer is stable, while the allosteric lobe dimer alters its helical interface during the simulations, presenting multiple conformations. The K101D/R102E mutations slightly weakens the allosteric lobe interface. However, the R41E/K42D mutations disrupt the effector lobe interface. Using the homo-oligomers prediction server, we obtained trimeric, tetrameric, and pentameric complexes with the allosteric lobe K-Ras4BG12D dimers. However, the allosteric lobe dimer with the K101D/R102E mutations is not capable of generating multiple higher order structures. Our detailed interface analysis may help to develop inhibitor design targeting functional Ras dimerization and high order oligomerization at the membrane signaling platform.

Structure, function and variants analysis of the androgen-regulated TMPRSS2, a drug target candidate for COVID-19 infection

SARS-CoV-2 is a novel virus causing mainly respiratory, but also gastrointestinal symptoms. Elucidating the molecular processes underlying SARS-CoV-2 infection, and how the genetic background of an individual is responsible for the variability in clinical presentation and severity of COVID-19 is essential in understanding this disease.Cell infection by the SARS-CoV-2 virus requires binding of its Spike (S) protein to the ACE2 cell surface protein and priming of the S by the serine protease TMPRSS2. One may expect that genetic variants leading to a defective TMPRSS2 protein can affect SARS-CoV-2 ability to infect cells. We used a range of bioinformatics methods to estimate the prevalence and pathogenicity of TMPRSS2 genetic variants in the human population, and assess whether TMPRSS2 and ACE2 are co-expressed in the intestine, similarly to what is observed in lungs.We generated a 3D structural model of the TMPRSS2 extracellular domain using the prediction server Phyre and studied 378 naturally-occurring TMPRSS2 variants reported in the GnomAD database. One common variant, p.V160M (rs12329760), is predicted damaging by both SIFT and PolyPhen2 and has a MAF of 0.25. Valine 160 is a highly conserved residue within the SRCS domain. The SRCS is found in proteins involved in host defence, such as CD5 and CD6, but its role in TMPRSS2 remains unknown. 84 rare variants (53 missense and 31 leading to a prematurely truncated protein, cumulative minor allele frequency (MAF) 7.34×10−4) cause structural destabilization and possibly protein misfolding, and are also predicted damaging by SIFT and PolyPhen2 prediction tools. Moreover, we extracted gene expression data from the human protein atlas and showed that both ACE2 and TMPRSS2 are expressed in the small intestine, duodenum and colon, as well as the kidneys and gallbladder.The implications of our study are that: i. TMPRSS2 variants, in particular p.V160M with a MAF of 0.25, should be investigated as a possible marker of disease severity and prognosis in COVID-19 and ii. in vitro validation of the co-expression of TMPRSS2 and ACE2 in gastro-intestinal is warranted.

AWSEM-Suite: a protein structure prediction server based on template-guided, coevolutionary-enhanced optimized folding landscapes

The accurate and reliable prediction of the 3D structures of proteins and their assemblies remains difficult even though the number of solved structures soars and prediction techniques improve. In this study, a free and open access web server, AWSEM-Suite, whose goal is to predict monomeric protein tertiary structures from sequence is described. The model underlying the server’s predictions is a coarse-grained protein force field which has its roots in neural network ideas that has been optimized using energy landscape theory. Employing physically motivated potentials and knowledge-based local structure biasing terms, the addition of homologous template and co-evolutionary restraints to AWSEM-Suite greatly improves the predictive power of pure AWSEM structure prediction. From the independent evaluation metrics released in the CASP13 experiment, AWSEM-Suite proves to be a reasonably accurate algorithm for free modeling, standing at the eighth position in the free modeling category of CASP13. The AWSEM-Suite server also features a front end with a user-friendly interface. The AWSEM-Suite server is a powerful tool for predicting monomeric protein tertiary structures that is most useful when a suitable structure template is not available. The AWSEM-Suite server is freely available at: https://awsem.rice.edu.