We introduce ProteinWorkshop, a comprehensive benchmark suite for
representation learning on protein structures with Geometric Graph Neural
Networks. We consider large-scale pre-training and downstream tasks on both
experimental and predicted structures to enable the systematic evaluation of
the quality of the learned structural representation and their usefulness in
capturing functional relationships for downstream tasks. We find that: (1)
large-scale pretraining on AlphaFold structures and auxiliary tasks
consistently improve the performance of both rotation-invariant and equivariant
GNNs, and (2) more expressive equivariant GNNs benefit from pretraining to a
greater extent compared to invariant models. We aim to establish a common
ground for the machine learning and computational biology communities to
rigorously compare and advance protein structure representation learning. Our
open-source codebase reduces the barrier to entry for working with large
protein structure datasets by providing: (1) storage-efficient dataloaders for
large-scale structural databases including AlphaFoldDB and ESM Atlas, as well
as (2) utilities for constructing new tasks from the entire PDB.
ProteinWorkshop is available at: github.com/a-r-j/ProteinWorkshop.

ProteinWorkshop 是一套用于蛋白质结构上的几何图神经网络表征学习的全面基准套件，通过对大规模预训练和下游任务进行系统评估，旨在建立机器学习和计算生物学社区之间的共同基础，以严谨地比较和推进蛋白质结构表征学习的研究。