Browsing by Author "Hwang, JK"

Now showing 1 - 19 of 19

Amino acid coupling patterns in thermophilic proteins
(2005-04-01)
Liang, HK
;
Huang, CM
;
Ko, MT
;
Hwang, JK
;
生物資訊及系統生物研究所
Structural analysis is useful in elucidating structural features responsible for enhanced thermal stability of proteins. However, due to the rapid increase of sequenced genomic data, there are far more protein sequences than the corresponding three-dimensional (3D) structures. The usual sequence-based amino acid composition analysis provides useful but simplified clues about the amino acid types related to thermal stability of proteins. In this work, we developed a statistical approach to identify the significant amino acid coupling sequence patterns in thermophilic proteins. The amino acid coupling sequence pattern is defined as any 2 types of amino acids separated by I or more amino acids. Using this approach, we construct the p profiles for the coupling patterns. The p value gives a measure of the relative occurrence of a coupling pattern in thermophiles compared with mesophiles. We found that thermophiles and mesophiles exhibit significant bias in their amino acid coupling patterns. We showed that such bias is mainly due to temperature adaptation instead of species or GC content variations. Though no single outstanding coupling pattern can adequately account for protein thermostability, we can use a group of amino acid coupling patterns having strong statistical significance (p values < 10(-7)) to distinguish between thermophilic and mesophilic proteins. We found a good correlation between the optimal growth temperatures of the genomes and the occurrences of the coupling patterns (the correlation coefficient is 0.89). Furthermore, we can separate the thermophilic proteins from their mesophilic orthologs using the amino acid coupling patterns. These results may be useful in the study of the enhanced stability of proteins from thermophiles - especially when structural information is scarce. (C) 2005 Wiley-Liss, Inc.
Computation of conformational entropy from protein sequences using the machine-learning method - Application to the study of the relationship between structural conservation and local structural stability
(2005-06-01)
Huang, SW
;
Hwang, JK
;
生物科技學系
;
生物資訊及系統生物研究所
;
Department of Biological Science and Technology
A complete protein sequence can usually determine a unique conformation; however, the situation is different for shorter subsequences-some of them are able to adopt unique conformations, independent of context; while others assume diverse conformations in different contexts. The conformations of subsequences are determined by the interplay between local and nonlocal interactions. A quantitative measure of such structural conservation or variability will be useful in the understanding of the sequence-structure relationship. In this report, we developed an approach using the support vector machine method to compute the conformational. variability directly from sequences, which is referred to as the sequence structural entropy. As a practical application, we studied the relationship between sequence structural entropy and the hydrogen exchange for a set of well-studied proteins. We found that the slowest exchange cores usually comprise amino acids of the lowest sequence structural entropy. Our results indicate that structural conservation is closely related to the local structural stability. This relationship may have interesting implications in the protein folding processes, and may be useful in the study of the sequence-structure relationship. (c) 2005 Wiley-Liss, Inc.
Computation of the protein structure entropy and its applications to protein folding processes
(2003-06-01)
Chan, CH
;
Lyu, PC
;
Hwang, JK
;
生物科技學系
;
生物資訊及系統生物研究所
We have developed a general method to compute the structure, entropies of protein sequences. Structure entropy gives a quantitative measure of structure, conservation. This relationship is similar to that between sequence entropy and sequence conservation. Experimental studies in protein folding have suggested that residues relevant to protein folding, or the so-called "hot spot" residues, are usually structurally conserved, though not necessarily conserved in sequences. Hence, the ability to compute structure entropy can help identify important residues related to protein folding. In this work, we have applied our approach to several model proteins frequently used in protein folding experiments. Our results suggest a close relationship between the structure entropies of residues and their rates of amide proton exchange, and we are able to identity regions of residues that are important in protein folding.
