Decoding the Energetic Blueprint of Extremophile Enzyme: A Multimodal AI Framework for Functional Discovery Beyond Sequence and Structure Homology.
ACCESS is a specialized open-source Python package in the field of biomanufacturing. It aims to offer high-precision EC number prediction for the rational design of industrial enzymes, screening of functional proteins, and enzyme reaction optimization using MIT license. By combining 3D protein structural features, side-chain features, and residue-level Rosetta energy, it uses a hybrid graph neural network architecture. Based on hierarchical contrastive learning and multi-label adaptive fine-tuning, it predicts protein functions. Through a topology-aware gradient attention mechanism, it precisely locates function - critical residues. This approach pioneers a multimodal feature fusion graph neural network architecture and an interpretable AI algorithm (“Activity Existence - EC Prediction - Rational Design”), surmounting the limitations of conventional tools.
Installation - Quick start - Tutorial - Documentation
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ACCESS is in a stage of rapid development so that we will carefully consider all aspects of your proposal. We hope future input will be given by both users and developers.