{"data":{"event":{"id":"429c04f2-e141-4d46-a4fe-83d38d5a9414","slug":"integrating-structural-and-biological-evidence-to-rerank-esmfold2-protei-a5dcae8d47","title":"\n\nIntegrating structural and biological evidence to rerank ESMFold2 protein-protein interactions \n\n","short_summary":"Large-scale protein structure prediction enables proteome-wide protein-protein interaction (PPI) screening, but distinguishing biologically meaningful interactions from spurious interfaces remains challenging. Here, we develop a scalable framework combining fast, MSA-free ESMFold2 prediction with PAE-guided domain parsing and evidence-based reranking. Three-recycle ESMFold2-Fast achieved 57% acceptable-or-better DockQ scores on FoldBench, comparable to AlphaFold2-Multimer while substantially reducing computation. PAE-guided parsing preserved 98.1% of XL-MS cross-links within parsed domain pair","full_description":"Large-scale protein structure prediction enables proteome-wide protein-protein interaction (PPI) screening, but distinguishing biologically meaningful interactions from spurious interfaces remains challenging. Here, we develop a scalable framework combining fast, MSA-free ESMFold2 prediction with PAE-guided domain parsing and evidence-based reranking. Three-recycle ESMFold2-Fast achieved 57% acceptable-or-better DockQ scores on FoldBench, comparable to AlphaFold2-Multimer while substantially reducing computation. PAE-guided parsing preserved 98.1% of XL-MS cross-links within parsed domain pairs. We then developed the Structure Prediction and Omics informed Classifier (SPOC)-ESMFold2, which integrates structural features with independent biological evidence to prioritize predicted interactions. SPOC-ESMFold2 achieved an AUROC of 0.93 and AUPR of 0.90, compared with 0.87 and 0.79 for a structural-only classifier. Under a 1:128 positive-to-negative ratio, SPOC-ESMFold2 achieved 17.2% recall at 5% false-discovery rate, substantially outperforming structural confidence metrics alone. This framework enables scalable PPI screening by integrating structural plausibility with orthogonal biological evidence to prioritize candidates for experimental investigation.","ledger_type":"benefit","primary_domain_id":"8f1af1b9-7302-4b51-aae9-fdfac04d158a","event_status":"provisional","event_date":"2026-09-17T00:00:00.000Z","discovery_date":"2026-09-17T00:00:00.000Z","first_published_date":"2026-09-17T00:00:00.000Z","last_reviewed_date":"2026-09-17T00:00:00.000Z","geographic_scope":"International","affected_population":null,"base_impact_tier":1,"base_score":"1.00","attribution_multiplier":"0.5000","evidence_multiplier":"0.3500","realization_multiplier":"0.4000","durability_multiplier":"0.5000","current_event_score":"0.035000","confidence_level":"low","score_explanation":"Auto-published from news ingest as a provisional placeholder. Score is conservative until a named release is identified and the record is rescored.","methodology_version_id":"8745541b-0f8e-4d5a-9b47-5291d3813a8d","original_methodology_version_id":"8745541b-0f8e-4d5a-9b47-5291d3813a8d","published_at":"2026-09-18T05:00:53.737Z","created_at":"2026-09-18T05:00:53.737Z","updated_at":"2026-09-18T05:00:53.737Z","flags":[],"domain_name":"Biology","domain_slug":"biology","methodology_version":"0.2"},"contributions":[{"id":"ecbfc308-41f0-4aa6-83fd-dd24d2581b0d","event_id":"429c04f2-e141-4d46-a4fe-83d38d5a9414","model_id":"535014f7-c941-4d39-aa9b-8f9c1d3d07b7","role_description":"Unspecified system mentioned or implied by a news item. Remap to a named release when identified.","attribution_multiplier":"0.5000","credit_share":"1.0000","contribution_score":"0.035000","attribution_rationale":"News ingest does not infer a named model from the publisher alone. Attribution stays unspecified until a release is identified.","attribution_confidence":"medium","first_used_date":"2026-09-17T00:00:00.000Z","model_version_if_known":null,"review_status":"approved","created_at":"2026-09-18T05:00:53.745Z","model_slug":"unspecified-ai-system","model_name":"Unspecified AI system","identity_class":"unknown","is_internal":false,"family_name":"Unspecified","family_slug":"unknown-unspecified","organization_name":"Unknown","organization_slug":"unknown"}],"sources":[{"id":"2cb2a411-95f7-469c-b2a1-f221cd766942","event_id":"429c04f2-e141-4d46-a4fe-83d38d5a9414","url":"\nhttps://www.biorxiv.org/content/10.64898/2026.09.16.751194v1?rss=1\n","canonical_url":"\nhttps://www.biorxiv.org/content/10.64898/2026.09.16.751194v1?rss=1\n","source_type":"preprint","publisher":"bioRxiv","author":null,"publication_date":"2026-09-17T00:00:00.000Z","retrieved_at":"2026-09-18T05:00:53.751Z","title":"\n\nIntegrating structural and biological evidence to rerank ESMFold2 protein-protein interactions \n\n","excerpt":"Large-scale protein structure prediction enables proteome-wide protein-protein interaction (PPI) screening, but distinguishing biologically meaningful interactions from spurious interfaces remains challenging. Here, we develop a scalable framework combining fast, MSA-free ESMFold2 prediction with PAE-guided domain parsing and evidence-based reranking. Three-recycle ESMFold2-Fast achieved 57% acceptable-or-better DockQ scores on FoldBench, comparable to AlphaFold2-Multimer while substantially red","content_hash":null,"source_reliability_class":"medium","is_primary_source":true,"is_independent":true,"is_peer_reviewed":false,"archived_url":null,"created_at":"2026-09-18T05:00:53.751Z"}],"claims":[{"id":"c4d5ba22-ca0b-455a-83f7-02aa2f049c38","event_id":"429c04f2-e141-4d46-a4fe-83d38d5a9414","claim_text":"\n\nIntegrating structural and biological evidence to rerank ESMFold2 protein-protein interactions \n\n","claim_type":"outcome","claim_status":"supported","confidence_score":null,"created_at":"2026-09-18T05:00:53.759Z","updated_at":"2026-09-18T05:00:53.759Z"}],"revisions":[{"id":"139ee116-adde-401f-a724-12133ae6737c","event_id":"429c04f2-e141-4d46-a4fe-83d38d5a9414","model_id":null,"previous_score":"0.000000","new_score":"0.035000","previous_factors":{},"new_factors":{"tier":1,"evidence":0.35,"base_score":1,"durability":0.5,"attribution":0.5,"realization":0.4},"change_reason":"Auto-published from news ingest.","trigger_type":"news_ingest","trigger_source_ids":null,"reviewer_id":null,"review_status":"published","created_at":"2026-09-18T05:00:53.766Z"}],"secondary":[]},"methodology_version":"0.1"}