Skip Navigation

This Article
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow FREE Full Text (Screen PDF)
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (9)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Portugaly, E.
Right arrow Articles by Linial, M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Portugaly, E.
Right arrow Articles by Linial, M.
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Bioinformatics Vol. 18 no. 7 2002
Pages 899-907
© 2002 Oxford University Press

Selecting targets for structural determination by navigating in a graph of protein families

Elon Portugaly 1, Ilona Kifer 1 and Michal Linial 2

1 Institute of Computer Sciences
2 Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University, Jerusalem 91904, Israel

Received on July 1, 2001 ; revised on December 21, 2001 and March 4, 2002 ; accepted on March 11, 2002

Motivation: A major goal in structural genomics is to enrich the catalogue of proteins whose 3D structures are known. In an attempt to address this problem we mapped over 10 000 proteins with solved structures onto a graph of all Swissprot protein sequences (release 36, ~73 000 proteins) provided by ProtoMap, with the goal of sorting proteins according to their likelihood of belonging to new superfamilies. We hypothesized that proteins within neighbouring clusters tend to share common structural superfamilies or folds. If true, the likelihood of finding new superfamilies increases in clusters that are distal from other solved structures within the graph.

Results: We defined an order relation between unsolved proteins according to their ‘distance’ from solved structures in the graph, and sorted ~48 000 proteins. Our list can be partitioned into three groups: ~35 000 proteins sharing a cluster with at least one known structure; ~6500 proteins in clusters with no solved structure but with neighbouring clusters containing known structures; and a third group contains the rest of the proteins, ~6100 (in 1274 clusters). We tested the quality of the order relation using thousands of recently solved structures that were not included when the order was defined. The tests show that our order is significantly better (P-value ~105) than a random order. More interestingly, the order within the union of the second and third groups, and the order within the third group alone, perform better than random (P-values: 0.0008 and 0.15, respectively) and are better than alternative orders created using PSI-BLAST. Herein, we present a method for selecting targets to be used in structural genomics projects.

Availability: List of proteins to be used for targets selection combined with a set of biological filters for narrowing down potential targets is in http://www.protarget.cs.huji.ac.il

Contact: michall{at}mail.ls.huji.ac.il


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
BioinformaticsHome page
O. Camoglu, T. Can, and A. K. Singh
Integrating multi-attribute similarity networks for robust representation of the protein space
Bioinformatics, July 1, 2006; 22(13): 1585 - 1592.
[Abstract] [Full Text] [PDF]


Home page
BioinformaticsHome page
M. Punta and B. Rost
PROFcon: novel prediction of long-range contacts
Bioinformatics, July 1, 2005; 21(13): 2960 - 2968.
[Abstract] [Full Text] [PDF]


Home page
BioinformaticsHome page
I. Kifer, O. Sasson, and M. Linial
Predicting fold novelty based on ProtoNet hierarchical classification
Bioinformatics, April 1, 2005; 21(7): 1020 - 1027.
[Abstract] [Full Text] [PDF]


Home page
Nucleic Acids ResHome page
O. Sasson, A. Vaaknin, H. Fleischer, E. Portugaly, Y. Bilu, N. Linial, and M. Linial
ProtoNet: hierarchical classification of the protein space
Nucleic Acids Res., January 1, 2003; 31(1): 348 - 352.
[Abstract] [Full Text] [PDF]



Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.