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

Jiangbin Zheng, Han Zhang, Qianqing Xu, An-Ping Zeng, Stan Z. Li

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.

Primary Subject Area: [Content] Multimodal Fusion

Secondary Subject Area: [Experience] Multimedia Applications

Relevance To Conference: This work significantly contributes to multimedia/multimodal processing by leveraging a multi-modal transformation architecture, the structure-to-sequence network, as a feature-driven starting point. By incorporating structural and sequential information, this approach enhances the representation capabilities crucial for understanding enzyme design tasks. Furthermore, the utilization of domain-specific techniques enables the generalization of specific enzyme design tasks under low-resource conditions. Through MetaEnzyme, the framework proposed in this work, these contributions facilitate seamless transitions between different design tasks, effectively addressing the challenges of multi-modal processing in enzyme design. This not only advances the field of enzyme design but also showcases the broader applicability of multi-modal processing techniques in solving complex biological problems.

Submission Number: 764