PROTA: A Robust Tool for Protamine Prediction Using a Hybrid Approach of Machine Learning and Deep Learning.

Protamines play a critical role in DNA compaction and stabilization in sperm cells, significantly influencing male fertility and various biotechnological applications. Traditionally, identifying these proteins is a challenging and time-consuming process due to their species-specific variability and complexity. Leveraging advancements in computational biology, we present PROTA, a novel tool that combines machine learning (ML) and deep learning (DL) techniques to predict protamines with high accuracy. For the first time, we integrate Generative Adversarial Networks (GANs) with supervised learning methods to enhance the accuracy and generalizability of protamine prediction. Our methodology evaluated multiple ML models, including Light Gradient-Boosting Machine (LIGHTGBM), Multilayer Perceptron (MLP), Random Forest (RF), eXtreme Gradient Boosting (XGBOOST), k-Nearest Neighbors (KNN), Logistic Regression (LR), Naive Bayes (NB), and Radial Basis Function-Support Vector Machine (RBF-SVM). During ten-fold cross-validation on our training dataset, the MLP model with GAN-augmented data demonstrated superior performance metrics: 0.997 accuracy, 0.997 F1 score, 0.998 precision, 0.997 sensitivity, and 1.0 AUC. In the independent testing phase, this model achieved 0.999 accuracy, 0.999 F1 score, 1.0 precision, 0.999 sensitivity, and 1.0 AUC. These results establish PROTA, accessible via a user-friendly web application. We anticipate that PROTA will be a crucial resource for researchers, enabling the rapid and reliable prediction of protamines, thereby advancing our understanding of their roles in reproductive biology, biotechnology, and medicine.

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.

Change in the swimming pattern of Salmo salar spermatozoa caused by the high temperature of the sperm motility activation medium.

Fish are ectotherms and many have an external reproductive mode. An environmental factor which triggers fish reproductive activity in fish is water temperature. However, climate change is causing increasingly frequent events in which the water temperature varies rapidly; as a result, both in hatchery and in natural conditions, fish sperm are exposed to varying environmental temperatures during their journey toward the egg. This study was based on two experiments: The first experiment was designed to determine how storage at 4 °C for four days affected the sperm functions of Atlantic salmon (Salmo salar) sperm collected by either abdominal massage (stripping/Pure) or testicular dissection (testicular macerate/Macerated). Further, computer-assisted semen analysis (CASA) was used to compare sperm velocity parameters (VCL, VSL, and VAP) and progressivity (STR, LIN, and WOB) after motility activation at different temperatures (8 and 16 °C) of sperm collected by both methods (Pure vs Macerated). The results show that spermatozoa from Macerated samples maintained a higher sperm function when stored at 4 °C for 4 days compared to Pure sperm samples. In the second experiment, CASA determined that all parameters for sperm velocity (VCL, VSL, and VAP) and progressivity (STR (50%/55%), LIN (25%-32%), and WOB (51%-57%) were affected by activation temperature (P < 0.05) and that the motility patterns after activation at 16 °C (P < 0.05), specifically the LIN or STR swimming trajectories of the sperm differed between the two groups. In conclusion, the sperm quality of testicular Macerate was superior to that of Pure sperm abdominal mass, based on the higher quality of various sperm functions during short-term storage. Moreover, there was a significant effect of the temperature of the activation medium on sperm speed and progressivity (motility pattern) in the collected samples of testicular macerate. The sensitivity of Salmo salar spermatozoa to elevated temperature varies markedly between collection methods (Pure and Macerated).

Novel Apoplastic Antifreeze Proteins of Deschampsia antarctica as Enhancer of Common Cell Freezing Media for Cryobanking of Genetic Resources, a Preliminary Study.

Antifreeze proteins (AFPs) are natural biomolecules found in cold-adapted organisms that lower the freezing point of water, allowing survival in icy conditions. These proteins have the potential to improve cryopreservation techniques by enhancing the quality of genetic material postthaw. Deschampsia antarctica, a freezing-tolerant plant, possesses AFPs and is a promising candidate for cryopreservation applications. In this study, we investigated the cryoprotective properties of AFPs from D. antarctica extracts on Atlantic salmon spermatozoa. Apoplastic extracts were used to determine ice recrystallization inhibition (IRI), thermal hysteresis (TH) activities and ice crystal morphology. Spermatozoa were cryopreserved using a standard cryoprotectant medium (C+) and three alternative media supplemented with apoplastic extracts. Flow cytometry was employed to measure plasma membrane integrity (PMI) and mitochondrial membrane potential (MMP) postthaw. Results showed that a low concentration of AFPs (0.05 mg/mL) provided significant IRI activity. Apoplastic extracts from D. antarctica demonstrated a cryoprotective effect on salmon spermatozoa, with PMI comparable to the standard medium. Moreover, samples treated with apoplastic extracts exhibited a higher percentage of cells with high MMP. These findings represent the first and preliminary report that suggests that AFPs derived from apoplastic extracts of D. antarctica have the potential to serve as cryoprotectants and could allow the development of novel freezing media.

