Linked Life Data

17623839

Source:http://linkedlifedata.com/resource/pubmed/id/17623839

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2007-10-24
pubmed:abstractText
The biophysical study of protein-protein interactions and docking has important implications in our understanding of most complex cellular signaling processes. Most computational approaches to protein docking involve a tradeoff between the level of detail incorporated into the model and computational power required to properly handle that level of detail. In this work, we seek to optimize that balance by showing that we can reduce the complexity of model representation and thus make the computation tractable with minimal loss of predictive performance. We also introduce a pair-wise statistical potential suitable for docking that builds on previous work and show that this potential can be incorporated into our fast fourier transform-based docking algorithm ZDOCK. We use the Protein Docking Benchmark to illustrate the improved performance of this potential compared with less detailed other scoring functions. Furthermore, we show that the new potential performs well on antibody-antigen complexes, with most predictions clustering around the Complementarity Determining Regions of antibodies without any manual intervention.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
1097-0134
pubmed:author
pubmed:copyrightInfo
(c) 2007 Wiley-Liss, Inc.
pubmed:issnType
Electronic
pubmed:day
15
pubmed:volume
69
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
511-20
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Integrating statistical pair potentials into protein complex prediction.
pubmed:affiliation
Bioinformatics Program, Boston University, Massachusetts 02215, USA.
pubmed:publicationType
Journal Article, Evaluation Studies