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G P Zhou

Publications and source records attributed to G P Zhou.

11 recordsLinked to original sources
PubMed2001

Some insights into protein structural class prediction.

It has been quite clear that the success rate for predicting protein structural class can be improved significantly by using the algorithms that incorporate the coupling effect among different amino acid components of a protein. However, there is still a lot of confusion in understanding the relationship of these advanced algorithms, such as the least Mahalanobis distance algorithm, the component-coupled algorithm, and the Bayes decision rule. In this communication, a simple, rigorous derivation is provided to prove that the Bayes decision rule introduced recently for protein structural class prediction is completely the same as the earlier component-coupled algorithm. Meanwhile, it is also very clear from the derivative equations that the least Mahalanobis distance algorithm is an approximation of the component-coupled algorithm, also named as the covariant-discriminant algorithm introduced by Chou and Elrod in protein subcellular location prediction (Protein Engineering, 1999; 12:107-118). Clarification of the confusion will help use these powerful algorithms effectively and correctly interpret the results obtained by them, so as to conduce to the further development not only in the structural prediction area, but in some other relevant areas in protein science as well.

Algorithms↗
PubMed2001

Support vector machines for predicting protein structural class.

BACKGROUND: We apply a new machine learning method, the so-called Support Vector Machine method, to predict the protein structural class. Support Vector Machine method is performed based on the database derived from SCOP, in which protein domains are classified based on known structures and the evolutionary relationships and the principles that govern their 3-D structure. RESULTS: High rates of both self-consistency and jackknife tests are obtained. The good results indicate that the structural class of a protein is considerably correlated with its amino acid composition. CONCLUSIONS: It is expected that the Support Vector Machine method and the elegant component-coupled method, also named as the covariant discrimination algorithm, if complemented with each other, can provide a powerful computational tool for predicting the structural classes of proteins.

Algorithms↗
PubMed1998

An intriguing controversy over protein structural class prediction.

A recent report by Bahar et al. [(1997), Proteins 29, 172-185] indicates that the coupling effects among different amino acid components as originally formulated by K. C. Chou [(1995), Proteins 21, 319-344] are important for improving the prediction of protein structural classes. These authors have further proposed a compact lattice model to illuminate the physical insight contained in the component-coupled algorithm. However, a completely opposite result was concluded by Eisenhaber et al. [(1996), Proteins 25, 169 179], using a different dataset constructed according to their definition. To address such an intriguing controversy, tests were conducted by various approaches for the datasets from an objective database, the SCOP database [Murzin et al. (1995), J. Mol. Biol. 247, 536-540]. The results obtained by both self-consistency and jackknife tests indicate that the overall rates of correct prediction by the algorithm incorporating the coupling effect among different amino acid components are significantly higher than those by the algorithms without counting such an effect. This is fully consistent with the physical reality that the folding of a protein is the result of a collective interaction among its constituent amino acid residues, and hence the coupling effects of different amino acid components must be incorporated in order to improve the prediction quality. It was found by a revisiting the calculation procedures by Eisenhaber et al. that there was a conceptual mistake in constructing the structural class datasets and a systematic mistake in applying the component-coupled algorithm. These findings are informative for understanding and utilizing the component-coupled algorithm to study the structural classes of proteins.

Algorithms↗
PubMed1994

Major histocompatibility complex class II antigens in steroid-sensitive nephrotic syndrome in Chinese children.

Steroid-sensitive nephrotic syndrome (SSNS) has been postulated to have an immunopathogenic basis. To determine whether SSNS is associated with specific class II antigens of the major histocompatibility complex, we studied HLA-DR and DQ in 40 children with SSNS. HLA-DR7 was found in 40% of SSNS patients compared with only 11.23% of controls (P = 0.00025). HLA-DR9 occurred in 71.40% of patients with frequent relapses, compared with 27.37% of controls (P = 0.016). It seems likely that SSNS has an immunogenetic basis.

Child↗
PubMed1993

[Studies on constituents of Ilex chinensis Sims].

Several compounds characterised as protocatechuic acid, caffeic acid, syringin, rotundic acid and pedunculoside were isolated from the leaves of Ilex chinensis, a Chinese crude drug. A novel compound cyclohexanone pedunculosyl-3,23-O-acetal was identified and proposed as a pedunculoside derivative produced in the extracting procedure.

Caffeic Acids↗
PubMed1981

The flexibility during the juxtaposition of reacting groups and the upper limits of enzyme reactions.

The combination between enzymes and substrates occur only after their reacting groups are in juxtaposition with each other. This will greatly reduce the probability of their effective encounters. However, the results calculated with the finite elements method show that the reaction limits will not decrease substantially if van der Waal's forces and a reasonable flexibility during such a juxtaposition are taken into account.

Binding Sites↗