Browsing by Author "Huang, Shao-Wei"

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A simple way to compute protein dynamics without a mechanical model
(2007-07-01)
Shih, Chien-Hua
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Huang, Shao-Wei
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Yen, Shih-Chung
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Lai, Yan-Long
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Yu, Sung-Huan
We found that in proteins the average atomic fluctuation is linearly related to the square of the atomic distance from the center of mass of the protein. Using this simple relation, we can accurately compute the temperature factors of proteins of a wide range of sizes and folds, and the correlation of the fluctuations in proteins. This simple relation provides a direct link between protein dynamics and the static protein's geometrical shape and offers a simple way to compute protein dynamics without either long time trajectory integration or any matrix operations.
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.
Crystal structure of Helicobacter pylori formamidase AmiF reveals a cysteine-glutamate-lysine catalytic triad
(2007-04-20)
Hung, Chiu-Lien
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Liu, Jia-Hsin
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Chiu, Wei-Chun
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Huang, Shao-Wei
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Hwang, Jenn-Kang
Helicobacter pylori AmiF formamidase that hydrolyzes formamide to produce formic acid and ammonia belongs to a member of the nitrilase superfamily. The crystal structure of AmiF was solved to 1.75 angstrom resolution using single-wavelength anomalous dispersion methods. The structure consists of a homohexamer related by 3-fold symmetry in which each subunit has an alpha-beta-beta-alpha four-layer architecture characteristic of the nitrilase superfamily. One exterior alpha layer faces the solvent, whereas the other one associates with that of the neighbor subunit, forming a tight alpha-beta-beta-alpha-alpha-beta-beta-alpha dimer. The apo and liganded crystal structures of an inactive mutant C166S were also determined to 2.50 and 2.30 angstrom, respectively. These structures reveal a small formamide-binding pocket that includes Cys(166), Glu(60), and Lys(133) catalytic residues, in which Cys166 acts as a nucleophile. Analysis of the liganded AmiF and N-carbamoyl D-amino acid amidohydrolase binding pockets reveals a common Cys-Glu-Lys triad, another conserved glutamate, and different subsets of ligand-binding residues. Molecular dynamic simulations show that the conserved triad has minimal fluctuations, catalyzing the hydrolysis of a specific nitrile or amide in the nitrilase superfamily efficiently.
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.
On the Relationship Between Catalytic Residues and their Protein Contact Number
(2011-09-01)
Huang, Shao-Wei
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Yu, Sung-Huan
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Shih, Chien-Hua
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Guan, Huei-Wen
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Huang, Tsun-Tsao
Due to advances in structural biology, an increasing number of protein structures of unknown function have been deposited in Protein Data Bank (PDB). These proteins are usually characterized by novel structures and sequences. Conventional comparative methodology (such as sequence alignment, structure comparison, or template search) is unable to determine their function. Thus, it is important to identify protein's function directly from its structure, but this is not an easy task. One of the strategies used is to analyze whether there are distinctive structure-derived features associated with functional residues. If so, one may be able to identify the functional residues directly from a single structure. Recently, we have shown that protein weighted contact number is related to atomic thermal fluctuations and can be used to derive motional correlations in proteins. In this report, we analyze the weighted contact-number profiles of both catalytic residues and non-catalytic residues for a dataset of 760 structures. We found that catalytic residues have distinct distributions of weighted contact numbers from those of non-catalytic residues. Using this feature, we are able to effectively differentiate catalytic residues from other residues with a single optimized threshold value. Our method is simple to implement and compares favourably with other more sophisticated methods. In addition, we discuss the physics behind the relationship between catalytic residues and their contact numbers as well as other features (such as residue centrality or B-factors) associated with catalytic residues.
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.
Prediction of NMR order parameters in proteins using weighted protein contact-number model
(2008-10-01)
Huang, Shao-Wei
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Shih, Chien-Hua
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Lin, Chih-Peng
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Hwang, Jenn-Kang
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生物資訊及系統生物研究所
In the NMR experiment, the protein backbone motion can be described by the N-H order parameters. Though protein dynamics is determined by a complex network of atomic interactions, we show that the order parameter of residues can be determined using a very simple method, the weighted protein contact number model. We computed for each C alpha atom the number of neighboring C alpha atoms weighted by the inverse distance squared between them. We show that the weighted contact number of each residue is directly related to its order parameter. Despite the simplicity of this model, it performs better than the other method. Since we can compute the order parameters directly from the topological properties (such as protein contact number) of protein structures, our study underscores a very direct link between protein topological structure and its dynamics.