CONAN: A Tool to Decode Dynamical Information from Molecular Interaction Maps

The analysis of contacts is a powerful tool to understand biomolecular function in a series of contexts, from the investigation of dynamical behavior at equilibrium to the study of nonequilibrium dynamics in which the system moves between multiple states. We thus propose a tool called CONtact ANalysis (CONAN) that, from molecular dynamics (MD) trajectories, analyzes interresidue contacts, creates videos of time-resolved contact maps, and performs correlation, principal component, and cluster analysis, revealing how specific contacts relate to functionally relevant states sampled by MD. We present how CONAN can identify features describing the dynamics of ubiquitin both at equilibrium and during mechanical unfolding. Additionally, we show the analysis of MD trajectories of an α-synuclein mutant peptide that undergoes an α-β conformational transition that can be easily monitored using CONAN, which identifies the multiple states that the peptide explores along its conformational dynamics. The high versatility and ease of use of the software make CONAN a tool that can significantly facilitate the understanding of the complex dynamical behavior of proteins or other biomolecules. CONAN and its documentation are freely available for download on GitHub.     

Resting-State Temporal Synchronization Networks Emerge from Connectivity Topology and Heterogeneity

Spatial patterns of coherent activity across different brain areas have been identified during the resting-state fluctuations of the brain. However, recent studies indicate that resting-state activity is not stationary, but shows complex temporal dynamics. We were interested in the spatiotemporal dynamics of the phase interactions among resting-state fMRI BOLD signals from human subjects. We found that the global phase synchrony of the BOLD signals evolves on a characteristic ultra-slow (<0.01Hz) time scale, and that its temporal variations reflect the transient formation and dissolution of multiple communities of synchronized brain regions. Synchronized communities reoccurred intermittently in time and across scanning sessions. We found that the synchronization communities relate to previously defined functional networks known to be engaged in sensory-motor or cognitive function, called resting-state networks (RSNs), including the default mode network, the somato-motor network, the visual network, the auditory network, the cognitive control networks, the self-referential network, and combinations of these and other RSNs. We studied the mechanism originating the observed spatiotemporal synchronization dynamics by using a network model of phase oscillators connected through the brain’s anatomical connectivity estimated using diffusion imaging human data. The model consistently approximates the temporal and spatial synchronization patterns of the empirical data, and reveals that multiple clusters that transiently synchronize and desynchronize emerge from the complex topology of anatomical connections, provided that oscillators are heterogeneous.     

Bacterial Protein Interaction Networks: Connectivity is Ruled by Gene Conservation,Essentiality and Function

BackgroundProtein-protein interaction (PPI) networks are the backbone of all processes in living cells. In this work, we relate conservation, essentiality and functional repertoire of a gene to the connectivity k (i.e. the number of interactions, links) of the corresponding protein in the PPI network.MethodsOn a set of 42 bacterial genomes of different sizes, and with reasonably separated evolutionary trajectories, we investigate three issues: i) whether the distribution of connectivities changes between PPI subnetworks of essential and nonessential genes; ii) how gene conservation, measured both by the evolutionary retention index (ERI) and by evolutionary pressures, is related to the connectivity of the corresponding protein; iii) how PPI connectivities are modulated by evolutionary and functional relationships, as represented by the Clusters of Orthologous Genes (COGs).ResultsWe show that conservation, essentiality and functional specialisation of genes constrain the connectivity of the corresponding proteins in bacterial PPI networks. In particular, we isolated a core of highly connected proteins (connectivities k≥40), which is ubiquitous among the species considered here, though mostly visible in the degree distributions of bacteria with small genomes (less than 1000 genes).ConclusionThe genes that support this highly connected core are conserved, essential and, in most cases, belong to the COG cluster J, related to ribosomal functions and the processing of genetic information.     

