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Microbial biofilms cause several environmental and industrial issues, even on the human health. Although they have represented a threaten due to their resistance to antibiotics, there are currently no approved antibiofilm agents for clinical treatments. The multi-functionality of antimicrobial peptides (AMPs) including the antibiofilm activity and their potentialities to target multiple mi-crobes motivated the synthesis of AMP relatives for developing antibiofilm agents for clinical purposes. Antibiofilm peptides (ABFPs) have been organized in databases that allowed the building of prediction tools which have assisted in the discovery/design of new antibiofilm agents. However, the complex network approach has been explored yet as an assistant tool for this aim. Herein, a kind of similarity networks, called the Half-Space Proximal Network (HSPN) is applied to represent/analyse the chemical space of the ABFPs aimed to identify promising scaf-folds for the development of next generation antimicrobials, able to target both planktonic and biofilm microbial forms. Such analyses also considered the metadata associated to the ABFPs such as origin, other activities, targets, etc. in which the relationships were projected by multilayer networks called metadata networks (METNs). From the complex networks mining, a reduced but informative set of 66 ABFPs representing the original antibiofilm space was extracted. This subset retained from the most central to atypical ABFPs, having some of them, desired properties for developing next generation antimicrobials. So, this subset is advisable for assisting the search/design both new antibiofilm/antimicrobial agents. The provided ABFP motifs list, dis-covered within the HSPN communities, is also useful for the same purpose.

Supplementary Materials

Peptide-based drugs are promising anticancer candidates due to their biocompatibility, and low toxicity. Particularly, tumor homing peptides (THPs) have the ability to bind specifically to can-cer cells receptors and tumor vasculature. Despite their potential to develop antitumor drugs, there are few available prediction tools to assist the discovery of new THPs. Two webservers based on machine learning models are currently active, the TumorHPD (https://webs.iiitd.edu.in/raghava/tumorhpd) and the THPep (http://codes.bio/thpep), and more recently the SCMTHP (SCMTHP (pmlabstack.pythonanywhere.com), based on scoring card method. Herein, a novel method based on network science and similarity searching implemented in the starPep toolbox (http://mobiosd-hub.com/starpep/) is presented for THPs discovery. The approach leverages from exploring the structural space of THPs with Chemical Space Networks (CSNs) and from applying centrality measures to identify the most relevant and non-redundant THPs sequences within the CSN. Such THPs were considered as queries (Qs) for multi-query similarity searches that applies a group fusion (MAX-SIM rule) model. The resulting multi-query similarity searching models (SSMs) were validated with three benchmarking datasets of THPs/non-THPs. Predictions achieved accuracies ranged from 92.64 to 99.18% and Matthews Correlation Coefficients between 0.894-0.98, outperforming state-of-the-art predictors. The best model was applied to repurpose AMPs from the starPep database as THPs, which were subse-quently optimized for the TH activity. Finally, 54 promising THP leads were discovered, and their sequences were analyzed to encounter novel motifs. These results demonstrate the potential of CSNs and multi-query similarity searching for a rapid and accurate identification of THPs.

The selenoenzyme glutathione peroxidase (GPx) like activity of stable organoselenium compounds has been evaluated through the initial rate (ν0) of the reduction reaction of H2O2, Cum-OOH, and t-BuOOH. A Quantitative Structure–Activity Relationships (QSAR) analysis based on different machine learning techniques was performed by employing atom-weighed algebraic maps indexes as descriptors. The predictive capability of the obtained models was statistically validated by mean of the correlation coefficient for adjusting (R2), leave one out cross validation (Q2LOO), and bootstrapping (Q2boot). For the case of H2O2 reduction, a model was obtained with six attributes (M2) and values of R2 = 0.907, Q2LOO = 0.867, and Q2boot = 0.852. For the cum-OOH reduction, a model was obtained with five attributes (M15) with the statistical parameters: R2 = 0.925, Q2LOO = 0.894, and Q2boot = 0.873. For the t-BuOOH reduction, a model with four descriptors (M19) was found with the values of R2 = 0.938, Q2LOO = 0.897, Q2boot = 0.856. The statistical parameters obtained for these three models suggest that they are robust enough with good predictive capability. Finally, screening analysis of some related compounds containing selenium was performed and two possible lead compounds were found (16 and 53), which can be used for the searching of candidates with GPx-like activity.

We define eight new macromolecular indices, and several related descriptors for proteins. The coarse grained methodology used for its deduction ensures its fast execution and becomes a powerful potential tool to explore large databases of protein structures. The indices are intended for stability studies, predicting Φ -values, predicting folding rate constants, protein QSAR/QSPR as well as protein alignment studies. Also, these indices could be used as scoring function in protein-protein docking or 3D protein structure prediction algorithms and any others applications which need a numerical code for proteins and/or residues from 2D or 3D format.

The compound VAM2-6 (1-methyl-7-nitro-4-(5-(piperidin-1-yl)pentyl)-3,4-dihydroquinoxalin-2(1H)-one) has previously been shown to have an in vitro efficacy of 100% at a concentration of 100μgml-1 against Trichomonas vaginalis, a protozoon parasite that causes the sexually transmitted disease trichomoniasis. Because VAM2-6 is a quinoxaline derivative and given the lack of studies on the genotoxic activity of this compound, the present study was undertaken to evaluate its ability to induce DNA damage in the peripheral blood of mice using single-cell gel electrophoresis (SCGE or comet assay) and the micronucleus (MN) assay. Cell viability was assessed using a fluorochrome-mediated viability test. The compound was tested on CD1 mice at 60, 40 and 10mgkg-1 body weight administrated intraperitoneal (i.p.) in a single dose. Peripheral blood samples were collected 24 and 48h after treatment. N-Ethyl-N-nitrosourea (ENU) was used as a positive control for the comet and micronucleus assays. The results showed that i.p. VAM2-6 induced single-strand DNA breaks and increased the average number of micronuclei in the treated mice in a dose-dependent manner at 60, 40 and 10mgkg-1. Cell viability decreased at 24h but recovered at 48h for all three evaluated doses. Therefore, the chemical structure of VAM2-6 should be modified to reduce its genotoxic potential. © 2012 John Wiley & Sons, Ltd.

In the present manuscript, an extension of the previously defined Graph Derivative Indices (GDIs) is discussed. To achieve this objective, the concept of a hypermatrix, conceived from the calculation of the frequencies of triple and quadruple atom relations in a set of connected sub-graphs, is introduced. This set of subgraphs is generated following a predefined criterion, known as the event (S), being in this particular case the connectivity among atoms. The triple and quadruple relations frequency matrices serve as a basis for the computation of triple and quadruple discrete derivative indices, respectively. The GDIs are implemented in a computational program denominated DIVATI (acronym for DIscrete DeriVAtive Type Indices), a module of TOMOCOMD-CARDD program. Shannon‟s entropy-based variability analysis demonstrates that the GDIs show major variability than others indices used in QSAR/QSPR researches. In addition, it can be appreciated when the indices are extended over n-elements from the graph, its quality increases, principally when they are used in a combined way. QSPR modeling of the physicochemical properties Log P and Log K of the 2-furylethylenes derivatives reveals that the GDIs obtained using the tripleand quadruple matrix approaches yield superior performance to the duplex matrix approach. Moreover, the statistical parameters for models obtained with the GDI method are superior to those reported in the literature by using other methods. It can therefore be suggested that the GDI method, seem to be a promissory tool to reckon on in QSAR/QSPR studies, virtual screening of compound datasets and similarity/dissimilarity evaluations.