Christopher D. Fjell

Designing antimicrobial peptides: form follows function

Multidrug-resistant bacteria are a severe threat to public health. Conventional antibiotics are becoming increasingly ineffective as a result of resistance, and it is imperative to find new antibacterial strategies. Natural antimicrobials, known as host defence peptides or antimicrobial peptides, defend host organisms against microbes but most have modest direct antibiotic activity. Enhanced variants have been developed using straightforward design and optimization strategies and are being tested clinically. Here, we describe advanced computer-assisted design strategies that address the difficult problem of relating primary sequence to peptide structure, and are delivering more potent, cost-effective, broad-spectrum peptides as potential next-generation antibiotics.

Endotoxin tolerance represents a distinctive state of alternative polarization (M2) in human mononuclear cells.

Classical (M1) and alternative (M2) polarization of mononuclear cells (MNCs) such as monocyte and macrophages is known to occur in response to challenges within a microenvironment, like the encounter of a pathogen. LPS, also known as endotoxin, is a potent inducer of inflammation and M1 polarization. LPS can also generate an effect in MNCs known as endotoxin tolerance, defined as the reduced capacity of a cell to respond to LPS activation after an initial exposure to this stimulus. Using systems biology approaches in PBMCs, monocytes, and monocyte-derived macrophages involving microarrays and advanced bioinformatic analysis, we determined that gene responses during endotoxin tolerance were similar to those found during M2 polarization, featuring gene and protein expression critical for the development of key M2 MNC functions, including reduced production of proinflammatory mediators, expression of genes involved in phagocytosis, as well as tissue remodeling. Moreover, expression of different metallothionein gene isoforms, known for their role in the control of oxidative stress and in immunomodulation, were also found to be consistently upregulated during endotoxin tolerance. These results demonstrate that after an initial inflammatory stimulus, human MNCs undergo an M2 polarization probably to control hyperinflammation and heal the affected tissue.

Screening and Characterization of Surface-Tethered Cationic Peptides for Antimicrobial Activity

There is an urgent need to coat the surfaces of medical devices, including implants, with antimicrobial agents to reduce the risk of infection. A peptide array technology was modified to permit the screening of short peptides for antimicrobial activity while tethered to a surface. Cellulose-amino-hydroxypropyl ether (CAPE) linker chemistry was used to synthesize, on a cellulose support, peptides that remained covalently bound during biological assays. Among 122 tested sequences, the best surface-tethered 9-, 12-, and 13-mer peptides were found to be highly antimicrobial against bacteria and fungi, as confirmed using alternative surface materials and coupling strategies as well as coupling through the C and N termini of the peptides. Structure-activity modeling of the structural features determining the activity of tethered peptides indicated that the extent and positioning of positive charges and hydrophobic residues were influential in determining activity.

Identification of Novel Antibacterial Peptides by Chemoinformatics and Machine Learning

The rise of antibiotic resistant pathogens is one of the most pressing global health issues. Discovery of new classes of antibiotics has not kept pace; new agents often suffer from cross-resistance to existing agents of similar structure. Short, cationic peptides with antimicrobial activity are essential to the host defenses of many organisms and represent a promising new class of antimicrobials. This paper reports the successful in silico screening for potent antibiotic peptides using a combination of QSAR and machine learning techniques. On the basis of initial high-throughput measurements of activity of over 1400 random peptides, artificial neural network models were built using QSAR descriptors and subsequently used to screen an in silico library of approximately 100,000 peptides. In vitro validation of the modeling showed 94% accuracy in identifying highly active peptides. The best peptides identified through screening were found to have activities comparable or superior to those of four conventional antibiotics and superior to the peptide most advanced in clinical development against a broad array of multiresistant human pathogens.

AMPer: a database and an automated discovery tool for antimicrobial peptides

MOTIVATION Increasing antibiotics resistance in human pathogens represents a pressing public health issue worldwide for which novel antibiotic therapies based on antimicrobial peptides (AMPs) may offer one possible solution. In the current study, we utilized publicly available data on AMPs to construct hidden Markov models (HMMs) that enable recognition of individual classes of antimicrobials peptides (such as defensins, cathelicidins, cecropins, etc.) with up to 99% accuracy and can be used for discovering novel AMP candidates. RESULTS HMM models for both mature peptides and propeptides were constructed. A total of 146 models for mature peptides and 40 for propeptides have been developed for individual AMP classes. These were created by clustering and analyzing AMP sequences available in the public sources and by consequent iterative scanning of the Swiss-Prot database for previously unknown gene-coded AMPs. As a result, an additional 229 additional AMPs have been identified from Swiss-Prot, and all but 34 could be associated with known antimicrobial activities according to the literature. The final set of 1045 mature peptides and 253 propeptides have been organized into the open-source AMPer database. AVAILABILITY The developed HMM-based tools and AMP sequences can be accessed through the AMPer resource at http://www.cnbi2.com/cgi-bin/amp.pl

INMEX--a web-based tool for integrative meta-analysis of expression data.

