Yixue Li

Predicting protein–protein interactions based only on sequences information

Protein–protein interactions (PPIs) are central to most biological processes. Although efforts have been devoted to the development of methodology for predicting PPIs and protein interaction networks, the application of most existing methods is limited because they need information about protein homology or the interaction marks of the protein partners. In the present work, we propose a method for PPI prediction using only the information of protein sequences. This method was developed based on a learning algorithm-support vector machine combined with a kernel function and a conjoint triad feature for describing amino acids. More than 16,000 diverse PPI pairs were used to construct the universal model. The prediction ability of our approach is better than that of other sequence-based PPI prediction methods because it is able to predict PPI networks. Different types of PPI networks have been effectively mapped with our method, suggesting that, even with only sequence information, this method could be applied to the exploration of networks for any newly discovered protein with unknown biological relativity. In addition, such supplementary experimental information can enhance the prediction ability of the method.

Hierarchical modularity of nested bow-ties in metabolic networks

BackgroundThe exploration of the structural topology and the organizing principles of genome-based large-scale metabolic networks is essential for studying possible relations between structure and functionality of metabolic networks. Topological analysis of graph models has often been applied to study the structural characteristics of complex metabolic networks.ResultsIn this work, metabolic networks of 75 organisms were investigated from a topological point of view. Network decomposition of three microbes (Escherichia coli, Aeropyrum pernix and Saccharomyces cerevisiae) shows that almost all of the sub-networks exhibit a highly modularized bow-tie topological pattern similar to that of the global metabolic networks. Moreover, these small bow-ties are hierarchically nested into larger ones and collectively integrated into a large metabolic network, and important features of this modularity are not observed in the random shuffled network. In addition, such a bow-tie pattern appears to be present in certain chemically isolated functional modules and spatially separated modules including carbohydrate metabolism, cytosol and mitochondrion respectively.ConclusionThe highly modularized bow-tie pattern is present at different levels and scales, and in different chemical and spatial modules of metabolic networks, which is likely the result of the evolutionary process rather than a random accident. Identification and analysis of such a pattern is helpful for understanding the design principles and facilitate the modelling of metabolic networks.

Nucleocapsid protein of SARS coronavirus tightly binds to human cyclophilin A

Abstract Severe acute respiratory syndrome coronavirus (SARS-CoV) is responsible for SARS infection. Nucleocapsid protein (NP) of SARS-CoV (SARS_NP) functions in enveloping the entire genomic RNA and interacts with viron structural proteins, thus playing important roles in the process of virus particle assembly and release. Protein–protein interaction analysis using bioinformatics tools indicated that SARS_NP may bind to human cyclophilin A (hCypA), and surface plasmon resonance (SPR) technology revealed this binding with the equilibrium dissociation constant ranging from 6 to 160nM. The probable binding sites of these two proteins were detected by modeling the three-dimensional structure of the SARS_NP–hCypA complex, from which the important interaction residue pairs between the proteins were deduced. Mutagenesis experiments were carried out for validating the binding model, whose correctness was assessed by the observed effects on the binding affinities between the proteins. The reliability of the binding sites derived by the molecular modeling was confirmed by the fact that the computationally predicted values of the relative free energies of the binding for SARS_NP (or hCypA) mutants to the wild-type hCypA (or SARS_NP) are in good agreement with the data determined by SPR. Such presently observed SARS_NP–hCypA interaction model might provide a new hint for facilitating the understanding of another possible SARS-CoV infection pathway against human cell.

Modular co-evolution of metabolic networks

BackgroundThe architecture of biological networks has been reported to exhibit high level of modularity, and to some extent, topological modules of networks overlap with known functional modules. However, how the modular topology of the molecular network affects the evolution of its member proteins remains unclear.ResultsIn this work, the functional and evolutionary modularity of Homo sapiens (H. sapiens) metabolic network were investigated from a topological point of view. Network decomposition shows that the metabolic network is organized in a highly modular core-periphery way, in which the core modules are tightly linked together and perform basic metabolism functions, whereas the periphery modules only interact with few modules and accomplish relatively independent and specialized functions. Moreover, over half of the modules exhibit co-evolutionary feature and belong to specific evolutionary ages. Peripheral modules tend to evolve more cohesively and faster than core modules do.ConclusionThe correlation between functional, evolutionary and topological modularity suggests that the evolutionary history and functional requirements of metabolic systems have been imprinted in the architecture of metabolic networks. Such systems level analysis could demonstrate how the evolution of genes may be placed in a genome-scale network context, giving a novel perspective on molecular evolution.

