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Introduction: Triple-negative breast cancer (TNBC) is an important subtype of breast cancer, which occurs in the absence of estrogen, progesterone and HER-2 receptors. According to the recent studies, TNBC may be a cancer testis antigen (CTA)-positive tumor, indicating that the CTA-based cancer vaccine can be a treatment option for the patients bearing such tumors. Of these antigens (Ags), the MAGE-A family and NY-ESO-1 as the most immunogenic CTAs are the potentially relevant targets for the development of an immunotherapeutic way of the breast cancer treatment. Methods: In the present study, immunoinformatics approach was used to design a multi-epitope peptide vaccine to combat the TNBC. The vaccine peptide was constructed by the fusion of three crucial components, including the CD8+ cytotoxic T lymphocytes (CTLs) epitopes, helper epitopes and adjuvant. The epitopes were predicted from the MAGE-A and NY-ESO-1 Ags. In addition, the granulocyte-macrophage-colony-stimulating factor (GM-CSF) was used as an adjuvant to promote the CD4+ T cells towards the T-helper for more strong induction of CTL responses. The components were conjugated by proper linkers. Results: The vaccine peptide was examined for different physiochemical characteristics to confirm the safety and immunogenic behavior. Furthermore, the 3D-structure of the vaccine peptide was predicted based on the homology modeling approach using the MODELLER v9.17 program. The vaccine structure was also subjected to the molecular dynamics simulation study for structure refinement. The results verified the immunogenicity and safety profile of the constructed vaccine as well as its capability for stimulating both the cellular and humoral immune responses. Conclusion: Based on our in-silico analyses, the proposed vaccine may be considered for the immunotherapy of TNBC.

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The structure of a novel psychrophilic β-mannanase enzyme from Glaciozyma antarctica PI12 yeast has been modelled and analysed in detail. To our knowledge, this is the first attempt to model a psychrophilic β-mannanase from yeast. To this end, a 3D structure of the enzyme was first predicted using a threading method because of the low sequence identity (<30%) using MODELLER9v12 and simulated using GROMACS at varying low temperatures for structure refinement. Comparisons with mesophilic and thermophilic mannanases revealed that the psychrophilic mannanase contains longer loops and shorter helices, increases in the number of aromatic and hydrophobic residues, reductions in the number of hydrogen bonds and salt bridges and numerous amino acid substitutions on the surface that increased the flexibility and its efficiency for catalytic reactions at low temperatures.

BackgroundIn structural biology, similarity analysis of protein structure is a crucial step in studying the relationship between proteins. Despite the considerable number of techniques that have been explored within the past two decades, the development of new alternative methods is still an active research area due to the need for high performance tools.ResultsIn this paper, we present TS-AMIR, a Topology String Alignment Method for Intensive Rapid comparison of protein structures. The proposed method works in two stages: In the first stage, the method generates a topology string based on the geometric details of secondary structure elements, and then, utilizes an n-gram modelling technique over entropy concept to capture similarities in these strings. This initial correspondence map between secondary structure elements is submitted to the second stage in order to obtain the alignment at the residue level. Applying the Kabsch method, a heuristic step-by-step algorithm is adopted in the second stage to align the residues, resulting in an optimal rotation matrix and minimized RMSD. The performance of the method was assessed in different information retrieval tests and the results were compared with those of CE and TM-align, representing two geometrical tools, and YAKUSA, 3D-BLAST and SARST as three representatives of linear encoding schemes. It is shown that the method obtains a high running speed similar to that of the linear encoding schemes. In addition, the method runs about 800 and 7200 times faster than TM-align and CE respectively, while maintaining a competitive accuracy with TM-align and CE.ConclusionsThe experimental results demonstrate that linear encoding techniques are capable of reaching the same high degree of accuracy as that achieved by geometrical methods, while generally running hundreds of times faster than conventional programs.