Biomedical subjects

Jenn-Kang Hwang

Publications and source records attributed to Jenn-Kang Hwang.

18 recordsLinked to original sources

PubMed2006

A three-coordinate and quadruply bonded Mo-Mo complex.

Reaction of MoCl3(THF)3 with [Me2Si{NLi(Dipp)}2]2 (Dipp = 2,6-i-PrC6H3) afforded a triply bonded dimolybdenum complex 1,2-Mo2Cl2[Me2Si(NDipp)2]2 1, spanned by two Me2Si[N(Dipp)]2 ligands, thus resulting in a syn conformation. The air- and moisture-sensitive compound 1 was characterized by NMR spectroscopic, elemental, and single-crystal X-ray crystallographic analysis. Reduction of 1 by Na/Hg yielded the quadruply bonded dimeric complex Mo2[Me2Si(NDipp)2]2 2, which was also characterized by the aforementioned analytical methods. The Mo-Mo bond was determined to be 2.1784(12) A, which is considered a long quadruple bond. In addition, density functional theory (DFT) computations on compound 2 provided insight into the intriguing Mo-Mo quadruple bond.

Crystallography↗

PubMed2006

Protein complexity, gene duplicability and gene dispensability in the yeast genome.

Using functional genomic and protein structural data we studied the effects of protein complexity (here defined as the number of subunit types in a protein) on gene dispensability and gene duplicability. We found that in terms of gene duplicability the major distinction in protein complexity is between hetero-complexes, each of which includes at least two different types of subunits (polypeptides), and homo-complexes, which include monomers and complexes that consist of only subunits of one polypeptide type. However, gene dispensability decreases only gradually as the number of subunit types in a protein complex increases. These observations suggest that the dosage balance hypothesis can explain well gene duplicability of complex proteins, but cannot completely explain the difference in dispensabilities between hetero-complex subunits. It is likely that knocking out a gene coding for a hetero-complex subunit would disrupt the function of the whole complex, so that the deletion effect on fitness would increase with protein complexity. We also found that multi-domain polypeptide genes are less dispensable but more duplicable than single-domain polypeptide genes. Duplicate genes derived from the whole genome duplication event in yeast are more dispensable (except for ribosomal protein genes) than other duplicate genes. Further, we found that subunits of the same protein complex tend to have similar expression levels and similar effects of gene deletion on fitness. Finally, we estimated that in yeast the contribution of duplicate genes to genetic robustness against null mutation is approximately 9%, smaller than previously estimated. In yeast, protein complexity may serve as a better indicator of gene dispensability than do duplicate genes.

Computational Biology↗

PubMed2006

Codon-usage bias versus gene conversion in the evolution of yeast duplicate genes.

Many Saccharomyces cerevisiae duplicate genes that were derived from an ancient whole-genome duplication (WGD) unexpectedly show a small synonymous divergence (K(S)), a higher sequence similarity to each other than to orthologues in Saccharomyces bayanus, or slow evolution compared with the orthologue in Kluyveromyces waltii, a non-WGD species. This decelerated evolution was attributed to gene conversion between duplicates. Using approximately 300 WGD gene pairs in four species and their orthologues in non-WGD species, we show that codon-usage bias and protein-sequence conservation are two important causes for decelerated evolution of duplicate genes, whereas gene conversion is effective only in the presence of strong codon-usage bias or protein-sequence conservation. Furthermore, we find that change in mutation pattern or in tDNA copy number changed codon-usage bias and increased the K(S) distance between K. waltii and S. cerevisiae. Intriguingly, some proteins showed fast evolution before the radiation of WGD species but little or no sequence divergence between orthologues and paralogues thereafter, indicating that functional conservation after the radiation may also be responsible for decelerated evolution in duplicates.

Codon↗

PubMed2006

Prediction of protein subcellular localization.

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.

Algorithms↗

PubMed2006

(PS)2: protein structure prediction server.

Protein structure prediction provides valuable insights into function, and comparative modeling is one of the most reliable methods to predict 3D structures directly from amino acid sequences. However, critical problems arise during the selection of the correct templates and the alignment of query sequences therewith. We have developed an automatic protein structure prediction server, (PS)2, which uses an effective consensus strategy both in template selection, which combines PSI-BLAST and IMPALA, and target-template alignment integrating PSI-BLAST, IMPALA and T-Coffee. (PS)2 was evaluated for 47 comparative modeling targets in CASP6 (Critical Assessment of Techniques for Protein Structure Prediction). For the benchmark dataset, the predictive performance of (PS)2, based on the mean GTD_TS score, was superior to 10 other automatic servers. Our method is based solely on the consensus sequence and thus is considerably faster than other methods that rely on the additional structural consensus of templates. Our results show that (PS)2, coupled with suitable consensus strategies and a new similarity score, can significantly improve structure prediction. Our approach should be useful in structure prediction and modeling. The (PS)2 is available through the website at http://ps2.life.nctu.edu.tw/.

Amino Acid Sequence↗

PubMed2006

The fragment transformation method to detect the protein structural motifs.

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 betabetaalpha-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 betabetaalpha-metal binding motif and the treble clef motif. The betabetaalpha-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.

Amino Acid Motifs↗

PubMed2006

Structure-stability-activity relationship in covalently cross-linked N-carbamoyl D-amino acid amidohydrolase and N-acylamino acid racemase.

N-Acylamino acid racemase (NAAAR) and N-carbamoyl-D-amino-acid amidohydrolase (D-NCAase) are important biocatalysts for producing enantiopure alpha-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 kcat/Km ratios, whereas mutant D-NCAases demonstrated increased kcat/Km 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.

Amidohydrolases↗

PubMed2005

Prediction of disulfide connectivity from protein sequences.

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.

Cysteine↗

PubMed2005

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.

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.

Amino Acid Sequence↗

PubMed2005

Amino acid coupling patterns in thermophilic proteins.

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 1 or more amino acids. Using this approach, we construct the rho profiles for the coupling patterns. The rho 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. Proteins 2005. (c) 2005 Wiley-Liss, Inc.

Amino Acid Sequence↗

PubMed2004

Relationship between local structural entropy and protein thermostability.

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.

Adenylate Kinase↗

PubMed2004

Prediction of the bonding states of cysteines using the support vector machines based on multiple feature vectors and cysteine state sequences.

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.

Amino Acids↗

PubMed2004

Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions.

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 tool 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.

Artificial Intelligence↗

PubMed2003

Relationship between protein structures and disulfide-bonding patterns.

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.

Disulfides↗

PubMed2003

Fine-grained protein fold assignment by support vector machines using generalized npeptide coding schemes and jury voting from multiple-parameter sets.

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.

Animals↗

PubMed2003

Computing motif correlations in proteins.

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/.

Algorithms↗

PubMed2002

GEM: a Gaussian Evolutionary Method for predicting protein side-chain conformations.

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 A 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.

Algorithms↗

PubMedDate not supplied

Numerical performance and throughput benchmark for electronic structure calculations in PC-Linux systems with new architectures, updated compilers, and libraries.

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 ifc 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.

Journal Article↗