dorsal/arxiv
View SchemaEmpirical Potential Function for Simplified Protein Models: Combining Contact and Local Sequence-Structure Descriptors
| Authors | Jinfeng Zhang, Rong Chen, Jie Liang |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0601029 |
| URL | https://arxiv.org/abs/q-bio/0601029 |
| DOI | 10.1002/prot.20809 |
Abstract
An effective potential function is critical for protein structure prediction and folding simulation. Simplified protein models such as those requiring only $C_\alpha$ or backbone atoms are attractive because they enable efficient search of the conformational space. We show residue specific reduced discrete state models can represent the backbone conformations of proteins with small RMSD values. However, no potential functions exist that are designed for such simplified protein models. In this study, we develop optimal potential functions by combining contact interaction descriptors and local sequence-structure descriptors. The form of the potential function is a weighted linear sum of all descriptors, and the optimal weight coefficients are obtained through optimization using both native and decoy structures. The performance of the potential function in test of discriminating native protein structures from decoys is evaluated using several benchmark decoy sets. Our potential function requiring only backbone atoms or $C_\alpha$ atoms have comparable or better performance than several residue-based potential functions that require additional coordinates of side chain centers or coordinates of all side chain atoms. By reducing the residue alphabets down to size 5 for local structure-sequence relationship, the performance of the potential function can be further improved. Our results also suggest that local sequence-structure correlation may play important role in reducing the entropic cost of protein folding.
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"abstract": "An effective potential function is critical for protein structure prediction\nand folding simulation. Simplified protein models such as those requiring only\n$C_\\alpha$ or backbone atoms are attractive because they enable efficient\nsearch of the conformational space. We show residue specific reduced discrete\nstate models can represent the backbone conformations of proteins with small\nRMSD values. However, no potential functions exist that are designed for such\nsimplified protein models. In this study, we develop optimal potential\nfunctions by combining contact interaction descriptors and local\nsequence-structure descriptors. The form of the potential function is a\nweighted linear sum of all descriptors, and the optimal weight coefficients are\nobtained through optimization using both native and decoy structures. The\nperformance of the potential function in test of discriminating native protein\nstructures from decoys is evaluated using several benchmark decoy sets. Our\npotential function requiring only backbone atoms or $C_\\alpha$ atoms have\ncomparable or better performance than several residue-based potential functions\nthat require additional coordinates of side chain centers or coordinates of all\nside chain atoms. By reducing the residue alphabets down to size 5 for local\nstructure-sequence relationship, the performance of the potential function can\nbe further improved. Our results also suggest that local sequence-structure\ncorrelation may play important role in reducing the entropic cost of protein\nfolding.",
"arxiv_id": "q-bio/0601029",
"authors": [
"Jinfeng Zhang",
"Rong Chen",
"Jie Liang"
],
"categories": [
"q-bio.BM"
],
"doi": "10.1002/prot.20809",
"title": "Empirical Potential Function for Simplified Protein Models: Combining Contact and Local Sequence-Structure Descriptors",
"url": "https://arxiv.org/abs/q-bio/0601029"
},
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