Stepwise identification of potent antimicrobial peptides from human genome

Molecular design of antimicrobial peptides based on hemagglutinin fusion domain to combat antibiotic resistance in bacterial infection

Antimicrobial peptides are derived from the viral fusion domain of influenza virus hemagglutinin based on rational analysis of the intermolecular interaction between peptides and bacterial outer membrane. It is revealed that the isolated viral fusion domain is a negatively charged peptide HAfp^1-23 that cannot effectively interact with the anionic membrane. Conversion of the native HAfp^1-23 to a positively charged peptide HAfp^1-23 _KK by E11K/D19K mutation can promote the peptide-membrane interaction substantially; this confers to the peptide a moderate antibacterial potency against antibiotic-resistant bacterial strains. Cyclization of the linear peptide HAfp^1-23 _KK results in a cyclic peptide cHAfp^1-23 _KK, which can largely minimize entropy penalty upon the peptide-membrane binding by pre-stabilizing peptide hairpin configuration in solvent, where the linear peptide would incur in a considerable conformational change/folding from intrinsic disorder (in water) to the structured hairpin conformation (in lipid). As might be expected, the cyclization considerably improves peptide antibacterial activity with minimum inhibitory concentration of 67 and 34 μg/mL against multidrug-resistant Pseudomonas aeruginosa and methicillin-resistant Staphylococcus aureus, respectively.

Computer-aided design, structural dynamics analysis, and in vitro susceptibility test of antibacterial peptides incorporating unnatural amino acids against microbial infections

BACKGROUND AND OBJECTIVE

Antibacterial peptides (ABPs) are essential components of host defense against microbial infections present in all domains of life. The AMPs incorporating unnatural amino acids (uABPs) exhibit several advantages over naturally occurring AMPs based on factors such as bioavailability, metabolic stability and overall toxicity.

METHODS

Computer-aided modeling and in vitro susceptibility test were combined to rationally design short uABPs with potent antimicrobial activity. In the procedure, peptide characterization and machine learning modeling were used to develop statistical regression predictors, which were then employed to guide the molecular design and structural optimization of uABPs, to which a number of commercially available unnatural amino acids were introduced.

RESULTS

An improved uABP population was obtained, from which several promising candidates were successfully prepared and their antibacterial potencies against three bacterial strains Staphylococcus aureus, Pseudomonas aeruginosa and Escherichia coli were measured using broth microdilution assay. Consequently, four uABPs with hybrid structure property were determined to have high potency against the tested strains with minimum inhibitory concentration (MIC) of <50 µg/ml.

CONCLUSIONS

Molecular dynamics (MD) simulations revealed that the designed uABPs are amphipathic helix in solution but they would largely unfold when spontaneously embedding into an artificial lipid bilayer that mimics microbial membrane.

The OPEP protein model: from single molecules, amyloid formation, crowding and hydrodynamics to DNA/RNA systems

The OPEP coarse-grained protein model has been applied to a wide range of applications since its first release 15 years ago. The model, which combines energetic and structural accuracy and chemical specificity, allows the study of single protein properties, DNA-RNA complexes, amyloid fibril formation and protein suspensions in a crowded environment. Here we first review the current state of the model and the most exciting applications using advanced conformational sampling methods. We then present the current limitations and a perspective on the ongoing developments.

Database-Guided Discovery of Potent Peptides to Combat HIV-1 or Superbugs

Antimicrobial peptides (AMPs), small host defense proteins, are indispensable for the protection of multicellular organisms such as plants and animals from infection. The number of AMPs discovered per year increased steadily since the 1980s. Over 2,000 natural AMPs from bacteria, protozoa, fungi, plants, and animals have been registered into the antimicrobial peptide database (APD). The majority of these AMPs (>86%) possess 11–50 amino acids with a net charge from 0 to +7 and hydrophobic percentages between 31–70%. This article summarizes peptide discovery on the basis of the APD. The major methods are the linguistic model, database screening, de novo design, and template-based design. Using these methods, we identified various potent peptides against human immunodeficiency virus type 1 (HIV-1) or methicillin-resistant Staphylococcus aureus (MRSA). While the stepwise designed anti-HIV peptide is disulfide-linked and rich in arginines, the ab initio designed anti-MRSA peptide is linear and rich in leucines. Thus, there are different requirements for antiviral and antibacterial peptides, which could kill pathogens via different molecular targets. The biased amino acid composition in the database-designed peptides, or natural peptides such as θ-defensins, requires the use of the improved two-dimensional NMR method for structural determination to avoid the publication of misleading structure and dynamics. In the case of human cathelicidin LL-37, structural determination requires 3D NMR techniques. The high-quality structure of LL-37 provides a solid basis for understanding its interactions with membranes of bacteria and other pathogens. In conclusion, the APD database is a comprehensive platform for storing, classifying, searching, predicting, and designing potent peptides against pathogenic bacteria, viruses, fungi, parasites, and cancer cells.