Abstract
The potential of hydrogen (pH) influences the function of the enzyme. Measuring or predicting the optimal pH (pH(opt)) at which enzymes exhibit maximal catalytic activity is crucial for enzyme design and application. The rapid development of enzyme mining and de novo design has produced a large number of new enzymes, making it impractical to measure their pHopt in the wet laboratory. Consequently, in-silico computational approaches such as machine learning and deep learning models, which offer pH prediction at minimal cost, have attracted considerable interest. This work presents Venus-DREAM, an enzyme pH(opt) prediction model based on the kNN algorithm and few-shot learning, which achieves state-of-the-art accuracy in pHopt prediction. Venus-DREAM regards the pH(opt) prediction of an enzyme as a few-shot learning task: learning from the k-closest labeled enzymes to predict the pH(opt) of the target enzyme. The value of k is determined by the optimal k-value of the kNN regression algorithm. And the distance between two enzymes is defined as the cosine similarity of their mean-pooled embeddings obtained from protein language models (PLMs). The few-shot learner is based on the Reptile algorithm, which first adapts to the k-nearest labeled enzymes to create a specialized model for the target enzyme and then predicts its pH(opt). This efficient method enables high-throughput virtual exploration of protein space, facilitating the identification of sequences with the desired pH(opt) ranges in a high-throughput manner. Moreover, our method can be easily adapted in other protein function prediction tasks.