Efficient chiral synthesis by Saccharomyces cerevisiae spore encapsulation of Candida parapsilosis Glu228Ser/(S)-carbonyl reductase II and Bacillus sp. YX-1 glucose dehydrogenase in organic solvents.

BACKGROUND: Saccharomyces cerevisiae AN120 osw2∆ spores were used as a host with good resistance to unfavorable environment. This work was undertaken to develop a new yeast spore-encapsulation of Candida parapsilosis Glu228Ser/(S)-carbonyl reductase II and Bacillus sp. YX-1 glucose dehydrogenase for efficient chiral synthesis in organic solvents. RESULTS: The spore microencapsulation of E228S/SCR II and GDH in S. cerevisiae AN120 osw2∆ catalyzed (R)-phenylethanol in a good yield with an excellent enantioselectivity (up to 99%) within 4 h. It presented good resistance and catalytic functions under extreme temperature and pH conditions. The encapsulation produced several chiral products with over 70% yield and over 99% enantioselectivity in ethyl acetate after being recycled for 4-6 times. It increased substrate concentration over threefold and reduced the reaction time two to threefolds compared to the recombinant Escherichia coli containing E228S and glucose dehydrogenase. CONCLUSIONS: This work first described sustainable enantioselective synthesis without exogenous cofactors in organic solvents using yeast spore-microencapsulation of coupled alcohol dehydrogenases.

In silico off-target profiling for enhanced drug safety assessment.

Ensuring drug safety in the early stages of drug development is crucial to avoid costly failures in subsequent phases. However, the economic burden associated with detecting drug off-targets and potential side effects through in vitro safety screening and animal testing is substantial. Drug off-target interactions, along with the adverse drug reactions they induce, are significant factors affecting drug safety. To assess the liability of candidate drugs, we developed an artificial intelligence model for the precise prediction of compound off-target interactions, leveraging multi-task graph neural networks. The outcomes of off-target predictions can serve as representations for compounds, enabling the differentiation of drugs under various ATC codes and the classification of compound toxicity. Furthermore, the predicted off-target profiles are employed in adverse drug reaction (ADR) enrichment analysis, facilitating the inference of potential ADRs for a drug. Using the withdrawn drug Pergolide as an example, we elucidate the mechanisms underlying ADRs at the target level, contributing to the exploration of the potential clinical relevance of newly predicted off-target interactions. Overall, our work facilitates the early assessment of compound safety/toxicity based on off-target identification, deduces potential ADRs of drugs, and ultimately promotes the secure development of drugs.

An overview of recent advances and challenges in predicting compound-protein interaction (CPI).

Compound-protein interactions (CPIs) are critical in drug discovery for identifying therapeutic targets, drug side effects, and repurposing existing drugs. Machine learning (ML) algorithms have emerged as powerful tools for CPI prediction, offering notable advantages in cost-effectiveness and efficiency. This review provides an overview of recent advances in both structure-based and non-structure-based CPI prediction ML models, highlighting their performance and achievements. It also offers insights into CPI prediction-related datasets and evaluation benchmarks. Lastly, the article presents a comprehensive assessment of the current landscape of CPI prediction, elucidating the challenges faced and outlining emerging trends to advance the field.

LogD7.4 prediction enhanced by transferring knowledge from chromatographic retention time, microscopic pKa and logP.

Lipophilicity is a fundamental physical property that significantly affects various aspects of drug behavior, including solubility, permeability, metabolism, distribution, protein binding, and toxicity. Accurate prediction of lipophilicity, measured by the logD7.4 value (the distribution coefficient between n-octanol and buffer at physiological pH 7.4), is crucial for successful drug discovery and design. However, the limited availability of data for logD modeling poses a significant challenge to achieving satisfactory generalization capability. To address this challenge, we have developed a novel logD7.4 prediction model called RTlogD, which leverages knowledge from multiple sources. RTlogD combines pre-training on a chromatographic retention time (RT) dataset since the RT is influenced by lipophilicity. Additionally, microscopic pKa values are incorporated as atomic features, providing valuable insights into ionizable sites and ionization capacity. Furthermore, logP is integrated as an auxiliary task within a multitask learning framework. We conducted ablation studies and presented a detailed analysis, showcasing the effectiveness and interpretability of RT, pKa, and logP in the RTlogD model. Notably, our RTlogD model demonstrated superior performance compared to commonly used algorithms and prediction tools. These results underscore the potential of the RTlogD model to improve the accuracy and generalization of logD prediction in drug discovery and design. In summary, the RTlogD model addresses the challenge of limited data availability in logD modeling by leveraging knowledge from RT, microscopic pKa, and logP. Incorporating these factors enhances the predictive capabilities of our model, and it holds promise for real-world applications in drug discovery and design scenarios.