In silico characterization of class II plant defensins from Arabidopsis thaliana.

Defensins comprise a polyphyletic group of multifunctional defense peptides. Cis-defensins, also known as cysteine stabilized αß (CSαß) defensins, are one of the most ancient defense peptide families. In plants, these peptides have been divided into two classes, according to their precursor organization. Class I defensins are composed of the signal peptide and the mature sequence, while class II defensins have an additional C-terminal prodomain, which is proteolytically cleaved. Class II defensins have been described in Solanaceae and Poaceae species, indicating this class could be spread among all flowering plants. Here, a search by regular expression (RegEx) was applied to the Arabidopsis thaliana proteome, a model plant with more than 300 predicted defensin genes. Two sequences were identified, A7REG2 and A7REG4, which have a typical plant defensin structure and an additional C-terminal prodomain. TraVA database indicated they are expressed in flower, ovules and seeds, and being duplicated genes, this indicates they could be a result of a subfunctionalization process. The presence of class II defensin sequences in Brassicaceae and Solanaceae and evolutionary distance between them suggest class II defensins may be present in other eudicots. Discovery of class II defensins in other plants could shed some light on flower, ovules and seed physiology, as this class is expressed in these locations.

Structure-guided identification of antimicrobial peptides in the spathe transcriptome of the non-model plant, arum lily (Zantedeschia aethiopica).

Antimicrobial peptides (AMPs) are small molecules present in all living beings. Despite their huge sequence variability, AMPs present great structural conservation, mainly in cysteine-stabilized families. Moreover, in non-model plants, it is possible to detect cysteine-stabilized AMPs (cs-AMPs) with different sequences not covered by conventional searches. Here, we described a threading application for cs-AMP identification in the non-model arum lily (Zantedeschia aethiopica) plant, exploring the spathe transcriptome. By using the predicted proteins from the Z. aethiopica transcriptome as our primary source of sequences, we have filtered by using structural alignments of 12 putative cs-AMP sequences. The two unreported sequences were submitted to PCR validation, and ZaLTP7 gene was confirmed. By using the structure alignments, we classified ZaLTP7 as an LTP type 2-like. The successful threading application for cs-AMP identification is an important advance in transcriptomic and proteomic data mining. Besides, the same approach could be applied to the use of NGS public data to discover molecules to combat multidrug-resistant bacteria.

In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design.

Plants are extensively used in traditional medicine, and several plant antimicrobial peptides have been described as potential alternatives to conventional antibiotics. However, after more than four decades of research no plant antimicrobial peptide is currently used for treating bacterial infections, due to their length, post-translational modifications or  high dose requirement for a therapeutic effect . Here we report the design of antimicrobial peptides derived from a guava glycine-rich peptide using a genetic algorithm. This approach yields guavanin peptides, arginine-rich α-helical peptides that possess an unusual hydrophobic counterpart mainly composed of tyrosine residues. Guavanin 2 is characterized as a prototype peptide in terms of structure and activity. Nuclear magnetic resonance analysis indicates that the peptide adopts an α-helical structure in hydrophobic environments. Guavanin 2 is bactericidal at low concentrations, causing membrane disruption and triggering hyperpolarization. This computational approach for the exploration of natural products could be used to design effective peptide antibiotics.

In silico analyses of deleterious missense SNPs of human apolipoprotein E3.

ApoE3 is the major chylomicron apolipoprotein, binding in a specific liver peripheral cell receptor, allowing transport and normal catabolism of triglyceride-rich lipoprotein constituents. Point mutations in ApoE3 have been associated with Alzheimer's disease, type III hyperlipoproteinemia, atherosclerosis, telomere shortening and impaired cognitive function. Here, we evaluate the impact of missense SNPs in APOE retrieved from dbSNP through 16 computational prediction tools, and further evaluate the structural impact of convergent deleterious changes using 100 ns molecular dynamics simulations. We have found structural changes in four analyzed variants (Pro102Arg, Arg132Ser, Arg176Cys and Trp294Cys), two of them (Pro102Arg and Arg176Cys) being previously associated with human diseases. In all cases, except for Trp294Cys, there was a loss in the number of hydrogen bonds between CT and NT domains that could result in their detachment. In conclusion, data presented here could increase the knowledge of ApoE3 activity and be a starting point for the study of the impact of variations on APOE gene.

