Multiquery Similarity Searching Models: An Alternative Approach for Predicting Hemolytic Activity from Peptide Sequence.

The desirable pharmacological properties and a broad number of therapeutic activities have made peptides promising drugs over small organic molecules and antibody drugs. Nevertheless, toxic effects, such as hemolysis, have hampered the development of such promising drugs. Hence, a reliable computational tool to predict peptide hemolytic toxicity is enormously useful before synthesis and experimental evaluation. Currently, four web servers that predict hemolytic activity using machine learning (ML) algorithms are available; however, they exhibit some limitations, such as the need for a reliable negative set and limited application domain. Hence, we developed a robust model based on a novel theoretical approach that combines network science and a multiquery similarity searching (MQSS) method. A total of 1152 initial models were constructed from 144 scaffolds generated in a previous report. These were evaluated on external data sets, and the best models were fused and improved. Our best MQSS model I1 outperformed all state-of-the-art ML-based models and was used to characterize the prevalence of hemolytic toxicity on therapeutic peptides. Based on our model's estimation, the number of hemolytic peptides might be 3.9-fold higher than the reported.

Rethinking the applicability domain analysis in QSAR models.

Notwithstanding the wide adoption of the OECD principles (or best practices) for QSAR modeling, disparities between in silico predictions and experimental results are frequent, suggesting that model predictions are often too optimistic. Of these OECD principles, the applicability domain (AD) estimation has been recognized in several reports in the literature to be one of the most challenging, implying that the actual reliability measures of model predictions are often unreliable. Applying tree-based error analysis workflows on 5 QSAR models reported in the literature and available in the QsarDB repository, i.e., androgen receptor bioactivity (agonists, antagonists, and binders, respectively) and membrane permeability (highest membrane permeability and the intrinsic permeability), we demonstrate that predictions erroneously tagged as reliable (AD prediction errors) overwhelmingly correspond to instances in subspaces (cohorts) with the highest prediction error rates, highlighting the inhomogeneity of the AD space. In this sense, we call for more stringent AD analysis guidelines which require the incorporation of model error analysis schemes, to provide critical insight on the reliability of underlying AD algorithms. Additionally, any selected AD method should be rigorously validated to demonstrate its suitability for the model space over which it is applied. These steps will ultimately contribute to more accurate estimations of the reliability of model predictions. Finally, error analysis may also be useful in "rational" model refinement in that data expansion efforts and model retraining are focused on cohorts with the highest error rates.

Elucidating the Racemization Mechanism of Aliphatic and Aromatic Amino Acids by In Silico Tools.

The racemization of biomolecules in the active site can reduce the biological activity of drugs, and the mechanism involved in this process is still not fully comprehended. The present study investigates the impact of aromaticity on racemization using advanced theoretical techniques based on density functional theory. Calculations were performed at the ωb97xd/6-311++g(d,p) level of theory. A compelling explanation for the observed aromatic stabilization via resonance is put forward, involving a carbanion intermediate. The analysis, employing Hammett's parameters, convincingly supports the presence of a negative charge within the transition state of aromatic compounds. Moreover, the combined utilization of natural bond orbital (NBO) analysis and intrinsic reaction coordinate (IRC) calculations confirms the pronounced stabilization of electron distribution within the carbanion intermediate. To enhance our understanding of the racemization process, a thorough examination of the evolution of NBO charges and Wiberg bond indices (WBIs) at all points along the IRC profile is performed. This approach offers valuable insights into the synchronicity parameters governing the racemization reactions.

A 2022 Update on Computational Approaches to the Discovery and Design of Antimicrobial Peptides.

The antimicrobial resistance process has been accelerated by the over-prescription and misuse of antibiotics [...].

Complex Networks Analyses of Antibiofilm Peptides: An Emerging Tool for Next-Generation Antimicrobials' Discovery.

Microbial biofilms cause several environmental and industrial issues, even affecting human health. Although they have long represented a threat due to their resistance to antibiotics, there are currently no approved antibiofilm agents for clinical treatments. The multi-functionality of antimicrobial peptides (AMPs), including their antibiofilm activity and their potential to target multiple microbes, has motivated the synthesis of AMPs and their relatives for developing antibiofilm agents for clinical purposes. Antibiofilm peptides (ABFPs) have been organized in databases that have allowed the building of prediction tools which have assisted in the discovery/design of new antibiofilm agents. However, the complex network approach has not yet been explored as an assistant tool for this aim. Herein, a kind of similarity network called the half-space proximal network (HSPN) is applied to represent/analyze the chemical space of ABFPs, aiming to identify privileged scaffolds for the development of next-generation antimicrobials that are able to target both planktonic and biofilm microbial forms. Such analyses also considered the metadata associated with 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 was extracted, representing the original antibiofilm space. This subset contained the most central to atypical ABFPs, some of them having the desired properties for developing next-generation antimicrobials. Therefore, this subset is advisable for assisting the search for/design of both new antibiofilms and antimicrobial agents. The provided ABFP motifs list, discovered within the HSPN communities, is also useful for the same purpose.

