Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes.

Motivation: With protein sequences entering into databanks at an explosive pace, the early determination of the family or subfamily class for a newly found enzyme molecule becomes important because this is directly related to the detailed information about which specific target it acts on, as well as to its catalytic process and biological function. Unfortunately, it is both time-consuming and costly to do so by experiments alone. In a previous study, the covariant-discriminant algorithm was introduced to identify the 16 subfamily classes of oxidoreductases.

Prediction of protein subcellular locations by GO-FunD-PseAA predictor.

The localization of a protein in a cell is closely correlated with its biological function. With the explosion of protein sequences entering into DataBanks, it is highly desired to develop an automated method that can fast identify their subcellular location. This will expedite the annotation process, providing timely useful information for both basic research and industrial application.

Insights from modelling the 3D structure of the extracellular domain of alpha7 nicotinic acetylcholine receptor.

Based on the crystal structure of acetylcholine-binding protein, the three-dimensional structures of the extracellular domain, or the ligand-binding domains, of the monomer, homodimer, and homopentamer of the alpha7 nicotinic acetylcholine receptor were derived. The interface between two subunits, where the ligand-binding site is located, was investigated. Furthermore, an explicit definition of the ligand-binding pocket was illustrated that might provide useful clues for conducting various mutagenesis studies for finding drugs against schizophrenia and Alzheimer's disease.

Predicting subcellular localization of proteins by hybridizing functional domain composition and pseudo-amino acid composition.

Recent advances in large-scale genome sequencing have led to the rapid accumulation of amino acid sequences of proteins whose functions are unknown. Since the functions of these proteins are closely correlated with their subcellular localizations, many efforts have been made to develop a variety of methods for predicting protein subcellular location. In this study, based on the strategy by hybridizing the functional domain composition and the pseudo-amino acid composition (Cai and Chou [2003]: Biochem.

A novel approach to predict active sites of enzyme molecules.

Enzymes are critical in many cellular signaling cascades. With many enzyme structures being solved, there is an increasing need to develop an automated method for identifying their active sites. However, given the atomic coordinates of an enzyme molecule, how can we predict its active site? This is a vitally important problem because the core of an enzyme molecule is its active site from the viewpoints of both pure scientific research and industrial application.

Prediction and classification of protein subcellular location-sequence-order effect and pseudo amino acid composition.

Given a protein sequence, how to identify its subcellular location? With the rapid increase in newly found protein sequences entering into databanks, the problem has become more and more important because the function of a protein is closely correlated with its localization. To practically deal with the challenge, a dataset has been established that allows the identification performed among the following 14 subcellular locations: (1) cell wall, (2) centriole, (3) chloroplast, (4) cytoplasm, (5) cytoskeleton, (6) endoplasmic reticulum, (7) extracellular, (8) Golgi apparatus, (9) lysosome, (10) mitochondria, (11) nucleus, (12) peroxisome, (13) plasma membrane, and (14) vacuole. Compared with the datasets constructed by the previous investigators, the current one represents the largest in the scope of localizations covered, and hence many proteins which were totally out of picture in the previous treatments, can now be investigated.

A new hybrid approach to predict subcellular localization of proteins by incorporating gene ontology.

Based on the recent development in the gene ontology and functional domain databases, a new hybridization approach is developed for predicting protein subcellular location by combining the gene product, functional domain, and quasi-sequence-order effects. As a showcase, the same prokaryotic and eukaryotic datasets, which were studied by many previous investigators, are used for demonstration. The overall success rate by the jackknife test for the prokaryotic set is 94.

Predicting protein quaternary structure by pseudo amino acid composition.

In the protein universe, many proteins are composed of two or more polypeptide chains, generally referred to as subunits, that associate through noncovalent interactions and, occasionally, disulfide bonds. With the number of protein sequences entering into data banks rapidly increasing, we are confronted with a challenge: how to develop an automated method to identify the quaternary attribute for a new polypeptide chain (i.e.

Binding mechanism of coronavirus main proteinase with ligands and its implication to drug design against SARS.

In order to stimulate the development of drugs against severe acute respiratory syndrome (SARS), based on the atomic coordinates of the SARS coronavirus main proteinase determined recently [Science 13 (May) (2003) (online)], studies of docking KZ7088 (a derivative of AG7088) and the AVLQSGFR octapeptide to the enzyme were conducted. It has been observed that both the above compounds interact with the active site of the SARS enzyme through six hydrogen bonds. Also, a clear definition of the binding pocket for KZ7088 has been presented.