The Versatility of Serine Proteases from Brazilian Bothrops Venom: Their Roles in Snakebites and Drug Discovery
Bibliographic record
Abstract
Serine proteases are multifunctional and versatile venom components found in viper snakes, including the Bothrops species, a widely distributed genus notorious for causing the highest number of snakebites across Latin America. These enzymes, representing a significant fraction of Bothrops venom proteomes, exhibit a wide range of biological activities that influence blood coagulation, fibrinolysis, and inflammation. This review provides a comprehensive overview of serine proteases, with a particular focus on those found in the venom of Brazilian Bothrops snakes. The discussion begins with a summary of snake species found in Brazil and their medical relevance. Specifically addressing the Bothrops genus, this review explores the distribution of these species across Brazilian territory and their associated medical importance. Subsequently, the article investigates the biochemistry of Bothrops venoms and the clinical manifestations induced by envenomation. Finally, it offers an in-depth discussion on the serine proteases, highlighting their biochemical properties, mechanisms of action, and potential therapeutic applications. Furthermore, this review provides an in-depth exploration of the diverse serine proteases found in Bothrops venoms and their functional significance, from thrombin-like effects to potent fibrinogenolytic actions, which determine the clinical manifestations of envenomation. This review delves into the evolutionary adaptations and biochemical diversity of serine proteases in Bothrops venoms, emphasizing their critical roles in venom functionality and the resulting pathophysiological effects. Additionally, it opens new avenues for utilizing these enzymes in biomedical applications, underscoring their potential beyond toxinology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".