Auto-Research Agent on MSA Organization to Improve Protein-Protein Complex Structure Prediction
The paper introduces an autonomous agent that treats a frozen AlphaFold 3 as a black-box instrument, reorganizing multiple sequence alignments (MSAs) per target through hypothesis-driven, confidence-guided iteration without retraining.
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- Primary sourceHugging Face
- Discovery linkHugging Face
- Linked in sourceView arXiv page
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The paper introduces an autonomous agent that treats a frozen AlphaFold 3 as a black-box instrument, reorganizing multiple sequence alignments (MSAs) per target through hypothesis-driven, confidence-guided iteration without retraining. Evaluated on 1,015 protein–protein interfaces across three benchmarks released after AF3's training cutoff, the agent improves interface DockQ over AF3's default pipeline and fixed MSA recipes on every benchmark while using on average 60% fewer MSA sequences. Gains are largest for inter-species heteromers.
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The agent improves overall interface DockQ over AF3's default pipeline and fixed MSA recipes on all three benchmarks.
Source: Hugging Face ↗Stored source-based brief · key point 1 · AINewsIQ paraphraseOn average the agent uses 60% fewer MSA sequences than the default pipeline.
Source: Hugging Face ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseMean DockQ on inter-species heteromers rises from 0.465 to 0.612 on FoldBench and from 0.482 to 0.562 on HD439.
Source: Hugging Face ↗Stored source-based brief · key point 3 · AINewsIQ paraphraseLimits from the stored brief
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Why this may matter
It suggests MSA construction and cross-chain pairing can be treated as target-specific choices made at test time, potentially improving structure prediction for scientific models without retraining their weights.
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