In silico assessment of missense point mutations on human cathelicidin LL-37

Cathelicidin antimicrobial peptides are a diverse family of cationic amphipathic peptides with multiple activities. In humans, cathelicidin LL-37 is one of the main host defense peptides with a remarkable medical and biotechnological potential. Deregulation of LL-37 expression has been associated with inflammatory diseases. However the effects of point mutations driven by single nucleotide polymorphisms (SNPs) on LL-37 are unknown. Here we applied an array of computational tools to investigate the effects of such mutations on LL-37 structure and activity. Due to the fact that, on cathelicidins, the prodomain is more conserved than the mature peptide, the SNP effect predictions were biased and, overall, resulted in neutral effects; and due to the slight changes in physicochemical properties, the antimicrobial predictions indicated the maintenance of such activity. Nonetheless, R07P, R07W, R29Q, R29W mutations reduced the peptide net charge, which in turn could result in less active LL-37 variants. Molecular dynamics data indicated that R07Q and N30Y mutations altered the LL-37 structure, leading to potential deleterious effects. In addition, the helix dipole is altered in G03A, R07P, R07W and L31P mutations, which could also alter the antimicrobial activity. Our results indicated that despite the mutations did not alter the residues from LL-37 active core, they could influence the antimicrobial activity and consequently, could be involved in inflammatory diseases.

Deciphering the structural basis for glucocorticoid resistance caused by missense mutations in the ligand binding domain of glucocorticoid receptor

The glucocorticoid resistance hereditary condition may emerge from the occurrence of point mutations in the glucocorticoid receptor (GR), which could impair its functionality. Because the main feature of such pathology is the resistance of the hypothalamic-pituitary-adrenal axis to the hormone cortisol, we used the GR ligand binding domain three-dimensional structure to perform computational analysis for eight variants known to cause this clinical condition (I559 N, V571A, D641V, G679S, F737L, I747 M, L753F and L773P), aiming to understand, on the atom scale, how they cause glucocorticoid resistance. We observed that the mutations generated a reduced affinity to cortisol and they alter some loop conformations, which could be a consequence from changes in protein motion, which in turn could result from the reduced stability of mutant GR structures. Therefore, the analyzed mutations compromise the GR ligand binding domain structure and cortisol binding, which could characterize the glucocorticoid resistance phenotype.

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.

Antimicrobial peptides are considered promising alternatives to antibiotics. Here the authors developed a computational algorithm that starts with peptides naturally occurring in plants and optimizes this starting material to yield new variants which are highly distinct from the parent peptide.

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.