Tun-Wen Pai

The family 21 carbohydrate-binding module of glucoamylase from Rhizopus oryzae consists of two sites playing distinct roles in ligand binding.

The starch-hydrolysing enzyme GA (glucoamylase) from Rhizopus oryzae is a commonly used glycoside hydrolase in industry. It consists of a C-terminal catalytic domain and an N-terminal starch-binding domain, which belong to the CBM21 (carbohydrate-binding module, family 21). In the present study, a molecular model of CBM21 from R. oryzae GA (RoGACBM21) was constructed according to PSSC (progressive secondary structure correlation), modified structure-based sequence alignment, and site-directed mutagenesis was used to identify and characterize potential ligand-binding sites. Our model suggests that RoGACBM21 contains two ligand-binding sites, with Tyr32 and Tyr67 grouped into site I, and Trp47, Tyr83 and Tyr93 grouped into site II. The involvement of these aromatic residues has been validated using chemical modification, UV difference spectroscopy studies, and both qualitative and quantitative binding assays on a series of RoGACBM21 mutants. Our results further reveal that binding sites I and II play distinct roles in ligand binding, the former not only is involved in binding insoluble starch, but also facilitates the binding of RoGACBM21 to long-chain soluble polysaccharides, whereas the latter serves as the major binding site mediating the binding of both soluble polysaccharide and insoluble ligands. In the present study we have for the first time demonstrated that the key ligand-binding residues of RoGACBM21 can be identified and characterized by a combination of novel bioinformatics methodologies in the absence of resolved three-dimensional structural information.

Inhibition of the interactions between eosinophil cationic protein and airway epithelial cells by traditional Chinese herbs

BackgroundThe eosinophil cationic protein (ECP) is cytotoxic to bacteria, viruses, parasites and mammalian cells. Cells are damaged via processes of pore formation, permeability alteration and membrane leaking. Some clinical studies indicate that ECP gathers in the bronchial tract of asthma sufferers, damages bronchial and airway epithelial cells, and leads to in breathing tract inflammation; therefore, prevention of the cytotoxicity caused by ECP may serve as an approach to treat airway inflammation. To achieve the purpose, reduction of the ECP-cell interactions is rational. In this work, the Chinese herbal combinative network was generated to predict and identify the functional herbs from the pools of prescriptions. It was useful to select the node herbs and to demonstrate the relative binding ability between ECP and Beas-2B cells with or withour herb treatments.ResultsEighty three Chinese herbs and prescriptions were tested and five effective herbs and six prescription candidates were selected. On the basis of effective single-herbal drugs and prescriptions, a combinative network was generated. We found that a single herb, Gan-cao, served as a node connecting five prescriptions. In addition, Sheng-di-huang, Dang-guei and Mu-tong also appeared in five, four and three kinds of prescriptions, respectively. The extracts of these three herbs indeed effectively inhibited the interactions between ECP and Beas-2B cells. According to the Chinese herbal combinative network, eight of the effective herbal extracts showed inhibitory effects for ECP internalizing into Beas-2B cells. The major components of Gang-cao and Sheng-di-huang, glycyrrhizic acid and verbascose, respectively, reduced the binding affinity between ECP and cells effectively.ConclusionsSince these Chinese herbs reduced the binding affinity between ECP and cells and inhibited subsequent ECP entrance into cells, they were potential for mitigating the airway inflammation symptoms. Additionally, we mentioned a new concept to study the Chinese herbs using combinative network in the field of systems biology. The functional single herbs could be identified from the set of prescriptions.

Integration of CNS survival and differentiation by HIF2α

Hypoxia-inducible factor (HIF) 1α and HIF2α and the inhibitor of apoptosis survivin represent prominent markers of many human cancers. They are also widely expressed in various embryonic tissues, including the central nervous system; however, little is known about their functions in embryos. Here, we show that zebrafish HIF2α protects neural progenitor cells and neural differentiation processes by upregulating the survivin orthologues birc5a and birc5b during embryogenesis. Morpholino-mediated knockdown of hif2α reduced the transcription of birc5a and birc5b, induced p53-independent apoptosis and abrogated neural cell differentiation. Depletion of birc5a and birc5b recaptured the neural development defects that were observed in the hif2α morphants. The phenotypes induced by HIF2α depletion were largely rescued by ectopic birc5a and birc5b mRNAs, indicating that Birc5a and Birc5b act downstream of HIF2α. Chromatin immunoprecipitation assay revealed that HIF2α binds to birc5a and birc5b promoters directly to modulate their transcriptions. Knockdown of hif2α, birc5a or birc5b reduced the expression of the cdk inhibitors p27/cdkn1b and p57/cdkn1c and increased ccnd1/cyclin D1 transcription in the surviving neural progenitor cells. The reduction in elavl3/HuC expression and enhanced pcna, nestin, ascl1b and sox3 expression indicate that the surviving neural progenitor cells in hif2α morphants maintain a high proliferation rate without terminally differentiating. We propose that a subset of developmental defects attributed to HIF2α depletion is due in part to the loss of survivin activity.

