Short cationic amphiphilic peptides with antimicrobial and/or immunomodulatory activities are present in virtually every life form, as an important component of (innate) immune defenses. These host-defense peptides provide a template for two separate classes of antimicrobial drugs. Direct-acting antimicrobial host-defense peptides can be rapid-acting and potent, and possess an unusually broad spectrum of activity; consequently, they have prospects as new antibiotics, although clinical trials to date have shown efficacy only as topical agents. But for these compounds to fulfill their therapeutic promise and overcome clinical setbacks, further work is needed to understand their mechanisms of action and reduce the potential for unwanted toxicity, to make them more resistant to protease degradation and improve serum half-life, as well as to devise means of manufacturing them on a large scale in a consistent and cost-effective manner. In contrast, the role of cationic host-defense peptides in modulating the innate immune response and boosting infection-resolving immunity while dampening potentially harmful pro-inflammatory (septic) responses gives these peptides the potential to become an entirely new therapeutic approach against bacterial infections.

Antimicrobial and host-defense peptides as new anti-infective therapeutic strategies.

Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement neighbor-joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest-neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N^2) space and O(N^2 L) time, but FastTree requires just O( NLa + N sqrt(N) ) memory and O( N sqrt(N) log(N) L a ) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 hours and 2.4 gigabytes of memory. Just computing pairwise Jukes-Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 hours and 50 gigabytes of memory. In simulations, FastTree was slightly more accurate than neighbor joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.

/pdf/fast-tree-computing-large-minimum-evolution-trees-with-4grq0gwfpy.pdf

Fast Tree: Computing Large Minimum-Evolution Trees with Profiles instead of a Distance Matrix

The major strategies for designing surfaces that prevent fouling due to proteins, bacteria, and marine organisms are reviewed. Biofouling is of great concern in numerous applications ranging from biosensors to biomedical implants and devices, and from food packaging to industrial and marine equipment. The two major approaches to combat surface fouling are based on either preventing biofoulants from attaching or degrading them. One of the key strategies for imparting adhesion resistance involves the functionalization of surfaces with poly(ethylene glycol) (PEG) or oligo(ethylene glycol). Several alternatives to PEG-based coatings have also been designed over the past decade. While protein-resistant coatings may also resist bacterial attachment and subsequent biofilm formation, in order to overcome the fouling-mediated risk of bacterial infection it is highly desirable to design coatings that are bactericidal. Traditional techniques involve the design of coatings that release biocidal agents, including antibiotics, quaternary ammonium salts (QAS), and silver, into the surrounding aqueous environment. However, the emergence of antibiotic- and silver-resistant pathogenic strains has necessitated the development of alternative strategies. Therefore, other techniques based on the use of polycations, enzymes, nanomaterials, and photoactive agents are being investigated. With regard to marine antifouling coatings, restrictions on the use of biocide-releasing coatings have made the generation of nontoxic antifouling surfaces more important. While considerable progress has been made in the design of antifouling coatings, ongoing research in this area should result in the development of even better antifouling materials in the future.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adma.201001215

Antifouling coatings: recent developments in the design of surfaces that prevent fouling by proteins, bacteria, and marine organisms.

Antimicrobial host defense peptides are produced by all complex organisms as well as some microbes and have diverse and complex antimicrobial activities. Collectively these peptides demonstrate a broad range of antiviral and antibacterial activities and modes of action, and it is important to distinguish between direct microbicidal and indirect activities against such pathogens. The structural requirements of peptides for antiviral and antibacterial activities are evaluated in light of the diverse set of primary and secondary structures described for host defense peptides. Peptides with antifungal and antiparasitic activities are discussed in less detail, although the broad-spectrum activities of such peptides indicate that they are important host defense molecules. Knowledge regarding the relationship between peptide structure and function as well as their mechanism of action is being applied in the design of antimicrobial peptide variants as potential novel therapeutic agents.

Peptide Antimicrobial Agents

Because of the high catalytic activities and substrate specificity, natural enzymes have been widely used in industrial, medical, and biological fields, etc. Although promising, they often suffer from intrinsic shortcomings such as high cost, low operational stability, and difficulties of recycling. To overcome these shortcomings, researchers have been devoted to the exploration of artificial enzyme mimics for a long time. Since the discovery of ferromagnetic nanoparticles with intrinsic horseradish peroxidase-like activity in 2007, a large amount of studies on nanozymes have been constantly emerging in the next decade. Nanozymes are one kind of nanomaterials with enzymatic catalytic properties. Compared with natural enzymes, nanozymes have the advantages such as low cost, high stability and durability, which have been widely used in industrial, medical, and biological fields. A thorough understanding of the possible catalytic mechanisms will contribute to the development of novel and high-efficient nanozymes, and the rational regulations of the activities of nanozymes are of great significance. In this review, we systematically introduce the classification, catalytic mechanism, activity regulation as well as recent research progress of nanozymes in the field of biosensing, environmental protection, and disease treatments, etc. in the past years. We also propose the current challenges of nanozymes as well as their future research focus. We anticipate this review may be of significance for the field to understand the properties of nanozymes and the development of novel nanomaterials with enzyme mimicking activities.

Nanozymes: Classification, Catalytic Mechanisms, Activity Regulation, and Applications

http://www.brasnutri.org.br/arquivos/estudos_tecnicos/Antimicrobial_porpertiers_of_lactoferrin.pdf

Antimicrobial properties of lactoferrin

Increased multiple antibiotic resistance in the face of declining antibiotic discovery is one of society's most pressing health issues. Antimicrobial peptides represent a promising new class of antibiotics. Here we ask whether it is possible to make small broad spectrum peptides employing minimal assumptions, by capitalizing on accumulating chemical biology information. Using peptide array technology, two large random 9-amino-acid peptide libraries were iteratively created using the amino acid composition of the most active peptides. The resultant data was used together with Artificial Neural Networks, a powerful machine learning technique, to create quantitative in silico models of antibiotic activity. On the basis of random testing, these models proved remarkably effective in predicting the activity of 100,000 virtual peptides. The best peptides, representing the top quartile of predicted activities, were effective against a broad array of multidrug-resistant "Superbugs" with activities that were equal to or better than four highly used conventional antibiotics, more effective than the most advanced clinical candidate antimicrobial peptide, and protective against Staphylococcus aureus infections in animal models.

Use of Artificial Intelligence in the Design of Small Peptide Antibiotics Effective against a Broad Spectrum of Highly Antibiotic-Resistant Superbugs

Antimicrobial peptides on calcium phosphate-coated titanium for the prevention of implant-associated infections.

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.

Håvard Jenssen

Papers

Peptide Antimicrobial Agents

Antimicrobial properties of lactoferrin

Use of Artificial Intelligence in the Design of Small Peptide Antibiotics Effective against a Broad Spectrum of Highly Antibiotic-Resistant Superbugs

Antimicrobial peptides on calcium phosphate-coated titanium for the prevention of implant-associated infections.

Identification of novel antibacterial peptides by chemoinformatics and machine learning.