Name
Abstract | ||
|---|---|---|
We present a new optimization of a threading scoring function. A scoring function is usually formulated in terms of the structural environment states that describe the protein fold model. We propose a method for the optimal selection of those structural environment states that naturally follows from the probabilistic description of the threading problem and is done prior to threading experiments. We demonstrate the selection of the optimal structural environment states for the solvent exposure of the amino acid position, and present the results of threading experiments performed using scoring functions designed with and without the optimization of the structural environment states. These results confirm that the optimal scoring function predicts the sequence-tostructure alignments most accurately. We also introduce a novel definition of the amino-acid-dependent structural environment state. This structural environment state allows the use of the detailed description of the structural environment but avoids the problem of increasing the statistical error in the amino acid likelihood estimates. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1145/299432.299446 | RECOMB |
Keywords | Field | DocType |
1,threading scoring function,optimal design,score function,amino acid,protein folding | Biology,Threading (manufacturing),Optimal design,Artificial intelligence,Bioinformatics,Machine learning | Conference |
ISBN | Citations | PageRank |
1-58113-069-4 | 0 | 0.34 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jadwiga R. Bienkowska | 1 | 16 | 5.96 |
| Robert G. Rogers, Jr. | 2 | 0 | 0.68 |
| Temple F. Smith | 3 | 139 | 73.26 |