RESUMO
Bleomycin, commonly employed in treating Hodgkin's lymphoma and testicular cancer, is associated with significant pulmonary toxicity. While various studies have assessed the toxic impact of chemotherapeutic agents on aquatic and terrestrial environments, limited data exist on bleomycin's effects, especially concerning higher plants. To address this gap, we utilized the Allium cepa assays, renowned for evaluating chemical and biochemical agents' toxic effects, to investigate bleomycin's impact on the terrestrial ecosystem. Our study aimed to assess bleomycin's cyto-genotoxic effects on A. cepa root tip cells at minimal concentrations (10-40 µg mL-1) and varied exposure durations (2, 4, 6, and 24 h). Analysis of nuclear and mitotic abnormalities in bleomycin-treated A. cepa root tip cells, alongside an acridine orange-ethidium bromide double staining assay, illuminated its influence on cell viability. Additionally, agarose gel electrophoresis determined the drug's potential for DNA degradation, unveiling the underlying mechanisms of cyto-genotoxicity. Results also demonstrated a decline in the mitotic index with increased bleomycin concentrations and exposure time, elevated frequencies of various cyto-genotoxic abnormalities, including sticky chromosomes, chromatid breaks, laggards, bridges, polar deviations, nuclear lesions, and hyperchromasia. The study indicated the potential risks of bleomycin even at low concentrations and brief exposures, highlighting its severe adverse effects on genetic material of plant, potentially contributing to cell death. Consequently, this investigation unveils bleomycin's cyto-genotoxic effects on higher plant system, underscoring its threat to terrestrial ecosystems, particularly upon chronic and unmonitored exposure.
RESUMO
Biocatalysis is a promising approach to sustainably synthesize pharmaceuticals, complex natural products, and commodity chemicals at scale. However, the adoption of biocatalysis is limited by our ability to select enzymes that will catalyze their natural chemical transformation on non-natural substrates. While machine learning and in silico directed evolution are well-posed for this predictive modeling challenge, efforts to date have primarily aimed to increase activity against a single known substrate, rather than to identify enzymes capable of acting on new substrates of interest. To address this need, we curate 6 different high-quality enzyme family screens from the literature that each measure multiple enzymes against multiple substrates. We compare machine learning-based compound-protein interaction (CPI) modeling approaches from the literature used for predicting drug-target interactions. Surprisingly, comparing these interaction-based models against collections of independent (single task) enzyme-only or substrate-only models reveals that current CPI approaches are incapable of learning interactions between compounds and proteins in the current family level data regime. We further validate this observation by demonstrating that our no-interaction baseline can outperform CPI-based models from the literature used to guide the discovery of kinase inhibitors. Given the high performance of non-interaction based models, we introduce a new structure-based strategy for pooling residue representations across a protein sequence. Altogether, this work motivates a principled path forward in order to build and evaluate meaningful predictive models for biocatalysis and other drug discovery applications.
