From Extraction of Local Structures of Protein Energy Landscapes to Improved Decoy Selection in Template-Free Protein Structure Prediction
Due to the essential role that the three-dimensional conformation of a protein plays in regulating interactions with molecular partners, wet and dry laboratories seek biologically-active conformations of a protein to decode its function. Computational approaches are gaining prominence due to the lab...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-01-01
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| Series: | Molecules |
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| Online Access: | http://www.mdpi.com/1420-3049/23/1/216 |
| _version_ | 1811328137848422400 |
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| author | Nasrin Akhter Amarda Shehu |
| author_facet | Nasrin Akhter Amarda Shehu |
| author_sort | Nasrin Akhter |
| collection | DOAJ |
| description | Due to the essential role that the three-dimensional conformation of a protein plays in regulating interactions with molecular partners, wet and dry laboratories seek biologically-active conformations of a protein to decode its function. Computational approaches are gaining prominence due to the labor and cost demands of wet laboratory investigations. Template-free methods can now compute thousands of conformations known as decoys, but selecting native conformations from the generated decoys remains challenging. Repeatedly, research has shown that the protein energy functions whose minima are sought in the generation of decoys are unreliable indicators of nativeness. The prevalent approach ignores energy altogether and clusters decoys by conformational similarity. Complementary recent efforts design protein-specific scoring functions or train machine learning models on labeled decoys. In this paper, we show that an informative consideration of energy can be carried out under the energy landscape view. Specifically, we leverage local structures known as basins in the energy landscape probed by a template-free method. We propose and compare various strategies of basin-based decoy selection that we demonstrate are superior to clustering-based strategies. The presented results point to further directions of research for improving decoy selection, including the ability to properly consider the multiplicity of native conformations of proteins. |
| first_indexed | 2024-04-13T15:19:57Z |
| format | Article |
| id | doaj.art-5bd700b6cbec4fe084f01d014333a4a3 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-04-13T15:19:57Z |
| publishDate | 2018-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-5bd700b6cbec4fe084f01d014333a4a32022-12-22T02:41:42ZengMDPI AGMolecules1420-30492018-01-0123121610.3390/molecules23010216molecules23010216From Extraction of Local Structures of Protein Energy Landscapes to Improved Decoy Selection in Template-Free Protein Structure PredictionNasrin Akhter0Amarda Shehu1Department of Computer Science, George Mason University, Fairfax, VA 22030, USADepartment of Computer Science, George Mason University, Fairfax, VA 22030, USADue to the essential role that the three-dimensional conformation of a protein plays in regulating interactions with molecular partners, wet and dry laboratories seek biologically-active conformations of a protein to decode its function. Computational approaches are gaining prominence due to the labor and cost demands of wet laboratory investigations. Template-free methods can now compute thousands of conformations known as decoys, but selecting native conformations from the generated decoys remains challenging. Repeatedly, research has shown that the protein energy functions whose minima are sought in the generation of decoys are unreliable indicators of nativeness. The prevalent approach ignores energy altogether and clusters decoys by conformational similarity. Complementary recent efforts design protein-specific scoring functions or train machine learning models on labeled decoys. In this paper, we show that an informative consideration of energy can be carried out under the energy landscape view. Specifically, we leverage local structures known as basins in the energy landscape probed by a template-free method. We propose and compare various strategies of basin-based decoy selection that we demonstrate are superior to clustering-based strategies. The presented results point to further directions of research for improving decoy selection, including the ability to properly consider the multiplicity of native conformations of proteins.http://www.mdpi.com/1420-3049/23/1/216template-free protein structure predictiondecoy selectionconformational spaceenergy landscapebasinsPareto optimality |
| spellingShingle | Nasrin Akhter Amarda Shehu From Extraction of Local Structures of Protein Energy Landscapes to Improved Decoy Selection in Template-Free Protein Structure Prediction Molecules template-free protein structure prediction decoy selection conformational space energy landscape basins Pareto optimality |
| title | From Extraction of Local Structures of Protein Energy Landscapes to Improved Decoy Selection in Template-Free Protein Structure Prediction |
| title_full | From Extraction of Local Structures of Protein Energy Landscapes to Improved Decoy Selection in Template-Free Protein Structure Prediction |
| title_fullStr | From Extraction of Local Structures of Protein Energy Landscapes to Improved Decoy Selection in Template-Free Protein Structure Prediction |
| title_full_unstemmed | From Extraction of Local Structures of Protein Energy Landscapes to Improved Decoy Selection in Template-Free Protein Structure Prediction |
| title_short | From Extraction of Local Structures of Protein Energy Landscapes to Improved Decoy Selection in Template-Free Protein Structure Prediction |
| title_sort | from extraction of local structures of protein energy landscapes to improved decoy selection in template free protein structure prediction |
| topic | template-free protein structure prediction decoy selection conformational space energy landscape basins Pareto optimality |
| url | http://www.mdpi.com/1420-3049/23/1/216 |
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