MultiToxPred 1.0: a novel comprehensive tool for predicting 27 classes of protein toxins using an ensemble machine learning approach.

Protein toxins are defense mechanisms and adaptations found in various organisms and microorganisms, and their use in scientific research as therapeutic candidates is gaining relevance due to their effectiveness and specificity against cellular targets. However, discovering these toxins is time-consuming and expensive. In silico tools, particularly those based on machine learning and deep learning, have emerged as valuable resources to address this challenge. Existing tools primarily focus on binary classification, determining whether a protein is a toxin or not, and occasionally identifying specific types of toxins. For the first time, we propose a novel approach capable of classifying protein toxins into 27 distinct categories based on their mode of action within cells. To accomplish this, we assessed multiple machine learning techniques and found that an ensemble model incorporating the Light Gradient Boosting Machine and Quadratic Discriminant Analysis algorithms exhibited the best performance. During the tenfold cross-validation on the training dataset, our model exhibited notable metrics: 0.840 accuracy, 0.827 F1 score, 0.836 precision, 0.840 sensitivity, and 0.989 AUC. In the testing stage, using an independent dataset, the model achieved 0.846 accuracy, 0.838 F1 score, 0.847 precision, 0.849 sensitivity, and 0.991 AUC. These results present a powerful next-generation tool called MultiToxPred 1.0, accessible through a web application. We believe that MultiToxPred 1.0 has the potential to become an indispensable resource for researchers, facilitating the efficient identification of protein toxins. By leveraging this tool, scientists can accelerate their search for these toxins and advance their understanding of their therapeutic potential.

VirusHound-I: prediction of viral proteins involved in the evasion of host adaptive immune response using the random forest algorithm and generative adversarial network for data augmentation.

Throughout evolution, pathogenic viruses have developed different strategies to evade the response of the adaptive immune system. To carry out successful replication, some pathogenic viruses encode different proteins that manipulate the molecular mechanisms of host cells. Currently, there are different bioinformatics tools for virus research; however, none of them focus on predicting viral proteins that evade the adaptive system. In this work, we have developed a novel tool based on machine and deep learning for predicting this type of viral protein named VirusHound-I. This tool is based on a model developed with the multilayer perceptron algorithm using the dipeptide composition molecular descriptor. In this study, we have also demonstrated the robustness of our strategy for data augmentation of the positive dataset based on generative antagonistic networks. During the 10-fold cross-validation step in the training dataset, the predictive model showed 0.947 accuracy, 0.994 precision, 0.943 F1 score, 0.995 specificity, 0.896 sensitivity, 0.894 kappa, 0.898 Matthew's correlation coefficient and 0.989 AUC. On the other hand, during the testing step, the model showed 0.964 accuracy, 1.0 precision, 0.967 F1 score, 1.0 specificity, 0.936 sensitivity, 0.929 kappa, 0.931 Matthew's correlation coefficient and 1.0 AUC. Taking this model into account, we have developed a tool called VirusHound-I that makes it possible to predict viral proteins that evade the host's adaptive immune system. We believe that VirusHound-I can be very useful in accelerating studies on the molecular mechanisms of evasion of pathogenic viruses, as well as in the discovery of therapeutic targets.

Enzyme Engineering Strategies for the Bioenhancement of L-Asparaginase Used as a Biopharmaceutical.

Over the past few years, there has been a surge in the industrial production of recombinant enzymes from microorganisms due to their catalytic characteristics being highly efficient, selective, and biocompatible. L-asparaginase (L-ASNase) is an enzyme belonging to the class of amidohydrolases that catalyzes the hydrolysis of L-asparagine into L-aspartic acid and ammonia. It has been widely investigated as a biologic agent for its antineoplastic properties in treating acute lymphoblastic leukemia. The demand for L-ASNase is mainly met by the production of recombinant type II L-ASNase from Escherichia coli and Erwinia chrysanthemi. However, the presence of immunogenic proteins in L-ASNase sourced from prokaryotes has been known to result in adverse reactions in patients undergoing treatment. As a result, efforts are being made to explore strategies that can help mitigate the immunogenicity of the drug. This review gives an overview of recent biotechnological breakthroughs in enzyme engineering techniques and technologies used to improve anti-leukemic L-ASNase, taking into account the pharmacological importance of L-ASNase.

Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides.

Viruses constitute a constant threat to global health and have caused millions of human and animal deaths throughout human history. Despite advances in the discovery of antiviral compounds that help fight these pathogens, finding a solution to this problem continues to be a task that consumes time and financial resources. Currently, artificial intelligence (AI) has revolutionized many areas of the biological sciences, making it possible to decipher patterns in amino acid sequences that encode different functions and activities. Within the field of AI, machine learning, and deep learning algorithms have been used to discover antimicrobial peptides. Due to their effectiveness and specificity, antimicrobial peptides (AMPs) hold excellent promise for treating various infections caused by pathogens. Antiviral peptides (AVPs) are a specific type of AMPs that have activity against certain viruses. Unlike the research focused on the development of tools and methods for the prediction of antimicrobial peptides, those related to the prediction of AVPs are still scarce. Given the significance of AVPs as potential pharmaceutical options for human and animal health and the ongoing AI revolution, we have reviewed and summarized the current machine learning and deep learning-based tools and methods available for predicting these types of peptides.

Current state of molecular and metabolic strategies for the improvement of L-asparaginase expression in heterologous systems.

Heterologous expression of L-asparaginase (L-ASNase) has become an important area of research due to its clinical and food industry applications. This review provides a comprehensive overview of the molecular and metabolic strategies that can be used to optimize the expression of L-ASNase in heterologous systems. This article describes various approaches that have been employed to increase enzyme production, including the use of molecular tools, strain engineering, and in silico optimization. The review article highlights the critical role that rational design plays in achieving successful heterologous expression and underscores the challenges of large-scale production of L-ASNase, such as inadequate protein folding and the metabolic burden on host cells. Improved gene expression is shown to be achievable through the optimization of codon usage, synthetic promoters, transcription and translation regulation, and host strain improvement, among others. Additionally, this review provides a deep understanding of the enzymatic properties of L-ASNase and how this knowledge has been employed to enhance its properties and production. Finally, future trends in L-ASNase production, including the integration of CRISPR and machine learning tools are discussed. This work serves as a valuable resource for researchers looking to design effective heterologous expression systems for L-ASNase production as well as for enzymes production in general.

Current Status of Omics in Biological Quality Elements for Freshwater Biomonitoring.

Freshwater ecosystems have been experiencing various forms of threats, mainly since the last century. The severity of this adverse scenario presents unprecedented challenges to human health, water supply, agriculture, forestry, ecological systems, and biodiversity, among other areas. Despite the progress made in various biomonitoring techniques tailored to specific countries and biotic communities, significant constraints exist, particularly in assessing and quantifying biodiversity and its interplay with detrimental factors. Incorporating modern techniques into biomonitoring methodologies presents a challenging topic with multiple perspectives and assertions. This review aims to present a comprehensive overview of the contemporary advancements in freshwater biomonitoring, specifically by utilizing omics methodologies such as genomics, metagenomics, transcriptomics, proteomics, metabolomics, and multi-omics. The present study aims to elucidate the rationale behind the imperative need for modernization in this field. This will be achieved by presenting case studies, examining the diverse range of organisms that have been studied, and evaluating the potential benefits and drawbacks associated with the utilization of these methodologies. The utilization of advanced high-throughput bioinformatics techniques represents a sophisticated approach that necessitates a significant departure from the conventional practices of contemporary freshwater biomonitoring. The significant contributions of omics techniques in the context of biological quality elements (BQEs) and their interpretations in ecological problems are crucial for biomonitoring programs. Such contributions are primarily attributed to the previously overlooked identification of interactions between different levels of biological organization and their responses, isolated and combined, to specific critical conditions.

Protein Modelling and Molecular Docking Analysis of Fasciola hepatica ß-Tubulin's Interaction Sites, with Triclabendazole, Triclabendazole Sulphoxide and Triclabendazole Sulphone.

