To adapt the ProteinBench framework for better capturing the synergies between different protein modalities, several strategies can be implemented:
Multi-Modal Evaluation Metrics: ProteinBench should develop and incorporate evaluation metrics that assess the interplay between sequence, structure, and function. For instance, metrics could be designed to evaluate how well a model predicts functional outcomes based on its structural predictions and sequence information. This would provide a more comprehensive understanding of model performance across modalities.
Integrated Datasets: The framework could curate integrated datasets that combine sequence, structural, and functional data. By using datasets that encompass all three modalities, ProteinBench can facilitate the evaluation of models in a more holistic context, allowing researchers to assess how well models leverage information from different sources.
Cross-Modal Training: ProteinBench could encourage the development of models that are explicitly designed to learn from multiple modalities simultaneously. This could involve providing guidelines or benchmarks for training multi-modal models, emphasizing the importance of capturing relationships between sequence, structure, and function.
User-Centric Analysis: The framework could include user-centric analysis tools that allow researchers to explore how different modalities contribute to model performance based on their specific objectives. For example, users could analyze how structural predictions influence functional outcomes in their specific applications, leading to insights that drive further model improvements.
By implementing these adaptations, ProteinBench can effectively capture the synergies between different protein modalities, enhancing the evaluation of protein foundation models and fostering advancements in the field of protein science.