ß-Carotene Supplementation Improves Pancreas Function during Moderate Ethanol Consumption: Initial Characterization from a Morphological Overview.

Alcohol is believed to harm acinar cells, pancreatic ductal epithelium, and pancreatic stellate cells. After giving ethanol and/or ß-carotene to C57BL/6 mice, our goal was to evaluate their biochemistry, histology, and morpho-quantitative features. There were six groups of C57BL/6 mice: 1. Group C (control), 2. Group LA (low-dose alcohol), 3. Group MA (moderate-dose alcohol), 4. Group B (ß-carotene), 5. Group LA + B (low-dose alcohol combined with ß-carotene), and 6. Group MA + B (moderate-dose alcohol combined with ß-carotene). After the animals were euthanized on day 28, each specimen's pancreatic tissue was taken. Lipase, uric acid, and amylase were assessed using biochemical assessment. Furthermore, the examination of the pancreatic structure was conducted using Ammann's fibrosis scoring system. Finally, the morpho-quantitative characteristics of the pancreatic islets and acinar cells were determined. In the serum of the MA + B group, there were higher amounts of total amylase (825.953 ± 193.412 U/L) and lower amounts of lipase (47.139 ± 6.099 U/L) (p < 0.05). Furthermore, Ammann's fibrosis punctuation in the pancreas revealed significant variations between the groups (p < 0.001). Finally, the stereological analysis of pancreatic islets showed that the groups were different (p < 0.001). These findings suggest that antioxidant treatments might help decrease the negative effects of ethanol exposure in animal models.

Role of long-chain polyunsaturated fatty acids, eicosapentaenoic and docosahexaenoic, in the regulation of gene expression during the development of obesity: a systematic review.

Introduction: There exists a correlation between obesity and the consumption of an excessive amount of calories, with a particular association between the intake of saturated and trans fats and an elevated body mass index. Omega-3 fatty acids, specifically eicosapentaenoic and docosahexaenoic acids, have been identified as potential preventive nutrients against the cardiometabolic hazards that are commonly associated with obesity. The objective of this comprehensive review was to elucidate the involvement of long-chain polyunsaturated fatty acids, specifically eicosapentaenoic acid and docosahexaenoic acid, in the modulation of gene expression during the progression of obesity. Methods: The present analysis focused on primary studies that investigated the association between long-chain polyunsaturated fatty acids, gene expression, and obesity in individuals aged 18 to 65 years. Furthermore, a comprehensive search was conducted on many databases until August 2023 to identify English-language scholarly articles utilizing MeSH terms and textual content pertaining to long-chain polyunsaturated fatty acids, gene expression, obesity, and omega-3. The protocol has been registered on PROSPERO under the registration number CRD42022298395. A comprehensive analysis was conducted on a total of nine primary research articles. All research collected and presented quantitative data. Results and Discussion: The findings of our study indicate that the incorporation of eicosapentaenoic and docosahexaenoic acid may have potential advantages and efficacy in addressing noncommunicable diseases, including obesity. This can be attributed to their anti-inflammatory properties and their ability to regulate genes associated with obesity, such as PPARγ and those within the ALOX family. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022298395, CRD42022298395.

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.

Effectiveness of Cerium Oxide Nanoparticles in Non-Alcoholic Fatty Liver Disease Evolution Using In Vivo and In Vitro Studies: A Systematic Review.

Nonalcoholic fatty liver disease (NAFLD) describes a spectrum of liver abnormalities, from benign steatosis to nonalcoholic steatohepatitis (NASH). Because of their antioxidant capabilities, CeNPs have sparked a lot of interest in biological applications. This review evaluated the effectiveness of CeNPs in NAFLD evolution through in vivo and in vitro studies. Databases such as MEDLINE, EMBASE, Scopus, and Web of Science were looked for studies published between 2012 and June 2023. Quality was evaluated using PRISMA guidelines. We looked at a total of nine primary studies in English carried out using healthy participants or HepG2 or LX2 cells. Quantitative data such as blood chemical markers, lipid peroxidation, and oxidative status were obtained from the studies. Our findings indicate that NPs are a possible option to make medications safer and more effective. In fact, CeNPs have been demonstrated to decrease total saturated fatty acids and foam cell production (steatosis), reactive oxygen species production and TNF-α (necrosis), and vacuolization in hepatic tissue when used to treat NAFLD. Thus, CeNP treatment may be considered promising for liver illnesses. However, limitations such as the variation in durations between studies and the utilization of diverse models to elucidate the etiology of NAFLD must be considered. Future studies must include standardized NAFLD models.

