DeepPH : A Multimodal Deep Learning Model for Predicting Enzyme Optimal pH Range

What is it about?

Enzymes are vital biological catalysts, but they only function within specific pH ranges. Finding these ranges through traditional lab experiments is slow and expensive. Meanwhile, existing computational models often fail because they only analyze protein sequences and overlook the enzyme's three-dimensional (3D) structure, which is a key determinant of pH sensitivity. Our research introduces DeepPH, a multimodal deep learning framework that bridges this gap. By combining sequence data with 3D structural information, DeepPH predicts the entire functional pH range rather than just a single point. It uses a multi-head attention mechanism to outperform current state-of-the-art models in accuracy and reliability.

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Photo by ANIRUDH on Unsplash

Photo by ANIRUDH on Unsplash

Why is it important?

This work addresses a fundamental gap in bioinformatics by moving beyond simple sequence analysis to incorporate the three-dimensional (3D) spatial determinants of enzyme activity. By leveraging an Equivariant Graph Neural Network (EGNN), DeepPH effectively captures structural features that previous sequence-only models overlook, providing a more comprehensive understanding of pH sensitivity. Furthermore, our "interval-aware" approach redefines how optimal pH is predicted, treating it as a functional range rather than a fixed point. This innovation better aligns with biological reality and offers researchers a robust, high-throughput alternative to time-consuming laboratory titrations. Ultimately, DeepPH empowers scientists to accelerate enzyme engineering for green chemistry, drug discovery, and industrial biotechnology with unprecedented precision.

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