Study of drug resistance of chicken influenza A virus (H5N1) from homology-modeled 3D structures of neuraminidases.

The spread of highly pathogenic H5N1 influenza virus in many Asian and European countries as well as its drug-resistance have raised serious worldwide concerns. In this paper, the structure-activity relationship between NA (neuraminidase) and its three inhibitors (DANA, zanamivir, and oseltamivir) was investigated. A homology model of H5N1-NA (BAE46950), which is the first reported oseltamivir-resistance virus strain, and the 108 homology-modeled 3D structures of chicken influenza H5N1 NAs downloaded from the website at , formed the molecular structural basis for the drug-resistance study.

Amino Acid Principal Component Analysis (AAPCA) and its applications in protein structural class prediction.

The extremely complicated nature of many biological problems makes them bear the features of fuzzy sets, such as with vague, imprecise, noisy, ambiguous, or input-missing information For instance, the current data in classifying protein structural classes are typically a fuzzy set To deal with this kind of problem, the AAPCA (Amino Acid Principal Component Analysis) approach was introduced. In the AAPCA approach the 20-dimensional amino acid composition space is reduced to an orthogonal space with fewer dimensions, and the original base functions are converted into a set of orthogonal and normalized base functions The advantage of such an approach is that it can minimize the random errors and redundant information in protein dataset through a principal component selection, remarkably improving the success rates in predicting protein structural classes It is anticipated that the AAPCA approach can be used to deal with many other classification problems in proteins as well.

Support vector machines for prediction of protein domain structural class.

The support vector machines (SVMs) method was introduced for predicting the structural class of protein domains. The results obtained through the self-consistency test, jack-knife test, and independent dataset test have indicated that the current method and the elegant component-coupled algorithm developed by Chou and co-workers, if effectively complemented with each other, may become a powerful tool for predicting the structural class of protein domains.