{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:CU5SIEIA5IDM64O6QG7LIGQOF3","short_pith_number":"pith:CU5SIEIA","schema_version":"1.0","canonical_sha256":"153b241100ea06cf71de81beb41a0e2ef877373146d667ea3436628e897b0c03","source":{"kind":"arxiv","id":"2305.05728","version":1},"attestation_state":"computed","paper":{"title":"Designing of knowledge-based potentials via B-spline basis functions for native proteins detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-bio.QM"],"primary_cat":"math.OC","authors_text":"Elmira Mirzabeigi, Hossein Naderi-Manesh, Rezvan Salehi, Saeed Mortezazadeh","submitted_at":"2023-05-09T19:19:21Z","abstract_excerpt":"Knowledge-based potentials were developed to investigate the differentiation of native structures from their decoy sets. This work presents the construction of two different distance-dependent potential energy functions based on two fundamental assumptions using mathematical modeling. Here, a model was developed using basic mathematical methods, and the carbon-alpha form is the simplest form of protein representation. We aimed to reduce computational volume and distinguish the native structure from the decoy structures. For this purpose, according to Anfinsens dogma, we assumed that the energy"},"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":"2305.05728","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2023-05-09T19:19:21Z","cross_cats_sorted":["q-bio.QM"],"title_canon_sha256":"6e81ee73b2f24e4ebcc24d98b3d39dc3a17af606e307b310ac66b10ff634a694","abstract_canon_sha256":"d6a73f797d7c782a736d6f83147c22ccdac98b3394a104b40a183bdaa787b730"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:08:44.657814Z","signature_b64":"VGM1ArhgOoRXWtFE+kOnqlhXXxJUQ3Z9t7UAr0VmRDELsgiVOLKOaz4x8JQA8PkVTnAxHW8v9Sy+vm8K3ZgvDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"153b241100ea06cf71de81beb41a0e2ef877373146d667ea3436628e897b0c03","last_reissued_at":"2026-07-05T06:08:44.657407Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:08:44.657407Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Designing of knowledge-based potentials via B-spline basis functions for native proteins detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-bio.QM"],"primary_cat":"math.OC","authors_text":"Elmira Mirzabeigi, Hossein Naderi-Manesh, Rezvan Salehi, Saeed Mortezazadeh","submitted_at":"2023-05-09T19:19:21Z","abstract_excerpt":"Knowledge-based potentials were developed to investigate the differentiation of native structures from their decoy sets. This work presents the construction of two different distance-dependent potential energy functions based on two fundamental assumptions using mathematical modeling. Here, a model was developed using basic mathematical methods, and the carbon-alpha form is the simplest form of protein representation. We aimed to reduce computational volume and distinguish the native structure from the decoy structures. For this purpose, according to Anfinsens dogma, we assumed that the energy"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.05728","kind":"arxiv","version":1},"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/2305.05728/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":"2305.05728","created_at":"2026-07-05T06:08:44.657462+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.05728v1","created_at":"2026-07-05T06:08:44.657462+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.05728","created_at":"2026-07-05T06:08:44.657462+00:00"},{"alias_kind":"pith_short_12","alias_value":"CU5SIEIA5IDM","created_at":"2026-07-05T06:08:44.657462+00:00"},{"alias_kind":"pith_short_16","alias_value":"CU5SIEIA5IDM64O6","created_at":"2026-07-05T06:08:44.657462+00:00"},{"alias_kind":"pith_short_8","alias_value":"CU5SIEIA","created_at":"2026-07-05T06:08:44.657462+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.04354","citing_title":"BridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck Equations","ref_index":3,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CU5SIEIA5IDM64O6QG7LIGQOF3","json":"https://pith.science/pith/CU5SIEIA5IDM64O6QG7LIGQOF3.json","graph_json":"https://pith.science/api/pith-number/CU5SIEIA5IDM64O6QG7LIGQOF3/graph.json","events_json":"https://pith.science/api/pith-number/CU5SIEIA5IDM64O6QG7LIGQOF3/events.json","paper":"https://pith.science/paper/CU5SIEIA"},"agent_actions":{"view_html":"https://pith.science/pith/CU5SIEIA5IDM64O6QG7LIGQOF3","download_json":"https://pith.science/pith/CU5SIEIA5IDM64O6QG7LIGQOF3.json","view_paper":"https://pith.science/paper/CU5SIEIA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.05728&json=true","fetch_graph":"https://pith.science/api/pith-number/CU5SIEIA5IDM64O6QG7LIGQOF3/graph.json","fetch_events":"https://pith.science/api/pith-number/CU5SIEIA5IDM64O6QG7LIGQOF3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CU5SIEIA5IDM64O6QG7LIGQOF3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CU5SIEIA5IDM64O6QG7LIGQOF3/action/storage_attestation","attest_author":"https://pith.science/pith/CU5SIEIA5IDM64O6QG7LIGQOF3/action/author_attestation","sign_citation":"https://pith.science/pith/CU5SIEIA5IDM64O6QG7LIGQOF3/action/citation_signature","submit_replication":"https://pith.science/pith/CU5SIEIA5IDM64O6QG7LIGQOF3/action/replication_record"}},"created_at":"2026-07-05T06:08:44.657462+00:00","updated_at":"2026-07-05T06:08:44.657462+00:00"}