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.

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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.

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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 β-defensins.

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.

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.

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