PURPOSE: Fasciola hepatica is a globally distributed trematode that causes significant economic losses. Triclabendazole is the primary pharmacological treatment for this parasite. However, the increasing resistance to triclabendazole limits its efficacy. Previous pharmacodynamics studies suggested that triclabendazole acts by interacting mainly with the ß monomer of tubulin. METHODS: We used a high-quality method to model the six isotypes of F. hepatica ß-tubulin in the absence of three-dimensional structures. Molecular dockings were conducted to evaluate the destabilization regions in the molecule against the ligands triclabendazole, triclabendazole sulphoxide and triclabendazole sulphone. RESULTS: The nucleotide binding site demonstrates higher affinity than the binding sites of colchicine, albendazole, the T7 loop and pßVII (p < 0.05). We suggest that the binding of the ligands to the polymerization site of ß-tubulin can lead a microtubule disruption. Furthermore, we found that triclabendazole sulphone exhibited significantly higher binding affinity than other ligands (p < 0.05) across all isotypes of ß-tubulin. CONCLUSIONS: Our investigation has yielded new insight on the mechanism of action of triclabendazole and its sulphometabolites on F. hepatica ß-tubulin through computational tools. These findings have significant implications for ongoing scientific research ongoing towards the discovery of novel therapeutics to treat F. hepatica infections.

In Silico Design of a Chimeric Humanized L-asparaginase.

Acute lymphoblastic leukemia (ALL) is the most common cancer among children worldwide, characterized by an overproduction of undifferentiated lymphoblasts in the bone marrow. The treatment of choice for this disease is the enzyme L-asparaginase (ASNase) from bacterial sources. ASNase hydrolyzes circulating L-asparagine in plasma, leading to starvation of leukemic cells. The ASNase formulations of E. coli and E. chrysanthemi present notorious adverse effects, especially the immunogenicity they generate, which undermine both their effectiveness as drugs and patient safety. In this study, we developed a humanized chimeric enzyme from E. coli L-asparaginase which would reduce the immunological problems associated with current L-asparaginase therapy. For these, the immunogenic epitopes of E. coli L-asparaginase (PDB: 3ECA) were determined and replaced with those of the less immunogenic Homo sapiens asparaginase (PDB:4O0H). The structures were modeled using the Pymol software and the chimeric enzyme was modeled using the SWISS-MODEL service. A humanized chimeric enzyme with four subunits similar to the template structure was obtained, and the presence of asparaginase enzymatic activity was predicted by protein-ligand docking.

Antibiotic discovery against Piscirickettsia salmonis using a combined in silico and in vitro approach.

Piscirickettsia salmonis is one of the main pathogens causing considerable economic losses in salmonid farming. The DNA gyrase of several pathogenic bacteria has been the target of choice for antibiotic design and discovery for years, due to its key function during DNA replication. In this study, we carried out a combined in silico and in vitro approach to antibiotic discovery targeting the GyrA subunit of Piscirickettsia salmonis. The in silico results of this work showed that flumequine (-6.6 kcal/mol), finafloxacin (-7.2 kcal/mol), rosoxacin (-6.6 kcal/mol), elvitegravir (-6.4 kcal/mol), sarafloxacin (-8.3 kcal/mol), orbifloxacin (-7.9 kcal/mol), and sparfloxacin (-7.2 kcal/mol) are docked with good affinities in the DNA binding domain of the Piscirickettsia salmonis GyrA subunit. In the in vitro inhibition assay, it was observed that most of these molecules inhibit the growth of Piscirickettsia salmonis, except for elvitegravir. We believe that this methodology could help to significantly reduce the time and cost of antibiotic discovery trials to combat Piscirickettsia salmonis within the salmonid farming industry.

Helicobacter pyloril-asparaginase: a study of immunogenicity from an in silico approach.

