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Auto-Research Agent on MSA Organization to Improve Protein-Protein Complex Structure Prediction — arXiv:2611.03183

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.03183
License (as archived): CC0-1.0
Archived: 2026-11-03T11:03:41.650Z
SHA-256 of the archived text: 135395de25b3f7c6ffeeef3f3e23ca6f8bec8c8b4c08cb0e1a997cc7da6b6aaa
Stable URL of this page: https://metraipolis.com/documento/dc_2d5151eddbd6

How to cite

Auto-Research Agent on MSA Organization to Improve Protein-Protein Complex Structure Prediction — arXiv:2611.03183 — arXiv (en), https://arxiv.org/abs/2611.03183 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2d5151eddbd6 (Metraipolis archive dc_2d5151eddbd6, sha256 135395de25b3f7c6ffeeef3f3e23ca6f8bec8c8b4c08cb0e1a997cc7da6b6aaa)

BibTeX

@misc{dc2d5151eddbd6,
  title = {Auto-Research Agent on MSA Organization to Improve Protein-Protein Complex Structure Prediction — arXiv:2611.03183},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.03183},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2d5151eddbd6; sha256 135395de25b3f7c6ffeeef3f3e23ca6f8bec8c8b4c08cb0e1a997cc7da6b6aaa},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.03183. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Protein-complex structure prediction with AlphaFold 3 depends on MSA content, depth, and cross-chain pairing. Current pipelines apply uniform recipes, typically maximizing depth and pairing by species, yet even whether cross-chain pairing helps AF3 remains contested. We show that the value of both depth and pairing is strongly target-dependent, so no single recipe is optimal across interfaces. We introduce an autonomous agent that treats a frozen AF3 as a black-box experimental instrument. For each target, the agent formulates biological hypotheses, analyzes and reorganizes the MSA, and iteratively refines sequence selection and pairing using predictor label-free signals alone. On 1{,}015 protein--protein interfaces from three benchmarks released after AF3's training cutoff, the agent improves overall interface DockQ over AF3's default pipeline and fixed MSA recipes on every benchmark, while using on average 60\% fewer MSA sequences than the default. Gains are largest for inter-species heteromers, where species-based pairing is unreliable: mean DockQ increases from 0.465 to 0.612 on FoldBench and from 0.482 to 0.562 on HD439. The improvements arise from both sequence construction and pairing policy, including deliberate this http URL results recast MSA design as a target-specific problem and show that an autonomous agent can adapt it at test time through biological reasoning and self-directed, confidence-guided experimentation.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.03183 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:59:45.881Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).