AI 中文总结
研究旨在弥合蛋白质功能注释与特性预测的差距，提出基于范畴论的AlphaFunctor平台，直接从序列预测蛋白质功能并映射到特性预测，经大量数据训练，无需特定任务网络设计，性能优于其他竞争预测器。
AI 中文摘要
蛋白质序列、结构、功能和物理化学性质之间的基本关系是生物学的核心原则。理论上蛋白质功能和特性应能直接从序列得出，但实际上相关预测方法基于特定数据集和子集设计，导致功能注释与特性预测存在巨大差距。为应对挑战，我们引入AlphaFunctor，一个基于范畴论的类基础模型平台来弥合差距。基于蛋白质功能和特性可直接从序列得出的假设，它直接从序列预测以基因本体术语表示的蛋白质功能，还用拓扑谱理论等将功能预测映射到下游特性预测。AlphaFunctor在近60万个蛋白质功能数据点上（预）训练，在三个基准数据集上实现了最先进的蛋白质功能注释。无需特定任务网络重新设计，它能将定性蛋白质功能注释映射到各种定性和定量蛋白质特性预测，优于其他特定数据集和任务的竞争预测器。
英文摘要
The fundamental relationship among protein sequence, structure, function, and physicochemical properties is a central principle in biology. While in principle protein function and properties should be able to be derived directly from protein sequence, in practice protein function and property prediction methods have been designed around specific datasets and specific property or function subsets, leading to an enormous gap between function annotation and property prediction. To address these challenges, we introduce AlphaFunctor, a category theory based foundation model-like platform to bridge the gap between protein function annotation and property prediction. Based on the hypothesis that protein function and properties can be directly derived from protein sequence, AlphaFunctor predicts protein functions as represented by Gene Ontology terms directly from sequence. Using these function predictions, AlphaFunctor further maps protein functions using topological spectral theory, path-complex neural networks, and protein domain analysis onto downstream property prediction. AlphaFunctor is (pre)trained in nearly 0.6 million protein function data points to deliver the state-of-the-art protein function annotation on three benchmark datasets. Without task-specific network redesign, AlphaFunctor maps qualitative protein function annotation to various qualitative and quantitative protein property predictions, outperforming other dataset-specific and task-specific competing predictors.
