MetaEnzyme: Meta Pan-Enzyme Learning for Task-Adaptive Redesign

TL;DR

MetaEnzyme introduces a unified framework for enzyme redesign that leverages cross-modal transformations and domain adaptation, effectively addressing low-resource challenges and demonstrating strong experimental validation.

Contribution

It presents a novel staged, unified enzyme design framework with cross-modal architecture and domain adaptation for low-resource enzyme redesign tasks.

Findings

Effective in functional, mutation, and sequence design tasks

Achieves superior performance over existing methods

Validated by wet lab experiments

Abstract

Enzyme design plays a crucial role in both industrial production and biology. However, this field faces challenges due to the lack of comprehensive benchmarks and the complexity of enzyme design tasks, leading to a dearth of systematic research. Consequently, computational enzyme design is relatively overlooked within the broader protein domain and remains in its early stages. In this work, we address these challenges by introducing MetaEnzyme, a staged and unified enzyme design framework. We begin by employing a cross-modal structure-to-sequence transformation architecture, as the feature-driven starting point to obtain initial robust protein representation. Subsequently, we leverage domain adaptive techniques to generalize specific enzyme design tasks under low-resource conditions. MetaEnzyme focuses on three fundamental low-resource enzyme redesign tasks: functional design (FuncDesign), mutation design (MutDesign), and sequence generation design (SeqDesign). Through novel unified paradigm and enhanced representation capabilities, MetaEnzyme demonstrates adaptability to diverse enzyme design tasks, yielding outstanding results. Wet lab experiments further validate these findings, reinforcing the efficacy of the redesign process.