Connecting Peptide Physicochemical and Antimicrobial Properties by a Rational Prediction Model

The increasing rate in antibiotic-resistant bacterial strains has become an imperative health issue. Thus, pharmaceutical industries have focussed their efforts to find new potent, non-toxic compounds to treat bacterial infections. Antimicrobial peptides (AMPs) are promising candidates in the fight against antibiotic-resistant pathogens due to their low toxicity, broad range of activity and unspecific mechanism of action. In this context, bioinformatics' strategies can inspire the design of new peptide leads with enhanced activity. Here, we describe an artificial neural network approach, based on the AMP's physicochemical characteristics, that is able not only to identify active peptides but also to assess its antimicrobial potency. The physicochemical properties considered are directly derived from the peptide sequence and comprise a complete set of parameters that accurately describe AMPs. Most interesting, the results obtained dovetail with a model for the AMP's mechanism of action that takes into account new concepts such as peptide aggregation. Moreover, this classification system displays high accuracy and is well correlated with the experimentally reported data. All together, these results suggest that the physicochemical properties of AMPs determine its action. In addition, we conclude that sequence derived parameters are enough to characterize antimicrobial peptides

A computational approach to chemical etiologies of diabetes.

Computational meta-analysis can link environmental chemicals to genes and proteins involved in human diseases, thereby elucidating possible etiologies and pathogeneses of non-communicable diseases. We used an integrated computational systems biology approach to examine possible pathogenetic linkages in type 2 diabetes (T2D) through genome-wide associations, disease similarities, and published empirical evidence. Ten environmental chemicals were found to be potentially linked to T2D, the highest scores were observed for arsenic, 2,3,7,8-tetrachlorodibenzo-p-dioxin, hexachlorobenzene, and perfluorooctanoic acid. For these substances we integrated disease and pathway annotations on top of protein interactions to reveal possible pathogenetic pathways that deserve empirical testing. The approach is general and can address other public health concerns in addition to identifying diabetogenic chemicals, and offers thus promising guidance for future research in regard to the etiology and pathogenesis of complex diseases

ChemProt: a disease chemical biology database

Systems pharmacology is an emergent area that studies drug action across multiple scales of complexity, from molecular and cellular to tissue and organism levels. There is a critical need to develop network-based approaches to integrate the growing body of chemical biology knowledge with network biology. Here, we report ChemProt, a disease chemical biology database, which is based on a compilation of multiple chemical–protein annotation resources, as well as disease-associated protein–protein interactions (PPIs). We assembled more than 700 000 unique chemicals with biological annotation for 30 578 proteins. We gathered over 2-million chemical–protein interactions, which were integrated in a quality scored human PPI network of 428 429 interactions. The PPI network layer allows for studying disease and tissue specificity through each protein complex. ChemProt can assist in the in silico evaluation of environmental chemicals, natural products and approved drugs, as well as the selection of new compounds based on their activity profile against most known biological targets, including those related to adverse drug events. Results from the disease chemical biology database associate citalopram, an antidepressant, with osteogenesis imperfect and leukemia and bisphenol A, an endocrine disruptor, with certain types of cancer, respectively. The server can be accessed at http://www.cbs.dtu.dk/services/ChemProt/

End-Tagging of Ultra-Short Antimicrobial Peptides by W/F Stretches to Facilitate Bacterial Killing

BACKGROUND: Due to increasing resistance development among bacteria, antimicrobial peptides (AMPs), are receiving increased attention. Ideally, AMP should display high bactericidal potency, but low toxicity against (human) eukaryotic cells. Additionally, short and proteolytically stable AMPs are desired to maximize bioavailability and therapeutic versatility. METHODOLOGY AND PRINCIPAL FINDINGS: A facile approach is demonstrated for reaching high potency of ultra-short antimicrobal peptides through end-tagging with W and F stretches. Focusing on a peptide derived from kininogen, KNKGKKNGKH (KNK10) and truncations thereof, end-tagging resulted in enhanced bactericidal effect against Gram-negative Escherichia coli and Gram-positive Staphylococcus aureus. Through end-tagging, potency and salt resistance could be maintained down to 4-7 amino acids in the hydrophilic template peptide. Although tagging resulted in increased eukaryotic cell permeabilization at low ionic strength, the latter was insignificant at physiological ionic strength and in the presence of serum. Quantitatively, the most potent peptides investigated displayed bactericidal effects comparable to, or in excess of, that of the benchmark antimicrobial peptide LL-37. The higher bactericidal potency of the tagged peptides correlated to a higher degree of binding to bacteria, and resulting bacterial wall rupture. Analogously, tagging enhanced peptide-induced rupture of liposomes, particularly anionic ones. Additionally, end-tagging facilitated binding to bacterial lipopolysaccharide, both effects probably contributing to the selectivity displayed by these peptides between bacteria and eukaryotic cells. Importantly, W-tagging resulted in peptides with maintained stability against proteolytic degradation by human leukocyte elastase, as well as staphylococcal aureolysin and V8 proteinase. The biological relevance of these findings was demonstrated ex vivo for pig skin infected by S. aureus and E. coli. CONCLUSIONS/SIGNIFICANCE: End-tagging by hydrophobic amino acid stretches may be employed to enhance bactericidal potency also of ultra-short AMPs at maintained limited toxicity. The approach is of general applicability, and facilitates straightforward synthesis of hydrophobically modified AMPs without the need for post-peptide synthesis modifications