Helicobacter pylori has become the causal agent of multiple forms of gastric disease worldwide, including gastric cancer. The enzyme l-asparaginase (ASNase) has been studied as a virulence factor. In this work, we performed an in silico investigation to characterize the immunological profile of H. pylori ASNase (HpASNase) to ascertain the possible implication of HpASNase immunogenicity in the H. pylori virulence mechanism. We applied a workflow based on bioinformatics tools, which, by calculating the relative frequency of immunogenic T-cell and B-cell epitopes, allowed us to predict the immunogenicity and allergenicity of HpASNase in silico. We also visualized the epitopes by mapping them into the native structure of the enzyme. We report for the first time the T-cell and B-cell epitope composition that contributes to the immunogenicity of this HpASNase, as well as the regions that could generate a hypersensitivity response in humans. ASNase from H. pylori resulted in highly immunogenic and allergenic. The high immunogenicity of HpASNase could imply the pathogenic mechanisms of H. pylori. This knowledge could be important for the development of new drugs against H. pylori infections. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-022-03359-0.

Anthocyanins Found in Pinot Noir Waste Induce Target Genes Related to the Nrf2 Signalling in Endothelial Cells.

Grape pomace is a source of anthocyanins, which can prevent cardiovascular diseases due to their antioxidant properties. Anthocyanin activity is associated with the ability to regulate oxidative stress through the transcription factor Nrf2. Thus, the present study aimed to evaluate if the anthocyanins found in Pinot noir pomace extract can affect the target genes related to the Nrf2 signalling pathway in endothelial cells. Our results highlight that the predominant anthocyanin in the Pinot noir pomace extract was malvidin-3-glucoside (3.7 ± 2.7 Eq. Malv-3-glu/kg). Molecular docking indicated that cyanidin-3-glucoside (-6.9 kcal/mol), malvidin-3-glucoside (-6.6 kcal/mol) and peonidin-3-glucoside (-6.6 kcal/mol) showed the highest affinities for the binding sites of the BTB domains in Keap1, suggesting that these components may modify the interaction of this protein with Nrf2. In addition, when HUVEC cells were exposed to different concentrations of Pinot noir pomace extract (100 µg/mL, 200 µg/mL, and 400 µg/mL), no changes in Nrf2 gene expression were observed. However, the gene expression of HO-1 and NQO1, which are in the signalling pathway of this transcription factor, increased according the concentrations of the extract (p = 0.0004 and p = 0.0084, respectively). In summary, our results show that anthocyanins play a very important role in Nrf2 activation and release, while at the same time not promoting its transcription. These preliminary results strongly suggest that the Pinot noir pomace extract can serve as a potent bioactive component source that protects cells against oxidative stress.

Analysis of Muscle Lipidome in Juvenile Rainbow Trout Fed Rapeseed Oil and Cochayuyo Meal.

This study aimed to analyze the effects on the lipidome of juvenile Oncorhynchus mykiss muscle fed 90% Brassica napus "rapeseed" oil and different amounts of Durvillaea antarctica "Cochayuyo" meal (1.5, 3 and 6%) as a replacement for cellulose. The analysis allowed for the identification of 329 lipids, mainly represented by phospholipids and fatty esters. The inclusion of Brassica napus oil significantly increased the levels of C18:2 species and fatty esters of hydroxylated fatty acids, which could play a bioactive role in human health. One of the most abundant lipids in all fillets was Phosphatidylcholine 33:6, which, according to the literature, could be considered a biomarker for the identification of Oncorhynchus mykiss. In all experimental diets, the species Phosphatidylethanolamine 15:1-18:24 showed four-fold higher levels than the control; increments of n-3- and n-6-rich phospholipids were also observed. Diets containing Durvillaea antarctica meal did not generate more significant variation in fish muscle phospholipids relative to the muscle of the rapeseed-oil-only group. These lipid species consist of medium- and long-chain fatty acids with different degrees of unsaturation. Still, it appears that the rapeseed oil masks the lipid contribution of the meal, possibly due to the low levels of total lipids in the macroalgae.

A bioinformatics analysis of the CatSper channel in the class Actinopterygii.

