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.

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.

Implications of Butyrate Signaling Pathways on the Motor Symptomatology of Parkinson's Disease and Neuroprotective Effects-Therapeutic Approaches: A Systematic Review.

Parkinson's Disease (PD) is a prevalent neurodegenerative disorder characterized by motor and non-motor symptoms. Emerging evidence suggests that gut microbiota alterations, specifically involving short-chain fatty acids (SCFAs) like butyrate, may influence PD pathogenesis and symptomatology. This Systematic Review aims to synthesize current research on the role of butyrate in modulating motor symptoms and its neuroprotective effects in PD, providing insights into potential therapeutic approaches. A systematic literature search was conducted in April 2024 across databases, including ScienceDirect, Scopus, Wiley, and Web of Science, for studies published between 2000 and 2024. Keywords used were "neuroprotective effects AND butyrate AND (Parkinson disease OR motor symptoms)". Four authors independently screened titles, abstracts, and full texts, applying inclusion criteria focused on studies investigating butyrate regulation and PD motor symptoms. A total of 1377 articles were identified, with 40 selected for full-text review and 14 studies meeting the inclusion criteria. Data extraction was performed on the study population, PD models, methodology, intervention details, and outcomes. Quality assessment using the SYRCLE RoB tool highlighted variability in study quality, with some biases noted in allocation concealment and blinding. Findings indicate that butyrate regulation has a significant impact on improving motor symptoms and offers neuroprotective benefits in PD models. The therapeutic modulation of gut microbiota to enhance butyrate levels presents a promising strategy for PD symptom management.

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.

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.

Joint Object Detection and Re-Identification for 3D Obstacle Multi-Camera Systems.

The growing on-board processing capabilities have led to more complex sensor configurations, enabling autonomous car prototypes to expand their operational scope. Nowadays, the joint use of LiDAR data and multiple cameras is almost a standard and poses new challenges for existing multi-modal perception pipelines, such as dealing with contradictory or redundant detections caused by inference on overlapping images. In this paper, we address this last issue in the context of sequential schemes like F-PointNets, where object candidates are obtained in the image space, and the final 3D bounding box is then inferred from point cloud information. To this end, we propose the inclusion of a re-identification branch into the 2D detector, i.e., Faster R-CNN, so that objects seen from adjacent cameras can be handled before the 3D box estimation takes place, removing duplicates and completing the object's cloud. Extensive experimental evaluations covering both the 2D and 3D domains affirm the effectiveness of the suggested methodology. The findings indicate that our approach outperforms conventional Non-Maximum Suppression (NMS) methods. Particularly, we observed a significant gain of over 5% in terms of accuracy for cars in camera overlap regions. These results highlight the potential of our upgraded detection and re-identification system in practical scenarios for autonomous driving.

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.

Time elapsed since peruvian children's last dental care and head of household educational attainment: findings from a national database.

BACKGROUND: It has been documented that the parents' highest level of education has an impact on their children's access to oral health services and the frequency of their use.This study aimed to determine the association between time elapsed since peruvian children's last dental care and head of household educational attainment. METHODS: Cross-sectional study using a database of children aged 0 to 11 years, with a final sample of 8012 participants. The dependent variable in this study was the time elapsed since last dental care and the independent variable was the head of household educational attainment. Other covariates considered were natural region, area of residence, place of residence, altitude, wealth index, health insurance coverage, sex and age. Descriptive, bivariate and multivariate statistical analyses were applied. RESULTS: Time elapsed since last dental care in the year 2021 was 5.68 years (SD = 5.25). A hierarchical multiple linear regression analysis was performed, analyzing the variables dimensions by separate and joint models. When head of household educational attainment was analyzed, there was no statistical significance (p = 0.262); however, other models did (p < 0.05). Model 4, which addresses all dimensions, was significant (p < 0.001) with an R2% of 0.011 and constant equal to 5.788; it showed significance with place of dental care, health insurance, altitude and age. CONCLUSIONS: No association was found between head of household educational attainment and time elapsed since last dental care; however, the latter was associated with place of care, health insurance coverage, altitude and age in Peruvian children.

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.

Effect of in vitro cold acclimation of Deschampsia antarctica on the accumulation of proteins with antifreeze activity.

