Biomedical subjects

Jinn-Moon Yang

Publications and source records attributed to Jinn-Moon Yang.

3 recordsLinked to original sources

PubMed2003

Relationship between protein structures and disulfide-bonding patterns.

We found that that disulfide-bonding patterns can be used to discriminate structure similarity. Our method, based on the hierarchical clustering scheme, is applicable to proteins with two or more disulfide bonds and is able to detect the structural similarities of proteins of low sequence identities (<25%). Our results show the surprisingly close relationship between disulfide-bonding patterns and proteins structures. Our findings should be useful in protein structure modeling.

Disulfides↗

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↗

PubMed2002

GEM: a Gaussian Evolutionary Method for predicting protein side-chain conformations.

We have developed an evolutionary approach to predicting protein side-chain conformations. This approach, referred to as the Gaussian Evolutionary Method (GEM), combines both discrete and continuous global search mechanisms. The former helps speed up convergence by reducing the size of rotamer space, whereas the latter, integrating decreasing-based Gaussian mutations and self-adaptive Gaussian mutations, continuously adapts dihedrals to optimal conformations. We tested our approach on 38 proteins ranging in size from 46 to 325 residues and showed that the results were comparable to those using other methods. The average accuracies of our predictions were 80% for chi(1), 66% for chi(1 + 2), and 1.36 A for the root mean square deviation of side-chain positions. We found that if our scoring function was perfect, the prediction accuracy was also essentially perfect. However, perfect prediction could not be achieved if only a discrete search mechanism was applied. These results suggest that GEM is robust and can be used to examine the factors limiting the accuracy of protein side-chain prediction methods. Furthermore, it can be used to systematically evaluate and thus improve scoring functions.

Algorithms↗