The regulation of sperm motility is controlled by several variables, including mainly ion concentrations. In fish, Ca2+ concentrations play an important role in the regulation of sperm motility, and several reports highlight the importance of certain Ca2+ channels in the regulation of this cell function. CatSper is a calcium channel scarcely studied in fish. In the species Salmo salar, it has been shown that it is key in the regulation of sperm motility. Taking into account the relevance of this channel in sperm activation in fish, in this study we evaluated the presence and probable functionality of this channel in the class Actinopterygii. For this purpose, a rational bioinformatic analysis was carried out, which had been previously validated using in vitro techniques by our group. The bioinformatic analysis of the present work revealed that the functionality of CatSper of the species of the class Actinopterygii could be exclusive to freshwater and anadromous fish species. The results of this study showed that only some anadromous and freshwater fish species contain 11 subunits of the CatSper channel, which are enough to trigger sperm motility. Consequently, this study provides new data for a better understanding of the sperm activation mechanism in fish.

PEP-PREDNa+: A web server for prediction of highly specific peptides targeting voltage-gated Na+ channels using machine learning techniques.

Voltage-gated sodium channel activity has long been associated with several diseases including epilepsy, chronic pain, cardiovascular diseases, cancers, immune system, neuromuscular and respiratory disorders. The strong participation of these channels in the development of diseases makes them excellent promising therapeutic targets. Voltage-gated Na+ channel blocking peptides come from a wide source of organisms such as venoms. However, the in vitro and in vivo identification and validation of these peptides are time-consuming and resource-intensive. In this work, we developed a bioinformatics tool called PEP-PREDNa + for the highly specific prediction of voltage-gated Na+ channel blocking peptides. PEP-PREDNa+ is based on the random forest algorithm, which presented excellent performance measures during the cross-validation (sensitivity = 0.81, accuracy = 0.83, precision = 0.85, F-score = 0.83, specificity = 0.86, and Matthew's correlation coefficient = 0.67) and testing (sensitivity = 0.88, accuracy = 0.92, precision = 0.96, F-score = 0.91, specificity = 0.96, and Matthew's correlation coefficient = 0.84) phases. The PEP-PREDNa + tool could be very useful in accelerating and reducing the costs of the discovery of new voltage-gated Na+ channel blocking peptides with therapeutic potential.

Neurobiological Promises of the Bitter Diterpene Lactone Andrographolide.

Andrographolide (ANDRO), a bitter diterpene lactone found in Andrographis paniculata (Burm.f.) Nees, possesses several biological effects such as antioxidant, anti-inflammatory, and organo-protective effects. Scientific reports suggest that it also has neuroprotective capacity in various test systems. The purpose of this review was to synthesize the neuropharmacological properties of ANDRO and highlight the molecular mechanisms of action that highlight these activities. A careful search was done in PubMed and Google Scholar databases using specific keywords. Findings suggest that ANDRO possess neuroprotective, analgesic, and antifatigue effects. Prominent effects were stated on neuro-inflammation, cerebral ischemia, Alzheimer's and Parkinson's diseases, multiple sclerosis, and brain cancer in mice and rats. Furthermore, ANDRO and its derivatives can enhance memory and learning capacity in experimental animals (rats) without causing any toxicity in the brain. Thus, ANDRO may be one of the most promising plant-based psychopharmacological lead compounds for new drug development.

VirVACPRED: A Web Server for Prediction of Protective Viral Antigens.

Viral antigens are key in the development of vaccines that prevent or eradicate infections caused by these pathogens. Bioinformatics tools are modern alternatives that facilitate the discovery of viral antigens, reducing the costs of experimental assays. We developed a bioinformatics tool called VirVACPRED, which is highly efficient in predicting viral antigens. In this study, we obtained a model based on the gradient boosting classifier, which showed high performance during the training, leave-one-out cross-validation (accuracy = 0.7402, sensitivity = 0.7319, precision = 0.7503, F1 = 0.7251, kappa = 0.4774, Matthews correlation coefficient = 0.4981) and testing (accuracy = 0.8889, sensitivity = 1.0, precision = 0.8276, F1 = 0.9057, kappa = 0.7734, Matthews correlation coefficient = 0.7941). VirVACPRED is a robust tool that can be of great help in the search and proposal of new viral antigens, which can be considered in the development of future vaccines against infections caused by viruses.

