[Paper Review] ProteinBench: A Holistic Evaluation of Protein Foundation Models

Fei Ye, Zaixiang Zheng+8 more|arXiv (Cornell University)|Sep 10, 2024

Biomedical Text Mining and OntologiesBiochemistry, Genetics and Molecular Biology3 citations

TL;DR

ProteinBench introduces a unified, holistic evaluation framework for protein foundation models, categorizing tasks by modality relationships and evaluating performance across quality, novelty, diversity, and robustness using standardized metrics. The benchmark reveals critical insights into model strengths and limitations, particularly in protein design and conformational dynamics, and is publicly released with code, datasets, and a leaderboard to standardize future evaluation.

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.

Motivation & Objective

• To address the lack of a unified evaluation framework for protein foundation models, which hinders fair comparison and transparency in performance assessment.
• To systematically classify protein modeling tasks based on their relationships across sequence, structure, and function modalities.
• To develop a multi-metric evaluation protocol assessing four key dimensions: quality, novelty, diversity, and robustness of model outputs.
• To provide in-depth, user-objective-driven analyses that reveal model capabilities and limitations across diverse biological domains.
• To release a public, modular toolkit with datasets, code, and a leaderboard to enable reproducible, standardized benchmarking and foster collaboration in the field.

Proposed method

• A taxonomic classification of protein modeling tasks into four main categories: structure design, sequence design, structure-sequence co-design, and antibody design, based on modality relationships.
• A multi-metric evaluation framework assessing performance across four dimensions: quality (via pLDDT, pTM, CA-clash, CA-break, PepBond-break), novelty (sequence identity to training data), diversity (sequence and structure variation), and robustness (sensitivity to input perturbations).
• Use of curated, diverse datasets spanning multiple biological domains, including BPTI, apo-holo, and ATLAS, to ensure comprehensive model evaluation.
• Implementation of 11 state-of-the-art protein foundation models, including AlphaFold2, ESMFold, RoseTTAFold2, EigenFold, Str2Str, AlphaFlow/ESMFlow, and ConfDiff, using standardized inference pipelines and hyperparameters.
• Application of structural validation metrics such as CA-clash %, CA-break %, and PepBond-break % to assess geometric plausibility and stability of predicted structures.
• Use of ensemble inference and confidence scoring (e.g., pLDDT, ELBO) to select optimal predictions and ensure robustness in evaluation.

Experimental results

Research questions

• RQ1 How do protein foundation models perform across different types of generative tasks, including structure design, sequence design, and conformational dynamics?
• RQ2 To what extent do model outputs exhibit novelty, diversity, and structural quality, and how do these metrics vary across model architectures?
• RQ3 How robust are protein foundation models to input perturbations, such as MSA subsampling or sequence modifications?
• RQ4 What are the key architectural and data-related factors that influence model performance across the four evaluation dimensions?
• RQ5 How do multi-modal models compare to uni-modal models in capturing complex sequence-structure-function relationships?

Key findings

• Protein foundation models exhibit strong performance in 3D structure prediction, with AlphaFold2 and ESMFold achieving high pLDDT and pTM scores, but significant variability in conformational dynamics tasks.
• Models like Str2Str and AlphaFlow/ESMFlow show improved performance in conformational sampling, with lower CA-clash and PepBond-break rates compared to traditional models.
• A substantial fraction of model-generated sequences (up to 30% in some cases) show low identity to training data, indicating high novelty, though this does not always correlate with structural quality.
• Diversity metrics reveal that models such as ConfDiff and Str2Str generate more structurally distinct conformations, suggesting better exploration of the conformational landscape.
• Robustness analysis shows that MSA subsampling significantly degrades performance in models like OpenFold and AlphaFold2, highlighting sensitivity to input data quality.
• The benchmark identifies critical gaps in current evaluation, particularly in standardized metrics for conformational dynamics and antibody design, where no prior benchmarks existed.