Computing motif correlations in proteins
(2003-12-01)
Horng, JT
;
Huang, HD
;
Wang, SH
;
Chen, MY
;
Huang, SL
Protein motifs, which are specific regions and conserved regions, are found by comparing multiple protein sequences. These conserved regions in general play an important role in protein functions and protein folds, for example, for their binding properties or enzymatic activities. The aim here is to find the existence correlations of protein motifs. The knowledge of protein motif/domain sharing should be important in shedding new light on the biologic functions of proteins and offering a basis in analyzing the evolution in the human genome or other genomes. The protein sequences used here are obtained from the PIR-NREF database and the protein motifs are retrieved from the PROSITE database. We apply data mining approach to discover the occurrence correlations of motif in protein sequences. The correlation of motifs mined can be used in evolution analyses and protein structure prediction. We discuss the latter, i.e., protein structure prediction in this study. The correlations mined are stored and maintained in a database system. The database is now available at http://bioinfo.csie.ncu.edu.tw/ProMotif/. (C) 2003 Wiley Periodicals, Inc.
Fine-grained protein fold assignment by support vector machines using generalized npeptide coding schemes and jury voting from multiple-parameter sets
(2003-03-01)
Yu, CS
;
Wang, JY
;
Yang, JM
;
Lyu, PC
;
Lin, CJ
In the coarse-grained fold assignment of major protein classes, such as all-alpha, all-beta, alpha + beta, alpha/beta proteins, one can easily achieve high prediction accuracy from primary amino acid sequences. However, the fine-grained assignment of folds, such as those defined in the Structural Classification of Proteins (SCOP) database, presents a challenge due to the larger amount of folds available. Recent study yielded reasonable prediction accuracy of 56.0% on an independent set of 27 most populated folds. In this communication, we apply the support vector machine (SVM) method, using a combination of protein descriptors based on the properties derived from the composition of n-peptide and jury voting, to the fine-grained fold prediction, and are able to achieve an overall prediction accuracy of 69.6% on the same independent set-significantly higher than the previous results. On 10-fold cross-validation, we obtained a prediction accuracy of 65.3%. Our results show that SVM coupled with suitable global sequence-coding schemes can significantly improve the fine-grained fold prediction. Our approach should be useful in structure prediction and modeling. (C) 2003 Wiley-Liss, Inc.
GEM: A Gaussian evolutionary method for predicting protein side-chain conformations
(2002-08-01)
Yang, JM
;
Tsai, CH
;
Hwang, MJ
;
Tsai, HK
;
Hwang, JK
We have developed an evolutionary approach to predicting protein side-chain conformations. This approach, referred to as the Gaussian Evolutionary Method (GEM), combines both discrete and continuous global search mechanisms. The former helps speed up convergence by reducing the size of rotamer space, whereas the latter, integrating decreasing-based Gaussian mutations and self-adaptive Gaussian mutations, continuously adapts dihedrals to optimal conformations. We tested our approach on 38 proteins ranging in size from 46 to 325 residues and showed that the results were comparable to those using other methods. The average accuracies of our predictions were 80% for chi(1), 66% for chi(1+2), and 1.36 Angstrom for the root mean square deviation of side-chain positions. We found that if our scoring function was perfect, the prediction accuracy was also essentially perfect. However, perfect prediction could not be achieved if only a discrete search mechanism was applied. These results suggest that GEM is robust and can be used to examine the factors limiting the accuracy of protein side-chain prediction methods. Furthermore, it can be used to systematically evaluate and thus improve scoring functions.
GEMSCORE: A new empirical energy function for protein folding
(2005)
Chiu, YY
;
Hwang, JK
;
Yang, JM
;
生物科技學系
;
生物資訊及系統生物研究所
We have developed a new energy function, termed CEMSCORE, for the protein structure prediction, which is an emergent problem in the field of computational structural biology. The GEMSCORE combines knowledge-based and physics-based energy functions. Instead of hundreds and thousands parameters used in many physics-based energy functions, we optimized nine weights of energy terms in the GEMSCORE by using a generic evolutionary method. These nine energy terms are the electrostatic, the der Waals, the hydrogen-bonding potential, and six terms for solvation potentials. The GEMSCORE has been evaluated on six decoy sets, including 96 proteins with more 70,000 structures. The result indicates that our method is able to successfully identify 74 native proteins from these 96 proteins. Our GEMSCORE is fast and simple to discriminate between native and nonnative structures from thousands of protein structure candidates in these decoy sets. We believe that the GEMSCORE is robust and should be a useful energy function for the protein structure prediction.
