Biomedical subjects

Kuo-Chen Chou

Publications and source records attributed to Kuo-Chen Chou.

101 records · Page 6Linked to original sources

PubMedDate not supplied

Prediction of membrane protein types by incorporating amphipathic effects.

According to their intramolecular arrangement and position in a cell, membrane proteins are generally classified into the following six types: (1) type I transmembrane, (2) type II transmembrane, (3) multipass transmembrane, (4) lipid chain-anchored membrane, (5) GPI-anchored membrane, and (6) peripheral membrane. Situated in a heteropolar environment, these six types of membrane proteins must have quite different amphiphilic sequence-order patterns in order to stabilize their respective frameworks. To incorporate such a feature into the predictor, the amphiphilic pseudo amino acid composition has been formulated that contains a series of hydrophobic and hydrophilic correlation factors. The success rates thus obtained have been remarkably enhanced in identifying the types of membrane proteins, as demonstrated by the jackknife test and independent data set test, respectively.

Algorithms↗

PubMedDate not supplied

Bioinformatical analysis of G-protein-coupled receptors.

G-protein-coupled receptors play a key role in cellular signaling networks that regulate various physiological processes, such as vision, smell, taste, neurotransmission, secretion, inflammatory, immune responses, cellular metabolism, and cellular growth. These proteins are very important for understanding human physiology and disease. Many efforts in pharmaceutical research have been aimed at understanding their structure and function. Unfortunately, because they are difficult to crystallize and most of them will not dissolve in normal solvents, so far very few G-protein-coupled receptor structures have been determined. In contrast, more than 1000 G-protein-coupled receptor sequences are known, and many more are expected to become known soon. In view of the extremely unbalanced state, it would be very useful to develop a fast sequence-based method to identify their different types. This would no doubt have practical value for both basic research and drug discovery because the function or binding specificity of a G-protein coupled receptor is determined by the particular type it belongs to. To realize this, a statistical analysis has been performed for 566 G-protein-coupled receptors classified into seven different types. The results indicate that the types of G-protein-coupled receptors are predictable to a considerable accurate extent if a good training data set can be established for such a goal.

Algorithms↗

PubMedDate not supplied

Prediction of enzyme family classes.

Classes of newly found enzyme sequences are usually determined either by biochemical analysis of eukaryotic and prokaryotic genomes or by microarray chips. These experimental methods are both time-consuming and costly. With the explosion of protein sequences entering into databanks, it is highly desirable to explore the feasibility of selectively classifying newly found enzyme sequences into their respective enzyme classes by means of an automated method. This is indeed important because knowing which family or subfamily an enzyme belongs to may help deduce its catalytic mechanism and specificity, giving clues to the relevant biological function. In this study, a bioinformatical analysis was conducted for 2640 oxidoreductases classified into 16 subclasses according to the different types of substrates they act on during the catalytic process. Although it is an extremely complicated problem and might involve the knowledge of 3-dimensional structure as well as many other physical chemistry factors, some quite promising results have been obtained indicating that the family or subfamily of an enzyme is predictable to a considerable degree by means of sequence-based approach alone if a good training dataset can be established.

Amino Acid Sequence↗

PubMedDate not supplied

Using functional domain composition to predict enzyme family classes.

According to their main EC (Enzyme Commission) numbers, enzymes are classified into the following 6 main classes: oxidoreductases, transferases, hydrolases, lyases, isomerases, and ligases. A new method has been developed to predict the enzymatic attribute of proteins by introducing the functional domain composition to formulate a given protein sequence. The advantage by doing so is that both the sequence-order-related features and the function-related features are naturally incorporated in the predictor. As a demonstration, the jackknife cross-validation test was performed on a dataset that consists of proteins with only less than 20% sequence identity to each other in order to get rid of any homologous bias. The overall success rate thus obtained was 85% in identifying the enzyme family classes (including the identification of nonenzyme protein sequences as well). The success rate is significantly higher than those obtained by the other methods on such a stringent dataset. This indicates that using the functional domain composition to represent protein samples for statistical prediction is indeed very promising, and will become a powerful tool in bioinformatics and proteomics.