Demonstration of two novel methods for predicting functional siRNA efficiency

BackgroundsiRNAs are small RNAs that serve as sequence determinants during the gene silencing process called RNA interference (RNAi). It is well know that siRNA efficiency is crucial in the RNAi pathway, and the siRNA efficiency for targeting different sites of a specific gene varies greatly. Therefore, there is high demand for reliable siRNAs prediction tools and for the design methods able to pick up high silencing potential siRNAs.ResultsIn this paper, two systems have been established for the prediction of functional siRNAs: (1) a statistical model based on sequence information and (2) a machine learning model based on three features of siRNA sequences, namely binary description, thermodynamic profile and nucleotide composition. Both of the two methods show high performance on the two datasets we have constructed for training the model.ConclusionBoth of the two methods studied in this paper emphasize the importance of sequence information for the prediction of functional siRNAs. The way of denoting a bio-sequence by binary system in mathematical language might be helpful in other analysis work associated with fixed-length bio-sequence.

Identification of β-barrel membrane proteins based on amino acid composition properties and predicted secondary structure

Unlike all-helices membrane proteins, beta-barrel membrane proteins can not be successfully discriminated from other proteins, especially from all-beta soluble proteins. This paper performs an analysis on the amino acid composition in membrane parts of 12 beta-barrel membrane proteins versus beta-strands of 79 all-beta soluble proteins. The average and variance of the amino acid composition in these two classes are calculated. Amino acids such as Gly, Asn, Val that are most likely associated with classification are selected based on Fishers discriminant ratio. A linear classifier built with these selected amino acids composition in observed beta-strands achieves 100% classification accuracy for 12 membrane proteins and 79 soluble proteins in a four-fold cross-validation experiment. Since at present the accuracy of secondary structure prediction is quite high, a promising method to identify beta-barrel membrane proteins is presented based on the linear classifier coupled with predicted secondary structure. Applied to 241 beta-barrel membrane proteins and 3855 soluble proteins with various structures, the method achieves 85.48% (206/241) sensitivity and 92.53% specificity (3567/3855).

Induced fit or conformational selection for RNA/U1A folding

The hairpin II of U1 snRNA can bind U1A protein with high affinity and specificity. NMR spectra suggest that the loop region of apo-RNA is largely unstructured and undergoes a transition from unstructured to well-folded upon U1Abinding. However, the mechanism that RNA folding coupled protein binding is poorly understood. To get an insight into the mechanism, we have performed explicit-solvent molecular dynamics (MD) to study the folding kinetics of bound RNA and apo-RNA. Room-temperature MD simulations suggest that the conformation of bound RNA has significant adjustment and becomes more stable upon U1A binding. Kinetic analysis of high-temperature MD simulations shows that bound RNA and apo-RNA unfold via a two-state process, respectively. Both kinetics and free energy landscape analyses indicate that bound RNA folds in the order of RNA contracting, U1A binding, and tertiary folding. The predicted Phi-values suggest that A8, C10, A11, and G16 are key bases for bound RNA folding. Mutant Arg52Gln analysis shows that electrostatic interaction and hydrogen bonds between RNA and U1A (Arg52Gln) decrease. These results are in qualitative agreement with experiments. Furthermore, this method could be used in other studies about biomolecule folding upon receptor binding.

Analysis of the dermatophyte Trichophyton rubrum expressed sequence tags

BackgroundDermatophytes are the primary causative agent of dermatophytoses, a disease that affects billions of individuals worldwide. Trichophyton rubrum is the most common of the superficial fungi. Although T. rubrum is a recognized pathogen for humans, little is known about how its transcriptional pattern is related to development of the fungus and establishment of disease. It is therefore necessary to identify genes whose expression is relevant to growth, metabolism and virulence of T. rubrum.ResultsWe generated 10 cDNA libraries covering nearly the entire growth phase and used them to isolate 11,085 unique expressed sequence tags (ESTs), including 3,816 contigs and 7,269 singletons. Comparisons with the GenBank non-redundant (NR) protein database revealed putative functions or matched homologs from other organisms for 7,764 (70%) of the ESTs. The remaining 3,321 (30%) of ESTs were only weakly similar or not similar to known sequences, suggesting that these ESTs represent novel genes.ConclusionThe present data provide a comprehensive view of fungal physiological processes including metabolism, sexual and asexual growth cycles, signal transduction and pathogenic mechanisms.