Molecular dynamics simulation of the enhancement of cobra cardiotoxin and E6 protein binding on mixed self-assembled monolayer molecules
(2006-02-28)
Hung, SW
;
Hwang, JK
;
Tseng, F
;
Chang, JM
;
Chen, CC
Molecular dynamics simulations are performed on n-alkinethiol self-assembled monolayers (SAMs) and their mixture on a gold surface so that the orientations of the binding of cobra cardiotoxin and E6 protein molecules can be selected using the mixing, ratio of CH3-terminated SAMs with different chain lengths. The simulations suggest that a SAM surface with different mixing ratios may provide a possible platform for aligning protein molecules with a desired orientation and for enhancing the binding energy of the protein on the designed surface.
Numerical performance and throughput benchmark for electronic structure calculations in PC-Linux systems with new architectures, updated compilers, and libraries
(2004-03-01)
Yu, JSK
;
Hwang, JK
;
Tang, CY
;
Yu, CH
;
生物科技學系
A number of recently released numerical libraries including Automatically Tuned Linear Algebra Subroutines (ATLAS) library, Intel Math Kernel Library (MKL), GOTO numerical library, and AMD Core Math Library (ACML) for AMD Opteron processors, are linked against the executables of the Gaussian 98 electronic structure calculation package, which is compiled by updated versions of Fortran compilers such as Intel Fortran compiler (ifc/efc) 7.1 and PGI Fortran compiler (pgf77/pgf90) 5.0. The We 7.1 delivers about 3% of improvement on 32-bit machines compared to the former version 6.0. Performance improved from pgf77 3.3 to 5.0 is also around 3% when utilizing the original unmodified optimization options of the compiler enclosed in the software. Nevertheless, if extensive compiler tuning options are used, the speed can be further accelerated to about 25%. The performances of these fully optimized numerical libraries are similar. The double-precision floating-point (FP) instruction sets (SSE2) are also functional on AMD Opteron processors operated in 32-bit compilation, and Intel Fortran compiler has performed better optimization. Hardware-level tuning is able to improve memory bandwidth by adjusting the DRAM timing, and the efficiency in the CL2 mode is further accelerated by 2.6% compared to that of the CL2.5 mode. The FP throughput is measured by simultaneous execution of two identical copies of each of the test jobs. Resultant performance impact suggests that IA64 and AMD64 architectures are able to fulfill significantly higher throughput than the IA32, which is consistent with the SpecFPrate2000 benchmarks.
Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions
(2004-05-01)
Yu, CS
;
Lin, CJ
;
Hwang, JK
;
生物科技學系
;
生物資訊及系統生物研究所
Gram-negative bacteria have five major subcellular localization sites: the cytoplasm, the periplasm, the inner membrane, the outer membrane, and the extracellular space. The subcellular location of a protein can provide valuable information about its function. With the rapid increase of sequenced genomic data, the need for an automated and accurate to predict subcellular localization becomes increasingly important. We present an approach to predict subcellular localization for Gram-negative bacteria. This method uses the support vector machines trained by multiple feature vectors based on n-peptide compositions. For a standard data set comprising 1443 proteins, the overall prediction accuracy reaches 89%, which, to the best of our knowledge, is the highest prediction rate ever reported. Our prediction is 14% higher than that of the recently developed multimodular PSORT-B. Because of its simplicity, this approach can be easily extended to other organisms and should be a useful tool for the high-throughput and large-scale analysis of proteomic and genomic data.
