The IL-1b plays a major role in inflammatory disorders and IL-1b production inhibitors can be used in the treatment of inflammatory and related diseases. In this study, quantitative relationships between the structures of 46 pyridazine derivatives (inhibitors of IL-1b production) and their activities were investigated by Multiple Linear Regression (MLR) technique Stepwise Regression Method (ES-SWR). The genetic algorithm (GA) has been proposed for improvement of the performance of the MLR modeling by choosing the most relevant descriptors. The results show that eight descriptors are able to describe about 83.70% of the variance in the experimental activity of the molecules in the training set. The physical meaning of the selected descriptors is discussed in detail. Power predictions of the QSAR models developed were evaluated using cross-validation, and validation through an external prediction set. The results showed satisfactory goodness-of-fit, robustness and perfect external predictive performance. The applicability domain was used to define the area of reliable predictions. Furthermore, the in silico screening technique was applied in order to predict the structure and potency of new compounds of this type using the proposed QSAR model.

Background: The arboviruses Zika virus (ZIKV) is a pathogen that threatens human health. Scientists have warned that a single mutation in the mosquito-borne ZIKV could spark another major outbreak of the disease in humans. Therefore, designing a suitable vaccine for this virus seems necessary. This study aimed to predict the protective epitopes of envelope protein from the Zika virus with bioinformatics methods for multi-epitope vaccine development. Materials and Methods: Computational studies including the identification of potential B-cell and T-cell epitopes were used. For generating a multi-epitopic vaccine construct (MEVC), selected epitopes are connected by suitable linkers. To enhance protein immunogenicity, Maltosebound protein was added to the MEVC after the prediction and refinement of the 3D structure of the designed vaccine. The binding mode of the MEVC with toll-like receptor was investigated by molecular docking technique. Finally, molecular dynamics and in silico cloning were performed for the designed vaccine. Results: This study showed that this recombinant vaccine is nontoxic, nonallergenic, and thermostable and elicits immune responses against the Zika virus. Conclusion: The computational data suggest that the MEVC has appropriate characteristics and a high-quality structure.

One of the newest diagnostic methods and treatment of cancer is to design new drugs. It is now possible to design a drug with desired properties in theory and evaluate its therapeutic effects through bioinformatics tools. Among the studied drugs, those based on cytokine genes, which increase the body's immunity against cancer, are of great interest. Cytokines are small proteins that play an essential role in cell signaling and can affect the function and behavior of surrounding cells. CCL21 chemokine is one of the cytokines that possess antitumor properties has the potential for chemoattraction of T lymphocytes and dendritic cells. Interleukin 1 beta (IL1β ) is a cytokine involving different cellular activities such as the activation of neutrophils, B-Cells, and T-Cells. In the present study, we designed a drug-based cytokine gene to activate T cells and B cells by inserting defined CCL21 epitope and IL1β peptide sequences into a protein construct. Molecular dynamics simulation was performed in Linux space using Gromex software. Results of RMSD, RMSF, and the radius of gyration obtained from the simulation showed the stability of both proteins, which indicated that there are no significant conformational differences between the commercial CCL21 and recombinant form. The interaction of synthetic construct and human CCL21 with the CCR7 receptor was also investigated by HADDOCK software. Obtained results showed no differences between these proteins, and recombinant protein has the same structural and conformational characteristics as human commercial CCL21.

Background and Aim: Mycobacterium tuberculosis is a health problem in countries. Despite the global prevalence of tuberculosis and the lack of appropriate drugs, further progress is still needed with the help of modern methods of preparing epitope-based vaccines for tuberculosis. Materials and Methods: In this study, specific T and B cell epitopes required for producing chimeric vaccines with the help of servers such as IEDB were determined. The antigenicity, allergenicity, and toxicity of the selected epitopes by various other servers such as VaxiJenv2. 0, AllerTOP, and Toxinpred were determined, and the vaccine of the epitope was then configured with the help of special linkers. Then, analyze the structure vaccine by some other bioinformatics servers such as PRABI, SWISS-MODEL, PROCHECK, and PEPCALC, was investigated and finally detected using docking techniques to evaluate the interaction with the epitope through MVD software. Results: The results showed that the vaccine, in terms of in silico evaluations of two-dimensional and three-dimensional structures, has a good condition. Also, the percentage of optimal placement of amino acids and bonds by PROCHECK server, % 99 percent of optimal placement of amino acids in the chimer structure was established. Also, it was non-toxic and nonallergenic and had the desired antigenicity. Conclusion: In general, the vaccine that was able to have a favorable interaction with some components of the immune system (HLA) in the docking process, which indicates the optimal identification of this structure by the humoral and cellular immune system, of course, more reliable proof of it requires clinical phase processes.

Background: In-silico analysis provides a fast, simple, and cost-free method for identifying potentially pathogenic single nucleotide variants. Objective: To propose a simple and relatively fast method for the prediction of variant pathogenicity using free online in-silico (IS) tools with AURKA gene as a model. Materials and Methods: We aim to propose a methodology to predict variants with high pathogenic potential using computational analysis, using AURKA gene as model. We predicted a protein model and analyzed 209 out of 64,369 AURKA variants obtained from Ensembl database. We used bioinformatic tools to predict pathogenicity. The results were compared through the VarSome website, which includes its own pathogenicity score and the American College of Medical Genetics (ACMG) classification. Results: Out of the 209 analyzed variants, 16 were considered pathogenic, and 13 were located in the catalytic domain. The most frequent protein changes were size and hydrophobicity modifications of amino acids. Proline and Glycine amino acid substitutions were the most frequent changes predicted as pathogenic. These bioinformatic tools predicted functional changes, such as protein up or down-regulation, gain or loss of molecule interactions, and structural protein modifications. When compared to the ACMG classification, 10 out of 16 variants were considered likely pathogenic, with 7 out of 10 changes at Proline/Glycine substitutions. Conclusion: This method allows quick and cost-free bulk variant screening to identify variants with pathogenic potential for further association and/or functional studies.

Mutations in the BRCA1 gene are known to be a major cause of hereditary breast cancer. However, characterizing the point mutations associated with cancer in BRCA1 is challenging because the functional impact of most of them is still unknown. Nowadays, a variety of methods are employed to identify cancer-associated mutations in BRCA1. This study is aimed to assess the functional effects of two mutations, Asp1733Gly and Val1714Gly, using a combination of in silico tools and yeast functional transcription activator assay. Our computational analysis showed that theVal1714Gly mutation was deleterious, while the other one, Asp1733Gly, predicted as neutral. Also using yeast functional transcription activator assay, we found that the Asp1733Gly mutation displayed similar ability with positive controls. In contrast, the Val1714Gly mutation completely abrogated transcriptional activity in the yeast. These results suggested that Val1714Gly and Asp1733Gly can be classified as pathogenic and benign mutations for the BRCA1, respectively.