ABP-Finder: A Tool to Identify Antibacterial Peptides and the Gram-Staining Type of Targeted Bacteria.

Multi-drug resistance in bacteria is a major health problem worldwide. To overcome this issue, new approaches allowing for the identification and development of antibacterial agents are urgently needed. Peptides, due to their binding specificity and low expected side effects, are promising candidates for a new generation of antibiotics. For over two decades, a large diversity of antimicrobial peptides (AMPs) has been discovered and annotated in public databases. The AMP family encompasses nearly 20 biological functions, thus representing a potentially valuable resource for data mining analyses. Nonetheless, despite the availability of machine learning-based approaches focused on AMPs, these tools lack evidence of successful application for AMPs' discovery, and many are not designed to predict a specific function for putative AMPs, such as antibacterial activity. Consequently, among the apparent variety of data mining methods to screen peptide sequences for antibacterial activity, only few tools can deal with such task consistently, although with limited precision and generally no information about the possible targets. Here, we addressed this gap by introducing a tool specifically designed to identify antibacterial peptides (ABPs) with an estimation of which type of bacteria is susceptible to the action of these peptides, according to their response to the Gram-staining assay. Our tool is freely available via a web server named ABP-Finder. This new method ranks within the top state-of-the-art ABP predictors, particularly in terms of precision. Importantly, we showed the successful application of ABP-Finder for the screening of a large peptide library from the human urine peptidome and the identification of an antibacterial peptide.

Network Science and Group Fusion Similarity-Based Searching to Explore the Chemical Space of Antiparasitic Peptides.

Antimicrobial peptides (AMPs) have appeared as promising compounds to treat a wide range of diseases. Their clinical potentialities reside in the wide range of mechanisms they can use for both killing microbes and modulating immune responses. However, the hugeness of the AMPs' chemical space (AMPCS), represented by more than 1065 unique sequences, has represented a big challenge for the discovery of new promising therapeutic peptides and for the identification of common structural motifs. Here, we introduce network science and a similarity searching approach to discover new promising AMPs, specifically antiparasitic peptides (APPs). We exploited the network-based representation of APPs' chemical space (APPCS) to retrieve valuable information by using three network types: chemical space (CSN), half-space proximal (HSPN), and metadata (METN). Some centrality measures were applied to identify in each network the most important and nonredundant peptides. Then, these central peptides were considered as queries (Qs) in group fusion similarity-based searches against a comprehensive collection of known AMPs, stored in the graph database StarPepDB, to propose new potential APPs. The performance of the resulting multiquery similarity-based search models (mQSSMs) was evaluated in five benchmarking data sets of APP/non-APPs. The predictions performed by the best mQSSM showed a strong-to-very-strong performance since their external Matthews correlation coefficient (MCC) values ranged from 0.834 to 0.965. Outstanding MCC values (>0.85) were attained by the mQSSM with 219 Qs from both networks CSN and HSPN with 0.5 as similarity threshold in external data sets. Then, the performance of our best mQSSM was compared with the APPs prediction servers AMPDiscover and AMPFun. The proposed model showed its relevance by outperforming state-of-the-art machine learning models to predict APPs. After applying the best mQSSM and additional filters on the non-APP space from StarPepDB, 95 AMPs were repurposed as potential APP hits. Due to the high sequence diversity of these peptides, different computational approaches were applied to identify relevant motifs for searching and designing new APPs. Lastly, we identified 11 promising APP lead candidates by using our best mQSSMs together with diversity-based network analyses, and 24 web servers for activity/toxicity and drug-like properties. These results support that network-based similarity searches can be an effective and reliable strategy to identify APPs. The proposed models and pipeline are freely available through the StarPep toolbox software at http://mobiosd-hub.com/starpep.

Fuzzy spherical truncation-based multi-linear protein descriptors: From their definition to application in structural-related predictions.

