VenusX: Unlocking Fine-Grained Functional Understanding of Proteins

• URL: http://arxiv.org/abs/2505.11812v1
• Date: Sat, 17 May 2025 03:44:23 GMT
• Title: VenusX: Unlocking Fine-Grained Functional Understanding of Proteins
• Authors: Yang Tan, Wenrui Gou, Bozitao Zhong, Liang Hong, Huiqun Yu, Bingxin Zhou,
• Abstract summary: VenusX is the first large-scale benchmark for fine-grained functional annotation and function-based protein pairing at the residue, fragment, and domain levels.<n>VenusX comprises three major task categories, including identifying critical active sites, binding sites, conserved sites motifs, domains, and pairwises.<n>Our benchmark enables a comprehensive assessment of model performance on both in-distribution and out-of-distribution scenarios.
• Score: 3.9151216020893544
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deep learning models have driven significant progress in predicting protein function and interactions at the protein level. While these advancements have been invaluable for many biological applications such as enzyme engineering and function annotation, a more detailed perspective is essential for understanding protein functional mechanisms and evaluating the biological knowledge captured by models. To address this demand, we introduce VenusX, the first large-scale benchmark for fine-grained functional annotation and function-based protein pairing at the residue, fragment, and domain levels. VenusX comprises three major task categories across six types of annotations, including residue-level binary classification, fragment-level multi-class classification, and pairwise functional similarity scoring for identifying critical active sites, binding sites, conserved sites, motifs, domains, and epitopes. The benchmark features over 878,000 samples curated from major open-source databases such as InterPro, BioLiP, and SAbDab. By providing mixed-family and cross-family splits at three sequence identity thresholds, our benchmark enables a comprehensive assessment of model performance on both in-distribution and out-of-distribution scenarios. For baseline evaluation, we assess a diverse set of popular and open-source models, including pre-trained protein language models, sequence-structure hybrids, structure-based methods, and alignment-based techniques. Their performance is reported across all benchmark datasets and evaluation settings using multiple metrics, offering a thorough comparison and a strong foundation for future research. Code and data are publicly available at https://github.com/ai4protein/VenusX.

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