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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
1
|
| pubmed:dateCreated |
1997-6-19
|
| pubmed:abstractText |
The accuracy of secondary structure prediction methods has been improved significantly by the use of aligned protein sequences. The PHD method and the NNSSP method reach 71 to 72% of sustained overall three-state accuracy when multiple sequence alignments are with neural networks and nearest-neighbor algorithms, respectively. We introduce a variant of the nearest-neighbor approach that can achieve similar accuracy using a single sequence as the query input. We compute the 50 best non-intersecting local alignments of the query sequence with each sequence from a set of proteins with known 3D structures. Each position of the query sequence is aligned with the database amino acids in alpha-helical, beta-strand or coil states. The prediction type of secondary structure is selected as the type of aligned position with the maximal total score. On the dataset of 124 non-membrane non-homologous proteins, used earlier as a benchmark for secondary structure predictions, our method reaches an overall three-state accuracy of 71.2%. The performance accuracy is verified by an additional test on 461 non-homologous proteins giving an accuracy of 71.0%. The main strength of the method is the high level of prediction accuracy for proteins without any known homolog. Using multiple sequence alignments as input the method has a prediction accuracy of 73.5%. Prediction of secondary structure by the SSPAL method is available via Baylor College of Medicine World Wide Web server.
|
| pubmed:language |
eng
|
| pubmed:journal | |
| pubmed:citationSubset |
IM
|
| pubmed:chemical | |
| pubmed:status |
MEDLINE
|
| pubmed:month |
Apr
|
| pubmed:issn |
0022-2836
|
| pubmed:author | |
| pubmed:issnType |
Print
|
| pubmed:day |
25
|
| pubmed:volume |
268
|
| pubmed:owner |
NLM
|
| pubmed:authorsComplete |
Y
|
| pubmed:pagination |
31-6
|
| pubmed:dateRevised |
2006-11-15
|
| pubmed:meshHeading |
pubmed-meshheading:9149139-Algorithms,
pubmed-meshheading:9149139-Amino Acid Sequence,
pubmed-meshheading:9149139-Databases, Factual,
pubmed-meshheading:9149139-Models, Molecular,
pubmed-meshheading:9149139-Molecular Sequence Data,
pubmed-meshheading:9149139-Protein Structure, Secondary,
pubmed-meshheading:9149139-Proteins,
pubmed-meshheading:9149139-Sequence Alignment
|
| pubmed:year |
1997
|
| pubmed:articleTitle |
Protein secondary structure prediction using local alignments.
|
| pubmed:affiliation |
Department of Cell Biology, Baylor College of Medicine, Houston, TX 77030, USA.
|
| pubmed:publicationType |
Journal Article,
Comparative Study,
Research Support, Non-U.S. Gov't
|