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The antimicrobial activity prediction tools aim to help the novel antimicrobial peptides (AMP) sequences discovery, utilizing machine learning methods. Such approaches have gained increasing importance in the generation of novel synthetic peptides by means of rational design techniques. This study focused on predictive ability of such approaches to determine the antimicrobial sequence activities, which were previously characterized at the protein level by in vitro studies. Using four web servers and one standalone software, we evaluated 78 sequences generated by the so-called linguistic model, being 40 designed and 38 shuffled sequences, with ∼60 and ∼25% of identity to AMPs, respectively. The ab initio molecular modelling of such sequences indicated that the structure does not affect the predictions, as both sets present similar structures. Overall, the systems failed on predicting shuffled versions of designed peptides, as they are identical in AMPs composition, which implies in accuracies below 30%. The prediction accuracy is negatively affected by the low specificity of all systems here evaluated, as they, on the other hand, reached 100% of sensitivity. Our results suggest that complementary approaches with high specificity, not necessarily high accuracy, should be developed to be used together with the current systems, overcoming their limitations.

The guanylate cyclase activator 2B, also known as uroguanylin, is part of the guanylin peptide family, which includes peptides such as guanylin and lymphoguanylin. The guanylin peptides could be related to sodium absorption inhibition and water secretion induction and their dysfunction may be related to various pathologies such as chronic renal failure, congestive heart failure and nephrotic syndrome. Besides, uroguanylin point mutations have been associated with essential hypertension. However, currently there are no studies on the impact of missense SNPs on uroguanylin structure. This study applied in silico SNP impact prediction tools to evaluate the impact of uroguanylin missense SNPs and to filter those considered as convergent deleterious, which were then further analyzed through long-term molecular dynamics simulations of 1 μs of duration. The simulations suggested that all missense SNPs considered as convergent deleterious caused some kind of structural change to the uroguanylin peptide. Additionally, four of these SNPs were also shown to cause modifications in peptide flexibility, possibly resulting in functional changes.

Guanylin peptides (GPs) are small cysteine-rich peptide hormones involved in salt absorption, regulation of fluids and electrolyte homeostasis. This family presents four members: guanylin (GN), uroguanylin (UGN), lymphoguanylin (LGN) and renoguanylin (RGN). GPs have been used as templates for the development of drugs for the treatment of gastrointestinal disorders. Currently, LGN is the only GP with only one disulfide bridge, making it a remarkable member of this family and a potential drug template; however, there is no structural information about this peptide. In fact, LGN is predicted to be highly disordered and flexible, making it difficult to obtain structural information using in vitro methods. Therefore, this study applied a series of 1 μs molecular dynamics simulations in order to understand the structural behavior of LGN, comparing it to the C115Y variant of GN, which shows the same Cys to Tyr modification. LGN showed to be more flexible than GN C115Y. While the negatively charged N-terminal, despite its repellent behavior, seems to be involved mainly in pH-dependent activity, the hydrophobic core showed to be the determinant factor in LGN's flexibility, which could be essential in its activity. These findings may be determinant in the development of new medicines to help in the treatment of gastrointestinal disorders. Moreover, our investigation of LGN structure clarified some issues in the structure-activity relationship of this peptide, providing new knowledge of guanylin peptides and clarifying the differences between GN C115Y and LGN.