Knowledge Retrieval from PubMed Abstracts and Electronic Medical Records with the Multiple Sclerosis Ontology

BackgroundIn order to retrieve useful information from scientific literature and electronic medical records (EMR) we developed an ontology specific for Multiple Sclerosis (MS).MethodsThe MS Ontology was created using scientific literature and expert review under the Protégé OWL environment. We developed a dictionary with semantic synonyms and translations to different languages for mining EMR. The MS Ontology was integrated with other ontologies and dictionaries (diseases/comorbidities, gene/protein, pathways, drug) into the text-mining tool SCAIView. We analyzed the EMRs from 624 patients with MS using the MS ontology dictionary in order to identify drug usage and comorbidities in MS. Testing competency questions and functional evaluation using F statistics further validated the usefulness of MS ontology.ResultsValidation of the lexicalized ontology by means of named entity recognition-based methods showed an adequate performance (F score = 0.73). The MS Ontology retrieved 80% of the genes associated with MS from scientific abstracts and identified additional pathways targeted by approved disease-modifying drugs (e.g. apoptosis pathways associated with mitoxantrone, rituximab and fingolimod). The analysis of the EMR from patients with MS identified current usage of disease modifying drugs and symptomatic therapy as well as comorbidities, which are in agreement with recent reports.ConclusionThe MS Ontology provides a semantic framework that is able to automatically extract information from both scientific literature and EMR from patients with MS, revealing new pathogenesis insights as well as new clinical information.     

PathVisio-MIM: PathVisio plugin for creating and editing Molecular Interaction Maps (MIMs)

MOTIVATION: A plugin for the Java-based PathVisio pathway editor has been developed to help users draw diagrams of bioregulatory networks according to the Molecular Interaction Map (MIM) notation. Together with the core PathVisio application, this plugin presents a simple to use and cross-platform application for the construction of complex MIM diagrams with the ability to annotate diagram elements with comments, literature references and links to external databases. This tool extends the capabilities of the PathVisio pathway editor by providing both MIM-specific glyphs and support for a MIM-specific markup language file format for exchange with other MIM-compatible tools and diagram validation. AVAILABILITY: The PathVisio-MIM plugin is freely available and works with versions of PathVisio 2.0.11 and later on Windows, Mac OS X and Linux. Information about MIM notation and the MIMML format is available at http://discover.nci.nih.gov/mim. The plugin, along with diagram examples, instructions and Java source code, may be downloaded at http://discover.nci.nih.gov/mim/mim_pathvisio.html.     

基于相互作用的蛋白质功能预测

蛋白质功能预测是后基因时代研究的热点问题。基于相互作用的蛋白质功能预测方法目前应用比较广泛,但是当"伙伴蛋白质"(interacting partners)数目k较小时,其预测准确率不高。从蛋白质相互作用网络入手,结合"小世界网络"特性,有效解决了k较小时预测准确率不高的问题。对酵母(Saccharomyces cerevisiae)蛋白质的相互作用网络进行预测,当k≤4时其预测准确率比相同条件下的GO(global optimization)方法有一定提高。实验结果表明:该方法能够有效的应用于伙伴蛋白质数目较小时的蛋白质功能预测。     

Automatic Context-Specific Subnetwork Discovery from Large Interaction Networks

Genes act in concert via specific networks to drive various biological processes, including progression of diseases such as cancer. Under different phenotypes, different subsets of the gene members of a network participate in a biological process. Single gene analyses are less effective in identifying such core gene members (subnetworks) within a gene set/network, as compared to gene set/network-based analyses. Hence, it is useful to identify a discriminative classifier by focusing on the subnetworks that correspond to different phenotypes. Here we present a novel algorithm to automatically discover the important subnetworks of closely interacting molecules to differentiate between two phenotypes (context) using gene expression profiles. We name it COSSY (COntext-Specific Subnetwork discoverY). It is a non-greedy algorithm and thus unlikely to have local optima problems. COSSY works for any interaction network regardless of the network topology. One added benefit of COSSY is that it can also be used as a highly accurate classification platform which can produce a set of interpretable features.     

