Application of a Topological Descriptor for Protein Interface Identification and Protein Binding Prediction
| dc.contributor.advisor | Vaisman, Iosif I. | |
| dc.contributor.author | Peters, Olivia | |
| dc.creator | Peters, Olivia | |
| dc.date | 2010-04-10 | |
| dc.date.accessioned | 2010-05-11T18:21:42Z | |
| dc.date.available | NO_RESTRICTION | |
| dc.date.available | 2010-05-11T18:21:42Z | |
| dc.date.issued | 2010-05-11T18:21:42Z | |
| dc.description.abstract | The identification of proteins which interact or form complexes is a critical step in advancing several aspects of computational biology, including intelligent protein design and functional prediction. Previous methods have focused primarily on sequence alignment or threading methods to accomplish this, requiring large libraries of sequences. This work is an attempt to advance the current field of protein prediction through the use of a structural geometry methodology proven successful for many other aspects of proteomic analyses. The method is extended in two ways; first, a classification approach is created to identify protein residues involved in the binding interface, with the intent of using this information to aid the prediction of protein complex formation. Results are promising, with better than eighty percent correct classification, comparable to the best techniques currently in use. Second, a methodology was created to score potential docking conformations. Of the 54 proteins in the test data set, 43 had a near-native structure in the top 100 positions, and a median ratio of successfully identified residue contacts of 0.57. The structural geometry method has been successfully applied to these two problems to advance the state of the field of proteomics. | |
| dc.identifier.uri | https://hdl.handle.net/1920/5805 | |
| dc.language.iso | en_US | |
| dc.subject | Proteomics | |
| dc.subject | Delaunay | |
| dc.subject | Interface | |
| dc.subject | Classification | |
| dc.subject | Docking | |
| dc.title | Application of a Topological Descriptor for Protein Interface Identification and Protein Binding Prediction | |
| dc.type | Dissertation | |
| thesis.degree.discipline | Bioinformatics and Computational Biology | |
| thesis.degree.grantor | George Mason University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy Bioinformatics and Computational Biology |