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.

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.

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.

L-Asparaginase as the gold standard in the treatment of acute lymphoblastic leukemia: a comprehensive review

L-Asparaginase is an antileukemic drug long approved for clinical use to treat childhood acute lymphoblastic leukemia, the most common cancer in this population worldwide. However, the efficacy and its use as a drug have been subject to debate due to the variety of adverse effects that patients treated with it present, as well as the prompt elimination in plasma, the need for multiple administrations, and high rates of allergic reactions. For this reason, the search for new, less immunogenic variants has long been the subject of study. This review presents the main aspects of the L-asparaginase enzyme from a structural, pharmacological, and clinical point of view, from the perspective of its use in chemotherapy protocols in conjunction with other drugs in the different treatment phases.

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.

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.

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.

PPLK+C: A Bioinformatics Tool for Predicting Peptide Ligands of Potassium Channels Based on Primary Structure Information

Potassium channels play a key role in regulating the flow of ions through the plasma membrane, orchestrating many cellular processes including cell volume regulation, hormone secretion and electrical impulse formation. Ligand peptides of potassium channels are molecules used in basic and applied research and are now considered promising alternatives in the treatment of many diseases, such as cardiovascular diseases and cancer. Currently, there are various bioinformatics tools focused on the prediction of peptides with different activities. However, none of the current tools can predict ligand peptides of potassium channels. In this work, we developed a tool called PPLK⁺C; this is the first tool that can predict peptide ligands of potassium channels. We also evaluated several amino acid molecular features and four machine-learning algorithms for the prediction of potassium channel ligand peptides: random forest, nearest neighbors, support vector machine and artificial neural network. All the biological data used in this study for training and validating models were obtained from peptides with experimentally verified activity. PPLK⁺C is a bioinformatics software written in the Python programming language, which showed a high predictive capacity with a model generated with the random forest algorithm: 0.77 sensitivity, 0.94 specificity, 0.91 accuracy and 0.70 Matthews correlation coefficient. PPLK⁺C is a novel tool with a friendly interface that can be used for the discovery of novel ligand peptides of potassium channels with high reliability, using only primary structure information.

From Synthesis to Characterization of Site-Selective PEGylated Proteins

Covalent attachment of therapeutic proteins to polyethylene glycol (PEG) is widely used for the improvement of its pharmacokinetic and pharmacological properties, as well as the reduction in reactogenicity and related side effects. This technique named PEGylation has been successfully employed in several approved drugs to treat various diseases, even cancer. Some methods have been developed to obtain PEGylated proteins, both in multiple protein sites or in a selected amino acid residue. This review focuses mainly on traditional and novel examples of chemical and enzymatic methods for site-selective PEGylation, emphasizing in N-terminal PEGylation, that make it possible to obtain products with a high degree of homogeneity and preserve bioactivity. In addition, the main assay methods that can be applied for the characterization of PEGylated molecules in complex biological samples are also summarized in this paper.

Role of voltage-gated L-type calcium channel in the spermatozoa motility of Atlantic salmon (Salmo salar)

Ca²⁺ cations play a key role in the initiation of spermatozoa motility in Atlantic salmon (Salmo salar). In this study we assess the importance of the voltage-gated L-type calcium channels in the spermatozoa motility of Atlantic salmon by combined in vitro and in silico approaches. The results of this study showed that as in other fish species, voltage-gated L-type calcium channels are significant in the spermatozoa motility of Salmo salar. The in vitro assays showed that total and progressive motilities decrease significantly (****p < .001) when Salmo salar spermatozoa are treated with verapamil, which has its binding site in the pore of the voltage-gated L-type calcium channel according to the in silico analysis.

The CatSper channel is present and plays a key role in sperm motility of the Atlantic salmon (Salmo salar)

Among all the Ca²⁺ channels, CatSper channels have been one of the most studied in sperm of different species due to their demonstrated role in the fertilization process. In fish sperm, the calcium channel plays a key role in sperm activation. However, the functionality of the CatSper channels has not been studied in any of the fish species. For the first time, we studied the relationship of the CatSper channel with sperm motility in a fish, using Atlantic salmon (Salmo salar) as the model. The results of our study showed that the CatSper channel in Salmo salar has chemical-physical characteristics similar to those reported for mammalian CatSper channels. In this work, it was shown that Salmo salar CatSper 3 protein has a molecular weight of approximately 55-kDa similar to Homo sapiens CatSper 3. In silico analyses suggest that this channel forms a heterotetramer sensitive to the specific inhibitor HC-056456, with a binding site in the center of the pore of the CatSper channel, hindering or preventing the influx of Ca²⁺ ions. The in vitro assay of the sperm motility inhibition of Salmo salar with the inhibitor HC-056456 showed that sperm treated with this inhibitor significantly reduced the total and progressive motility (p < .0001), demonstrating the importance of this ionic channel for this cell. The complementation of the in silico and in vitro analyses of the present work demonstrates that the CatSper channel plays a key role in the regulation of sperm motility in Atlantic salmon.

AntiVPP 1.0: A portable tool for prediction of antiviral peptides

Viruses are worldwide pathogens with a high impact on the human population. Despite the constant efforts to fight viral infections, there is a need to discover and design new drug candidates. Antiviral peptides are molecules with confirmed activity and constitute excellent alternatives for the treatment of viral infections. In the present study, we developed AntiVPP 1.0, an accurate bioinformatic tool that uses the Random Forest algorithm for antiviral peptide predictions. The model of AntiVPP 1.0 for antiviral peptide predictions uses several features of 1088 peptides for training and validation. During the validation of the model we achieved the TPR = 0.87, SPC = 0.97, ACC = 0.93 and MCC = 0.87 performance measures, which were indicative of a robust model. AntiVPP 1.0 is a fast, accurate and intuitive software focused on the assessment of antiviral peptides candidates. AntiVPP 1.0 is available at https://github.com/bio-coding/AntiVPP.

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Random Forest algorithm was used for antiviral peptides prediction.

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AntiVPP 1.0 can be used for assessing various physical and chemical properties of antiviral peptides.

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Anti VPP 1.0 is a software with a simple interface and a high performance for academic and commercial purposes.