GPCR-GIA: a web-server for identifying G-protein coupled receptors and their families with grey incidence analysis.

G-protein-coupled receptors (GPCRs) play fundamental roles in regulating various physiological processes as well as the activity of virtually all cells. Different GPCR families are responsible for different functions. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop an automated method to address the two problems: given the sequence of a query protein, can we identify whether it is a GPCR? If it is, what family class does it belong to? Here, a two-layer ensemble classifier called GPCR-GIA was proposed by introducing a novel scale called 'grey incident degree'.

GPCR-CA: A cellular automaton image approach for predicting G-protein-coupled receptor functional classes.

Given an uncharacterized protein sequence, how can we identify whether it is a G-protein-coupled receptor (GPCR) or not? If it is, which functional family class does it belong to? It is important to address these questions because GPCRs are among the most frequent targets of therapeutic drugs and the information thus obtained is very useful for "comparative and evolutionary pharmacology," a technique often used for drug development. Here, we present a web-server predictor called "GPCR-CA," where "CA" stands for "Cellular Automaton" (Wolfram, S. Nature 1984, 311, 419), meaning that the CA images have been utilized to reveal the pattern features hidden in piles of long and complicated protein sequences.

Application of protein grey incidence degree measure to predict protein quaternary structural types.

Many proteins are composed of two or more subunits, each associated with different polypeptide chains. The number and arrangement of subunits forming a protein are referred to as quaternary structure. It has been known for long that the functions of proteins are closely related to their quaternary structure.

Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image.

A novel approach was developed for predicting the structural classes of proteins based on their sequences. It was assumed that proteins belonging to the same structural class must bear some sort of similar texture on the images generated by the cellular automaton evolving rule [Wolfram, S., 1984.

Using grey dynamic modeling and pseudo amino acid composition to predict protein structural classes.

Using the pseudo amino acid (PseAA) composition to represent the sample of a protein can incorporate a considerable amount of sequence pattern information so as to improve the prediction quality for its structural or functional classification. However, how to optimally formulate the PseAA composition is an important problem yet to be solved. In this article the grey modeling approach is introduced that is particularly efficient in coping with complicated systems such as the one consisting of many proteins with different sequence orders and lengths.

Digital coding of amino acids based on hydrophobic index.

Analysis of amino acid sequences can provide useful insights into the tertiary structures of proteins and their biological functions. One of the critical problems in amino acid analysis is how to establish a digital coding system to better reflect the properties of amino acids and their degeneracy. Based on the hydrophobic index, a one-to-one relationship has been established between the amino acid sequence and the digital signal process.