This study introduces a set of fuzzy spherically truncated three-dimensional (3D) multi-linear descriptors for proteins. These indices codify geometric structural information from kth spherically truncated spatial-(dis)similarity two-tuple and three-tuple tensors. The coefficients of these truncated tensors are calculated by applying a smoothing value to the 3D structural encoding based on the relationships between two and three amino acids of a protein embedded into a sphere. At considering, the geometrical center of the protein matches with center of the sphere, the distance between each amino acid involved in any specific interaction and the geometrical center of the protein can be computed. Then, the fuzzy membership degree of each amino acid from an spherical region of interest is computed by fuzzy membership functions (FMFs). The truncation value is finally a combination of the membership degrees from interacting amino acids, by applying the arithmetic mean as fusion rule. Several fuzzy membership functions with diverse biases on the calculation of amino acids memberships (e.g., Z-shaped (close to the center), PI-shaped (middle region), and A-Gaussian (far from the center)) were considered as well as traditional truncation functions (e.g., Switching). Such truncation functions were comparatively evaluated by exploring: 1) the frequency of membership degrees, 2) the variability and orthogonality analyses among them based on the Shannon Entropy's and Principal Component's methods, respectively, and 3) the prediction performance of alignment-free prediction of protein folding rates and structural classes. These analyses unraveled the singularity of the proposed fuzzy spherically truncated MDs with respect to the classical (non-truncated) ones and respect to the MDs truncated with traditional functions. They also showed an improved prediction power by attaining an external correlation coefficient of 95.82% in the folding rate modelling and an accuracy of 100% in distinguishing structural protein classes. These outcomes are better than the ones attained by existing approaches, justifying the theoretical contribution of this report. Thus, the fuzzy spherically truncated-based protein descriptors from MuLiMs-MCoMPAs (http://tomocomd.com/mulims-mcompas) are promising alignment-free predictors for modeling protein functions and properties.

Emerging Computational Approaches for Antimicrobial Peptide Discovery.

In the last two decades many reports have addressed the application of artificial intelligence (AI) in the search and design of antimicrobial peptides (AMPs). AI has been represented by machine learning (ML) algorithms that use sequence-based features for the discovery of new peptidic scaffolds with promising biological activity. From AI perspective, evolutionary algorithms have been also applied to the rational generation of peptide libraries aimed at the optimization/design of AMPs. However, the literature has scarcely dedicated to other emerging non-conventional in silico approaches for the search/design of such bioactive peptides. Thus, the first motivation here is to bring up some non-standard peptide features that have been used to build classical ML predictive models. Secondly, it is valuable to highlight emerging ML algorithms and alternative computational tools to predict/design AMPs as well as to explore their chemical space. Another point worthy of mention is the recent application of evolutionary algorithms that actually simulate sequence evolution to both the generation of diversity-oriented peptide libraries and the optimization of hit peptides. Last but not least, included here some new considerations in proteogenomic analyses currently incorporated into the computational workflow for unravelling AMPs in natural sources.

Computational Design of Inhibitors Targeting the Catalytic ß Subunit of Escherichia coli FOF1-ATP Synthase.

With the uncontrolled growth of multidrug-resistant bacteria, there is an urgent need to search for new therapeutic targets, to develop drugs with novel modes of bactericidal action. FoF1-ATP synthase plays a crucial role in bacterial bioenergetic processes, and it has emerged as an attractive antimicrobial target, validated by the pharmaceutical approval of an inhibitor to treat multidrug-resistant tuberculosis. In this work, we aimed to design, through two types of in silico strategies, new allosteric inhibitors of the ATP synthase, by targeting the catalytic ß subunit, a centerpiece in communication between rotor subunits and catalytic sites, to drive the rotary mechanism. As a model system, we used the F1 sector of Escherichia coli, a bacterium included in the priority list of multidrug-resistant pathogens. Drug-like molecules and an IF1-derived peptide, designed through molecular dynamics simulations and sequence mining approaches, respectively, exhibited in vitro micromolar inhibitor potency against F1. An analysis of bacterial and Mammalia sequences of the key structural helix-turn-turn motif of the C-terminal domain of the ß subunit revealed highly and moderately conserved positions that could be exploited for the development of new species-specific allosteric inhibitors. To our knowledge, these inhibitors are the first binders computationally designed against the catalytic subunit of FOF1-ATP synthase.

A Novel Network Science and Similarity-Searching-Based Approach for Discovering Potential Tumor-Homing Peptides from Antimicrobials.

