Multimodal Mixture-of-Experts with Retrieval Augmentation for Protein Active Site Identification

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• MERA achieves 90% AUPRC on active site prediction, demonstrating state-of-the-art performance.result 95%
• MERA is the first retrieval-augmented framework for protein active site identification.method 90%
• MERA employs hierarchical multi-expert retrieval that dynamically aggregates contextual information from chain, sequence, and active-site perspectives.method 85%
• MERA uses a reliability-aware fusion strategy based on Dempster-Shafer evidence theory to prevent modality degradation.technical 85%
• MERA shows significant gains on peptide-binding site identification compared to existing methods.result 80%
• MERA's effectiveness could diminish without reliable access to external datasets and embeddings.limitation 75%
• Deploying MERA at scale could require substantial computational resources.limitation 70%
• MERA outperforms existing solutions in protein active site identification tasks.result 85%

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