A probabilistic fragment-based protein structure prediction algorithm.
Conformational sampling is one of the bottlenecks in fragment-based protein structure prediction approaches. They generally start with a coarse-grained optimization where mainchain atoms and centroids of side chains are considered, followed by a fine-grained optimization with an all-atom representat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2012-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22829868/?tool=EBI |
| _version_ | 1818722679787618304 |
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| author | David Simoncini Francois Berenger Rojan Shrestha Kam Y J Zhang |
| author_facet | David Simoncini Francois Berenger Rojan Shrestha Kam Y J Zhang |
| author_sort | David Simoncini |
| collection | DOAJ |
| description | Conformational sampling is one of the bottlenecks in fragment-based protein structure prediction approaches. They generally start with a coarse-grained optimization where mainchain atoms and centroids of side chains are considered, followed by a fine-grained optimization with an all-atom representation of proteins. It is during this coarse-grained phase that fragment-based methods sample intensely the conformational space. If the native-like region is sampled more, the accuracy of the final all-atom predictions may be improved accordingly. In this work we present EdaFold, a new method for fragment-based protein structure prediction based on an Estimation of Distribution Algorithm. Fragment-based approaches build protein models by assembling short fragments from known protein structures. Whereas the probability mass functions over the fragment libraries are uniform in the usual case, we propose an algorithm that learns from previously generated decoys and steers the search toward native-like regions. A comparison with Rosetta AbInitio protocol shows that EdaFold is able to generate models with lower energies and to enhance the percentage of near-native coarse-grained decoys on a benchmark of [Formula: see text] proteins. The best coarse-grained models produced by both methods were refined into all-atom models and used in molecular replacement. All atom decoys produced out of EdaFold's decoy set reach high enough accuracy to solve the crystallographic phase problem by molecular replacement for some test proteins. EdaFold showed a higher success rate in molecular replacement when compared to Rosetta. Our study suggests that improving low resolution coarse-grained decoys allows computational methods to avoid subsequent sampling issues during all-atom refinement and to produce better all-atom models. EdaFold can be downloaded from http://www.riken.jp/zhangiru/software.html [corrected]. |
| first_indexed | 2024-12-17T20:58:28Z |
| format | Article |
| id | doaj.art-ac0972fe533943f886e4f1d6b9473e36 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-17T20:58:28Z |
| publishDate | 2012-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-ac0972fe533943f886e4f1d6b9473e362022-12-21T21:32:48ZengPublic Library of Science (PLoS)PLoS ONE1932-62032012-01-0177e3879910.1371/journal.pone.0038799A probabilistic fragment-based protein structure prediction algorithm.David SimonciniFrancois BerengerRojan ShresthaKam Y J ZhangConformational sampling is one of the bottlenecks in fragment-based protein structure prediction approaches. They generally start with a coarse-grained optimization where mainchain atoms and centroids of side chains are considered, followed by a fine-grained optimization with an all-atom representation of proteins. It is during this coarse-grained phase that fragment-based methods sample intensely the conformational space. If the native-like region is sampled more, the accuracy of the final all-atom predictions may be improved accordingly. In this work we present EdaFold, a new method for fragment-based protein structure prediction based on an Estimation of Distribution Algorithm. Fragment-based approaches build protein models by assembling short fragments from known protein structures. Whereas the probability mass functions over the fragment libraries are uniform in the usual case, we propose an algorithm that learns from previously generated decoys and steers the search toward native-like regions. A comparison with Rosetta AbInitio protocol shows that EdaFold is able to generate models with lower energies and to enhance the percentage of near-native coarse-grained decoys on a benchmark of [Formula: see text] proteins. The best coarse-grained models produced by both methods were refined into all-atom models and used in molecular replacement. All atom decoys produced out of EdaFold's decoy set reach high enough accuracy to solve the crystallographic phase problem by molecular replacement for some test proteins. EdaFold showed a higher success rate in molecular replacement when compared to Rosetta. Our study suggests that improving low resolution coarse-grained decoys allows computational methods to avoid subsequent sampling issues during all-atom refinement and to produce better all-atom models. EdaFold can be downloaded from http://www.riken.jp/zhangiru/software.html [corrected].https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22829868/?tool=EBI |
| spellingShingle | David Simoncini Francois Berenger Rojan Shrestha Kam Y J Zhang A probabilistic fragment-based protein structure prediction algorithm. PLoS ONE |
| title | A probabilistic fragment-based protein structure prediction algorithm. |
| title_full | A probabilistic fragment-based protein structure prediction algorithm. |
| title_fullStr | A probabilistic fragment-based protein structure prediction algorithm. |
| title_full_unstemmed | A probabilistic fragment-based protein structure prediction algorithm. |
| title_short | A probabilistic fragment-based protein structure prediction algorithm. |
| title_sort | probabilistic fragment based protein structure prediction algorithm |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22829868/?tool=EBI |
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