Browsing by Author "Lu, Chih-Hao"

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CELLO2GO: A Web Server for Protein subCELlular LOcalization Prediction with Functional Gene Ontology Annotation
(2014-06-09)
Yu, Chin-Sheng
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Cheng, Chih-Wen
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Su, Wen-Chi
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Chang, Kuei-Chung
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Huang, Shao-Wei
CELLO2GO (http://cello.life.nctu.edu.tw/cello2go/) is a publicly available, web-based system for screening various properties of a targeted protein and its subcellular localization. Herein, we describe how this platform is used to obtain a brief or detailed gene ontology (GO)-type categories, including subcellular localization(s), for the queried proteins by combining the CELLO localization-predicting and BLAST homology-searching approaches. Given a query protein sequence, CELLO2GO uses BLAST to search for homologous sequences that are GO annotated in an in-house database derived from the UniProt KnowledgeBase database. At the same time, CELLO attempts predict at least one subcellular localization on the basis of the species in which the protein is found. When homologs for the query sequence have been identified, the number of terms found for each of their GO categories, i.e., cellular compartment, molecular function, and biological process, are summed and presented as pie charts representing possible functional annotations for the queried protein. Although the experimental subcellular localization of a protein may not be known, and thus not annotated, CELLO can confidentially suggest a subcellular localization. CELLO2GO should be a useful tool for research involving complex subcellular systems because it combines CELLO and BLAST into one platform and its output is easily manipulated such that the user-specific questions may be readily addressed.
Deriving protein dynamical properties from weighted protein contact number
(2008-08-15)
Lin, Chih-Peng
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Huang, Shao-Wei
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Lai, Yan-Long
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Yen, Shih-Chung
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Shih, Chien-Hua
It has recently been shown that in proteins the atomic mean-square displacement (or B-factor) can be related to the number of the neighboring atoms (or protein contact number), and that this relationship allows one to compute the B-factor profiles directly from protein contact number. This method, referred to as the protein contact model, is appealing, since it requires neither trajectory integration nor matrix diagonalization. As a result, the protein contact model can be applied to very large proteins and can be implemented as a high-throughput computational toot to compute atomic fluctuations in proteins. Here, we show that this relationship can be further refined to that between the atomic mean-square displacement and the weighted protein contact-number, the weight being the square of the reciprocal distance between the contacting pair. In addition, we show that this relationship can be utilized to compute the cross-correlation of atomic motion (the B-factor is essentially the auto-correlation of atomic motion). For a nonhomologous dataset comprising 972 high-resolution X-ray protein structures (resolution < 2.0 angstrom and sequence identity < 25%), the mean correlation coefficient between the X-ray and computed B-factors based on the weighted protein contact-number model is 0.61, which is better than those of the original contact-number model (0.51) and other methods. We also show that the computed correlation maps based on the weighted contact-number model are globally similar to those computed through normal model analysis for some selected cases. Our results underscore the relationship between protein dynamics and protein packing. We believe that our method will be useful in the study of the protein structure-dynamics relationship.
MI6: Metal Ion-Binding Site Prediction and Docking Server
(2016-12)
Lin, Yu-Feng
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Cheng, Chih-Wen
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Shih, Chung-Shiuan
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Hwang, Jenn-Kang
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Yu, Chin-Sheng
The structure of a protein determines its biological function(s) and its interactions with other factors; the binding regions tend to be conserved in sequence and structure, and the interacting residues involved are usually in close 3D space. The Protein Data Bank currently contains more than 110 000 protein structures, approximately one-third of which contain metal ions. Identifying and characterizing metal ion-binding sites is thus essential for investigating a protein\'s function(s) and interactions. However, experimental approaches are time-consuming and costly. The web server reported here was built to predict metal ion-binding residues and to generate the predicted metal ion-bound 3D structure. Binding templates have been constructed for regions that bind 12 types of metal ion-binding residues have been used to construct binding templates. The templates include residues within 3.5 angstrom of the metal ion, and the fragment transformation method was used for structural comparison between query proteins and templates without any data training. Through the adjustment of scoring functions, which are based on the similarity of structure and binding residues. Twelve kinds of metal ions (Ca2+, Cu2+, Fe3+, Mn2+, Zn2+, Cd2+, Fe2+, Ni2+, Hg2+, Co2+, and Cu+) binding residues prediction are supported. MIB also provides the metal ions docking after prediction. The MIB server is available at http://bioinfo.cmu.edu.tw/MIB/.
