Polymerase Chain Reaction (PCR) based techniques for DNA methylation techniques includes MS-HRM technique. Methylation Sensitive High-Resolution Melting (MS-HRM) primer-design requires a set of necessary recommendations for such DNA methylation assessment. However, there were not any available software that allows an automatic design of this kind primers. We present Softepigen, the first complete MS-HRM primer design software. Softepigen allows to search for primers in a genomic region following Wojdacz's recommendations and targets primer binding regions with high linguistic complexity sequences that increase the specificity of the converted sequence of the human genome. We performed in-silico PCR analysis through BiSearch ePCR tool to validate the specificity of the of the primers designed using Softepigen. Softepigen for MS-HRM performance in our genomic regions of interest show satisfactory specificity measurements, and we implemented it for freely available use in web-based interface in www.soft-epigen.com.

Bacillus thuringiensis (Bt) is a bacterium capable of producing Cry toxins, which are recognized for their bio-controlling actions against insects. However, a few Bt strains encode proteins lacking insecticidal activity but showing cytotoxic activity against different cancer cell lines and low or no cytotoxicity toward normal human cells. A subset of Cry anticancer proteins, termed parasporins (PSs), has recently arisen as a potential alternative for cancer treatment. However, the molecular receptors that allow the binding of PSs to cells and their cytotoxic mechanisms of action have not been well established. Nonetheless, their selective cytotoxic activity against different types of cancer cell lines places PSs as a promising alternative treatment modality. In this review, we provide an overview of the classification, structures, mechanisms of action, and insights obtained from genetic modification approaches for PS proteins.

Directed evolution methods mimic in vitro Darwinian evolution, inducing random mutations and selective pressure in genes to obtain proteins with enhanced characteristics. These techniques are developed using trial-and-error testing at an experimental level with a high degree of uncertainty. Therefore, in silico modeling of directed evolution is required to support experimental assays. Several in silico approaches have reproduced directed evolution, using statistical, thermodynamic, and kinetic models in an attempt to recreate experimental conditions. Likewise, optimization techniques using heuristic models have been used to understand and find the best scenarios of directed evolution. Our study uses an in silico model named HeurIstics DirecteD EvolutioN, which is based on a genetic algorithm designed to generate chimeric libraries from 2 parental genes, cry11Aa and cry11Ba, of Bacillus thuringiensis. These genes encode crystal-shaped δ-endotoxins with 3 conserved domains. Cry11 toxins are of biotechnological interest because they have shown to be effective as biopesticides for disease-spreading vectors. With our heuristic model, we considered experimental parameters such as DNA fragmentation length, number of generations or simulation cycles, and mutation rate, to get characteristics of Cry11 chimeric libraries such as percentage of population identity, truncation of variants obtained from the presence of internal stop codons, percentage of thermodynamic diversity, and stability of variants. Our study allowed us to focus on experimental conditions that may be useful for the design of in vitro and in silico experiments of directed evolution with Cry toxins of 3 conserved domains. Furthermore, we obtained in silico libraries of Cry11 variants, in which structural characteristics of wild Cry families were observed in a review of a sample of in silico sequences. We consider that future studies could use our in silico libraries and heuristic computational models, as the one suggested here, to support in vitro experiments of directed evolution.

Antimicrobial resistance is increasing at an alarming rate and the number of new antibiotics developed and approved has decreased in the last decades, basically for economic and regulatory obstacles. Pathogenic bacteria that are resistant to multiple or all available antibiotics are isolated frequently. Hence, new antibacterial agents are urgently needed and antimicrobial peptides are being considered as a potential solution to this important threat. These molecules are small host defense proteins that are part of the immune systems of most living organisms such as plants, bacteria, invertebrates, vertebrates, and mammals. These peptides are found in those parts of organisms that are exposed to pathogens and they are active against multiple organisms such as virus, bacteria, and parasites, among others. This review shows different strategies in the computational design of new antibacterial peptides, the physicochemical properties that are considered as the most relevant for the antibacterial activity and toxicity, and it suggests guidelines in order to help in the finding of new non-toxic antibacterial peptides through the development of computational models.