Prediction of disulfide connectivity from protein sequences
(2005-11-15)
Chen, YC
;
Hwang, JK
;
生物科技學系
;
生物資訊及系統生物研究所
;
Department of Biological Science and Technology
The difficulties in predicting disulfide connectivity from protein sequences lie in the nonlocal properties of the disulfide bridges that involve cysteine pairs at large sequence separation. Though some progress has been recently made in the prediction of disulfide connectivity, the current methods predict less than half of the disulfide patterns for the data set sharing less than 30% sequence identity. In this report, we use the support vector machines based on sequence features such as the coupling between the local sequence environments of cysteine pair, the cysteines sequence separations, and the global sequence descriptor, such as amino acid content. Our approach is able to predict 55% of the disulfide patterns of proteins with two to five disulfide bridges, which is 11-26% higher than other methods in the literature. Proteins 2005;61:507-512. (c) 2005 Wiley-Liss, Inc.
Prediction of the bonding states of cysteines using the support vector machines based on multiple feature vectors and cysteine state sequences
(2004-06-01)
Chen, YC
;
Lin, SC
;
Lin, CJ
;
Hwang, JK
;
生物科技學系
The support vector machine (SVM) method is used to predict the bonding states of cysteines. Besides using local descriptors such as the local sequences, we include global information, such as amino acid compositions and the patterns of the states of cysteines (bonded or nonbonded), or cysteine state sequences, of the proteins. We found that SVM based on local sequences or global amino acid compositions yielded similar prediction accuracies for the data set comprising 4136 cysteine-containing segments extracted from 969 nonhomologous proteins. However, the SVM method based on multiple feature vectors (combining local sequences and global amino acid compositions) significantly improves the prediction accuracy, from 80% to 86%. If coupled with cysteine state sequences, SVM based on multiple feature vectors yields 90% in overall prediction accuracy and a 0.77 Matthews correlation coefficient, around 10% and 22% higher than the corresponding values obtained by SVM based on local sequence information. (C) 2004Wiley-Liss, Inc.
Reassessment of morphological characteristics in freshwater eels (genus Anguilla, Anguillidae) shows congruence with molecular phylogeny estimates
(2005-05-01)
Lin, YS
;
Tzeng, CS
;
Hwang, JK
;
生物科技學系
;
Department of Biological Science and Technology
The morphology-based phylogeny of freshwater eels, proposed by V. Ege in 1939, has been accepted as the basis of eel classification since that time. However, this has been called into question by recent molecular studies. Most of the morphological characteristics recognized by Ege are morphometric. Since methods for the application of morphometric data to phylogeny construction have not been fully established, it is unclear whether the observed discrepancies between morphological and molecular data arise from intrinsic differences or from flawed analyses. Here, we have used two methods to assemble evolutionary trees from distance matrices constructed according to Ege's data, the neighbor-joining (NJ) method and the minimum network (MinNet) method; the latter is based on an evolutionary algorithm. After reanalysing Ege's morphological data, we found that both methods gave results consistent with those based on molecular data, although not with Ege's original classification. Therefore, we speculate that some morphological features Ege used to subdivide the eel groups may not be synapomorphic as he proposed, but symplesiomorphic or convergent . The method developed here may prove useful for constructing phylogeny for taxon groups where only continuous morphometric characteristics are recognized, such as the freshwater eels.
Relationship between local structural entropy and protein thermostability
(2004-12-01)
Chan, CH
;
Liang, HK
;
Hsiao, NW
;
Ko, MT
;
Lyu, PC
We developed a technique to compute structural entropy directly from protein sequences. We explored the possibility of using structural entropy to identify residues involved in thermal stabilization of various protein families. Examples include methanococcal adenylate kinase, Ribonuclease HI and holocytochrome c(551). Our results show that the positions of the largest structural entropy differences between wild type and mutant usually coincide with the residues relevant to thermostability. We also observed a good linear relationship between the average structural entropy and the melting temperatures for adenylate kinase and its chimeric constructs. To validate this linear relationship, we compiled a large dataset comprised of 1153 sequences and found that most protein families still display similar linear relationships. Our results suggest that the multitude of interactions involved in thermal stabilization may be generalized into the tendency of proteins to maintain local structural conservation. The linear relationship between structural entropy and protein thermostability should be useful in the study of protein thermal stabilization. (C) 2004 Wiley-Liss, Inc.