Hypoxia-Inducible Factor 2 Alpha Is Essential for Hepatic Outgrowth and Functions via the Regulation of leg1 Transcription in the Zebrafish Embryo

The liver plays a vital role in metabolism, detoxification, digestion, and the maintenance of homeostasis. During development, the vertebrate embryonic liver undergoes a series of morphogenic processes known as hepatogenesis. Hepatogenesis can be separated into three interrelated processes: endoderm specification, hepatoblast differentiation, and hepatic outgrowth. Throughout this process, signaling molecules and transcription factors initiate and regulate the coordination of cell proliferation, apoptosis, differentiation, intercellular adhesion, and cell migration. Hifs are already recognized to be essential in embryonic development, but their role in hepatogenesis remains unknown. Using the zebrafish embryo as a model organism, we report that the lack of Hif2-alpha but not Hif1-alpha blocks hepatic outgrowth. While Hif2-alpha is not involved in hepatoblast specification, this transcription factor regulates hepatocyte cell proliferation during hepatic outgrowth. Furthermore, we demonstrated that the lack of Hif2-alpha can reduce the expression of liver-enriched gene 1 (leg1), which encodes a secretory protein essential for hepatic outgrowth. Additionally, exogenous mRNA expression of leg1 can rescue the small liver phenotype of hif2-alpha morphants. We also showed that Hif2-alpha directly binds to the promoter region of leg1 to control leg1 expression. Interestingly, we discovered overrepresented, high-density Hif-binding sites in the potential upstream regulatory sequences of leg1 in teleosts but not in terrestrial mammals. We concluded that hif2-alpha is a key factor required for hepatic outgrowth and regulates leg1 expression in zebrafish embryos. We also proposed that the hif2-alpha-leg1 axis in liver development may have resulted from the adaptation of teleosts to their environment.

In Silico Prediction and In Vitro Characterization of Multifunctional Human RNase3

Human ribonucleases A (hRNaseA) superfamily consists of thirteen members with high-structure similarities but exhibits divergent physiological functions other than RNase activity. Evolution of hRNaseA superfamily has gained novel functions which may be preserved in a unique region or domain to account for additional molecular interactions. hRNase3 has multiple functions including ribonucleolytic, heparan sulfate (HS) binding, cellular binding, endocytic, lipid destabilization, cytotoxic, and antimicrobial activities. In this study, three putative multifunctional regions, 34RWRCK38 (HBR1), 75RSRFR79 (HBR2), and 101RPGRR105 (HBR3), of hRNase3 have been identified employing in silico sequence analysis and validated employing in vitro activity assays. A heparin binding peptide containing HBR1 is characterized to act as a key element associated with HS binding, cellular binding, and lipid binding activities. In this study, we provide novel insights to identify functional regions of hRNase3 that may have implications for all hRNaseA superfamily members.

Detection and alignment of 3D domain swapping proteins using angle-distance image-based secondary structural matching techniques.

This work presents a novel detection method for three-dimensional domain swapping (DS), a mechanism for forming protein quaternary structures that can be visualized as if monomers had “opened” their “closed” structures and exchanged the opened portion to form intertwined oligomers. Since the first report of DS in the mid 1990s, an increasing number of identified cases has led to the postulation that DS might occur in a protein with an unconstrained terminus under appropriate conditions. DS may play important roles in the molecular evolution and functional regulation of proteins and the formation of depositions in Alzheimers and prion diseases. Moreover, it is promising for designing auto-assembling biomaterials. Despite the increasing interest in DS, related bioinformatics methods are rarely available. Owing to a dramatic conformational difference between the monomeric/closed and oligomeric/open forms, conventional structural comparison methods are inadequate for detecting DS. Hence, there is also a lack of comprehensive datasets for studying DS. Based on angle-distance (A-D) image transformations of secondary structural elements (SSEs), specific patterns within A-D images can be recognized and classified for structural similarities. In this work, a matching algorithm to extract corresponding SSE pairs from A-D images and a novel DS score have been designed and demonstrated to be applicable to the detection of DS relationships. The Matthews correlation coefficient (MCC) and sensitivity of the proposed DS-detecting method were higher than 0.81 even when the sequence identities of the proteins examined were lower than 10%. On average, the alignment percentage and root-mean-square distance (RMSD) computed by the proposed method were 90% and 1.8Å for a set of 1,211 DS-related pairs of proteins. The performances of structural alignments remain high and stable for DS-related homologs with less than 10% sequence identities. In addition, the quality of its hinge loop determination is comparable to that of manual inspection. This method has been implemented as a web-based tool, which requires two protein structures as the input and then the type and/or existence of DS relationships between the input structures are determined according to the A-D image-based structural alignments and the DS score. The proposed method is expected to trigger large-scale studies of this interesting structural phenomenon and facilitate related applications.