Highly Selective End-Tagged Antimicrobial Peptides Derived from PRELP

Background: Antimicrobial peptides (AMPs) are receiving increasing attention due to resistance development against conventional antibiotics. Pseudomonas aeruginosa and Staphylococcus aureus are two major pathogens involved in an array of infections such as ocular infections, cystic fibrosis, wound and post-surgery infections, and sepsis. The goal of the study was to design novel AMPs against these pathogens. Methodology and Principal Findings: Antibacterial activity was determined by radial diffusion, viable count, and minimal inhibitory concentration assays, while toxicity was evaluated by hemolysis and effects on human epithelial cells. Liposome and fluorescence studies provided mechanistic information. Protease sensitivity was evaluated after subjection to human leukocyte elastase, staphylococcal aureolysin and V8 proteinase, as well as P. aeruginosa elastase. Highly active peptides were evaluated in ex vivo skin infection models. C-terminal end-tagging by W and F amino acid residues increased antimicrobial potency of the peptide sequences GRRPRPRPRP and RRPRPRPRP, derived from proline arginine-rich and leucine-rich repeat protein (PRELP). The optimized peptides were antimicrobial against a range of Gram-positive S. aureus and Gram-negative P. aeruginosa clinical isolates, also in the presence of human plasma and blood. Simultaneously, they showed low toxicity against mammalian cells. Particularly W-tagged peptides displayed stability against P. aeruginosa elastase, and S. aureus V8 proteinase and aureolysin, and the peptide RRPRPRPRPWWWW-NH2 was effective against various "superbugs'' including vancomycin-resistant enterococci, multi-drug resistant P. aeruginosa, and methicillin-resistant S. aureus, as well as demonstrated efficiency in an ex vivo skin wound model of S. aureus and P. aeruginosa infection. Conclusions/Significance: Hydrophobic C-terminal end-tagging of the cationic sequence RRPRPRPRP generates highly selective AMPs with potent activity against multiresistant bacteria and efficiency in ex vivo wound infection models. A precise "tuning'' of toxicity and proteolytic stability may be achieved by changing tag-length and adding W-or F-amino acid tags

Integrate mechanistic evidence from new approach methodologies (NAMs) into a read-across assessment to characterise trends in shared mode of action

This read-across case study characterises thirteen, structurally similar carboxylic acids demonstrating the application of in vitro and in silico human-based new approach methods, to determine biological similarity. Based on data from in vivo animal studies, the read-across hypothesis is that all analogues are steatotic and so should be considered hazardous. Transcriptomic analysis to determine differentially expressed genes (DEGs) in hepatocytes served as first tier testing to confirm a common mode-of-action and identify differences in the potency of the analogues. An adverse outcome pathway (AOP) network for hepatic steatosis, informed the design of an in vitro testing battery, targeting AOP relevant MIEs and KEs, and Dempster-Shafer decision theory was used to systematically quantify uncertainty and to define the minimal testing scope. The case study shows that the read-across hypothesis is the critical core to designing a robust, NAM-based testing strategy. By summarising the current mechanistic understanding, an AOP enables the selection of NAMs covering MIEs, early KEs, and late KEs. Experimental coverage of the AOP in this way is vital since MIEs and early KEs alone are not confirmatory of progression to the AO. This strategy exemplifies the workflow previously published by the EUTOXRISK project driving a paradigm shift towards NAM-based NGRA.Toxicolog

Established and Emerging Trends in Computational Drug Discovery in the Structural Genomics Era

Bioinformatics and chemoinformatics approaches contribute to hit discovery, hit-to-lead optimization, safety profiling, and target identification and enhance our overall understanding of the health and disease states. A vast repertoire of computational methods has been reported and increasingly combined in order to address more and more challenging targets or complex molecular mechanisms in the context of large-scale integration of structure and bioactivity data produced by private and public drug research. This review explores some key computational methods directly linked to drug discovery and chemical biology with a special emphasis on compound collection preparation, virtual screening, protein docking, and systems pharmacology. A list of generally freely available software packages and online resources is provided, and examples of successful applications are briefly commented upon