PubstractHelper: A Web-based Text-Mining Tool for Marking Sentences in Abstracts from PubMed Using Multiple User-Defined Keywords

While a huge amount of information about biological literature can be obtained by searching the PubMed database, reading through all the titles and abstracts resulting from such a search for useful information is inefficient. Text mining makes it possible to increase this efficiency. Some websites use text mining to gather information from the PubMed database; however, they are database-oriented, using pre-defined search keywords while lacking a query interface for user-defined search inputs. We present the PubMed Abstract Reading Helper (PubstractHelper) website which combines text mining and reading assistance for an efficient PubMed search. PubstractHelper can accept a maximum of ten groups of keywords, within each group containing up to ten keywords. The principle behind the text-mining function of PubstractHelper is that keywords contained in the same sentence are likely to be related. PubstractHelper highlights sentences with co-occurring keywords in different colors. The user can download the PMID and the abstracts with color markings to be reviewed later. The PubstractHelper website can help users to identify relevant publications based on the presence of related keywords, which should be a handy tool for their research.

Availability

http://bio.yungyun.com.tw/ATM/PubstractHelper.aspx and http://holab.med.ncku.edu.tw/ATM/PubstractHelper.aspx     

The Evolutionary Dynamics of Protein-Protein Interaction Networks Inferred from the Reconstruction of Ancient Networks

Cellular functions are based on the complex interplay of proteins, therefore the structure and dynamics of these protein-protein interaction (PPI) networks are the key to the functional understanding of cells. In the last years, large-scale PPI networks of several model organisms were investigated. A number of theoretical models have been developed to explain both the network formation and the current structure. Favored are models based on duplication and divergence of genes, as they most closely represent the biological foundation of network evolution. However, studies are often based on simulated instead of empirical data or they cover only single organisms. Methodological improvements now allow the analysis of PPI networks of multiple organisms simultaneously as well as the direct modeling of ancestral networks. This provides the opportunity to challenge existing assumptions on network evolution. We utilized present-day PPI networks from integrated datasets of seven model organisms and developed a theoretical and bioinformatic framework for studying the evolutionary dynamics of PPI networks. A novel filtering approach using percolation analysis was developed to remove low confidence interactions based on topological constraints. We then reconstructed the ancient PPI networks of different ancestors, for which the ancestral proteomes, as well as the ancestral interactions, were inferred. Ancestral proteins were reconstructed using orthologous groups on different evolutionary levels. A stochastic approach, using the duplication-divergence model, was developed for estimating the probabilities of ancient interactions from today''s PPI networks. The growth rates for nodes, edges, sizes and modularities of the networks indicate multiplicative growth and are consistent with the results from independent static analysis. Our results support the duplication-divergence model of evolution and indicate fractality and multiplicative growth as general properties of the PPI network structure and dynamics.     

Pharmacological Characterization of Cultivated Neuronal Networks: Relevance to Synaptogenesis and Synaptic Connectivity

Mental disorders, such as schizophrenia or Alzheimer’s disease, are associated with impaired synaptogenesis and/or synaptic communication. During development, neurons assemble into neuronal networks, the primary supracellular mediators of information processing. In addition to the orchestrated activation of genetic programs, spontaneous electrical activity and associated calcium signaling have been shown to be critically involved in the maturation of such neuronal networks. We established an in vitro model that recapitulates the maturation of neuronal networks, including spontaneous electrical activity. Upon plating, mouse primary hippocampal neurons grow neurites and interconnect via synapses to form a dish-wide neuronal network. Via live cell calcium imaging, we identified a limited period of time in which the spontaneous activity synchronizes across neurons, indicative of the formation of a functional network. After establishment of network activity, the neurons grow dendritic spines, the density of which was used as a morphological readout for neuronal maturity and connectivity. Hence, quantification of neurite outgrowth, synapse density, spontaneous neuronal activity, and dendritic spine density allowed to study neuronal network maturation from the day of plating until the presence of mature neuronal networks. Via acute pharmacological intervention, we show that synchronized network activity is mediated by the NMDA-R. The balance between kynurenic and quinolinic acid, both neuro-active intermediates in the tryptophan/kynurenine pathway, was shown to be decisive for the maintenance of network activity. Chronic modulation of the neurotrophic support influenced the network formation and revealed the extreme sensitivity of calcium imaging to detect subtle alterations in neuronal physiology. Given the reproducible cultivation in a 96-well setup in combination with fully automated analysis of the calcium recordings, this approach can be used to build a high-content screening assay usable for neurotoxicity screening, target identification/validation, or phenotypic drug screening.     