Algorithms↗

PubMedDate not supplied

Molecular therapeutic target for type-2 diabetes.

Many lines of evidences indicate that increased flux of glucose through the pathway, in which glutamine:fructose-6-phosphate amidotransferase (GFPT or GFAT) is a key catalyst while uridine-5'-diphosphate-N-acetylglucosamine (UDP-GlcNAc) functions as an energy sensor, can lead to the insulin resistance that is characteristic of Type-2 diabetes. In view of this, GFAT and its interaction mechanism with UDP-GlcNAc may become a novel therapeutic target for the treatment of type 2 diabetes. To stimulate the structure-based drug design, the three-dimensional structures of human GFAT1 monomer and dimer have been developed. It has been found by docking UDP-GlcNAc to the dimer (the smallest unit for catalyzing the substrate) that UDP-GlcNAc is bound to the interface of the dimer by 12 hydrogen bonds. On the basis of the docking results, a binding pocket of human GFAT1 dimer for UDP-GlcNAc is defined. All of these findings can serve as a reference or footing in developing new therapeutic strategy for the treatment of type-2 diabetes.

Amino Acid Sequence↗

PubMedDate not supplied

Insights from modeling the tertiary structure of human BACE2.

BACE1, or beta-secretase, is a putative prime therapeutic target for the treatment of Alzheimer's disease. Mapping to the Down syndrome critical region (chromosome 21) and identified as a homologue of BACE1, BACE2 also cleaves amyloid precursor protein at the beta-site. Thus, BACE2, named also as Asp1 or Memapsin1, represents a second beta-secretase candidate. In this paper, the tertiary structure of the protease domain of BACE2 was developed. Although the overall structural topology between BACE1 and BACE2 protease domains is quite similar, the former contains 3 disulfide bonds but the latter only two. Particularly, a subtle structural difference around the DTG/DSG active site between the two structures has been observed that is useful for the in-depth selectivity study of BACE1 and BACE2 inhibitors, stimulating new therapeutic strategies for the treatment of Alzheimer's disease and Down syndrome as well.

Alzheimer Disease↗

PubMedDate not supplied

Insights from modeling three-dimensional structures of the human potassium and sodium channels.

Ion channels allow the movement of ions across cell membranes. Nearly all cells have membranes spanned by ion channels, without which human nerves simply would not work. Ion channels are formed by the aggregation of subunits into a cylindrical configuration that allows a pore, thus forming a kind of tube for ion trafficking. In the present study, the subunits of the human potassium channel are formed by four identical protein chains, whereas for the case of the human sodium channel, the corresponding subunits are actually four hetero-domains formed by the folding of a very large but single protein chain. Since both of the two ion channels are important targets for drug discovery, the 3D (dimensional) structures of their pore regions were developed. On the basis of the 3D models, some important molecular biological mechanisms were discussed that may stimulate novel strategies for therapeutic treatment of the diseases related to ion channel disorders, such as long QT syndrome and chronic pain.

Amino Acid Sequence↗

PubMedDate not supplied

Predicting enzyme subclass by functional domain composition and pseudo amino acid composition.

As a continuous effort to use the sequence approach to identify enzymatic function at a deeper level, investigations are extended from the main enzyme classes (Protein Sci. 2004, 13, 2857-2863) to their subclasses. This is indispensable if we wish to understand the molecular mechanism of an enzyme at a deeper level. For each of the 6 main enzyme classes (i.e., oxidoreductase, transferase, hydrolase, lyase, isomerase, and ligase), a subclass training dataset is constructed. To reduce homologous bias, a stringent cutoff was imposed that all the entries included in the datasets have less than 40% sequence identity to each other. To catch the core feature that is intimately related to the biological function, the sample of a protein is represented by hybridizing the functional domain composition and pseudo amino acid composition. On the basis of such a hybridization representation, the FunD-PseAA predictor is established. It is demonstrated by the jackknife cross-validation tests that the overall success rate in identifying the 21 subclasses of oxidoreductases is above 86%, and the corresponding rates in identifying the subclasses of the other 5 main enzyme classes are 94-97%. The high success rates imply that the FunD-PseAA predictor may become a useful tool in bioinformatics and proteomics of the post-genomic era.

