Kazuhiro Takemoto

HSEpred: predict half-sphere exposure from protein sequences

MOTIVATION Half-sphere exposure (HSE) is a newly developed two-dimensional solvent exposure measure. By conceptually separating an amino acids sphere in a protein structure into two half spheres which represent its distinct spatial neighborhoods in the upward and downward directions, the HSE-up and HSE-down measures show superior performance compared with other measures such as accessible surface area, residue depth and contact number. However, currently there is no existing method for the prediction of HSE measures from sequence data. RESULTS In this article, we propose a novel approach to predict the HSE measures and infer residue contact numbers using the predicted HSE values, based on a well-prepared non-homologous protein structure dataset. In particular, we employ support vector regression (SVR) to quantify the relationship between HSE measures and protein sequences and evaluate its prediction performance. We extensively explore five sequence-encoding schemes to examine their effects on the prediction performance. Our method could achieve the correlation coefficients of 0.72 and 0.68 between the predicted and observed HSE-up and HSE-down measures, respectively. Moreover, contact number can be accurately predicted by the summation of the predicted HSE-up and HSE-down values, which has further enlarged the application of this method. The successful application of SVR approach in this study suggests that it should be more useful in quantifying the protein sequence-structure relationship and predicting the structural property profiles from protein sequences. AVAILABILITY The prediction webserver and supplementary materials are accessible at http://sunflower.kuicr.kyoto-u.ac.jp/~sjn/hse/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Identification of chemogenomic features from drug–target interaction networks using interpretable classifiers

Motivation: Drug effects are mainly caused by the interactions between drug molecules and their target proteins including primary targets and off-targets. Identification of the molecular mechanisms behind overall drug–target interactions is crucial in the drug design process. Results: We develop a classifier-based approach to identify chemogenomic features (the underlying associations between drug chemical substructures and protein domains) that are involved in drug–target interaction networks. We propose a novel algorithm for extracting informative chemogenomic features by using L1 regularized classifiers over the tensor product space of possible drug–target pairs. It is shown that the proposed method can extract a very limited number of chemogenomic features without loosing the performance of predicting drug–target interactions and the extracted features are biologically meaningful. The extracted substructure–domain association network enables us to suggest ligand chemical fragments specific for each protein domain and ligand core substructures important for a wide range of protein families. Availability: Softwares are available at the supplemental website. Contact: yamanishi@bioreg.kyushu-u.ac.jp Supplementary Information: Datasets and all results are available at http://cbio.ensmp.fr/~yyamanishi/l1binary/ .

Correlation between structure and temperature in prokaryotic metabolic networks

BackgroundIn recent years, an extensive characterization of network structures has been made in an effort to elucidate design principles of metabolic networks, providing valuable insights into the functional organization and the evolutionary history of organisms. However, previous analyses have not discussed the effects of environmental factors (i.e., exogenous forces) in shaping network structures. In this work, we investigate the effect of temperature, which is one of the environmental factors that may have contributed to shaping structures of metabolic networks.ResultsFor this, we investigate the correlations between several structural properties characterized by graph metrics like the edge density, the degree exponent, the clustering coefficient, and the subgraph concentration in the metabolic networks of 113 prokaryotes and optimal growth temperature. As a result, we find that these structural properties are correlated with the optimal growth temperature. With increasing temperature, the edge density, the clustering coefficient and the subgraph concentration decrease and the degree exponent becomes large.ConclusionThis result implies that the metabolic networks transit with temperature as follows. The density of chemical reactions becomes low, the connectivity of the networks becomes homogeneous such as random networks and both the network modularity, based on the graph-theoretic clustering coefficient, and the frequency of recurring subgraphs decay. In short, metabolic networks undergo a change from heterogeneous and high-modular structures to homogeneous and low-modular structures, such as random networks, with temperature. This finding may suggest that the temperature plays an important role in the design principles of metabolic networks.

PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework

Determining the catalytic residues in an enzyme is critical to our understanding the relationship between protein sequence, structure, function, and enhancing our ability to design novel enzymes and their inhibitors. Although many enzymes have been sequenced, and their primary and tertiary structures determined, experimental methods for enzyme functional characterization lag behind. Because experimental methods used for identifying catalytic residues are resource- and labor-intensive, computational approaches have considerable value and are highly desirable for their ability to complement experimental studies in identifying catalytic residues and helping to bridge the sequence-structure-function gap. In this study, we describe a new computational method called PREvaIL for predicting enzyme catalytic residues. This method was developed by leveraging a comprehensive set of informative features extracted from multiple levels, including sequence, structure, and residue-contact network, in a random forest machine-learning framework. Extensive benchmarking experiments on eight different datasets based on 10-fold cross-validation and independent tests, as well as side-by-side performance comparisons with seven modern sequence- and structure-based methods, showed that PREvaIL achieved competitive predictive performance, with an area under the receiver operating characteristic curve and area under the precision-recall curve ranging from 0.896 to 0.973 and from 0.294 to 0.523, respectively. We demonstrated that this method was able to capture useful signals arising from different levels, leveraging such differential but useful types of features and allowing us to significantly improve the performance of catalytic residue prediction. We believe that this new method can be utilized as a valuable tool for both understanding the complex sequence-structure-function relationships of proteins and facilitating the characterization of novel enzymes lacking functional annotations.

An automated system for evaluation of the potential functionome: MAPLE version 2.1.0.