Deschampsia antarctica has managed to colonize the maritime Antarctic. One of the main factors associated with its tolerance to low temperatures is the presence of apoplastic proteins with antifreeze activity. This work focuses on the effect of cold acclimation of D. antarctica on the accumulation of apoplastic proteins with antifreeze activity. Antifreeze proteins present in apoplastic extracts were purified by ice affinity purification, and their identity was determined by protein sequencing. D. antarctica plants were subjected to 22 days of cold acclimation at 4 °C. The highest content of apoplastic proteins with antifreeze activity was obtained at between 12 and 16 days of acclimation. Protein sequencing allowed their identification with >95% probability. Percentage coverage was 74% with D. antarctica ice recrystallization inhibition protein 1 (DaIRIP1) and 55% with DaIRIP3. Cold acclimation of D. antarctica improved the yield of apoplastic proteins, and resulted in an increase in the antifreeze activity of apoplastic extracts. An in silico analysis suggested that the fluctuations presented by the three-dimensional structures of DaIRIPs help to explain the presence of certain DaIRIPs in apoplastic extracts under the cold acclimation conditions evaluated.

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.

Composition and distribution of medically important phlebotomines (Diptera: Psychodidae) in the municipalities of Tierralta and Valencia (Córdoba, Colombia).

BACKGROUND & OBJECTIVES: Ecoepidemiological studies of cutaneous leishmaniasis and regular monitoring of Lutzomyia species have generated a knowledge base that can be used for control and prevention strategies targeted at the disease transmission dynamics in focal areas of Colombia. In this study, the presence and spatial distribution of phlebotomines of medical importance in the municipalities of Tierralta (El Loro and Tuis Tuis villages) and Valencia (Guadua and Mieles villages) were determined. METHODS: Entomological surveys were performed in 2015 (months of June, September and November) and samples were collected via CDC-traps located in intradomicilary and peridomiciliary areas in the municipalities of Tierralta and Valencia (Department of Córdoba, Colombia). Active searches were also carried out with a mouth aspirator to collect adult phlebotomines from resting sites. ANOVA and Kruskal-Wallis tests were performed to assess if the differences between the communities of phlebotomines. Spatial distribution maps of the Lutzomyia species were generated. RESULTS: A high species diversity of Lutzomyia was observed with a total of 1677 Lutzomyia individuals belonging to 12 species. Among these species, Lu. panamensis was the most abundant (80.18%). The composition of the intradomicilary and peridomiciliary phlebotomines varied significantly (F = 0.9962; df = 1; p = 0.02895). Species like Lu. carpenteri, Lu. camposi, Lu. dysponeta, Lu. atroclavata and Lu. yuilli yuilli were recorded for the first time in the Department of Córdoba, Colombia. INTERPRETATION & CONCLUSION: The spatial distribution shows that Lu. panamensis and Lu. gomezi are predominant and present in areas with high concentration of houses. This study provides basic information on new records of phlebotomines in the Department of Córdoba. The results suggest that greater vector-human contact occurs in the peridomiciliary environment and that a high number of Lutzomyia species associated with the transmission of leishmaniasis are present in Colombia.

Can preoperative imaging help to predict postoperative outcome after wisdom tooth removal? A randomized controlled trial using panoramic radiography versus cone-beam CT.

OBJECTIVES: The primary objective of the study was to compare the postoperative complications following surgical removal of impacted third molars using panoramic radiography (PAN) images- and cone-beam computed tomography (CBCT)-based surgeries for "moderate-risk" cases of impacted third mandibular molars. The secondary objective was to compare the reliability of CBCT with that of PAN in preoperative radiographic determination of the position of the third molar, number of roots, and apical divergence. MATERIALS AND METHODS: A randomized controlled multicenter trial was conducted to compare the surgical complications of PAN- and CBCT-based surgeries of impacted third molars. The sample consisted of impacted third molars from 256 patients with a close relation to the inferior alveolar nerve (IAN). Exclusion criteria were "no risk" and "high risk" of damage to the IAN based on the assessment of the panoramic radiograph. Patients were divided into two groups: the CBCT group (n = 126) and the PAN group (n = 130). The incidences of IAN sensory disturbance and other postoperative complications were recorded for each group at 7 days after surgery. Statistical analysis (kappa values) was used to compare the diagnoses of five trained dentomaxillofacial radiologists and to relate radiologic diagnoses to perioperative findings. Logistic regression was used to determine whether the imaging modality influenced occurrence of postoperative complications. RESULTS: Two extractions (1.5%) in the CBCT group and five (3.8%) in the PAN group resulted in IAN sensory disturbance (p = 0.45). Logistic regression models did not show that CBCT modality decreased postoperative complications following surgical removal of impacted third molars. Yet, CBCT revealed the number of roots and apical divergence of the roots more reliably than panoramic radiographs. CONCLUSIONS: CBCT was not better than panoramic radiography in predicting postoperative complications for moderate-risk cases of impacted third mandibular molars. Nonetheless, a CBCT buccolingual view can accurately confirm the number of roots and root morphology of the third molar better than PAN. CLINICAL RELEVANCE: For management of postoperative complications for moderate-risk cases of impacted mandibular third molars, careful preoperative radiographic planning followed by an atraumatic surgical approach seems to be valuable, irrespective of the 2D or 3D nature of the preoperative images.