An online conserved SSR discovery through cross-species comparison.

Simple sequence repeats (SSRs) play important roles in gene regulation and genome evolution. Although there exist several online resources for SSR mining, most of them only extract general SSR patterns without providing functional information. Here, an online search tool, CG-SSR (Comparative Genomics SSR discovery), has been developed for discovering potential functional SSRs from vertebrate genomes through cross-species comparison. In addition to revealing SSR candidates in conserved regions among various species, it also combines accurate coordinate and functional genomics information. CG-SSR is the first comprehensive and efficient online tool for conserved SSR discovery.

A local average distance descriptor for flexible protein structure comparison

BackgroundProtein structures are flexible and often show conformational changes upon binding to other molecules to exert biological functions. As protein structures correlate with characteristic functions, structure comparison allows classification and prediction of proteins of undefined functions. However, most comparison methods treat proteins as rigid bodies and cannot retrieve similarities of proteins with large conformational changes effectively.ResultsIn this paper, we propose a novel descriptor, local average distance (LAD), based on either the geodesic distances (GDs) or Euclidean distances (EDs) for pairwise flexible protein structure comparison. The proposed method was compared with 7 structural alignment methods and 7 shape descriptors on two datasets comprising hinge bending motions from the MolMovDB, and the results have shown that our method outperformed all other methods regarding retrieving similar structures in terms of precision-recall curve, retrieval success rate, R-precision, mean average precision and F1-measure.ConclusionsBoth ED- and GD-based LAD descriptors are effective to search deformed structures and overcome the problems of self-connection caused by a large bending motion. We have also demonstrated that the ED-based LAD is more robust than the GD-based descriptor. The proposed algorithm provides an alternative approach for blasting structure database, discovering previously unknown conformational relationships, and reorganizing protein structure classification.

Hydrophilic Aromatic Residue and in silico Structure for Carbohydrate Binding Module

Carbohydrate binding modules (CBMs) are found in polysaccharide-targeting enzymes and increase catalytic efficiency. Because only a relatively small number of CBM structures have been solved, computational modeling represents an alternative approach in conjunction with experimental assessment of CBM functionality and ligand-binding properties. An accurate target-template sequence alignment is the crucial step during homology modeling. However, low sequence identities between target/template sequences can be a major bottleneck. We therefore incorporated the predicted hydrophilic aromatic residues (HARs) and secondary structure elements into our feature-incorporated alignment (FIA) algorithm to increase CBM alignment accuracy. An alignment performance comparison for FIA and six others was made, and the greatest average sequence identities and similarities were achieved by FIA. In addition, structure models were built for 817 representative CBMs. Our models possessed the smallest average surface-potential z scores. Besides, a large true positive value for liagnd-binding aromatic residue prediction was obtained by HAR identification. Finally, the pre-simulated CBM structures have been deposited in the Database of Simulated CBM structures (DS-CBMs). The web service is publicly available at http://dscbm.life.nthu.edu.tw/ and http://dscbm.cs.ntou.edu.tw/.

Feature-incorporated alignment based ligand-binding residue prediction for carbohydrate-binding modules

MOTIVATION Carbohydrate-binding modules (CBMs) share similar secondary and tertiary topology, but their primary sequence identity is low. Computational identification of ligand-binding residues allows biologists to better understand the protein-carbohydrate binding mechanism. In general, functional characterization can be alternatively solved by alignment-based manners. As alignment accuracy based on conventional methods is often sensitive to sequence identity, low sequence identity among query sequences makes it difficult to precisely locate small portions of relevant features. Therefore, we propose a feature-incorporated alignment (FIA) to flexibly align conserved signatures in CBMs. Then, an FIA-based target-template prediction model was further implemented to identify functional ligand-binding residues. RESULTS Arabidopsis thaliana CBM45 and CBM53 were used to validate the FIA-based prediction model. The predicted ligand-binding residues residing on the surface in the hypothetical structures were verified to be ligand-binding residues. In the absence of 3D structural information, FIA demonstrated significant improvement in the estimation of sequence similarity and identity for a total of 808 sequences from 11 different CBM families as compared with six leading tools by Friedman rank test.