Biomedical subjects

Yun-Ping Zhu

Publications and source records attributed to Yun-Ping Zhu.

7 recordsLinked to original sources

PubMed2006

Network analysis of the protein chain tertiary structures of heterocomplexes.

In this paper, the tertiary structures of protein chains of heterocomplexes were mapped to 2D networks; based on the mapping approach, statistical properties of these networks were systematically studied. Firstly, our experimental results confirmed that the networks derived from protein structures possess small-world properties. Secondly, an interesting relationship between network average degree and the network size was discovered, which was quantified as an empirical function enabling us to estimate the number of residue contacts of the protein chains accurately. Thirdly, by analyzing the average clustering coefficient for nodes having the same degree in the network, it was found that the architectures of the networks and protein structures analyzed are hierarchically organized. Finally, network motifs were detected in the networks which are believed to determine the family or superfamily the networks belong to. The study of protein structures with the new perspective might shed some light on understanding the underlying laws of evolution, function and structures of proteins, and therefore would be complementary to other currently existing methods.

Models, Molecular↗

PubMed2005

Prediction of protein secondary structure using improved two-level neural network architecture.

In this paper we propose constructing an improved two-level neural network to predict protein secondary structure. Firstly, we code the whole protein composition information as the inputs to the first-level network besides the evolutionary information. Secondly, we calculate the reliability score for each residue position based on the output of the first-level network, and the role of the second-level network is to take full advantage of the residues with a higher reliability score to impact the neighboring residues with a lower one for improving the whole prediction accuracy. Thirdly, considering it is indeed a problem that the target protein can be lost in the multiple sequence alignment we propose to code single sequence into the second-level network. The experimental results show that our proposed method can efficiently improve the prediction accuracy.

Algorithms↗

PubMed2005

[Strategy for the protein identification of human proteome expression profile: selection of searching database].

Widely used method of protein identification for high-throughout proteome expression profile studies was database-dependent, so the selection of databases for the protein identification was very important. Despite the deficiency of available human protein databases, the complementarity of human proteins could be got mainly from human genome but not from the protein databases of other organisms. According to the comparison of the current protein databases from different aspects, IPI was recommended for the basic identification for the studies of human proteome expression profile, and other human protein or nucleic acid databases were needed for the complementary identification and novel protein mining.

Animals↗

PubMed2003

[Subcellular localization of basic Krüppel-like factor].

To understand the function of basic Krüppel-like factor (BKLF), it was confirmed by direct fluorescence and indirect fluorescence observation that hBKLF was localized in nucleus, and distributed throughout nucleoplasm in a speckled pattern, except the nucleoli. This pattern is similar to many but not all transcription factors. To clarify the specific sequence responsible for its nuclear localization, a series of deletion mutants of GFP/hBKLF were constructed. By observing their subcellular localization, it was found that the three zinc fingers of hBKLF and the N-terminal aside from the fingers all served as nuclear localization signals (NLS); the sub-NLS of hBKLF was located in the N-terminus, including the CtBP-binding motif and the proline rich domain. These results provided a basis for further clarifying the function of BKLF.

Animals↗

PubMed2003

[Transcriptional regulation of gamma- and epsilon-globin genes by basic Krüppel-like factor].

To study the transcription regulatory function of basic Krüppel-like factor (BKLF)on gamma- and epsilon-globin genes, recombinant expression vectors containing the full-length human BKLF gene, and a deletion mutant that lost N-terminal 40 amino acids, were constructed and used, respectively, to transiently transfect COS7 cells in order to assay their reporter activities. Results showed that hBKLF was able to repress the activity of gamma- and epsilon-globin gene promoters, while the antisense nucleic acid specific for hBKLF activated the transcription of these promoters. Deleting 40 amino acids from N-terminus did not influence the transcriptional repression of hBKLF. The stimulatory function of FKLF on gamma- and epsilon-globin gene promoters was also significantly reduced by hBKLF. In addition, BKLF bound the CACCC element in the SHP1 (SH2-containing protein tyrosine phosphatase 1) gene promoter. These results suggest that gamma- and epsilon-globin genes may be transcriptional targets of BKLF, providing evidence for further studies on the role of BKLF in participating the transcriptional regulation of haemocyte-specific genes.

3T3 Cells↗

PubMed2003

[Gene prediction and function research of SARS-CoV(BJ01)].

Through reading the articles, this study points out the shortage of gene prediction and function research about SARS-CoV, and predict it again for developing effective drugs and future vaccines. Using twelve gene prediction methods to predict coronavirus known genes, we select four better methods including Heuristic models, Gene Identification, ZCURVE_CoV and ORF FINDER to predict SARS-CoV(BJ01), and use ATGpr for analyzing probability of initiation codon and Kozak rule, search transcription regulating sequence(TRS) in order to improve the accuracy of predicted genes. Twenty-one probable new genes with more than 50 amino acids have been obtained excluding 13 ORFs which are similar to the genes of NCBI and relative articles. For predicted proteins, we use ProtParam to analyse physical and chemical features; SignalP to analyse signal peptide; BLAST, FASTA to search similar sequences; TMPred, TMHMM, PFAM and HMMTOP to analyse domain and motif in order to improve reliability of gene function prediction. At the same time, we separate the 21 ORFs into four classes using codition of four gene prediction methods, match score, match expection and match length between predicted gene and Coronavirus known gene. In the end, we discuss the results and analyse the reasons.

Computational Biology↗

PubMed2003

[The genome comparison of SARS-CoV and other coronaviruses].

The genome comparison of inter-species and intra-species can give us much information about the origin and evolution of viruses. There are 137 mutation sites in the 17 genomes of SARS-CoV,and the mutation rate is about 8.04 x 10(-3) substitution/site/year. The distribution of the segregating sites is not steady,the most variable region appears in S1 protein,and the nucleotide sequence of RNA-dependent RNA polymerase has very few mutation sites. The substitution bias of nucleotide acids and amino acids indicates the non-random drift products. The comparison of genome structures of SARS-CoV and other coronaviruses shows that SARS-CoV and IBV share the same genome structure. Phylogenetic analyses of conserved genes of coronaviruses indicate that SARS-CoV is a new branch of coronaviruses and appears more close to the group II coronaviruses. Interestingly,SARS-CoV shares some different features with different groups of coronaviruses. Additional analyses show that the first ORFs between S and E genes of some coronaviruses are transmembrane proteins and share the common motif,indicating the possible common ancestor. From the host distribution of different groups of coronaviruses and the phylogeny of s2m,we can deduce that avian is the probable natural host of SARS-CoV.

English Abstract↗