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Introduction: Targeting tumor microenvironment is beneficial and present an ideal setting for the development of futuristic immunotherapy. Here, we make use of Nuclear prelamin A recognition factor (NARF), a protein linked to the coactivation of transcriptional regulators in human breast cancer stem cells (CSC) in our investigation. Methods: In this study, we initially computed the epitope regions possessing the ability to stimulate both T and B cells within the NARF protein. These identified epitope areas were fused with an adjuvant such as RpfB and RpfE as well as linkers like AAY, GPGPG, KK, and EAAAK. The constructed vaccine was further characterized by assessing its physicochemical properties and population coverage. The potential interactions of the designed vaccine with different toll-like receptors were examined by a sequence of computational studies. Of note, docking study were employed to understand its mechanism of action. Molecular dynamics and immune simulation studies were conducted to comprehend more into their structural stability and immune responses. The resultant vaccine was back-translated, codon-optimised and introduced into pET-28 (+) vector. Results and discussion: We hypothesize from the results that the designed NARF protein-based vaccine in our analysis could effectively provoke the immune responses in the target organism through TLR-7 binding and promotes MHC class-II mediated antigen presentation. Indeed, comprehensive evaluations conducted in both in vitro and in vivo settings are imperative to substantiate the safety and efficacy of the developed vaccine. [ABSTRACT FROM AUTHOR]

Rice blast disease, caused by Magnaporthe oryzae, is the most devastating cereal killer worldwide. Note that melanin pigment is an essential factor of M. oryzae virulence, thus fungicides interfering with melanin biosynthesizing enzymes would reduce the pathogenicity. Scytalone dehydratase (SDH) is the key target for commercial fungicides, like carpropamid, due to its role in the dehydration reaction of the fungal melanin pathway. However, a single-point mutation (V75M) in SDH elicits resistance to carpropamid. A lack of effective fungicides against this resistant strain expedited the quest for novel bioactive inhibitors. Currently, bacterial endophytes like Streptomyces have been heralded for synthesizing bioactive metabolites to protect plants from phytopathogens. The literature search led to the identification of 21 Streptomyces spp. symbionts of paddy that can suppress M. oryzae growth. An antiSMASH server was used to explore Streptomyces spp. gene clusters and found 4463 putative metabolites. Besides, 745 unique metabolites were subjected to a series of virtual screening techniques. Ideally, this process identified five potential SDH inhibitors. The docking result highlights that the metabolite pseudopyronine A interacted hydrophobically with both Val75 of SDHWT and Met75 of SDHV75M targets. Moreover, pseudopyronine A has a higher binding free energy with SDHWT (− 89.94 kcal/mol) and SDHV75M (− 71.95 kcal/mol). Interestingly, the pyranones scaffold of pseudopyronine A was reported for antifungal activity against phytopathogens. Dynamic behavior confirms that pseudopyronine A has excellent conformational states with both SDHWT and SDHV75M. Altogether, we hope that this study creates a new avenue for the discovery of novel phytopathogen inhibitors from endophytes. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has been applied recently to develop prognostic classification models that can be used in individual cancer patients to forecast outcomes. Here, four different ML algorithms were built to predict survival rate of lung cancer patients using 1600 metadata records. Of note, the generated models were validated using test set and external validation data set consisting of 400 patient records each together with 10-fold cross-validation technique. The extratree classifier algorithm was employed to identify the influential descriptors for patients survival after incidence of metachronous second primary lung cancer. The models were assessed using five different performance metrices. The results from our study highlight that logistic regression model with all features and important features achieved an accuracy of 94% and 96%, respectively, for stratifying the survival status of lung cancer patients. On the other hand, logistic regression also outperformed external validation with an accuracy of 85%. Indeed, the results from our study will provide meaningful insights for the treatment and management of large community of lung cancer patients. [ABSTRACT FROM AUTHOR]

Over the years, phytochemical compounds have shown compelling evidences in exhibiting powerful antitumor properties. Moreover, due to the lack of safety and high cost of cancer therapies, opportunities are being sought out in these compounds as an alternative treatment modality. Therefore, in the present study, 1,574 compounds from NPACT library were examined to excavate potent and nontoxic anaplastic lymphoma kinase (ALK) inhibitors. Notably, two pharmacophore hypotheses (AAAHP and DDRRR) were generated using ligand‐based and energy‐based techniques, respectively, to eliminate false‐positive prediction in database screening. Furthermore, molecular docking and Prime MM/GBSA analysis were performed on the screened compounds to examine inhibitory activity against ALK. The analysis revealed that the two hits, namely, NPACT00018 and NPACT01077, exhibited better docking scores, binding energies, and also ensured excellent drug‐likeness properties than the reference compound, crizotinib. Finally, the results were subjected to molecular dynamics studies to gain insight into the stability of these compounds in the binding pocket of ALK protein. Indeed, the useful predictions generated by the present computational models are of immense importance and could further speed up the anticancer drug development in the near future. [ABSTRACT FROM AUTHOR]

Drug repurposing is a powerful technique which has been recently employed in both industry and academia to discover and validate previously approved drugs for new indications. It provides the quickest possible transition from bench to bedside. In essence, computational strategies are appealing because they putatively nominate the most encouraging candidate for a given indication. A wide range of computational methods exist for repositioning. In this chapter we present the guidelines for performing integrated drug repurposing strategy by combining disease-disease association and molecular simulation analysis.

Drug resistant S. typhimurium pose important public health problem. The development of effective drugs with novel mechanism(s) of action is needed to overcome issues pertaining to drug resistance. Drug repurposing based on computational analyses is considered a viable alternative strategy to circumvent this issue. In this context, 1309 FDA-approved drugs molecules from Mantra 2.0 database were analyzed for this study, against S. typhimurium. Sixteen compounds having similar profiles of gene expression as quinolones were identified from the database, Mantra 2.0. Further, the pharmacophore characteristics of each resultant molecule were identified and compared with the features of nalidixic acid, using the PharamGist program. Subsequently, the activities of these compounds against S. typhimurium DNA gyrase were identified, using molecular docking study. Side effects analysis was also performed for the identified compounds. Molecular dynamics simulation was carried out for the compound to validate its binding efficiency. Further, characterization of screened compound revealed IC50 values in micromolar concentration range, of which flufenamic acid showed comparable in vitro activity alongside ciprofloxacin and nalidixic acid. Thus represent interesting starting points for further optimization against S. typhimurium infections. It may be noted that the results we have obtained are the first experimental evidence of flufenamic acid activity against S. typhimurium., (© 2017 Wiley Periodicals, Inc.)