Metabolic and physiological potential evaluator (MAPLE) is an automatic system that can perform a series of steps used in the evaluation of potential comprehensive functions (functionome) harboured in the genome and metagenome. MAPLE first assigns KEGG Orthology (KO) to the query gene, maps the KO-assigned genes to the Kyoto Encyclopedia of Genes and Genomes (KEGG) functional modules, and then calculates the module completion ratio (MCR) of each functional module to characterize the potential functionome in the user’s own genomic and metagenomic data. In this study, we added two more useful functions to calculate module abundance and Q-value, which indicate the functional abundance and statistical significance of the MCR results, respectively, to the new version of MAPLE for more detailed comparative genomic and metagenomic analyses. Consequently, MAPLE version 2.1.0 reported significant differences in the potential functionome, functional abundance, and diversity of contributors to each function among four metagenomic datasets generated by the global ocean sampling expedition, one of the most popular environmental samples to use with this system. MAPLE version 2.1.0 is now available through the web interface (http://www.genome.jp/tools/maple/) 17 June 2016, date last accessed.

Evolving networks by merging cliques

We propose a model for evolving networks by merging building blocks represented as complete graphs, reminiscent of modules in biological system or communities in sociology. The model shows power-law degree distributions, power-law clustering spectra, and high average clustering coefficients independent of network size. The analytical solutions indicate that a degree exponent is determined by the ratio of the number of merging nodes to that of all nodes in the blocks, demonstrating that the exponent is tunable, and are also applicable when the blocks are classical networks such as Erdös-Rényi or regular graphs. Our model becomes the same model as the Barabási-Albert model under a specific condition.

Heterogeneity in ecological mutualistic networks dominantly determines community stability

Although the hypothesis that nestedness determines mutualistic ecosystem dynamics is accepted in general, results of some recent data analyses and theoretical studies have begun to cast doubt on the impact of nestedness on ecosystem stability. However, definite conclusions have not yet been reached because previous studies are mainly based on numerical simulations. Therefore, we reveal a mathematical architecture in the relationship between ecological mutualistic networks and local stability based on spectral graph analysis. In particular, we propose a theoretical method for estimating the dominant eigenvalue (i.e., spectral radius) of quantitative (or weighted) bipartite networks by extending spectral graph theory, and provide a theoretical prediction that the heterogeneity of node degrees and link weights primarily determines the local stability; on the other hand, nestedness additionally affects it. Numerical simulations demonstrate the validity of our theory and prediction. This study emphasizes the importance of ecological network heterogeneity in ecosystem dynamics, and it enhances our understanding of structure–stability relationships.

Metabolic Network Modularity in Archaea Depends on Growth Conditions

Network modularity is an important structural feature in metabolic networks. A previous study suggested that the variability in natural habitat promotes metabolic network modularity in bacteria. However, since many factors influence the structure of the metabolic network, this phenomenon might be limited and there may be other explanations for the change in metabolic network modularity. Therefore, we focus on archaea because they belong to another domain of prokaryotes and show variability in growth conditions (e.g., trophic requirement and optimal growth temperature), but not in habitats because of their specialized growth conditions (e.g., high growth temperature). The relationship between biological features and metabolic network modularity is examined in detail. We first show the absence of a relationship between network modularity and habitat variability in archaea, as archaeal habitats are more limited than bacterial habitats. Although this finding implies the need for further studies regarding the differences in network modularity, it does not contradict previous work. Further investigations reveal alternative explanations. Specifically, growth conditions, trophic requirement, and optimal growth temperature, in particular, affect metabolic network modularity. We have discussed the mechanisms for the growth condition-dependant changes in network modularity. Our findings suggest different explanations for the changes in network modularity and provide new insights into adaptation and evolution in metabolic networks, despite several limitations of data analysis.

Structure of n-clique networks embedded in a complex network

We propose the n-clique network as a powerful tool for understanding global structures of combined highly-interconnected subgraphs, and provide theoretical predictions for statistical properties of the n-clique networks embedded in a complex network using the degree distribution and the clustering spectrum. Furthermore, using our theoretical predictions, we find that the statistical properties are invariant between 3-clique networks and original networks for several observable real-world networks with the scale-free connectivity and the hierarchical modularity. The result implies that structural properties are identical between the 3-clique networks and the original networks.

Modular organization of cancer signaling networks is associated with patient survivability

Molecular signaling networks are believed to determine cancer robustness. Although cancer patient survivability was reported to correlate with the heterogeneous connectivity of the signaling networks inspired by theoretical studies on the increase of network robustness due to the heterogeneous connectivity, other theoretical and data analytic studies suggest an alternative explanation: the impact of modular organization of networks on biological robustness or adaptation to changing environments. In this study, thus, we evaluate whether the modularity-robustness hypothesis is applicable to cancer using network analysis. We focus on 14 specific cancer types whose molecular signaling networks are available in databases, and show that modular organization of cancer signaling networks is associated with the patient survival rate. In particular, the cancers with less modular signaling networks are more curable. This result is consistent with a prediction from the modularity-robustness hypothesis. Furthermore, we show that the network modularity is a better descriptor of the patient survival rate than the heterogeneous connectivity. However, these results do not contradict the importance of the heterogeneous connectivity. Rather, they provide new and different insights into the relationship between cellular networks and cancer behaviors. Despite several limitations of data analysis, these findings enhance our understanding of adaptive and evolutionary mechanisms of cancer cells.