Design of Bactericidal Peptides Against Escherichia coli O157:H7, Pseudomonas aeruginosa and methicillin-resistant Staphylococcus aureus.

BACKGROUND: Antimicrobial peptides are on the first line of defense against pathogenic microorganisms of many living beings. These compounds are considered natural antibiotics that can overcome bacterial resistance to conventional antibiotics. Due to this characteristic, new peptides with improved properties are quite appealing for designing new strategies for fighting pathogenic bacteria. METHODS: Sixteen designed peptides were synthesized using Fmoc chemistry; five of them are new cationic antimicrobial peptides (CAMPs) designed using a genetic algorithm that optimizes the antibacterial activity based on selected physicochemical descriptors and 11 analog peptides derived from these five peptides were designed and constructed by single amino acid substitutions. These 16 peptides were structurally characterized and their biological activity was determined against Escherichia coli O157:H7 (E. coli O157:H7), and methicillin-resistant strains of Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa (P. aeruginosa) were determined. RESULTS: These 16 peptides were folded into an α-helix structure in membrane-mimicking environment. Among these 16 peptides, GIBIM-P5S9K (ATKKCGLFKILKGVGKI) showed the highest antimicrobial activity against E. coli O157:H7 (MIC=10µM), methicillin-resistant Staphylococcus aureus (MRSA) (MIC=25µM) and Pseudomonas aeruginosa (MIC=10 µM). Peptide GIBIM-P5S9K caused permeabilization of the bacterial membrane at 25 µM as determined by the Sytox Green uptake assay and the labelling of these bacteria by using the fluoresceinated peptide. GIBIM-P5S9K seems to be specific for these bacteria because at 50 µM, it provoked lower than 40% of erythrocyte hemolysis. CONCLUSION: New CAMPs have been designed using a genetic algorithm based on selected physicochemical descriptors and single amino acid substitution. These CAMPs interacted quite specifically with the bacterial cell membrane, GIBIM-P5S9K exhibiting high antibacterial activity on Escherichia coli O157:H7, methicillin-resistant strains of Staphylococcus aureus and P. aeruginosa.

DNA secondary structure formation by DNA shuffling of the conserved domains of the Cry protein of Bacillus thuringiensis.

BACKGROUND: The Cry toxins, or δ-endotoxins, are a diverse group of proteins produced by Bacillus thuringiensis. While DNA secondary structures are biologically relevant, it is unknown if such structures are formed in regions encoding conserved domains of Cry toxins under shuffling conditions. We analyzed 5 holotypes that encode Cry toxins and that grouped into 4 clusters according to their phylogenetic closeness. The mean number of DNA secondary structures that formed and the mean Gibbs free energy [Formula: see text] were determined by an in silico analysis using different experimental DNA shuffling scenarios. In terms of spontaneity, shuffling efficiency was directly proportional to the formation of secondary structures but inversely proportional to ∆G. RESULTS: The results showed a shared thermodynamic pattern for each cluster and relationships among sequences that are phylogenetically close at the protein level. The regions of the cry11Aa, Ba and Bb genes that encode domain I showed more spontaneity and thus a greater tendency to form secondary structures (<∆G). In the region of domain III; this tendency was lower (>∆G) in the cry11Ba and Bb genes. Proteins that are phylogenetically closer to Cry11Ba and Cry11Bb, such as Cry2Aa and Cry18Aa, maintained the same thermodynamic pattern. More distant proteins, such as Cry1Aa, Cry1Ab, Cry30Aa and Cry30Ca, featured different thermodynamic patterns in their DNA. CONCLUSION: These results suggest the presence of thermodynamic variations associated to the formation of secondary structures and an evolutionary relationship with regions that encode highly conserved domains in Cry proteins. The findings of this study may have a role in the in silico design of cry gene assembly by DNA shuffling techniques.

Machine learning in the rational design of antimicrobial peptides.

One of the most important public health issues is the microbial and bacterial resistance to conventional antibiotics by pathogen microorganisms. In recent years, many researches have been focused on the development of new antibiotics. Among these, antimicrobial peptides (AMPs) have raised as a promising alternative to combat antibioticresistant microorganisms. For this reason, many theoretical efforts have been done in the development of new computational tools for the rational design of both better and effective AMPs. In this review, we present an overview of the rational design of AMPs using machine learning techniques and new research fields.

A new linear muscle fiber model for neural control of saccades.

A comprehensive model for the control of horizontal saccades is presented using a new muscle fiber model for the lateral and medial rectus muscles. The importance of this model is that each muscle fiber has a separate neural input. This model is robust and accounts for the neural activity for both large and small saccades. The muscle fiber model consists of serial sequences of muscle fibers in parallel with other serial sequences of muscle fibers. Each muscle fiber is described by a parallel combination of a linear length tension element, viscous element and active state tension generator.

Brain tumour classification using Gaussian decomposition and neural networks.

The development, implementation and use of computer-based medical decision support systems (MDSS) based on pattern recognition techniques holds the promise of substantially improving the quality of medical practice in diagnostic and prognostic tasks. In this study, the core of a decision support system for brain tumour classification from magnetic resonance spectroscopy (MRS) data is presented. It combines data pre-processing using Gaussian decomposition, dimensionality reduction using moving window with variance analysis, and classification using artificial neural networks (ANN). This combination of techniques is shown to yield high diagnostic classification accuracy in problems concerning diverse brain tumour pathologies, some of which have received little attention in the literature.

Linear homeomorphic models for muscles in the head-neck region.

The linear homeomorphic muscle model proposed by Enderle and coworkers for the rectus eye muscle is fitted to reflect the dynamics of muscles in the head-neck complex, specifically in muscles involved in gaze shifts. This parameterization of the model for different muscles in the neck region will serve to drive a 3D dynamic computer model for the movement of the head-neck complex, including bony structures and soft tissues, and aimed to study the neural control of the complex during fast eye and head movements such as saccades and gaze shifts. Parameter values for the different muscles in the neck region were obtained by optimization using simulated annealing. These linear homeomorphic muscle models provide non-linear force-velocity profiles and linear length tension profiles, which are in agreement with results from the more complex Virtual Muscle model, which is based on Zajac's non-linear muscle model.

3D dynamic computer model of the head-neck complex.

A 3D dynamic computer model for the movement of the head is presented that incorporates anatomically correct information about the diverse elements forming the system. The skeleton is considered as a set of interconnected rigid 3D bodies following the Newton-Euler laws of movement. The muscles are modeled using Enderle's linear model. Finally, the soft tissues, namely the ligaments, intervertebral disks, and zigapophysial joints, are modeled using the finite elements approach. The model is intended to study the neural network that controls movement and maintains the balance of the head-neck complex during eye movements.