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| Content Provider | PubMed Central |
|---|---|
| Author | Vallat, Brinda Kizhakke Jaroslaw, Pillardy Peter, Májek Meller, Jaroslaw Blom, Thomas Cao, Baoqiang Ron, Elber |
| Abstract | One approach to predict a protein fold from a sequence (a target) is based on structures of related proteins that are used as templates. We present an algorithm that examines a set of candidates for templates, builds from each of the templates an atomically detailed model, and ranks the models. The algorithm performs a hierarchical selection of the best model using a diverse set of signals. After a quick and suboptimal screening of template candidates from the protein data bank, the current method fine-tunes the selection to a few models. More detailed signals test the compatibility of the sequence and the proposed structures, and are merged to give a global fitness measure using linear programming. This algorithm is a component of the prediction server LOOPP (http://www.loopp.org). Large scale training and tests sets were designed and are presented. Recent results of the LOOPP server in CASP8 are discussed. |
| Related Links | http://dx.doi.org/10.1002/prot.22401 |
| Ending Page | 945 |
| Page Count | 16 |
| Starting Page | 930 |
| File Format | |
| ISSN | 08873585 |
| e-ISSN | 10970134 |
| Journal | Proteins |
| Issue Number | 4 |
| Volume Number | 76 |
| Language | English |
| Publisher Date | 2009-09-01 |
| Access Restriction | Open |
| Subject Keyword | Medicine(all) Research in Higher Education |
| Content Type | Text |
| Resource Type | Article |
| Subject | Structural Biology Biochemistry Molecular Biology |
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