Tackling Ischemic Reperfusion Injury With the Aid of Stem Cells and Tissue Engineering.

Ischemia is a severe condition in which blood supply, including oxygen (O), to organs and tissues is interrupted and reduced. This is usually due to a clog or blockage in the arteries that feed the affected organ. Reinstatement of blood flow is essential to salvage ischemic tissues, restoring O, and nutrient supply. However, reperfusion itself may lead to major adverse consequences. Ischemia-reperfusion injury is often prompted by the local and systemic inflammatory reaction, as well as oxidative stress, and contributes to organ and tissue damage. In addition, the duration and consecutive ischemia-reperfusion cycles are related to the severity of the damage and could lead to chronic wounds. Clinical pathophysiological conditions associated with reperfusion events, including stroke, myocardial infarction, wounds, lung, renal, liver, and intestinal damage or failure, are concomitant in due process with a disability, morbidity, and mortality. Consequently, preventive or palliative therapies for this injury are in demand. Tissue engineering offers a promising toolset to tackle ischemia-reperfusion injuries. It devises tissue-mimetics by using the following: (1) the unique therapeutic features of stem cells, i.e., self-renewal, differentiability, anti-inflammatory, and immunosuppressants effects; (2) growth factors to drive cell growth, and development; (3) functional biomaterials, to provide defined microarchitecture for cell-cell interactions; (4) bioprocess design tools to emulate the macroscopic environment that interacts with tissues. This strategy allows the production of cell therapeutics capable of addressing ischemia-reperfusion injury (IRI). In addition, it allows the development of physiological-tissue-mimetics to study this condition or to assess the effect of drugs. Thus, it provides a sound platform for a better understanding of the reperfusion condition. This review article presents a synopsis and discusses tissue engineering applications available to treat various types of ischemia-reperfusions, ultimately aiming to highlight possible therapies and to bring closer the gap between preclinical and clinical settings.

TAP 1.0: A robust immunoinformatic tool for the prediction of tumor T-cell antigens based on AAindex properties.

Immunotherapy is a research area with great potential in drug discovery for cancer treatment. Because of the capacity of tumor antigens to activate the immune response and promote the destruction of tumor cells, they are considered excellent immunotherapeutic drugs. In this work, we evaluated fifteen machine learning algorithms for the classification of tumor antigens. For this purpose, we build robust datasets, carefully selected from the TANTIGEN and IEDB databases. The feature computation of all antigens in this study was performed by developing a script written in Python 3.8, which allowed the calculation of 544 physicochemical and biochemical properties extracted from the AAindex database. All classifiers were subjected to the training, 10-fold cross-validation, and testing on an independent dataset. The results of this study showed that the quadratic discriminant classifier presented the best performance measures over the independent dataset, accuracy = 0.7384, AUC = 0.817, recall = 0.676, precision = 0.7857, F1 = 0.713, kappa = 0.4764, and Matthews correlation coefficient = 0.4834, outperforming common machine learning classifiers used in the bioinformatics area. We believe that our prediction model could be of great importance in the field of cancer immunotherapy for the search of potential tumor antigens. Taking all aspects mentioned before, we developed an immunoinformatic tool called TAP 1.0 with a friendly interface for tumor antigens prediction, available at https://tapredictor.herokuapp.com/.

The voltage-gated T-type Ca2+ channel is key to the sperm motility of Atlantic salmon (Salmo salar).

Ca2+ is a key element in the sperm activation process of Salmo salar. However, the molecular mechanisms by which this ion enters the sperm cell have been poorly studied. In this study, we examined, for the first time, the role of the voltage-gated T-type Ca2+ channel in the activation of sperm motility of Salmo salar. Using an in vitro inhibition assay, a significant decrease in total and progressive motility (P < 0.0001) was observed in Salmo salar sperm when they were treated with NNC-55-0396, a highly selective blocker. The in silico analysis showed that this blocker is docked with a strong affinity for the pore of the voltage-gated T-type calcium channel suggesting the blocking of Ca2+ ions. The results show that the T-type voltage-gated Ca2+ channel is key to sperm motility in Salmo salar.