Abstract
Background: Structural genomics initiatives are producing increasing numbers of three-dimensional (3D) structures for which there is little functional information. Structure-based annotation of molecular function is therefore becoming critical. We previously presented FEATURE, a method for describing microenvironments around functional sites in proteins. However, FEATURE uses supervised machine learning and so is limited to building models for sites of known importance and location. We hypothesized that there are a large number of sites in proteins that are associated with function that have not yet been recognized. Toward that end, we have developed a method for clustering protein microenvironments in order to evaluate the potential for discovering novel sites that have not been previously identified. Results: We have prototyped a computational method for rapid clustering of millions of microenvironments in order to discover residues whose surrounding environments are similar and which may therefore share a functional or structural role. We clustered nearly 2,000,000 environments from 9,600 protein chains and defined 4,550 clusters. As a preliminary validation, we asked whether known 3D environments associated with PROSITE motifs were "rediscovered". We found examples of clusters highly enriched for residues that share PROSITE sequence motifs. Conclusion: Our results demonstrate that we can cluster protein environments successfully using a simplified representation and K-means clustering algorithm. The rediscovery of known 3D motifs allows us to calibrate the size and intercluster distances that characterize useful clusters. This information will then allow us to find new clusters with similar characteristics that represent novel structural or functional sites.
| Original language | English |
|---|---|
| Article number | S10 |
| Journal | BMC bioinformatics |
| Volume | 8 |
| Issue number | SUPPL. 4 |
| DOIs | |
| Publication status | Published - 2007 May 22 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Structural Biology
- Biochemistry
- Molecular Biology
- Computer Science Applications
- Applied Mathematics
Cite this
}
Clustering protein environments for function prediction : Finding PROSITE motifs in 3D. / Yoon, Sungroh; Ebert, Jessica C.; Chung, Eui Young; De Micheli, Giovanni; Altman, Russ B.
In: BMC bioinformatics, Vol. 8, No. SUPPL. 4, S10, 22.05.2007.Research output: Contribution to journal › Article
TY - JOUR
T1 - Clustering protein environments for function prediction
T2 - Finding PROSITE motifs in 3D
AU - Yoon, Sungroh
AU - Ebert, Jessica C.
AU - Chung, Eui Young
AU - De Micheli, Giovanni
AU - Altman, Russ B.
PY - 2007/5/22
Y1 - 2007/5/22
N2 - Background: Structural genomics initiatives are producing increasing numbers of three-dimensional (3D) structures for which there is little functional information. Structure-based annotation of molecular function is therefore becoming critical. We previously presented FEATURE, a method for describing microenvironments around functional sites in proteins. However, FEATURE uses supervised machine learning and so is limited to building models for sites of known importance and location. We hypothesized that there are a large number of sites in proteins that are associated with function that have not yet been recognized. Toward that end, we have developed a method for clustering protein microenvironments in order to evaluate the potential for discovering novel sites that have not been previously identified. Results: We have prototyped a computational method for rapid clustering of millions of microenvironments in order to discover residues whose surrounding environments are similar and which may therefore share a functional or structural role. We clustered nearly 2,000,000 environments from 9,600 protein chains and defined 4,550 clusters. As a preliminary validation, we asked whether known 3D environments associated with PROSITE motifs were "rediscovered". We found examples of clusters highly enriched for residues that share PROSITE sequence motifs. Conclusion: Our results demonstrate that we can cluster protein environments successfully using a simplified representation and K-means clustering algorithm. The rediscovery of known 3D motifs allows us to calibrate the size and intercluster distances that characterize useful clusters. This information will then allow us to find new clusters with similar characteristics that represent novel structural or functional sites.
AB - Background: Structural genomics initiatives are producing increasing numbers of three-dimensional (3D) structures for which there is little functional information. Structure-based annotation of molecular function is therefore becoming critical. We previously presented FEATURE, a method for describing microenvironments around functional sites in proteins. However, FEATURE uses supervised machine learning and so is limited to building models for sites of known importance and location. We hypothesized that there are a large number of sites in proteins that are associated with function that have not yet been recognized. Toward that end, we have developed a method for clustering protein microenvironments in order to evaluate the potential for discovering novel sites that have not been previously identified. Results: We have prototyped a computational method for rapid clustering of millions of microenvironments in order to discover residues whose surrounding environments are similar and which may therefore share a functional or structural role. We clustered nearly 2,000,000 environments from 9,600 protein chains and defined 4,550 clusters. As a preliminary validation, we asked whether known 3D environments associated with PROSITE motifs were "rediscovered". We found examples of clusters highly enriched for residues that share PROSITE sequence motifs. Conclusion: Our results demonstrate that we can cluster protein environments successfully using a simplified representation and K-means clustering algorithm. The rediscovery of known 3D motifs allows us to calibrate the size and intercluster distances that characterize useful clusters. This information will then allow us to find new clusters with similar characteristics that represent novel structural or functional sites.
UR - http://www.scopus.com/inward/record.url?scp=34447637608&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34447637608&partnerID=8YFLogxK
U2 - 10.1186/1471-2105-8-S4-S10
DO - 10.1186/1471-2105-8-S4-S10
M3 - Article
C2 - 17570144
AN - SCOPUS:34447637608
VL - 8
JO - BMC Bioinformatics
JF - BMC Bioinformatics
SN - 1471-2105
IS - SUPPL. 4
M1 - S10
ER -