Homology modeling study of bovine µ-calpain inhibitor-binding domains.

The activated mammalian CAPN-structures, the CAPN/CAST complex in particular, have become an invaluable target model using the structure-based virtual screening of drug candidates from the discovery phase to development for over-activated CAPN linked to several diseases, such as post-ischemic injury and cataract formation. The effect of Ca²âº-binding to the enzyme is thought to include activation, as well as the dissociation, aggregation, and autolysis of small regular subunits. Unfortunately, the Ca²âº-activated enzyme tends to aggregate when provided as a divalent ion at the high-concentration required for the protease crystallization. This is also makes it very difficult to crystallize the whole-length enzyme itself, as well as the enzyme-inhibitor complex. Several parameters that influence CAPN activity have been investigated to determine its roles in Ca²âº-modulation, autoproteolysis, phosphorylation, and intracellular distribution and inhibition by its endogenous inhibitor CAST. CAST binds and inhibits CAPN via its CAPN-inhibitor domains (four repeating domains 1-4; CAST1-4) when CAPN is activated by Ca²âº-binding. An important key to understanding CAPN1 inhibition by CAST is to determine how CAST interacts at the molecular level with CAPN1 to inhibit its protease activity. In this study, a 3D structure model of a CAPN1 bound bovine CAST4 complex was built by comparative modeling based on the only known template structure of a rat CAPN2/CAST4 complex. The complex model suggests certain residues of bovine CAST4, notably, the TIPPKYQ motif sequence, and the structural elements of these residues, which are important for CAPN1 inhibition. In particular, as CAST4 docks near the flexible active site of CAPN1, conformational changes at the interaction site after binding could be directly related to CAST4 inhibitory activity. These functional interfaces can serve as a guide to the site-mutagenesis in research on bovine CAPN1 structure-function relationships for the design of small molecules inhibitors to prevent uncontrolled and unspecific degradation in the proteolysis of key protease substrates.

Understanding the interaction determinants of CAPN1 inhibition by CAST4 from bovines using molecular modeling techniques.

HCV-induced CAPN activation and its effects on virus-infected cells in a host-immune system have been studied recently. It has been shown that the HCV-nonstructural 5A protein acts as both an inducer and a substrate for host CAPN protease; it participates in suppressing the TNF-α-induced apoptosis response and downstream IFN-induced antiviral processes. However, little is known regarding the disturbance of antiviral responses generated by bovine CAPN activation by BVDV, which is a surrogate model of HCV and is one of the most destructive diseases leading to great economic losses in cattle herds worldwide. This is also thought to be associated with the effects of either small CAPN inhibitors or the natural inhibitor CAST. They mainly bind to the binding site of CAPN substrate proteins and competitively inhibit the binding of the enzyme substrates to possibly defend against the two viruses (HCV and BVDV) for anti-viral immunity. To devise a new stratagem to discover lead candidates for an anti-BVDV drug, we first attempted to understand the bovine CAPN-CAST interaction sites and the interaction constraints of local binding architectures, were well reflected in the geometry between the pharmacophore features and its shape constraints identified using our modeled bovine CAPN1/CAST4 complex structures. We propose a computer-aided molecular design of an anti-BVDV drug as a mimetic CAST inhibitor to develop a rule-based screening function for adjusting the puzzle of relationship between bovine CAPN1 and the BVDV nonstructural proteins from all of the data obtained in the study.

Identification of tissue-specific targeting peptide.

Using phage display technique, we identified tissue-targeting peptide sets that recognize specific tissues (bone-marrow dendritic cell, kidney, liver, lung, spleen and visceral adipose tissue). In order to rapidly evaluate tissue-specific targeting peptides, we performed machine learning studies for predicting the tissue-specific targeting activity of peptides on the basis of peptide sequence information using four machine learning models and isolated the groups of peptides capable of mediating selective targeting to specific tissues. As a representative liver-specific targeting sequence, the peptide "DKNLQLH" was selected by the sequence similarity analysis. This peptide has a high degree of homology with protein ligands which can interact with corresponding membrane counterparts. We anticipate that our models will be applicable to the prediction of tissue-specific targeting peptides which can recognize the endothelial markers of target tissues.

Machine learning study for the prediction of transdermal peptide.

In order to develop a computational method to rapidly evaluate transdermal peptides, we report approaches for predicting the transdermal activity of peptides on the basis of peptide sequence information using Artificial Neural Network (ANN), Partial Least Squares (PLS) and Support Vector Machine (SVM). We identified 269 transdermal peptides by the phage display technique and use them as the positive controls to develop and test machine learning models. Combinations of three descriptors with neural network architectures, the number of latent variables and the kernel functions are tried in training to make appropriate predictions. The capacity of models is evaluated by means of statistical indicators including sensitivity, specificity, and the area under the receiver operating characteristic curve (ROC score). In the ROC score-based comparison, three methods proved capable of providing a reasonable prediction of transdermal peptide. The best result is obtained by SVM model with a radial basis function and VHSE descriptors. The results indicate that it is possible to discriminate between transdermal peptides and random sequences using our models. We anticipate that our models will be applicable to prediction of transdermal peptide for large peptide database for facilitating efficient transdermal drug delivery through intact skin.

