RESUMO

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.

Assuntos

RESUMO

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).

Assuntos

RESUMO

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.

Assuntos

RESUMO

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.

Assuntos

RESUMO

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.

Assuntos

RESUMO

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.

Assuntos

RESUMO

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.

RESUMO

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.

Assuntos

RESUMO

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.

RESUMO

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.

Assuntos