Browsing by Author "Lyu, PC"

Now showing 1 - 4 of 4

Computation of the protein structure entropy and its applications to protein folding processes
(2003-06-01)
Chan, CH
;
Lyu, PC
;
Hwang, JK
;
生物科技學系
;
生物資訊及系統生物研究所
We have developed a general method to compute the structure, entropies of protein sequences. Structure entropy gives a quantitative measure of structure, conservation. This relationship is similar to that between sequence entropy and sequence conservation. Experimental studies in protein folding have suggested that residues relevant to protein folding, or the so-called "hot spot" residues, are usually structurally conserved, though not necessarily conserved in sequences. Hence, the ability to compute structure entropy can help identify important residues related to protein folding. In this work, we have applied our approach to several model proteins frequently used in protein folding experiments. Our results suggest a close relationship between the structure entropies of residues and their rates of amide proton exchange, and we are able to identity regions of residues that are important in protein folding.
Fine-grained protein fold assignment by support vector machines using generalized npeptide coding schemes and jury voting from multiple-parameter sets
(2003-03-01)
Yu, CS
;
Wang, JY
;
Yang, JM
;
Lyu, PC
;
Lin, CJ
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. (C) 2003 Wiley-Liss, Inc.
Relationship between local structural entropy and protein thermostability
(2004-12-01)
Chan, CH
;
Liang, HK
;
Hsiao, NW
;
Ko, MT
;
Lyu, PC
We developed a technique to compute structural entropy directly from protein sequences. We explored the possibility of using structural entropy to identify residues involved in thermal stabilization of various protein families. Examples include methanococcal adenylate kinase, Ribonuclease HI and holocytochrome c(551). Our results show that the positions of the largest structural entropy differences between wild type and mutant usually coincide with the residues relevant to thermostability. We also observed a good linear relationship between the average structural entropy and the melting temperatures for adenylate kinase and its chimeric constructs. To validate this linear relationship, we compiled a large dataset comprised of 1153 sequences and found that most protein families still display similar linear relationships. Our results suggest that the multitude of interactions involved in thermal stabilization may be generalized into the tendency of proteins to maintain local structural conservation. The linear relationship between structural entropy and protein thermostability should be useful in the study of protein thermal stabilization. (C) 2004 Wiley-Liss, Inc.
Relationship between protein structures and disulfide bonding patterns
(2003-10-01)
Chuang, CC
;
Chen, CY
;
Yang, JM
;
Lyu, PC
;
Hwang, JK
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. (C) 2003 Wiley-Liss, Inc.