Antimicrobial activity predictors benchmarking analysis using shuffled and designed synthetic peptides.

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.

Theoretical structural characterization of lymphoguanylin: A potential candidate for the development of drugs to treat gastrointestinal disorders.

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 1µs 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.

Structural impact analysis of missense SNPs present in the uroguanylin gene by long-term molecular dynamics simulations.

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 1µs 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.

Prediction of the impact of coding missense and nonsense single nucleotide polymorphisms on HD5 and HBD1 antibacterial activity against Escherichia coli.

Defensins confer host defense against microorganisms and are important for human health. Single nucleotide polymorphisms (SNPs) in defensin gene-coding regions could lead to less active variants. Using SNP data available at the dbSNP database and frequency information from the 1000 Genomes Project, two DEFA5 (L26I and R13H) and eight DEFB1 (C35S, K31T, K33R, R29G, V06I, C12Y, Y28* and C05*) missense and nonsense SNPs that are located within mature regions of the coded defensins were retrieved. Such SNPs are rare and population restricted. In order to assess their antibacterial activity against Escherichia coli, two linear regression models were used from a previous work, which models the antibacterial activity as a function of solvation potential energy, using molecular dynamics data. Regarding only the antibacterial predictions, for HD5, no biological differences between wild-type and its variants were observed; while for HBD1, the results suggest that the R29G, K31T, Y28* and C05* variants could be less active than the wild-type one. The data here reported could lead to a substantial improvement in knowledge about the impact of missense SNPs in human defensins and their world distribution. © 2016 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 106: 633-644, 2016.

HD5 and HBD1 variants' solvation potential energy correlates with their antibacterial activity against Escherichia coli.

The structure-activity relationship of defensins is not clear. It is known that point mutations in HD5 and HBD1 could modify their activities; however, these mutations do not seem to alter their three-dimensional structures. Here, applying molecular dynamics simulations, this relationship was studied in depth. There are modifications in flexibility, solvent accessible surface area and radius of gyration, but these properties are not reflected in the activity. Only alterations in the solvation potential energy were correlated to antibacterial activity against Escherichia coli. Data here reported could lead to a better understanding of structural and functional aspects of α- and ß-defensins.

CS-AMPPred: an updated SVM model for antimicrobial activity prediction in cysteine-stabilized peptides.

The antimicrobial peptides (AMP) have been proposed as an alternative to control resistant pathogens. However, due to multifunctional properties of several AMP classes, until now there has been no way to perform efficient AMP identification, except through in vitro and in vivo tests. Nevertheless, an indication of activity can be provided by prediction methods. In order to contribute to the AMP prediction field, the CS-AMPPred (Cysteine-Stabilized Antimicrobial Peptides Predictor) is presented here, consisting of an updated version of the Support Vector Machine (SVM) model for antimicrobial activity prediction in cysteine-stabilized peptides. The CS-AMPPred is based on five sequence descriptors: indexes of (i) α-helix and (ii) loop formation; and averages of (iii) net charge, (iv) hydrophobicity and (v) flexibility. CS-AMPPred was based on 310 cysteine-stabilized AMPs and 310 sequences extracted from PDB. The polynomial kernel achieves the best accuracy on 5-fold cross validation (85.81%), while the radial and linear kernels achieve 84.19%. Testing in a blind data set, the polynomial and radial kernels achieve an accuracy of 90.00%, while the linear model achieves 89.33%. The three models reach higher accuracies than previously described methods. A standalone version of CS-AMPPred is available for download at and runs on any Linux machine.