Biomedical subjects

Zheng Yuan

Publications and source records attributed to Zheng Yuan.

6 recordsLinked to original sources

PubMed2003

Flexibility analysis of enzyme active sites by crystallographic temperature factors.

Protein flexibility is inherent to protein structural behavior. Experimental evidence for protein flexibility is extensive both in solution and in the solid state. A major question is whether the flexibility observed in enzymes is simply an inherent property of proteins that must always be borne in mind or is essential for catalysis or substrate binding. The temperature factors or B-values, as determined crystallographically, are linearly related to the mean square displacement of an atom and give an indication of atomic flexibility in the crystalline state. In this paper, we describe the frequency distributions of the normalized B-factor (B'-factor) for the active site and non-active site residues in the selected 69 apo-enzymes. This analysis was performed over the entire sequences and for different structural subsets defined by the three-dimensional structure of proteins, as alpha-helices, beta-structures and coil conformation and buried and non-buried residues. The results show that in all cases, the active site residues predominantly occur in region of low B'-factor and the non-active site residues have a tendency to exist in the high B'-factor region. This observation suggests that the active site residues, in general, are less flexible than the non-active site residues and therefore the vibrational and the fast collective motions of the C(alpha) atoms of proteins appear not to have clear biological significance.

Amino Acids↗

PubMed2003

Mouse proteome analysis.

A general overview of the protein sequence set for the mouse transcriptome produced during the FANTOM2 sequencing project is presented here. We applied different algorithms to characterize protein sequences derived from a nonredundant representative protein set (RPS) and a variant protein set (VPS) of the mouse transcriptome. The functional characterization and assignment of Gene Ontology terms was done by analysis of the proteome using InterPro. The Superfamily database analyses gave a detailed structural classification according to SCOP and provide additional evidence for the functional characterization of the proteome data. The MDS database analysis revealed new domains which are not presented in existing protein domain databases. Thus the transcriptome gives us a unique source of data for the detection of new functional groups. The data obtained for the RPS and VPS sets facilitated the comparison of different patterns of protein expression. A comparison of other existing mouse and human protein sequence sets (e.g., the International Protein Index) demonstrates the common patterns in mammalian proteomes. The analysis of the membrane organization within the transcriptome of multiple eukaryotes provides valuable statistics about the distribution of secretory and transmembrane proteins

Animals↗

PubMed2003

The mouse secretome: functional classification of the proteins secreted into the extracellular environment.

We have developed a computational strategy to identify the set of soluble proteins secreted into the extracellular environment of a cell. Within the protein sequences predominantly derived from the RIKEN representative transcript and protein set, we identified 2033 unique soluble proteins that are potentially secreted from the cell. These proteins contain a signal peptide required for entry into the secretory pathway and lack any transmembrane domains or intracellular localization signals. This class of proteins, which we have termed the mouse secretome, included >500 novel proteins and 92 proteins <100 amino acids in length. Functional analysis of the secretome included identification of human orthologs, functional units based on InterPro and SCOP Superfamily predictions, and expression of the proteins within the RIKEN READ microarray database. To highlight the utility of this information, we discuss the CUB domain-containing protein family.

Animals↗

PubMed2002

Prediction of protein solvent accessibility using support vector machines.

A Support Vector Machine learning system has been trained to predict protein solvent accessibility from the primary structure. Different kernel functions and sliding window sizes have been explored to find how they affect the prediction performance. Using a cut-off threshold of 15% that splits the dataset evenly (an equal number of exposed and buried residues), this method was able to achieve a prediction accuracy of 70.1% for single sequence input and 73.9% for multiple alignment sequence input, respectively. The prediction of three and more states of solvent accessibility was also studied and compared with other methods. The prediction accuracies are better than, or comparable to, those obtained by other methods such as neural networks, Bayesian classification, multiple linear regression, and information theory. In addition, our results further suggest that this system may be combined with other prediction methods to achieve more reliable results, and that the Support Vector Machine method is a very useful tool for biological sequence analysis.

Bayes Theorem↗

PubMed2002

Prediction of Golgi Type II membrane proteins based on their transmembrane domains.

MOTIVATION: A major issue in cell biology today is how distinct intracellular regions of the cell, like the Golgi Apparatus, maintain their unique composition of proteins and lipids. The cell differentially separates Golgi resident proteins from proteins that move through the organelle to other subcellular destinations. We set out to determine if we could distinguish these two types of transmembrane proteins using computational approaches. RESULTS: A new method has been developed to predict Golgi membrane proteins based on their transmembrane domains. To establish the prediction procedure, we took the hydrophobicity values and frequencies of different residues within the transmembrane domains into consideration. A simple linear discriminant function was developed with a small number of parameters derived from a dataset of Type II transmembrane proteins of known localization. This can discriminate between proteins destined for Golgi apparatus or other locations (post-Golgi) with a success rate of 89.3% or 85.2%, respectively on our redundancy-reduced data sets. AVAILABILITY: See http://microarray.imb.uq.edu.au/golgi/

Algorithms↗

PubMed1996

Electronic Structures of Copper(I) and Silver(I) beta-Diketonate Complexes.

The valence electronic structures of [Cu(hfac)L] (hfac = CF(3)C(O)CHC(O)CF(3); L = PMe(3), CNMe), [Ag(hfac)(PMe(3))], and [Ag(fod)(PEt(3))] (fod = t-BuC(O)CHC(O)C(3)F(7)) have been studied by recording their photoelectron spectra and by performing Xalpha-SW calculations on the model compounds [M(dfm)(PH(3))] (dfm = HC(O)CHC(O)H; M = Cu, Ag) and [Cu(dfm)(CNH)]. For the copper complexes, the spectra were recorded between 21 and 160 eV using He I, He II and synchrotron radiation; while, for the silver complexes, He I and He II, spectra were recorded. Assignments were made by comparison of experimental and calculated values of band energies, and, for the copper complexes, by similar comparison of experimental and theoretical branching ratios as a function of photon energy. For the silver complexes, a more limited comparison of band intensities in the He I and He II spectra was made. In analogous compounds, it is shown that the binding energies follow the sequence Ag 4d > Cu 3d, with an energy difference of almost 2 eV.

Journal Article↗