RESUMEN
Descriptors play a pivotal role in enzyme design for the greener synthesis of biochemicals, as they could characterize enzymes and chemicals from the physicochemical and evolutionary perspective. This study examined the effects of various descriptors on the performance of Random Forest model used for enzyme-chemical relationships prediction. We curated activity data of seven specific enzyme families from the literature and developed the pipeline for evaluation the machine learning model performance using 10-fold cross-validation. The influence of protein and chemical descriptors was assessed in three scenarios, which were predicting the activity of unknown relations between known enzymes and known chemicals (new relationship evaluation), predicting the activity of novel enzymes on known chemicals (new enzyme evaluation), and predicting the activity of new chemicals on known enzymes (new chemical evaluation). The results showed that protein descriptors significantly enhanced the classification performance of model on new enzyme evaluation in three out of the seven datasets with the greatest number of enzymes, whereas chemical descriptors appear no effect. A variety of sequence-based and structure-based protein descriptors were constructed, among which the esm-2 descriptor achieved the best results. Using enzyme families as labels showed that descriptors could cluster proteins well, which could explain the contributions of descriptors to the machine learning model. As a counterpart, in the new chemical evaluation, chemical descriptors made significant improvement in four out of the seven datasets, while protein descriptors appear no effect. We attempted to evaluate the generalization ability of the model by correlating the statistics of the datasets with the performance of the models. The results showed that datasets with higher sequence similarity were more likely to get better results in the new enzyme evaluation and datasets with more enzymes were more likely beneficial from the protein descriptor strategy. This work provides guidance for the development of machine learning models for specific enzyme families.
RESUMEN
Perennial interest in enzyme catalysis has been expanding its applicability from aqueous phases where enzymes are naturally evolved to organic solvents in which the majority of industrial chemical syntheses are carried out. Although conjugating an enzyme with a soluble polymer has been attempted to enhance enzyme activity in organic solvents, the underlying mechanism remains poorly understood in terms of the conformational dynamics and enzyme activity. Herein, we combine LF-NMR measurements and MD simulations to investigate the effects of polymer grafting on the conformational dynamics of CalB in organic solvents and the consequential impacts on the catalytic kinetics, using the lipase-catalyzed transesterification reaction as a model system. LF-NMR measurements confirm that conjugation with a soluble polymer improves the enzyme flexibility in organic solvents, leading to an increase in the catalytic efficiency of up to two orders of magnitude. MD simulations suggest that the conjugated enzyme samples a larger conformational space, compared to its native counterpart, validating the hypothesis that polymer motion enhances enzyme dynamics. These experimental and simulation results provide new insights for enhancing enzyme conformational dynamics and thereby catalytic kinetics in organic solvents.