Biomedical subjects

Jung-Ying Wang

Publications and source records attributed to Jung-Ying Wang.

2 recordsLinked to original sources

PubMed2004

Look-up tables for protein solvent accessibility prediction and nearest neighbor effect analysis.

We developed dictionaries of two-, three-, and five-residue patterns in proteins and computed the average solvent accessibility of the central residues in their native proteins. These dictionaries serve as a look-up table for making subsequent predictions of solvent accessibility of amino acid residues. We find that predictions made in this way are very close to those made using more sophisticated methods of solvent accessibility prediction. We also analyzed the effect of immediate neighbors on the solvent accessibility of residues. This helps us in understanding how the same residue type may have different accessible surface areas in different proteins and in different positions of the same protein. We observe that certain residues have a tendency to increase or decrease the solvent accessibility of their neighboring residues in C- or N-terminal positions. Interestingly, the C-terminal and N-terminal neighbor residues are found to have asymmetric roles in modifying solvent accessibility of residues. As expected, similar neighbors enhance the hydrophobic or hydrophilic character of residues. Detailed look-up tables are provided on the web at www.netasa.org/look-up/.

Amino Acid Sequence↗

PubMed2003

Fine-grained protein fold assignment by support vector machines using generalized npeptide coding schemes and jury voting from multiple-parameter sets.

In the coarse-grained fold assignment of major protein classes, such as all-alpha, all-beta, alpha + beta, alpha/beta proteins, one can easily achieve high prediction accuracy from primary amino acid sequences. However, the fine-grained assignment of folds, such as those defined in the Structural Classification of Proteins (SCOP) database, presents a challenge due to the larger amount of folds available. Recent study yielded reasonable prediction accuracy of 56.0% on an independent set of 27 most populated folds. In this communication, we apply the support vector machine (SVM) method, using a combination of protein descriptors based on the properties derived from the composition of n-peptide and jury voting, to the fine-grained fold prediction, and are able to achieve an overall prediction accuracy of 69.6% on the same independent set-significantly higher than the previous results. On 10-fold cross-validation, we obtained a prediction accuracy of 65.3%. Our results show that SVM coupled with suitable global sequence-coding schemes can significantly improve the fine-grained fold prediction. Our approach should be useful in structure prediction and modeling.

Animals↗