Amino Acid Sequence↗

PubMedDate not supplied

Prediction of G-protein-coupled receptor classes.

Being the largest family of cell surface receptors, G-protein-coupled receptors (GPCRs) are among the most frequent targets of therapeutic drugs. The functions of many of GPCRs are unknown, and it is both time-consuming and expensive to determine their ligands and signaling pathways. This forces us to face a critical challenge: how to develop an automated method for classifying the family of GPCRs so as to help us in classifying drugs and expedite the process of drug discovery. Owing to their highly divergent nature, it is difficult to predict the classification of GPCRs by means of conventional sequence alignment approaches. To cope with such a situation, the CD (Covariant Discriminant) predictor was introduced to predict the families of GPCRs. The overall success rate thus obtained by jack-knife test for 1238 GPCRs classified into three main families, i.e., class A-"rhodopsin like", class B-"secretin like", and class C-"metabotrophic/glutamate/pheromone", was over 97%. The high success rate suggests that the CD predictor holds very high potential to become a useful tool for understanding the actions of drugs that target GPCRs and designing new medications with fewer side effects and greater efficacy.

Amino Acids↗

PubMedDate not supplied

Insights from modeling the 3D structure of DNA-CBF3b complex.

A 3-dimensional model for a DNA-protein interaction has been developed. The protein component is the 61-residue fragment (res. 11-71) of subunit b of the yeast centromeric DNA binding factor 3, CBF3b. The CBF3b fragments bind to the 17 base pairs (5'-CGGAGGACTGTCCTCCG-3') as a symmetric homodimer, with each folded into three distinct conformations: a compact, zinc-binding domain (res. 11-44); an extended linker (res. 45-57); and an alpha-helical dimerization element (res. 58-71). The DNA fragment in the complex is featured by a relatively straight conformation with only slight deviation from a standard B-structure, and a large part of the major groove not blocked by the protein. The large DNA open area provides the necessary space for the other subunits of CBF3 to bind coordinately with CBF3b, fully consistent with the observation that the cooperation of all four CBF3 components is absolutely required to constitute an activity that specifically interacts with centromere DNA. The model also provides a footing for further considering the possible binding arrangements of the other three subunits, namely CBF3a, CBF3c, and CBF3d.

Amino Acid Motifs↗

PubMedDate not supplied

Coupling interaction between thromboxane A2 receptor and alpha-13 subunit of guanine nucleotide-binding protein.

G protein-coupled receptors (GPCRs) form a large superfamily of membrane proteins that play an essential role in modulating many vital physiological events, such as cell communication, neurotransmission, sensory perception, and chemotaxis. Understanding of the 3D (dimensional) structures of these receptors and their binding interactions with G proteins will help in the design of drugs for the treatment of GPCR-related diseases. By means of the approach of structural bioinformatics, the 3D structures of human alpha-13 subunit of guanine nucleotide-binding protein (G alpha 13) and human thromboxane A2 (TXA2) receptor were developed. The former plays an important role in the control of cell growth that may serve as a prototypical G protein; the latter is a target for nitric oxide-mediated desensitization that may serve as a prototypical GPCR. On the basis of the 3D models, their coupling interactions were investigated via docking studies. It has been found that the two proteins are coupled with each other mainly through the interaction between the minigene of G alpha 13 and the 3rd intracellular loop of the TXA2 receptor, consistent with the existing deduction in the literatures. However, it has also been observed via a close view that some residues of the TXA2 receptor that are sequentially far away but spatially quite close to the loop region are also involved in forming hydrogen bonds with the minigene of G alpha 13. These findings may provide useful information for conducting mutagenesis and reveal the molecular mechanism how the human TXA2 receptor interact with G alpha 13 to activate intracellular signaling. The findings may also provide useful insights for stimulating new therapeutic approaches by manipulating the interaction of the receptor with the relevant G proteins.

Allosteric Site↗