ProteinBench: A Holistic Evaluation of Protein Foundation Models (2409.06744v2)

Abstract: Recent years have witnessed a surge in the development of protein foundation models, significantly improving performance in protein prediction and generative tasks ranging from 3D structure prediction and protein design to conformational dynamics. However, the capabilities and limitations associated with these models remain poorly understood due to the absence of a unified evaluation framework. To fill this gap, we introduce ProteinBench, a holistic evaluation framework designed to enhance the transparency of protein foundation models. Our approach consists of three key components: (i) A taxonomic classification of tasks that broadly encompass the main challenges in the protein domain, based on the relationships between different protein modalities; (ii) A multi-metric evaluation approach that assesses performance across four key dimensions: quality, novelty, diversity, and robustness; and (iii) In-depth analyses from various user objectives, providing a holistic view of model performance. Our comprehensive evaluation of protein foundation models reveals several key findings that shed light on their current capabilities and limitations. To promote transparency and facilitate further research, we release the evaluation dataset, code, and a public leaderboard publicly for further analysis and a general modular toolkit. We intend for ProteinBench to be a living benchmark for establishing a standardized, in-depth evaluation framework for protein foundation models, driving their development and application while fostering collaboration within the field.

• The paper presents ProteinBench, a comprehensive evaluation framework that categorizes protein tasks and employs multi-metric performance analysis.
• The paper demonstrates trade-offs in model performance for tasks like sequence recovery and backbone design, revealing strengths and limitations.
• The paper provides actionable insights through a public leaderboard and open-source code, fostering transparency and collaboration in protein research.

A Holistic Evaluation of Protein Foundation Models through ProteinBench

The development and nuanced evaluation of protein foundation models have become critical to advancing biological and bioengineering domains. The paper "ProteinBench: A Holistic Evaluation of Protein Foundation Models" by Fei Ye et al. introduces a new comprehensive benchmarking framework designed to evaluate the performance of these models systematically. This framework, ProteinBench, bridges the gap in understanding the intricate capabilities and limitations of various protein foundation models by offering a detailed and standardized evaluation protocol across diverse tasks in the protein domain.

Key Contributions

The authors present several pivotal contributions through ProteinBench:

1. Taxonomic Classification of Protein Tasks: ProteinBench categorizes the main challenges in protein science into well-defined tasks, which include protein design (both single-modal and multi-modal) as well as protein conformational dynamics. This classification spans sequence, structure, and function modality predictions, allowing for a nuanced analysis of model capabilities.
2. Multi-Metric Evaluation: The evaluation approach is multi-dimensional, assessing models on key performance metrics: quality, novelty, diversity, and robustness. This extends beyond traditional single metrics, offering a more intricate view of model efficiency and effectiveness in protein modeling tasks.
3. In-depth User Objective Analyses: Recognizing the diversity of user objectives in protein modeling, ProteinBench includes analyses from varied perspectives to cater to different end goals. This customization ensures that practical application domains can leverage specific insights relevant to their unique requirements.
4. Public Leaderboard and Codebase: To promote transparency and encourage collaboration, the authors released an open-source code framework and a public leaderboard. This allows researchers to benchmark their models against established baselines and contributes to collective advancements in the field.

Evaluation Insights

Protein Design Tasks:

• Inverse Folding: The paper underscores that no single model excels across all objectives. LLMs like LM-Design demonstrate high sequence recovery rates for native structures but struggle with de novo backbones, highlighting a trade-off between evolutionary distribution fitting and robustness.
• Backbone Design: Structure-based models, such as RFdiffusion and FrameFlow, show strengths in generating robust backbones across various chain lengths, although performance declines significantly for longer chains, indicating a need for improved methods for extensive protein backbones.
• Sequence Design: Different models exhibit varied strengths: DPLM is noted for high-quality sequence generation with impressive pLDDT scores, whereas EvoDiff excels in generating diverse sequences. The balance between sequence quality and diversity remains a critical consideration.
• Structure-Sequence Co-Design: Multiflow and ProteinGenerator provide robust performance across lengths, showcasing a balanced capability in co-design tasks. However, consistent challenges arise in maintaining accuracy and novelty for longer sequences.
• Motif Scaffolding and Antibody Design: For motif scaffolding, structure-based methods like RFdiffusion excel in generating high-quality scaffolds, while sequence-based methods lag behind. In antibody design, comprehensive metrics reveal no clear frontrunner, with methods like AbDPO++ showing commendable balance across accuracy, specificity, and rationality.

Protein Conformation Prediction Tasks:

• Single-State Folding: AlphaFold2 and OpenFold consistently demonstrate superior performance, leveraging advanced MSA-based approaches. In contrast, models like EigenFold lag in accuracy due to architectural limitations.
• Multiple-State Prediction: ConfDiff models with classifier-free guidance and physical information incorporation provide the best performance in ensemble accuracy, indicating effective sampling of diverse conformations.
• Distribution Prediction: Generative models trained with MD conformations (AlphaFlow/ESMFlow, ConfDiff) outperform perturbation-based techniques in predicting dynamic features and approximating target distributions, although significant performance gaps remain compared to MD simulations.

Implications and Future Directions

The findings from ProteinBench emphasize the importance of considering multiple dimensions of evaluation to accurately gauge the capabilities of protein foundation models. The highlighted trade-offs between robustness, novelty, and diversity inform future work on model improvement and application-specific optimization.

Future research directions should focus on standardizing datasets across models to enable direct comparisons of architectural innovations and extending benchmark tasks to include broader protein science challenges. By continually updating and expanding ProteinBench, the research community can stabilize and accelerate advancements in protein modeling and design, ultimately driving innovative solutions in bioengineering and therapeutic development.

In summary, ProteinBench establishes a robust, holistic framework for evaluating protein foundation models, providing a critical tool for researchers to deeply understand and further enhance the current state-of-the-art in protein science.