The widespread applications of various ‘omics’ technologies in biomedical research together with the emergence of public data repositories have resulted in a plethora of data sets for almost any given physiological state or disease condition. Properly combining or integrating these data sets with similar basic hypotheses can help reduce study bias, increase statistical power and improve overall biological understanding. However, the difficulties in data management and the complexities of analytical approaches have significantly limited data integration to enable meta-analysis. Here, we introduce integrative meta-analysis of expression data (INMEX), a user-friendly web-based tool designed to support meta-analysis of multiple gene-expression data sets, as well as to enable integration of data sets from gene expression and metabolomics experiments. INMEX contains three functional modules. The data preparation module supports flexible data processing, annotation and visualization of individual data sets. The statistical analysis module allows researchers to combine multiple data sets based on P-values, effect sizes, rank orders and other features. The significant genes can be examined in functional analysis module for enriched Gene Ontology terms or Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, or expression profile visualization. INMEX has built-in support for common gene/metabolite identifiers (IDs), as well as 45 popular microarray platforms for human, mouse and rat. Complex operations are performed through a user-friendly web interface in a step-by-step manner. INMEX is freely available at http://www.inmex.ca.

Evaluating Different Descriptors for Model Design of Antimicrobial Peptides with Enhanced Activity Toward P. aeruginosa

The number of isolated drug‐resistant pathogenic microbes has increased drastically over the past decades, demonstrating an urgent need for new therapeutic interventions. Antimicrobial peptides have for a long time been looked upon as an interesting template for drug optimization. However, the process of optimizing peptide antimicrobial activity and specificity, using large peptide libraries is both tedious and expensive. Here, we describe the construction of a mathematical model for prediction, prior to synthesis, of peptide antibacterial activity toward Pseudomonas aeruginosa. By use of novel descriptors quantifying the contact energy between neighboring amino acids in addition to a set of inductive and conventional quantitative structure–activity relationship descriptors, we are able to model the peptides antibacterial activity. Cross‐correlation and optimization of the implemented descriptor values have enabled us to build a model (Bac2a‐ #2) that was able to correctly predict the activity of 84% of the tested peptides, within a twofold deviation window of the corresponding IC50 values, measured earlier. The predictive power, is an average of 10 submodels, each predicting the activity of 20 randomly excluded peptides, with a predictive success of 16.7 ± 1.6 peptides. The model has also been proven significantly more accurate than a simpler model (Bac2a‐ #1), where the inductive and conventional quantitative structure–activity relationship descriptors were excluded.

Optimization of Antibacterial Peptides by Genetic Algorithms and Cheminformatics

Pathogens resistant to available drug therapies are a pressing global health problem. Short, cationic peptides represent a novel class of agents that have lower rates of drug resistance than derivatives of current antibiotics. Previously, we created a software system utilizing artificial neural networks that were trained on quantitative structure‐activity relationship descriptors calculated for a total of 1400 synthetic peptides for which antibacterial activity was determined. Using the trained system, we correctly identified additional peptides with activity of 94% accuracy; active peptides were 47 of the top rated 50 peptides chosen from an in silico library of nearly 100 000 sequences. Here, we report a method of generating candidate peptide sequences using the heuristic evolutionary programming method of genetic algorithms (GA), which provided a large (19‐fold) improvement in identification of novel antibacterial peptides. Approximately 0.50% of peptides evaluated during the GA method were classified as highly active, while only 0.026% of the nearly 100 000 sequences we previously screened were classified as highly active. A selection of these peptides was tested in vitro and activities reported here. While GA significantly improves the possibility of identifying candidate peptides, we encountered important pitfalls to this method that should be considered when using GA.

Innate defense regulator peptide 1018 protects against perinatal brain injury

There is currently no pharmacological treatment that provides protection against brain injury in neonates. It is known that activation of an innate immune response is a key, contributing factor in perinatal brain injury; therefore, the neuroprotective therapeutic potential of innate defense regulator peptides (IDRs) was investigated.

Synthetic Cationic Peptide IDR-1018 Modulates Human Macrophage Differentiation

Macrophages play a critical role in the innate immune response. To respond in a rapid and efficient manner to challenges in the micro-environment, macrophages are able to differentiate towards classically (M1) or alternatively (M2) activated phenotypes. Synthetic, innate defense regulators (IDR) peptides, designed based on natural host defence peptides, have enhanced immunomodulatory activities and reduced toxicity leading to protection in infection and inflammation models that is dependent on innate immune cells like monocytes/macrophages. Here we tested the effect of IDR-1018 on macrophage differentiation, a process essential to macrophage function and the immune response. Using transcriptional, protein and systems biology analysis, we observed that differentiation in the presence of IDR-1018 induced a unique signature of immune responses including the production of specific pro and anti-inflammatory mediators, expression of wound healing associated genes, and increased phagocytosis of apoptotic cells. Transcription factor IRF4 appeared to play an important role in promoting this IDR-1018-induced phenotype. The data suggests that IDR-1018 drives macrophage differentiation towards an intermediate M1–M2 state, enhancing anti-inflammatory functions while maintaining certain pro-inflammatory activities important to the resolution of infection. Synthetic peptides like IDR-1018, which act by modulating the immune system, could represent a powerful new class of therapeutics capable of treating the rising number of multidrug resistant infections as well as disorders associated with dysregulated immune responses.