Peptide-based drugs are promising anticancer candidates due to their biocompatibility and low toxicity. In particular, tumor-homing peptides (THPs) have the ability to bind specifically to cancer cell 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 and the THPep, and more recently the SCMTHP. Herein, a novel method based on network science and similarity searching implemented in the starPep toolbox is presented for THP 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 THP sequences within the CSN. Such THPs were considered as queries (Qs) for multi-query similarity searches that apply 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. The predictions achieved accuracies that 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 subsequently 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 the rapid and accurate identification of THPs.

DISTATIS: A Promising Framework to Integrate Distance Matrices in Molecular Phylogenetics.

BACKGROUND: Molecular phylogenetic algorithms frequently disagree with the approaches considering reproductive compatibility and morphological criteria for species delimitation. The question stems if the resulting species boundaries from molecular, reproductive and/or morphological data are definitively not reconcilable; or if the existing phylogenetic methods are not sensitive enough to agree morphological and genetic variation in species delimitation. OBJECTIVE: We propose DISTATIS as an integrative framework to combine alignment-based (AB) and alignment-free (AF) distance matrices from ITS2 sequences/structures to shed light whether Gelasinospora and Neurospora are sister but independent genera. METHODS: We aimed at addressing this standing issue by harmonizing genus-specific classification based on their ascospore morphology and ITS2 molecular data. To validate our proposal, three phylogenetic approaches: i) traditional alignment-based, ii) alignment-free and iii) novel distance integrative (DI)-based were comparatively evaluated on a set of Gelasinospora and Neurospora species. All considered species have been extensively characterized at both the morphological and reproductive levels and there are known incongruences between their ascospore morphology and molecular data that hampers genus-specific delimitation. RESULTS: Traditional AB phylogenetic analyses fail at resolving the Gelasinospora and Neurospora genera into independent monophyletic clades following ascospore morphology criteria. In contrast, AF and DI approaches produced phylogenetic trees that could properly delimit the expected monophyletic clades. CONCLUSION: The DI approach outperformed the AF one in the sense that it could also divide the Neurospora species according to their reproduction mode.

Engineering protein fragments via evolutionary and protein-protein interaction algorithms: de novo design of peptide inhibitors for FO F1 -ATP synthase.

Enzyme subunit interfaces have remarkable potential in drug design as both target and scaffold for their own inhibitors. We show an evolution-driven strategy for the de novo design of peptide inhibitors targeting interfaces of the Escherichia coli FoF1-ATP synthase as a case study. The evolutionary algorithm ROSE was applied to generate diversity-oriented peptide libraries by engineering peptide fragments from ATP synthase interfaces. The resulting peptides were scored with PPI-Detect, a sequence-based predictor of protein-protein interactions. Two selected peptides were confirmed by in vitro inhibition and binding tests. The proposed methodology can be widely applied to design peptides targeting relevant interfaces of enzymatic complexes.

Putative Antimicrobial Peptides of the Posterior Salivary Glands from the Cephalopod Octopus vulgaris Revealed by Exploring a Composite Protein Database.

Cephalopods, successful predators, can use a mixture of substances to subdue their prey, becoming interesting sources of bioactive compounds. In addition to neurotoxins and enzymes, the presence of antimicrobial compounds has been reported. Recently, the transcriptome and the whole proteome of the Octopus vulgaris salivary apparatus were released, but the role of some compounds-e.g., histones, antimicrobial peptides (AMPs), and toxins-remains unclear. Herein, we profiled the proteome of the posterior salivary glands (PSGs) of O. vulgaris using two sample preparation protocols combined with a shotgun-proteomics approach. Protein identification was performed against a composite database comprising data from the UniProtKB, all transcriptomes available from the cephalopods' PSGs, and a comprehensive non-redundant AMPs database. Out of the 10,075 proteins clustered in 1868 protein groups, 90 clusters corresponded to venom protein toxin families. Additionally, we detected putative AMPs clustered with histones previously found as abundant proteins in the saliva of O. vulgaris. Some of these histones, such as H2A and H2B, are involved in systemic inflammatory responses and their antimicrobial effects have been demonstrated. These results not only confirm the production of enzymes and toxins by the O. vulgaris PSGs but also suggest their involvement in the first line of defense against microbes.

ShadowCaster: Compositional Methods under the Shadow of Phylogenetic Models to Detect Horizontal Gene Transfers in Prokaryotes.