On the relationship between the protein structure and protein dynamics
(2008-08-01)
Lu, Chih-Hao
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Huang, Shao-Wei
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Lai, Yan-Long
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Lin, Chih-Peng
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Shih, Chien-Hua
Recently, we have developed a method (Shih et al., Proteins: Structure, Function, and Bioinformatics 2007,68: 34-38) to compute correlation of fluctuations of proteins. This method, referred to as the protein fixed-point (PFP) model, is based on the positional vectors of atoms issuing from the fixed point, which is the point of the least fluctuations in proteins. One corollary from this model is that atoms lying on the same shell centered at the fixed point will have the same thermal fluctuations. In practice, this model provides a convenient way to compute the average dynamical properties of proteins directly from the geometrical shapes of proteins without the need of any mechanical models, and hence no trajectory integration or sophisticated matrix operations are needed. As a result, it is more efficient than molecular dynamics simulation or normal mode analysis. Though in the previous study the PFP model has been successfully applied to a number of proteins of various folds, it is not clear to what extent this model will be applied. In this article, we have carried out the comprehensive analysis of the PFP model for a dataset comprising 972 high-resolution X-ray structures with pairwise sequence identity <= 25%. We found that in most cases the PFP model works well. However, in case of proteins comprising multiple domains, each domain should be treated separately as an independent dynamical module with its own fixed point, and in case of the protein complex comprising it number of subunits, if functioning as a biological unit, the whole complex should be considered as one single dynamical module with one fixed point. Under such considerations, the resultant correlation coefficient between the computed and the X-ray structural B-factors for the data set is 0.59 and 75% (727/972) of proteins with a correlation coefficient >= 0.5. Our result shows that the fixed-point model is indeed quite general and will be a useful tool for high throughput analysis of dynamical properties of proteins.
Periostin promotes epithelial-mesenchymal transition via the MAPK/miR-381 axis in lung cancer
(2017-09-05)
Hu, Wei-Wei
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Chen, Po-Chun
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Chen, Jun-Ming
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Wu, Yue-Ming
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Liu, Po-Yi
Periostin (POSTN, PN, or osteoblast-specific factor OSF-2) is a multifunctional cytokine that signals between the cell and the extracellular matrix. Periostin plays an important role in tumor development and is involved in carcinoma cell epithelial-mesenchymal transition (EMT), whereby mature epithelial cells undergo phenotypic morphological changes and become invasive, motile cells. Here, we discuss the molecular mechanisms involved in periostin-induced promotion of EMT in lung cancer cells. Online TCGA datasets demonstrate the prognostic relevance of periostin in lung cancer; a higher periostin level correlates with poor overall survival. Similarly, our IHC results show that high periostin expression is positively correlated with the EMT markers Snail and Twist, as well as stage of lung cancer. We found that recombinant periostin induces the EMT phenotype in lung cancer cells through the p38/ERK pathway, while pretreatment with chemical inhibitors prevented periostin-induced EMT induction. Moreover, we found that periostin regulates EMT by repressing microRNA-381 (miR-381) expression, which targets both Snail and Twist. Using the miR-381 mimic, we dramatically reversed periostin-induced Snail and Twist expression. Furthermore, periostin knockdown dramatically affected EMT markers and cell migration potential. The role of periostin in lung cancer progression is elucidated by the in vivo mouse model. Our findings indicate that changes in periostin expression in lung cancer may serve as a therapeutic target for the treatment of lung cancer metastasis.
Predicting disulfide connectivity patterns
(2007-05-01)
Lu, Chih-Hao
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Chen, Yu-Ching
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Yu, Chin-Sheng
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Hwang, Jenn-Kang
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生物資訊及系統生物研究所
Disulfide bonds play an important role in stabilizing protein structure and regulating protein function. Therefore, the ability to infer disulfide connectivity from protein sequences will be valuable in structural modeling and functional analysis. However, to predict disulfide connectivity directly from sequences presents a challenge to computational biologists due to the nonlocal nature of disulfide bonds, i.e., the close spatial proximity of the cysteine pair that forms the disulfide bond does not necessarily imply the short sequence separation of the cysteine residues. Recently, Chen and Hwang (Proteins 2005;61:507-512) treated this problem as a multiple class classification by defining each distinct disulfide pattern as a class. They used multiple support vector machines based on a variety of sequence features to predict the disulfide patterns. Their results compare favorably with those in the literature for a benchmark dataset sharing less than 30% sequence identity. However, since the number of disulfide patterns grows rapidly when the number of disulfide bonds increases, their method performs unsatisfactorily for the cases of large number of disulfide bonds. In this work, we propose a novel method to represent disulfide connectivity in terms of cysteine pairs, instead of disulfide patterns. Since the number of bonding states of the cysteine pairs is independent of that of disulfide bonds, the problem of class explosion is avoided. The bonding states of the cysteine pairs are predicted using the support vector machines together with the genetic algorithm optimization for feature selection. The complete disulfide patterns are then determined from the connectivity matrices that are constructed from the predicted bonding states of the cysteine pairs. Our approach outperforms the current approaches in the literature.