Polypy: A Framework to Interpret Polymer Properties from Mass Spectroscopy Data.

Mass spectroscopy (MS) is a robust technique for polymer characterization, and it can provide the chemical fingerprint of a complete sample regarding polymer distribution chains. Nevertheless, polymer chemical properties such as polydispersity (Pd), average molecular mass (Mn), weight average molecular mass (Mw) and others are not determined by MS, as they are commonly characterized by gel permeation chromatography (GPC). In order to calculate polymer properties from MS, a Python script was developed to interpret polymer properties from spectroscopic raw data. Polypy script can be considered a peak detection and area distribution method, and represents the result of combining the MS raw data filtered using Root Mean Square (RMS) calculation with molecular classification based on theoretical molar masses. Polypy filters out areas corresponding to repetitive units. This approach facilitates the identification of the polymer chains and calculates their properties. The script also integrates visualization graphic tools for data analysis. In this work, aryl resin (poly(2,2-bis(4-oxy-(2-(methyloxirane)phenyl)propan) was the study case polymer molecule, and is composed of oligomer chains distributed mainly in the range of dimers to tetramers, in some cases presenting traces of pentamers and hexamers in the distribution profile of the oligomeric chains. Epoxy resin has Mn = 607 Da, Mw = 631 Da, and polydispersity (Pd) of 1.015 (data given by GPC). With Polypy script, calculations resulted in Mn = 584.42 Da, Mw = 649.29 Da, and Pd = 1.11, which are consistent results if compared with GPC characterization. Additional information, such as the percentage of oligomer distribution, was also calculated and for this polymer matrix it was not possible to retrieve it from the GPC method. Polypy is an approach to characterizing major polymer chemical properties using only MS raw spectra, and it can be utilized with any MS raw data for any polymer matrix.

Diagnosis and Treatment in Unilateral Condylar Hyperplasia.

Unilateral condylar hyperplasia (UCH) is an uncommon disease involving progressive facial asymmetry. The aim of this research was to perform an analysis of the diagnosis and treatment of patients with UCH in a clinical series. An observational retrospective study was performed on subjects with progressive facial asymmetry in the lower third of the face; all the subjects were under treatment with condylectomy and orthodontics to improve occlusion and face balance. Variables such as age, sex, clinical type, SPECT (single photon emission computed tomography) intensity and a requirement for secondary surgery were included; the Shapiro Wilk test was performed to analyze the normality of the data and nonparametric analysis and the Kruskal-Wallis or Mann-Whitney tests were used to assess the association between the SPECT difference and the variables, where 2-tailed p values < 0.05 were considered to be statistically significant. Forty-nine patients between 10 and 45 y.o. (average age: 19.1 ± 7.4 y.o.) were included in the study. There were 41 female (83.6%) and 8 male (16.4%) subjects. The SPECT analysis comparing the right and left condyles with more than 10% in caption of the isotope was present in 46 subjects; the results obtained using SPECT were not statistically related to the age or sex of the sample (p = 0.277). The patients were classified into clinical types I, II and III, and no correlations could be confirmed between the clinical type and other variables. High condylectomy was conducted on all patients, among which 14 patients underwent a secondary surgery for orthognathic or cosmetic treatment, and was not related to the initial variables used in diagnosis (p = 0.98); interestingly, the second surgical treatment was more present in the clinical type I and in subjects under 16 years old with no statistical differences. Clinical analysis, medical records, 3D imaging and SPECT should be used as a complementary analysis in assessing the diagnosis of UCH and progressive facial asymmetry.

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.

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.

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.