Horizontal gene transfer (HGT) plays an important role for evolutionary innovations within prokaryotic communities and is a crucial event for their survival. Several computational approaches have arisen to identify HGT events in recipient genomes. However, this has been proven to be a complex task due to the generation of a great number of false positives and the prediction disagreement among the existing methods. Phylogenetic reconstruction methods turned out to be the most reliable ones, but they are not extensible to all genes/species and are computationally demanding when dealing with large datasets. In contrast, the so-called surrogate methods that use heuristic solutions either based on nucleotide composition patterns or phyletic distribution of BLAST hits can be applied easily to the genomic scale, but they fail in identifying common HGT events. Here, we present ShadowCaster, a hybrid approach that sequentially combines nucleotide composition-based predictions by support vector machines (SVMs) under the shadow of phylogenetic models independent of tree reconstruction, to improve the detection of HGT events in prokaryotes. ShadowCaster successfully predicted close and distant HGT events in both artificial and bacterial genomes. ShadowCaster detected HGT related to heavy metal resistance in the genome of Rhodanobacter denitrificans with higher accuracy than the most popular state-of-the-art computational approaches, encompassing most of the predicted cases made by other methods. ShadowCaster is released at the GitHub platform as an open-source software under the GPLv3 license.

Shotgun Proteomics of Ascidians Tunic Gives New Insights on Host-Microbe Interactions by Revealing Diverse Antimicrobial Peptides.

Ascidians are marine invertebrates associated with diverse microbial communities, embedded in their tunic, conferring special ecological and biotechnological relevance to these model organisms used in evolutionary and developmental studies. Next-generation sequencing tools have increased the knowledge of ascidians' associated organisms and their products, but proteomic studies are still scarce. Hence, we explored the tunic of three ascidian species using a shotgun proteomics approach. Proteins extracted from the tunic of Ciona sp., Molgula sp., and Microcosmus sp. were processed using a nano LC-MS/MS system (Ultimate 3000 liquid chromatography system coupled to a Q-Exactive Hybrid Quadrupole-Orbitrap mass spectrometer). Raw data was searched against UniProtKB - the Universal Protein Resource Knowledgebase (Bacteria and Metazoa section) using Proteome Discoverer software. The resulting proteins were merged with a non-redundant Antimicrobial Peptides (AMPs) database and analysed with MaxQuant freeware. Overall, 337 metazoan and 106 bacterial proteins were identified being mainly involved in basal metabolism, cytoskeletal and catalytic functions. 37 AMPs were identified, most of them attributed to eukaryotic origin apart from bacteriocins. These results and the presence of "Biosynthesis of antibiotics" as one of the most highlighted pathways revealed the tunic as a very active tissue in terms of bioactive compounds production, giving insights on the interactions between host and associated organisms. Although the present work constitutes an exploratory study, the approach employed revealed high potential for high-throughput characterization and biodiscovery of the ascidians' tunic and its microbiome.

Graph Theory-Based Sequence Descriptors as Remote Homology Predictors.

Alignment-free (AF) methodologies have increased in popularity in the last decades as alternative tools to alignment-based (AB) algorithms for performing comparative sequence analyses. They have been especially useful to detect remote homologs within the twilight zone of highly diverse gene/protein families and superfamilies. The most popular alignment-free methodologies, as well as their applications to classification problems, have been described in previous reviews. Despite a new set of graph theory-derived sequence/structural descriptors that have been gaining relevance in the detection of remote homology, they have been omitted as AF predictors when the topic is addressed. Here, we first go over the most popular AF approaches used for detecting homology signals within the twilight zone and then bring out the state-of-the-art tools encoding graph theory-derived sequence/structure descriptors and their success for identifying remote homologs. We also highlight the tendency of integrating AF features/measures with the AB ones, either into the same prediction model or by assembling the predictions from different algorithms using voting/weighting strategies, for improving the detection of remote signals. Lastly, we briefly discuss the efforts made to scale up AB and AF features/measures for the comparison of multiple genomes and proteomes. Alongside the achieved experiences in remote homology detection by both the most popular AF tools and other less known ones, we provide our own using the graphical-numerical methodologies, MARCH-INSIDE, TI2BioP, and ProtDCal. We also present a new Python-based tool (SeqDivA) with a friendly graphical user interface (GUI) for delimiting the twilight zone by using several similar criteria.

The Harderian gland transcriptomes of Caraiba andreae, Cubophis cantherigerus and Tretanorhinus variabilis, three colubroid snakes from Cuba.

