Name
Abstract | ||
|---|---|---|
This article describes an implementation of a set of rules for automatically embedding a minimal functional sequence pattern in a structural hidden Markov model. An HMM is constructed from the atomic coordinates of a protein structure deposited in the PDB (protein databank) by an entirely automated procedure. This procedure generates a set of HMMs that represent distinct three-dimensional folds of protein structures. The next step generates a sequence pattern for the functional family from a set of homologous sequences. The strictly conserved positions from the pattern are selected for embedding in each structural HMM. The final product is a library of wide-ranging fold models with encoded information about the functional families. This library is used to assign the fold models to protein sequences. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1109/5254.972074 | Intelligent Systems, IEEE |
Keywords | Field | DocType |
biology computing,hidden Markov models,molecular biophysics,molecular configurations,pattern classification,proteins,scientific information systems,sequences,automatically embedded minimal functional sequence pattern,encoded information,fold model library,functional families,protein structure models,structural hidden Markov model | Variable-order Bayesian network,Maximum-entropy Markov model,Embedding,Pattern recognition,Final product,Computer science,Ranging,Artificial intelligence,Sequence pattern,Hidden Markov model,Protein structure | Journal |
Volume | Issue | ISSN |
16 | 6 | 1541-1672 |
Citations | PageRank | References |
3 | 0.45 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jadwiga R. Bienkowska | 1 | 16 | 5.96 |
| Hongxian He | 2 | 3 | 0.45 |
| Temple F. Smith | 3 | 139 | 73.26 |