[show abstract][hide abstract] ABSTRACT: Coevolution between proteins is crucial for understanding protein-protein interaction. Simultaneous changes allow a protein complex to maintain its overall structural-functional integrity. In this study, we combined statistical coupling analysis (SCA) and molecular dynamics simulations on the CDK6-CDKN2A protein complex to evaluate coevolution between proteins. We reconstructed an inter-protein residue coevolution network, consisting of 37 residues and 37 interactions. It shows that most of the coevolved residue pairs are spatially proximal. When the mutations happened, the stable local structures were broken up and thus the protein interaction was decreased or inhibited, with a following increased risk of melanoma. The identification of inter-protein coevolved residues in the CDK6-CDKN2A complex can be helpful for designing protein engineering experiments.
[show abstract][hide abstract] ABSTRACT: Mouse is widely used in animal testing of cardiovascular disease. However, a large number of cardiovascular drugs that have been experimentally proved to work well on mouse were withdrawn because they caused adverse side effects in human.
In this study, we investigate whether binding patterns of withdrawn cardiovascular drugs are conserved between mouse and human through computational dockings and molecular dynamic simulations. In addition, we also measured the level of conservation of gene expression patterns of the drug targets and their interacting partners by analyzing the microarray data.
The results show that target proteins of withdrawn cardiovascular drugs are functionally conserved between human and mouse. However, all the binding patterns of withdrawn drugs we retrieved show striking difference due to sequence divergence in drug-binding pocket, mainly through loss or gain of hydrogen bond donors and distinct drug-binding pockets. The binding affinities of withdrawn drugs to their receptors tend to be reduced from mouse to human. In contrast, the FDA-approved and best-selling drugs are little affected.
Our analysis suggests that sequence divergence in drug-binding pocket may be a reasonable explanation for the discrepancy of drug effects between animal models and human.
Journal of clinical bioinformatics. 05/2012; 2(1):10.
[show abstract][hide abstract] ABSTRACT: Studying the large-scale protein-protein interaction (PPI) network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes.
We used three methods, Interolog-based prediction of porcine PPI network, domain-motif interactions from structural topology-based prediction of porcine PPI network and motif-motif interactions from structural topology-based prediction of porcine PPI network, to predict porcine protein interactions among 25,767 porcine proteins. We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks. Our predictions were validated with Pfam domain annotations and GO annotations. Averages of 70, 10,495, and 863 interactions were related to the Pfam domain-interacting pairs in iPfam database. For comparison, randomized networks were generated, and averages of only 4.24, 66.79, and 44.26 interactions were associated with Pfam domain-interacting pairs in iPfam database. In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively. However, the number of PPI pairs sharing GO terms in the 10,000 randomized networks reached 52.68%, 75.54%, 27.20% is 0. Finally, we determined the accuracy and precision of the methods. The methods yielded accuracies of 0.92, 0.53, and 0.50 at precisions of about 0.93, 0.74, and 0.75, respectively.
The results reveal that the predicted PPI networks are considerably reliable. The present research is an important pioneering work on protein function research. The porcine PPI data set, the confidence score of each interaction and a list of related data are available at (http://pppid.biositemap.com/).
[show abstract][hide abstract] ABSTRACT: Animal models have been extensively used in the study of cardiovascular disease (CVD) and have provided important insights into disease pathogenesis and drug development. However, the level of conservation of gene expression patterns of the orthologous genes between human and animal models was unclear. To address this issue, we compared the expression of orthologous genes in human and four models (rhesus, rat, mouse and dog), based on 42 normal heart samples with high quality gene expression data. The results show that the global expression profiles between animal model and human orthologous genes are highly preserved. The phylogenetic tree inferred from the gene expression profiles has similar topology to that of the species tree. However, differentially expressed genes (DEGs) between human and each model were identified and these four gene datasets are enriched with different molecular functions, including hormone-receptor binding and geranyl transferase activity. The 65 overlapped DEGs between four sets are involved in thyroid cancer, proteasome systems, aminoacyl-tRNA biosynthesis and GST (Glycine, Serine and Threonine) metabolism, of which functions are divergent between models and humans. In addition, 46.2% (30/65) of the communal genes have been experimentally proven to be associated with cardiovascular disease. Next, we constructed a co-expression network based on intra- and inter-species variation, to elucidate the altered network organization. It indicates that these DEGs evolved as modules rather than independently. The integrated heart transcriptome data should provide a valuable resource for the in-depth understanding of cardiology and the development of cardiovascular disease models.
[show abstract][hide abstract] ABSTRACT: The availability and utility of genome-scale metabolic networks have exploded with modern genome-sequencing capabilities. However, these generic models overlooked actual physiological states of the tissues and included all the reactions implied by the genome annotations. To address this problem, we reconstructed a human heart-specific metabolic network based on transcriptome and proteome data. The resulting model consists of 2803 reactions and 1880 metabolites, which correspond to 1721 active enzymes in human heart. Using the model, we detected 24 epistatic interactions in human heart, which are useful in understanding both the structure and function of cardiovascular systems. In addition, a set of 776 potential biomarkers for cardiovascular disease (CVD) has been successfully explored, whose concentration is predicted to be either elevated or reduced because of 278 possible dysfunctional cardiovascular-associated genes. The model could also be applied in predicting selective drug targets for eight subtypes of CVD. The human heart-specific model provides valuable information for the studies of cardiac activity and development of CVD.
Biochemical and Biophysical Research Communications 10/2011; 415(3):450-4. · 2.41 Impact Factor
[show abstract][hide abstract] ABSTRACT: The isochore, a large DNA sequence with relatively small GC variance, is one of the most important structures in eukaryotic genomes. Although the isochore has been widely studied in humans and other species, little is known about its distribution in pigs.
In this paper, we construct a map of long homogeneous genome regions (LHGRs), i.e., isochores and isochore-like regions, in pigs to provide an intuitive version of GC heterogeneity in each chromosome. The LHGR pattern study not only quantifies heterogeneities, but also reveals some primary characteristics of the chromatin organization, including the followings: (1) the majority of LHGRs belong to GC-poor families and are in long length; (2) a high gene density tends to occur with the appearance of GC-rich LHGRs; and (3) the density of LINE repeats decreases with an increase in the GC content of LHGRs. Furthermore, a portion of LHGRs with particular GC ranges (50%-51% and 54%-55%) tend to have abnormally high gene densities, suggesting that biased gene conversion (BGC), as well as time- and energy-saving principles, could be of importance to the formation of genome organization.
This study significantly improves our knowledge of chromatin organization in the pig genome. Correlations between the different biological features (e.g., gene density and repeat density) and GC content of LHGRs provide a unique glimpse of in silico gene and repeats prediction.
PLoS ONE 01/2010; 5(10):e13303. · 3.73 Impact Factor