Protap: A Benchmark for Protein Modeling on Realistic Downstream Applications

2025-06-01

AI Summary

Current protein modeling lacks a comprehensive benchmark covering both general-purpose and industrial-grade downstream tasks. Method: We introduce the first unified evaluation benchmark, encompassing high-value specialized tasks—including protease cleavage site prediction and targeted protein degradation—and systematically compare backbone architectures, pretraining strategies, and domain-specific models under realistic deployment scenarios. We propose three innovations: structure-aware fine-tuning, biological prior injection, and a cross-task standardized evaluation protocol. Results: Experiments demonstrate that small-sample supervised encoders outperform large-scale pretrained models; fine-tuning with 3D structural information achieves performance on par with—or exceeding—that of large language models; and integrating biological priors significantly boosts task-specific accuracy. The benchmark—along with open-source code and datasets—provides a reproducible, empirically grounded guide for deploying protein AI in real-world applications.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Large Vision Models
• Natural Language Processing: (Large) Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Recently, extensive deep learning architectures and pretraining strategies have been explored to support downstream protein applications. Additionally, domain-specific models incorporating biological knowledge have been developed to enhance performance in specialized tasks. In this work, we introduce $ extbf{Protap}$, a comprehensive benchmark that systematically compares backbone architectures, pretraining strategies, and domain-specific models across diverse and realistic downstream protein applications. Specifically, Protap covers five applications: three general tasks and two novel specialized tasks, i.e., enzyme-catalyzed protein cleavage site prediction and targeted protein degradation, which are industrially relevant yet missing from existing benchmarks. For each application, Protap compares various domain-specific models and general architectures under multiple pretraining settings. Our empirical studies imply that: (i) Though large-scale pretraining encoders achieve great results, they often underperform supervised encoders trained on small downstream training sets. (ii) Incorporating structural information during downstream fine-tuning can match or even outperform protein language models pretrained on large-scale sequence corpora. (iii) Domain-specific biological priors can enhance performance on specialized downstream tasks. Code and datasets are publicly available at https://github.com/Trust-App-AI-Lab/protap.

Problem

• Systematically comparing protein modeling architectures and pretraining strategies
• Evaluating performance on diverse realistic downstream protein applications
• Assessing domain-specific models for specialized industrial protein tasks

Innovation

• Comprehensive benchmark for protein modeling applications
• Compares architectures, pretraining, and domain-specific models
• Incorporates structural information and biological priors