dorsal/arxiv
View SchemaKnowledge-based energy functions for computational studies of proteins
| Authors | Xiang Li, Jie Liang |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0601026 |
| URL | https://arxiv.org/abs/q-bio/0601026 |
| DOI | 10.1007/978-0-387-68372-0_3 |
Abstract
This chapter discusses theoretical framework and methods for developing knowledge-based potential functions essential for protein structure prediction, protein-protein interaction, and protein sequence design. We discuss in some details about the Miyazawa-Jernigan contact statistical potential, distance-dependent statistical potentials, as well as geometric statistical potentials. We also describe a geometric model for developing both linear and non-linear potential functions by optimization. Applications of knowledge-based potential functions in protein-decoy discrimination, in protein-protein interactions, and in protein design are then described. Several issues of knowledge-based potential functions are finally discussed.
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"abstract": "This chapter discusses theoretical framework and methods for developing\nknowledge-based potential functions essential for protein structure prediction,\nprotein-protein interaction, and protein sequence design. We discuss in some\ndetails about the Miyazawa-Jernigan contact statistical potential,\ndistance-dependent statistical potentials, as well as geometric statistical\npotentials. We also describe a geometric model for developing both linear and\nnon-linear potential functions by optimization. Applications of knowledge-based\npotential functions in protein-decoy discrimination, in protein-protein\ninteractions, and in protein design are then described. Several issues of\nknowledge-based potential functions are finally discussed.",
"arxiv_id": "q-bio/0601026",
"authors": [
"Xiang Li",
"Jie Liang"
],
"categories": [
"q-bio.BM"
],
"doi": "10.1007/978-0-387-68372-0_3",
"title": "Knowledge-based energy functions for computational studies of proteins",
"url": "https://arxiv.org/abs/q-bio/0601026"
},
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