Reduced Functional Connectivity of Default Mode and Set-Maintenance Networks in Ornithine Transcarbamylase Deficiency

Background and Purpose

Ornithine transcarbamylase deficiency (OTCD) is an X-chromosome linked urea cycle disorder (UCD) that causes hyperammonemic episodes leading to white matter injury and impairments in executive functioning, working memory, and motor planning. This study aims to investigate differences in functional connectivity of two resting-state networks—default mode and set-maintenance—between OTCD patients and healthy controls.

Methods

Sixteen patients with partial OTCD and twenty-two control participants underwent a resting-state scan using 3T fMRI. Combining independent component analysis (ICA) and region-of-interest (ROI) analyses, we identified the nodes that comprised each network in each group, and assessed internodal connectivity.

Results

Group comparisons revealed reduced functional connectivity in the default mode network (DMN) of OTCD patients, particularly between the anterior cingulate cortex/medial prefrontal cortex (ACC/mPFC) node and bilateral inferior parietal lobule (IPL), as well as between the ACC/mPFC node and the posterior cingulate cortex (PCC) node. Patients also showed reduced connectivity in the set-maintenance network, especially between right anterior insula/frontal operculum (aI/fO) node and bilateral superior frontal gyrus (SFG), as well as between the right aI/fO and ACC and between the ACC and right SFG.

Conclusion

Internodal functional connectivity in the DMN and set-maintenance network is reduced in patients with partial OTCD compared to controls, most likely due to hyperammonemia-related white matter damage. Because several of the affected areas are involved in executive functioning, it is postulated that this reduced connectivity is an underlying cause of the deficits OTCD patients display in this cognitive domain.     

Connectivity of Neutral Networks,Overdispersion, and Structural Conservation in Protein Evolution

Abstract Protein structures are much more conserved than sequences during evolution. Based on this observation, we investigate the consequences of structural conservation on protein evolution. We study seven of the most studied protein folds, determining that an extended neutral network in sequence space is associated with each of them. Within our model, neutral evolution leads to a non-Poissonian substitution process, due to the broad distribution of connectivities in neutral networks. The observation that the substitution process has non-Poissonian statistics has been used to argue against the original Kimura neutral theory, while our model shows that this is a generic property of neutral evolution with structural conservation. Our model also predicts that the substitution rate can strongly fluctuate from one branch to another of the evolutionary tree. The average sequence similarity within a neutral network is close to the threshold of randomness, as observed for families of sequences sharing the same fold. Nevertheless, some positions are more difficult to mutate than others. We compare such structurally conserved positions to positions conserved in protein evolution, suggesting that our model can be a valuable tool to distinguish structural from functional conservation in databases of protein families. These results indicate that a synergy between database analysis and structurally based computational studies can increase our understanding of protein evolution.     