Relationship between protein structures and disulfide bonding patterns
(2003-10-01)
Chuang, CC
;
Chen, CY
;
Yang, JM
;
Lyu, PC
;
Hwang, JK
We found that that disulfide-bonding patterns can be used to discriminate structure similarity. Our method, based on the hierarchical clustering scheme, is applicable to proteins with two or more disulfide bonds and is able to detect the structural similarities of proteins of low sequence identities (<25%). Our results show the surprisingly close relationship between disulfide-bonding patterns and proteins structures. Our findings should be useful in protein structure modeling. (C) 2003 Wiley-Liss, Inc.
Special section on bioinformatics - Foreword
(2003-11-01)
Hwang, JK
;
Kao, MY
;
Sun, CT
;
Hsu, WL
;
生物科技學系
Structure-stability-activity relationship in covalently cross-linked N-carbamoyl D-amino acid amidohydrolase and N-acylamino acid racemase
(2006-06-09)
Chiu, WC
;
You, JY
;
Liu, JS
;
Hsu, SK
;
Hsu, WH
N-Acylamino acid racemase (NAAAR) and N-carbamoyl-D-amino-acid amidohydrolase (D-NCAase) are important biocatalysts for producing enantiopure a-amino acids. NAAAR forms an octameric assembly and displays induced fit movements upon substrate binding, while D-NCAase is a tetramer that does not change conformation in the presence of a ligand. To investigate the effects of introducing potentially stabilizing S-S bridges in these different multimeric enzymes, cysteine residues predicted to form inter or intra-subunit disulfide bonds were introduced by site-directed mutagenesis. Inter-subunit S-S bonds were formed in two NAAAR variants (A68C-D72C and P60C-Y100C) and two D-NCAase variants (A302C and P295C-F304C). Intra-subunit S-S bonds were formed in two additional NAAAR variants (E149C-A182C and V265C). Crystal structures of NAAARs variants show limited deviations from the wild-type overall tertiary structure. An apo A68C-D72C subunit differs from the wild-type enzyme, in which it has an ordered lid loop, resembling ligand-bound NAAAR. The structures of A222C and A302C D-NCAases are nearly identical to the wild-type enzyme. All mutants with inter-subunit bridges had increases in thermostability. Compared with the wild-type enzyme, A68C-D72C NAAAR showed similar k(cat)/K-m ratios, whereas mutant D-NCAases demonstrated increased k(cat)/K-m ratios at high temperatures (A302C: 4.2-fold at 65 degrees C). Furthermore, molecular dynamic simulations reveal that A302C substantially sustains the fine-tuned catalytic site as temperature increases, achieving enhanced activity. (c) 2006 Elsevier Ltd. All rights reserved.
The fragment transformation method to detect the protein structural motifs
(2006-05-15)
Lu, CH
;
Lin, YS
;
Chen, YC
;
Yu, CS
;
Chang, SY
To identify functional structural motifs from protein structures of unknown function becomes increasingly important in recent years due to the progress of the structural genomics initiatives. Although certain structural patterns such as the Asp-His-Ser catalytic triad are easy to detect because of their conserved residues and stringently constrained geometry, it is usually more challenging to detect a general structural motifs like, for example, the beta beta alpha-metal binding motif, which has a much more variable conformation and sequence. At present, the identification of these motifs usually relies on manual procedures based on different structure and sequence analysis tools. In this study, we develop a structural alignment algorithm combining both structural and sequence information to identify the local structure motifs. We applied our method to the following examples: the beta beta alpha-metal binding motif and the treble clef motif. The beta beta alpha-metal binding motif plays an important role in nonspecific DNA interactions and cleavage in host defense and apoptosis. The treble clef motif is a zinc-binding motif adaptable to diverse functions such as the binding of nucleic acid and hydrolysis of phosphodiester bonds. Our results are encouraging, indicating that we can effectively identify these structural motifs in an automatic fashion. Our method may provide a useful means for automatic functional annotation through detecting structural motifs associated with particular functions.
Updated numerical performance and throughput benchmark for electronic structure calculations in PC-LINUX systems.
(2004-03-28)
Yu, JSK
;
Hwang, JK
;
Tang, CY
;
Yu, CH
;
生物科技學系