dorsal/arxiv
View SchemaPrediction of protein secondary structure based on residue pairs
| Authors | Xin Liu, Li-mei Zhang, Wei-Mou Zheng |
|---|---|
| Categories | |
| ArXiv ID | physics/0212065 |
| URL | https://arxiv.org/abs/physics/0212065 |
Abstract
The GOR program for predicting protein secondary structure is extended to include triple correlation. A score system for a residue pair to be at certain conformation state is derived from the conditional weight matrix describing amino acid frequencies at each position of a window flanking the pair under the condition for the pair to be at the fixed state. A program using this score system to predict protein secondary structure is established. After training the model with a learning set created from PDB\_SELECT, the program is tested with two test sets. As a method using single sequence for predicting secondary structures, the approach achieves a high accuracy near 70%.
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"date_created": "2026-03-02T18:00:43.060000Z",
"date_modified": "2026-03-02T18:00:43.060000Z",
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"abstract": "The GOR program for predicting protein secondary structure is extended to\ninclude triple correlation. A score system for a residue pair to be at certain\nconformation state is derived from the conditional weight matrix describing\namino acid frequencies at each position of a window flanking the pair under the\ncondition for the pair to be at the fixed state. A program using this score\nsystem to predict protein secondary structure is established. After training\nthe model with a learning set created from PDB\\_SELECT, the program is tested\nwith two test sets. As a method using single sequence for predicting secondary\nstructures, the approach achieves a high accuracy near 70%.",
"arxiv_id": "physics/0212065",
"authors": [
"Xin Liu",
"Li-mei Zhang",
"Wei-Mou Zheng"
],
"categories": [
"physics.bio-ph",
"q-bio"
],
"title": "Prediction of protein secondary structure based on residue pairs",
"url": "https://arxiv.org/abs/physics/0212065"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "4710235b-8f19-4fa7-9617-c4952ac4f9e7",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
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