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.

Application of Quantitative Structure-Activity Relationships in the Prediction of New Compounds with Anti-Leukemic Activity.

Leukemia invades the bone marrow progressively and, through unknown mechanisms, outcompetes healthy hematopoiesis. Protein arginine methyltransferases 1 (PRMT1) are found in prokaryotes and eukaryotes cells. They are necessary for a number of biological processes and have been linked to several human diseases, including cancer. Small compounds that target PRMT1 have a significant impact on both functional research and clinical disease treatment. In fact, numerous PRMT1 inhibitors targeting the S-adenosyl-L-methionine binding region have been studied. Through topographical descriptors, quantitative structure-activity relationships (QSAR) were developed in order to identify the most effective PRMT1 inhibitors among 17 compounds. The model built using linear discriminant analysis allows us to accurately classify over 90% of the investigated active substances. Antileukemic activity is predicted using a multilinear regression analysis, and it can account for more than 56% of the variation. Both analyses are validated using an internal "leave some out" test. The developed model could be utilized in future preclinical experiments with novel drugs.

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 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.

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.

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.

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.

An Updated Overview of the Role of CYP450 during Xenobiotic Metabolization in Regulating the Acute Myeloid Leukemia Microenvironment.

Oxidative stress is associated with several acute and chronic disorders, including hematological malignancies such as acute myeloid leukemia, the most prevalent acute leukemia in adults. Xenobiotics are usually harmless compounds that may be detrimental, such as pharmaceuticals, environmental pollutants, cosmetics, and even food additives. The storage of xenobiotics can serve as a defense mechanism or a means of bioaccumulation, leading to adverse effects. During the absorption, metabolism, and cellular excretion of xenobiotics, three steps may be distinguished: (i) inflow by transporter enzymes, (ii) phases I and II, and (iii) phase III. Phase I enzymes, such as those in the cytochrome P450 superfamily, catalyze the conversion of xenobiotics into more polar compounds, contributing to an elevated acute myeloid leukemia risk. Furthermore, genetic polymorphism influences the variability and susceptibility of related myeloid neoplasms, infant leukemias associated with mixed-lineage leukemia (MLL) gene rearrangements, and a subset of de novo acute myeloid leukemia. Recent research has shown a sustained interest in determining the regulators of cytochrome P450, family 2, subfamily E, member 1 (CYP2E1) expression and activity as an emerging field that requires further investigation in acute myeloid leukemia evolution. Therefore, this review suggests that CYP2E1 and its mutations can be a therapeutic or diagnostic target in acute myeloid leukemia.

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.

Intraoperative Fascial Plane Blocks Facilitate Earlier Tracheal Extubation and Intensive Care Unit Discharge After Cardiac Surgery: A Retrospective Cohort Analysis.

OBJECTIVES: Novel fascial plane blocks may allow early tracheal extubation and discharge from the intensive care unit (ICU). The present study primarily aimed to determine whether fascial plane blocks, in comparison with intravenous analgesia alone, significantly shortened tracheal extubation times in patients undergoing cardiac surgery. The secondary objectives were to compare each block's performance with that of intravenous analgesia alone in terms of the individual tracheal extubation time and length of ICU stay. DESIGN: Retrospective observational study. SETTING: Single-center study. PARTICIPANTS: Patients who underwent cardiac surgery between 2018 and 2019 were identified from a prospective clinical registry. After obtaining ethics approval, the clinical and electronic records of patients undergoing cardiac surgery in 2018 were analyzed. Data of patients receiving fascial plane blocks (erector spinae plane [ESP], pectoral plane I and II [PECs], and serratus anterior plane [SAP] blocks) with intravenous analgesia were compared with those of patients receiving only intravenous analgesia. A propensity score (PS) model was used to control for differences in the baseline characteristics. Adjusted p < 0.05 was considered statistically significant. MEASUREMENTS AND MAIN RESULTS: Of the 589 patients screened, 532 met the inclusion criteria; 404 received a fascial plane block. After PS matching, weighted linear regression revealed that by receiving a block, the predicted extubation time difference was 9.29 hours (b coefficient; 95% CI: -11.98, -6.60; p = 0.022). Similar results were obtained using PS weighting, with a reduction of 7.82 hours (b coefficient; 95% CI: -11.89, -3.75; p < 0.001) in favor of the block. In the fascial-plane-block group, ESP block achieved the best performance. The length of ICU stay decreased by 1.1 days (b coefficient; 95% CI: -1.43, -0.79; p = 0.0001) in the block group. No complications were reported. CONCLUSIONS: Fascial plane block is associated with reduced extubation times and lengths of ICU stay. ESP block achieved the best performance, followed by PECs and SAP blocks. After PS matching, only ESP block reduced the extubation time.