The prediction of enzyme kinetic parameters is crucial for screening enzymes with high catalytic efficiency and desired characteristics to catalyze natural or non-natural reactions. Data-driven machine learning models have been explored to reduce exp...
Sheng wu gong cheng xue bao = Chinese journal of biotechnology
40170309
Natural components serve the survival instincts of cells that are obtained through long-term evolution, while they often fail to meet the demands of engineered cells for efficiently performing biological functions in special industrial environments. ...
Journal of chemical information and modeling
40127128
The potential of hydrogen (pH) influences the function of the enzyme. Measuring or predicting the optimal pH (pH) at which enzymes exhibit maximal catalytic activity is crucial for enzyme design and application. The rapid development of enzyme mining...
Accurate prediction of enzyme kinetic parameters is crucial for enzyme exploration and modification. Existing models face the problem of either low accuracy or poor generalization ability due to overfitting. In this work, we first developed unbiased ...
Tools available for inferring enzyme function from general sequence, fold, or evolutionary information are generally successful. However, they can lead to misclassification if a deviation in local structural features influences the function. Here, we...
An accurate deep learning predictor is needed for enzyme optimal temperature (${T}_{opt}$), which quantitatively describes how temperature affects the enzyme catalytic activity. In comparison with existing models, a new model developed in this study,...
Journal of agricultural and food chemistry
40066931
Since the structure of enzymes determines their function, elucidating the structure of enzymes lays a solid foundation for deciphering their catalytic mechanism and enabling rational design. The development of artificial intelligence (AI) has sparked...
The aaccurate prediction of enzymes with environment detoxification functions is crucial, not only to achieve a better understanding of bioremediation strategies, but also to alleviate environmental pollution. In the present study, a novel machine le...
Traditional methods for synthesizing nanozymes are often time-consuming and complex, hindering efficiency. Artificial intelligence (AI) has the potential to simplify these processes, but there are very few dedicated nanozyme databases available, limi...
Enzyme design has struggled to emulate the complexity and catalytic proficiency of natural enzymes. Lauko et al. show that with the help of deep learning, the design of serine hydrolases that rival nature's ingenuity is possible.