Synbiotics Containing Nanoprebiotics: A Novel Therapeutic Strategy to Restore Gut Dysbiosis.

A new formulation, nanoprebiotics [e.g., phthalyl pullulan nanoparticles (PPNs)], was demonstrated to enhance the antimicrobial activity of probiotics [e.g., Lactobacillus plantarum (LP)] in vitro through intracellular stimulation better than that by backbone prebiotics, which are commonly used. In this study, we aimed to investigate whether this combination would exert distinct effects as synbiotics in vivo. Synbiotics combinations of LP, pullulan, and PPNs were used as experimental treatments in a dysbiosis-induced murine model, and their restorative effect was assessed using pathogen Escherichia coli K99 challenge. Our results showed that the E. coli infection was suppressed markedly in the experimental group fed with synbiotics containing PPNs. In addition, the decrease in serum endotoxin level after synbiotics treatment suggested the reinforcement of the gut barrier. Comparison of treatment groups, including a normal control group, showed that synbiotics containing PPNs increased microbial diversity, which is a representative parameter of healthy status. Furthermore, distinct from probiotics treatment alone, synbiotics showed additive effects of enrichment of several well-known beneficial bacteria such as Lactobacillus, Bifidobacterium, and other butyrate-producing bacteria including Faecalibacterium. Collectively, our results indicate that synbiotics containing PPNs are effective at restoring gut dysbiosis, suppressing pathogenic infection, and increasing microbial diversity, suggesting that synbiotics with nanoprebiotics have the potential to be a novel strategy for ameliorating gut dysbiosis and infectious diseases.

Artificial neural network study on organ-targeting peptides.

We report a new approach to studying organ targeting of peptides on the basis of peptide sequence information. The positive control data sets consist of organ-targeting peptide sequences identified by the peroral phage-display technique for four organs, and the negative control data are prepared from random sequences. The capacity of our models to make appropriate predictions is validated by statistical indicators including sensitivity, specificity, enrichment curve, and the area under the receiver operating characteristic (ROC) curve (the ROC score). VHSE descriptor produces statistically significant training models and the models with simple neural network architectures show slightly greater predictive power than those with complex ones. The training and test set statistics indicate that our models could discriminate between organ-targeting and random sequences. We anticipate that our models will be applicable to the selection of organ-targeting peptides for generating peptide drugs or peptidomimetics.

Artificial neural network models for prediction of intestinal permeability of oligopeptides.

BACKGROUND: Oral delivery is a highly desirable property for candidate drugs under development. Computational modeling could provide a quick and inexpensive way to assess the intestinal permeability of a molecule. Although there have been several studies aimed at predicting the intestinal absorption of chemical compounds, there have been no attempts to predict intestinal permeability on the basis of peptide sequence information. To develop models for predicting the intestinal permeability of peptides, we adopted an artificial neural network as a machine-learning algorithm. The positive control data consisted of intestinal barrier-permeable peptides obtained by the peroral phage display technique, and the negative control data were prepared from random sequences. RESULTS: The capacity of our models to make appropriate predictions was validated by statistical indicators including sensitivity, specificity, enrichment curve, and the area under the receiver operating characteristic (ROC) curve (the ROC score). The training and test set statistics indicated that our models were of strikingly good quality and could discriminate between permeable and random sequences with a high level of confidence. CONCLUSION: We developed artificial neural network models to predict the intestinal permeabilities of oligopeptides on the basis of peptide sequence information. Both binary and VHSE (principal components score Vectors of Hydrophobic, Steric and Electronic properties) descriptors produced statistically significant training models; the models with simple neural network architectures showed slightly greater predictive power than those with complex ones. We anticipate that our models will be applicable to the selection of intestinal barrier-permeable peptides for generating peptide drugs or peptidomimetics.

Membrane fluidity sensoring microbial fuel cell.

A study has been performed to examine the effect of temperature and ethanolic stresses on the coulombic efficiency of a microbial fuel cell. The conventional-type fuel cell containing Gram-negative bacteria, Proteus vulgaris, was investigated as a model system. From current output measurements, it was found that the coulombic yields were altered by environmental stresses such as temperature shock or ethanol treatment to the bacteria. While high-temperature or ethanolic shock led to a remarkable decrement in coulombic output, the low-temperature shock induced a slight increase in microbial fuel cell efficiency. These results indicate that the membrane fluidity is affected considerably by environmental stress, which in turn affects the electron transfer process through the bacterial cell membrane to and from the electrode. This interpretation was confirmed by the cyclic voltammetric study of a mediator on an electrode surface modified with the lipids extracted from the membrane of P. vulgaris under the given stress. Markedly different electrochemical behaviors were observed depending on the environmental stress. A reciprocal relationship between coulomb output and the ratio of saturation/unsaturation of fatty acids has been observed. This is the first report, to our knowledge, that the structural adaptation of membrane fatty acids in response to the environmental shock can regulate the coulombic efficiency of a microbial fuel cell.