ProteinConformers: Benchmark Dataset for Simulating Protein Conformational Landscape Diversity and Plausibility
Yihang Zhou, Chen Wei, Minghao Sun, Jin Song, Yang Li, Lin Wang, Yang Zhang
Understanding the conformational landscape of proteins is essential for elucidatingprotein function and facilitating drug design. However, existing protein confor-mation benchmarks fail to capture the full energy landscape, limiting their abilityto evaluate the diversity and physical plausibility of AI-generated structures. Weintroduce ProteinConformers, a large-scale benchmark dataset comprising over381,000 physically realistic conformations for 87 CASP targets. These were de-rived from more than 40,000 structural decoys via extensive all-atom moleculardynamics simulations totaling over 6 million CPU hours. Using this dataset,we propose novel metrics to evaluate conformational diversity and plausibility,and systematically benchmark six protein conformation generative models. Ourresults highlight that leveraging large-scale protein sequence data can enhance amodel’s ability to explore conformational space, potentially reducing reliance onMD-derived data. Additionally, we find that PDB and MD datasets influence modelperformance differently, current models perform well on inter-atomic distance pre-diction but struggle with inter-residue orientation generation. Overall, our dataset,evaluation metrics, and benchmarking results provide the first comprehensive foun-dation for assessing generative models in protein conformational modeling. Datasetand instructions are available at https://huggingface.co/datasets/Jim990908/ProteinConformers/tree/main.