Predicting Flavin and Nicotinamide Adenine Dinucleotide-Binding Sites in Proteins Using the Fragment Transformation Method
(2015-01-01)
Lu, Chih-Hao
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Yu, Chin-Sheng
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Lin, Yu-Feng
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Chen, Jin-Yi
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生物資訊及系統生物研究所
We developed a computational method to identify NAD- and FAD-binding sites in proteins. First, we extracted from the Protein Data Bank structures of proteins that bind to at least one of these ligands. NAD-/FAD-binding residue templates were then constructed by identifying binding residues through the ligand-binding database BioLiP. The fragment transformation method was used to identify structures within query proteins that resembled the ligand-binding templates. By comparing residue types and their relative spatial positions, potential binding sites were identified and a ligand-binding potential for each residue was calculated. Setting the false positive rate at 5%, our method predicted NAD- and FAD-binding sites at true positive rates of 67.1% and 68.4%, respectively. Our method provides excellent results for identifying FAD- and NAD-binding sites in proteins, and the most important is that the requirement of conservation of residue types and local structures in the FAD- and NAD-binding sites can be verified.
Prediction of Metal Ion-Binding Sites in Proteins Using the Fragment Transformation Method
(2012-06-18)
Lu, Chih-Hao
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Lin, Yu-Feng
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Lin, Jau-Ji
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Yu, Chin-Sheng
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生物資訊及系統生物研究所
The structure of a protein determines its function and its interactions with other factors. Regions of proteins that interact with ligands, substrates, and/or other proteins, tend to be conserved both in sequence and structure, and the residues involved are usually in close spatial proximity. More than 70,000 protein structures are currently found in the Protein Data Bank, and approximately one-third contain metal ions essential for function. Identifying and characterizing metal ion-binding sites experimentally is time-consuming and costly. Many computational methods have been developed to identify metal ion-binding sites, and most use only sequence information. For the work reported herein, we developed a method that uses sequence and structural information to predict the residues in metal ion-binding sites. Six types of metal ion-binding templates-those involving Ca2+, Cu2+, Fe3+, Mg2+, Mn2+, and Zn2+-were constructed using the residues within 3.5 angstrom of the center of the metal ion. Using the fragment transformation method, we then compared known metal ion-binding sites with the templates to assess the accuracy of our method. Our method achieved an overall 94.6 % accuracy with a true positive rate of 60.5 % at a 5 % false positive rate and therefore constitutes a significant improvement in metal-binding site prediction.
Prediction of protein subcellular localization
(2006-08-15)
Yu, Chin-Sheng
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Chen, Yu-Ching
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Lu, Chih-Hao
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Hwang, Jenn-Kang
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生物科技學系
Because the protein's function is usually related to its subcellular localization, the ability to predict subcellular localization directly from protein sequences will be useful for inferring protein functions. Recent years have seen a surging interest in the development of novel computational tools to predict subcellular localization. At present, these approaches, based on a wide range of algorithms, have achieved varying degrees of success for specific organisms and for certain localization categories. A number of authors have noticed that sequence similarity is useful in predicting subcellular localization. For example, Nair and Rost (Protein Sci 2002;11:2836-2847) have carried out extensive analysis of the relation between sequence similarity and identity in subcellular localization, and have found a close relationship between them above a certain similarity threshold. However, many existing benchmark data sets used for the prediction accuracy assessment contain highly homologous sequences-some data sets comprising sequences up to 80-90% sequence identity. Using these benchmark test data will surely lead to overestimation of the performance of the methods considered. Here, we develop an approach based on a two-level support vector machine (SVM) system: the first level comprises a number of SVM classifiers, each based on a specific type of feature vectors derived from sequences; the second level SVM classifier functions as the jury machine to generate the probability distribution of decisions for possible localizations. We compare our approach with a global sequence alignment approach and other existing approaches for two benchmark data sets-one comprising prokaryotic sequences and the other eukaryotic sequences. Furthermore, we carried out all-against-all sequence alignment for several data sets to investigate the relationship between sequence homology and subcellular localization. Our results, which are consistent with previous studies, indicate that the homology search approach performs well down to 30% sequence identity, although its performance deteriorates considerably for sequences sharing lower sequence identity. A data set of high homology levels will undoubtedly lead to biased assessment of the performances of the predictive approaches-especially those relying on homology search or sequence annotations. Our two-level classification system based on SVM does not rely on homology search; therefore, its performance remains relatively unaffected by sequence homology. When compared with other approaches, our approach performed significantly better. Furthermore, we also develop a practical hybrid method, which combines the two-level SVM classifier and the homology search method, as a general tool for the sequence annotation of subcellular localization.
