{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:3RF5BDORSKNY4BS7ZBQ2X76CVR","short_pith_number":"pith:3RF5BDOR","schema_version":"1.0","canonical_sha256":"dc4bd08dd1929b8e065fc861abffc2ac65ba2ff474472f3d7d8808d113216b6c","source":{"kind":"arxiv","id":"2012.00672","version":2},"attestation_state":"computed","paper":{"title":"Dynamics-based peptide-MHC binding optimization by a convolutional variational autoencoder: a use-case model for CASTELO","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.QM","authors_text":"Chih-Chieh Yang, David Bell, Giacomo Domeniconi, Guojing Cong, Leili Zhang, Ruhong Zhou","submitted_at":"2020-11-29T13:41:18Z","abstract_excerpt":"An unsolved challenge in the development of antigen specific immunotherapies is determining the optimal antigens to target. Comprehension of antigen-MHC binding is paramount towards achieving this goal. Here, we present CASTELO, a combined machine learning-molecular dynamics (ML-MD) approach to design novel antigens of increased MHC binding affinity for a Type 1 diabetes (T1D)-implicated system. We build upon a small molecule lead optimization algorithm by training a convolutional variational autoencoder (CVAE) on MD trajectories of 48 different systems across 4 antigens and 4 HLA serotypes. W"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2012.00672","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.QM","submitted_at":"2020-11-29T13:41:18Z","cross_cats_sorted":[],"title_canon_sha256":"74537fa5d63e22cd6cde1250e4ec6e99e68850d38adac0ba2c05644f14d8be45","abstract_canon_sha256":"bf74e0e48fcb56a166d6523c74e565399be96d20026bb162bb9527290d78f876"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:57:59.649387Z","signature_b64":"9vTITNR8Cs12sy7dkf/tXZFc8dI9fMm8g5nOaos8PFcZHUqxkuLbqzH5N594k8fnhaz1duF2yZuh83+52BPuCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dc4bd08dd1929b8e065fc861abffc2ac65ba2ff474472f3d7d8808d113216b6c","last_reissued_at":"2026-07-05T01:57:59.648978Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:57:59.648978Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dynamics-based peptide-MHC binding optimization by a convolutional variational autoencoder: a use-case model for CASTELO","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.QM","authors_text":"Chih-Chieh Yang, David Bell, Giacomo Domeniconi, Guojing Cong, Leili Zhang, Ruhong Zhou","submitted_at":"2020-11-29T13:41:18Z","abstract_excerpt":"An unsolved challenge in the development of antigen specific immunotherapies is determining the optimal antigens to target. Comprehension of antigen-MHC binding is paramount towards achieving this goal. Here, we present CASTELO, a combined machine learning-molecular dynamics (ML-MD) approach to design novel antigens of increased MHC binding affinity for a Type 1 diabetes (T1D)-implicated system. We build upon a small molecule lead optimization algorithm by training a convolutional variational autoencoder (CVAE) on MD trajectories of 48 different systems across 4 antigens and 4 HLA serotypes. W"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.00672","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2012.00672/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2012.00672","created_at":"2026-07-05T01:57:59.649035+00:00"},{"alias_kind":"arxiv_version","alias_value":"2012.00672v2","created_at":"2026-07-05T01:57:59.649035+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.00672","created_at":"2026-07-05T01:57:59.649035+00:00"},{"alias_kind":"pith_short_12","alias_value":"3RF5BDORSKNY","created_at":"2026-07-05T01:57:59.649035+00:00"},{"alias_kind":"pith_short_16","alias_value":"3RF5BDORSKNY4BS7","created_at":"2026-07-05T01:57:59.649035+00:00"},{"alias_kind":"pith_short_8","alias_value":"3RF5BDOR","created_at":"2026-07-05T01:57:59.649035+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3RF5BDORSKNY4BS7ZBQ2X76CVR","json":"https://pith.science/pith/3RF5BDORSKNY4BS7ZBQ2X76CVR.json","graph_json":"https://pith.science/api/pith-number/3RF5BDORSKNY4BS7ZBQ2X76CVR/graph.json","events_json":"https://pith.science/api/pith-number/3RF5BDORSKNY4BS7ZBQ2X76CVR/events.json","paper":"https://pith.science/paper/3RF5BDOR"},"agent_actions":{"view_html":"https://pith.science/pith/3RF5BDORSKNY4BS7ZBQ2X76CVR","download_json":"https://pith.science/pith/3RF5BDORSKNY4BS7ZBQ2X76CVR.json","view_paper":"https://pith.science/paper/3RF5BDOR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2012.00672&json=true","fetch_graph":"https://pith.science/api/pith-number/3RF5BDORSKNY4BS7ZBQ2X76CVR/graph.json","fetch_events":"https://pith.science/api/pith-number/3RF5BDORSKNY4BS7ZBQ2X76CVR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3RF5BDORSKNY4BS7ZBQ2X76CVR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3RF5BDORSKNY4BS7ZBQ2X76CVR/action/storage_attestation","attest_author":"https://pith.science/pith/3RF5BDORSKNY4BS7ZBQ2X76CVR/action/author_attestation","sign_citation":"https://pith.science/pith/3RF5BDORSKNY4BS7ZBQ2X76CVR/action/citation_signature","submit_replication":"https://pith.science/pith/3RF5BDORSKNY4BS7ZBQ2X76CVR/action/replication_record"}},"created_at":"2026-07-05T01:57:59.649035+00:00","updated_at":"2026-07-05T01:57:59.649035+00:00"}