DCGL v2.0: an R package for unveiling differential regulation from differential co-expression.

Motivation Differential co-expression analysis (DCEA) has emerged in recent years as a novel, systematic investigation into gene expression data. While most DCEA studies or tools focus on the co-expression relationships among genes, some are developing a potentially more promising research domain, differential regulation analysis (DRA). In our previously proposed R package DCGL v1.0, we provided functions to facilitate basic differential co-expression analyses; however, the output from DCGL v1.0 could not be translated into differential regulation mechanisms in a straightforward manner. Results To advance from DCEA to DRA, we upgraded the DCGL package from v1.0 to v2.0. A new module named “Differential Regulation Analysis” (DRA) was designed, which consists of three major functions: DRsort, DRplot, and DRrank. DRsort selects differentially regulated genes (DRGs) and differentially regulated links (DRLs) according to the transcription factor (TF)-to-target information. DRrank prioritizes the TFs in terms of their potential relevance to the phenotype of interest. DRplot graphically visualizes differentially co-expressed links (DCLs) and/or TF-to-target links in a network context. In addition to these new modules, we streamlined the codes from v1.0. The evaluation results proved that our differential regulation analysis is able to capture the regulators relevant to the biological subject. Conclusions With ample functions to facilitate differential regulation analysis, DCGL v2.0 was upgraded from a DCEA tool to a DRA tool, which may unveil the underlying differential regulation from the observed differential co-expression. DCGL v2.0 can be applied to a wide range of gene expression data in order to systematically identify novel regulators that have not yet been documented as critical. Availability DCGL v2.0 package is available at http://cran.r-project.org/web/packages/DCGL/index.html or at our project home page http://lifecenter.sgst.cn/main/en/dcgl.jsp.

Genomic characterization of ribitol teichoic acid synthesis in Staphylococcus aureus: genes, genomic organization and gene duplication

BackgroundStaphylococcus aureus or MRSA (Methicillin Resistant S. aureus), is an acquired pathogen and the primary cause of nosocomial infections worldwide. In S. aureus, teichoic acid is an essential component of the cell wall, and its biosynthesis is not yet well characterized. Studies in Bacillus subtilis have discovered two different pathways of teichoic acid biosynthesis, in two strains W23 and 168 respectively, namely teichoic acid ribitol (tar) and teichoic acid glycerol (tag). The genes involved in these two pathways are also characterized, tarA, tarB, tarD, tarI, tarJ, tarK, tarL for the tar pathway, and tagA, tagB, tagD, tagE, tagF for the tag pathway. With the genome sequences of several MRSA strains: Mu50, MW2, N315, MRSA252, COL as well as methicillin susceptible strain MSSA476 available, a comparative genomic analysis was performed to characterize teichoic acid biosynthesis in these S. aureus strains.ResultsWe identified all S. aureus tar and tag gene orthologs in the selected S. aureus strains which would contribute to teichoic acids sythesis.Based on our identification of genes orthologous to tarI, tarJ, tarL, which are specific to tar pathway in B. subtilis W23, we also concluded that tar is the major teichoic acid biogenesis pathway in S. aureus. Further analyses indicated that the S. aureus tar genes, different from the divergon organization in B. subtilis, are organized into several clusters in cis. Most interesting, compared with genes in B. subtilis tar pathway, the S. aureus tar specific genes (tarI,J,L) are duplicated in all six S. aureus genomes.ConclusionIn the S. aureus strains we analyzed, tar (teichoic acid ribitol) is the main teichoic acid biogenesis pathway. The tar genes are organized into several genomic groups in cis and the genes specific to tar (relative to tag): tarI, tarJ, tarL are duplicated. The genomic organization of the S. aureus tar pathway suggests their regulations are different when compared to B. subtilis tar or tag pathway, which are grouped in two operons in a divergon structure.