Thrombospondin 2 promotes tumor metastasis by inducing matrix metalloproteinase-13 production in lung cancer cells
(2018-09-01)
Liu, Ju-Fang
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Lee, Chiang-Wen
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Tsai, Ming-Horng
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Tang, Chih-Hsin
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Chen, Po-Chun
Thrombospondin (TSP)-2, a matricellular glycoprotein of the TSP family, regulates multiple biological functions, including proliferation, angiogenesis, cell adhesion, and extracellular matrix (ECM) modeling. The clinical relevance of TSP-2 has been explored in many different cancers. TSP-2 expression levels vary between different cancer types, and their role in tumor progression remains controversial. Although previous studies have reported higher serum TSP-2 levels in patients with non-small cell lung cancer, the role of TSP-2 in lung cancer progression remains to be addressed. A total of 585 lung adenocarcinoma datasets, including mRNA sequencing and clinical data, were retrieved from The Cancer Genome Atlas (TCGA). Forty paired adjacent normal tissues and lung tumor tissue datasets were used to examine TSP-2 expression levels. Tumor microarray were performed with immunohistochemical staining to examine TSP-2 expression in lung cancer patients. Transwell migration assay, quantitative real-time PCR and Western blot were used to investigate molecular mechanism of TSP-2 in lung cancer cell. TSP-2 promotes matrix metalloproteinase-13 (MMP-13) expression, cell migration, and cell invasion by mediating integrin alpha v beta 3/FAK/Akt/NF-kappa B signal transduction. Using TSP-2 knockdown stable cell lines, we found that TSP-2 knockdown reduces MMP-13 expression and cell mobility. When we manipulated the tumor tissue microarray and TCGA datasets to investigate the clinical relevance of TSP-2, we found high TSP-2 expression levels in lung cancer specimens. The present study demonstrates that TSP-2 regulates cell mobility through MMP-13 expression in lung cancer cells. In addition, TSP-2 expression was associated with MMP-13 expression and poor prognosis in lung cancer. TSP-2 may therefore be a promising novel target for lung cancer treatment.
Understanding the functional properties of bio-inorganic nanoflowers as biocatalysts by deciphering the metal-binding sites of enzymes
(2017-06-21)
Escobar, Sindy
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Velasco-Lozano, Susana
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Lu, Chih-Hao
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Lin, Yu-Feng
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Mesa, Monica
The biomineralisation of metal phosphates is a promising approach to develop more efficient nano-biocatalysts; however, the interactions between the protein and the inorganic mineral are poorly understood. Elucidating which protein regions most likely participate in the mineral formation will guide the fabrication of more efficient biocatalysts based on metal-phosphate nanoflowers. We have biomineralised the lipase from Thermomyces lanuginosus using three calcium, zinc and copper phosphates to fabricate different types of bio-inorganic nanoflowers. To better understand how the biomineralisation process affects the enzyme properties, we have computationally predicted the protein regions with a higher propensity for binding Ca2+, Cu2+ and Zn2+. These binding sites can be considered as presumable nucleation points where the biomineralisation process starts and explain why different metals can form bio-inorganic nanoflowers of the same enzyme with different functional properties. The formation of calcium, copper and zinc phosphates in the presence of this lipase gives rise to nanoflowers with different morphologies and different enzymatic properties such as activity, stability, hyperactivation and activity-pH profile; these functional differences are supported by structural studies based on fluorescence spectroscopy and can be explained by the different locations of the predicted nucleation sites for the different metals. Among the three metals used herein, the mineralisation of this lipase with zinc-phosphate enables the fabrication of bio-inorganic nanoflowers 34 times more stable than the soluble enzyme. These bio-inorganic nanoflowers were reused for 8 reaction cycles achieving 100% yield in the hydrolysis of p-nitrophenol butyrate but losing more than 50% of their initial activity after 6 operational cycles. Finally, this heterogeneous biocatalyst was more active and enantioselective than the soluble enzyme (ee = 79%(R)) towards the kinetic resolution of rac-1-phenylethyl acetate yielding the R enantiomer with ee = 84%.