The Harderian gland is a cephalic structure, widely distributed among vertebrates. In snakes, the Harderian gland is anatomically connected to the vomeronasal organ via the nasolacrimal duct, and in some species can be larger than the eyes. The function of the Harderian gland remains elusive, but it has been proposed to play a role in the production of saliva, pheromones, thermoregulatory lipids and growth factors, among others. Here, we have profiled the transcriptomes of the Harderian glands of three non-front-fanged colubroid snakes from Cuba: Caraiba andreae (Cuban Lesser Racer); Cubophis cantherigerus (Cuban Racer); and Tretanorhinus variabilis (Caribbean Water Snake), using Illumina HiSeq2000 100 bp paired-end. In addition to ribosomal and non-characterized proteins, the most abundant transcripts encode putative transport/binding, lipocalin/lipocalin-like, and bactericidal/permeability-increasing-like proteins. Transcripts coding for putative canonical toxins described in venomous snakes were also identified. This transcriptional profile suggests a more complex function than previously recognized for this enigmatic organ.

Surveying alignment-free features for Ortholog detection in related yeast proteomes by using supervised big data classifiers.

BACKGROUND: The development of new ortholog detection algorithms and the improvement of existing ones are of major importance in functional genomics. We have previously introduced a successful supervised pairwise ortholog classification approach implemented in a big data platform that considered several pairwise protein features and the low ortholog pair ratios found between two annotated proteomes (Galpert, D et al., BioMed Research International, 2015). The supervised models were built and tested using a Saccharomycete yeast benchmark dataset proposed by Salichos and Rokas (2011). Despite several pairwise protein features being combined in a supervised big data approach; they all, to some extent were alignment-based features and the proposed algorithms were evaluated on a unique test set. Here, we aim to evaluate the impact of alignment-free features on the performance of supervised models implemented in the Spark big data platform for pairwise ortholog detection in several related yeast proteomes. RESULTS: The Spark Random Forest and Decision Trees with oversampling and undersampling techniques, and built with only alignment-based similarity measures or combined with several alignment-free pairwise protein features showed the highest classification performance for ortholog detection in three yeast proteome pairs. Although such supervised approaches outperformed traditional methods, there were no significant differences between the exclusive use of alignment-based similarity measures and their combination with alignment-free features, even within the twilight zone of the studied proteomes. Just when alignment-based and alignment-free features were combined in Spark Decision Trees with imbalance management, a higher success rate (98.71%) within the twilight zone could be achieved for a yeast proteome pair that underwent a whole genome duplication. The feature selection study showed that alignment-based features were top-ranked for the best classifiers while the runners-up were alignment-free features related to amino acid composition. CONCLUSIONS: The incorporation of alignment-free features in supervised big data models did not significantly improve ortholog detection in yeast proteomes regarding the classification qualities achieved with just alignment-based similarity measures. However, the similarity of their classification performance to that of traditional ortholog detection methods encourages the evaluation of other alignment-free protein pair descriptors in future research.

Known Hepatoprotectors Act as Antioxidants and Immune Stimulators in Stressed Mice: Perspectives in Animal Health Care.

BACKGROUND: Oxygen is involved in a variety of physiological reactions in aerobic organisms, such as those produced in the electron transport chain, hydroxylation, and oxygenation. Reactive oxygen species (ROS) are naturally formed as byproducts from these previously reactions involving the O2 molecule; they are made up of superoxide anion (O2-), hydroxyl radical (HO-), hydrogen peroxide (H2O2), nitric oxide (NO), peroxyl (ROO-), and reactive aldehyde (ROCH). Under certain environmental stress conditions, ROS are accumulated causing cellular damage but also triggering the overexpression of several enzyme classes such as superoxide dismutases (SOD), catalases (CAT) and glutathione peroxidases (GPx), which represent an important intrinsic antioxidant defence line. Liver is a key organ in vertebrates including farm animals and human. The oxidative stress plays an important role in systemic malfunctions including hepatic, renal and immunological, disorders. METHODS: This review presents a brief update about the relationship of oxidative stress with hepatic, renal and immunological malfunctions in stressed organisms. Cellular and exogenous hepatoprotective compounds share also the ability to scavenge ROS acting as antioxidants and in many cases as stimulators of immune response in stressed organisms. We present the effect of some hepatoprotectors on the hepatic, renal and immunological function in stressed mice by the jointed evaluation of biological and oxidative stress markers. CONCLUSION: Hepatoprotective effect of several exogenous compounds is very associated with their antioxidant capacity. This fact is relevant for keeping oxidant/antioxidant balance in the respective organs, but also for maintaining the physiological status of the whole organism.