A protein folding potential that places the native states of a large number of proteins near a local minimum
Background
We present a simple method to train a potential function for the protein folding problem which, even though trained using a small number of proteins, is able to place a significantly large number of native conformations near a local minimum. The training relies on generating decoys by energy minimization of the native conformations using the current potential and using a physically meaningful objective function (derivative of energy with respect to torsion angles at the native conformation) during the quadratic programming to place the native conformation near a local minimum.
Results
We also compare the performance of three different types of energy functions and find that while the pairwise energy function is trainable, a solvation energy function by itself is untrainable if decoys are generated by minimizing the current potential starting at the native conformation. The best results are obtained when a pairwise interaction energy function is used with solvation energy function.
Conclusions
We are able to train a potential function using six proteins which places a total of 42 native conformations within ~4 Å rmsd and 71 native conformations within ~6 Å rmsd of a local minimum out of a total of 91 proteins. Furthermore, the threading test using the same 91 proteins ranks 89 native conformations to be first and the other two as second.
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
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|
| description | Background We present a simple method to train a potential function for the protein folding problem which, even though trained using a small number of proteins, is able to place a significantly large number of native conformations near a local minimum. The training relies on generating decoys by energy minimization of the native conformations using the current potential and using a physically meaningful objective function (derivative of energy with respect to torsion angles at the native conformation) during the quadratic programming to place the native conformation near a local minimum. Results We also compare the performance of three different types of energy functions and find that while the pairwise energy function is trainable, a solvation energy function by itself is untrainable if decoys are generated by minimizing the current potential starting at the native conformation. The best results are obtained when a pairwise interaction energy function is used with solvation energy function. Conclusions We are able to train a potential function using six proteins which places a total of 42 native conformations within ~4 Å rmsd and 71 native conformations within ~6 Å rmsd of a local minimum out of a total of 91 proteins. Furthermore, the threading test using the same 91 proteins ranks 89 native conformations to be first and the other two as second. |
| distribution |
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"@type": "dcat:Distribution",
"title": "Official Government Data Source",
"mediaType": "text/html",
"description": "Visit the original government dataset for complete information, documentation, and data access.",
"downloadURL": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC126205/"
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| identifier | https://healthdata.gov/api/views/dbgv-u6mf |
| issued | 2025-07-14 |
| keyword |
[
"energy-minimization",
"native-conformation",
"nih",
"potential-energy-function",
"protein-folding"
]
|
| landingPage | https://healthdata.gov/d/dbgv-u6mf |
| modified | 2025-09-06 |
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| publisher |
{
"name": "National Institutes of Health",
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}
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| theme |
[
"NIH"
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|
| title | A protein folding potential that places the native states of a large number of proteins near a local minimum |