Independently Outgrowing Neurons and Geometry-Based Synapse Formation Produce Networks with Realistic Synaptic Connectivity

Neuronal signal integration and information processing in cortical networks critically depend on the organization of synaptic connectivity. During development, neurons can form synaptic connections when their axonal and dendritic arborizations come within close proximity of each other. Although many signaling cues are thought to be involved in guiding neuronal extensions, the extent to which accidental appositions between axons and dendrites can already account for synaptic connectivity remains unclear. To investigate this, we generated a local network of cortical L2/3 neurons that grew out independently of each other and that were not guided by any extracellular cues. Synapses were formed when axonal and dendritic branches came by chance within a threshold distance of each other. Despite the absence of guidance cues, we found that the emerging synaptic connectivity showed a good agreement with available experimental data on spatial locations of synapses on dendrites and axons, number of synapses by which neurons are connected, connection probability between neurons, distance between connected neurons, and pattern of synaptic connectivity. The connectivity pattern had a small-world topology but was not scale free. Together, our results suggest that baseline synaptic connectivity in local cortical circuits may largely result from accidentally overlapping axonal and dendritic branches of independently outgrowing neurons.     

The Selective Interaction between Silica Nanoparticles and Enzymes from Molecular Dynamics Simulations

Nanoscale particles have become promising materials in many fields, such as cancer therapeutics, diagnosis, imaging, drug delivery, catalysis, as well as biosensors. In order to stimulate and facilitate these applications, there is an urgent need for the understanding of the interaction mode between the nano-particles and proteins. In this study, we investigate the orientation and adsorption between several enzymes (cytochrome c, RNase A, lysozyme) and 4 nm/11 nm silica nanoparticles (SNPs) by using molecular dynamics (MD) simulation. Our results show that three enzymes are adsorbed onto the surfaces of both 4 nm and 11 nm SNPs during our MD simulations and the small SNPs induce greater structural stabilization. The active site of cytochrome c is far away from the surface of 4 nm SNPs, while it is adsorbed onto the surface of 11 nm SNPs. We also explore the influences of different groups (-OH, -COOH, -NH₂ and CH₃) coated onto silica nanoparticles, which show significantly different impacts. Our molecular dynamics results indicate the selective interaction between silicon nanoparticles and enzymes, which is consistent with experimental results. Our study provides useful guides for designing/modifying nanomaterials to interact with proteins for their bio-applications.     

Investigating the Dynamic Aspects of Drug-Protein Recognition through a Combination of MD and NMR Analyses: Implications for the Development of Protein-Protein Interaction Inhibitors

In this paper, we investigate the dynamic aspects of the molecular recognition between a small molecule ligand and a flat, exposed protein surface, representing a typical target in the development of protein-protein interaction inhibitors. Specifically, we analyze the complex between the protein Fibroblast Growth Factor 2 (FGF2) and a recently discovered small molecule inhibitor, labeled sm27 for which the binding site and the residues mainly involved in small molecule recognition have been previously characterized. We have approached this problem using microsecond MD simulations and NMR-based characterizations of the dynamics of the apo and holo states of the system. Using direct combination and cross-validation of the results of the two techniques, we select the set of conformational states that best recapitulate the principal dynamic and structural properties of the complex. We then use this information to generate a multi-structure representation of the sm27-FGF2 interaction. We propose this kind of representation and approach as a useful tool in particular for the characterization of systems where the mutual dynamic influence between the interacting partners is expected to play an important role. The results presented can also be used to generate new rules for the rational expansion of the chemical diversity space of FGF2 inhibitors.     

High Density Molecular Linkage Maps of the Tomato and Potato Genomes

High density molecular linkage maps, comprised of more than 1000 markers with an average spacing between markers of approximately 1.2 cM (ca. 900 kb), have been constructed for the tomato and potato genomes. As the two maps are based on a common set of probes, it was possible to determine, with a high degree of precision, the breakpoints corresponding to 5 chromosomal inversions that differentiate the tomato and potato genomes. All of the inversions appear to have resulted from single breakpoints at or near the centromeres of the affected chromosomes, the result being the inversion of entire chromosome arms. While the crossing over rate among chromosomes appears to be uniformly distributed with respect to chromosome size, there is tremendous heterogeneity of crossing over within chromosomes. Regions of the map corresponding to centromeres and centromeric heterochromatin, and in some instances telomeres, experience up to 10-fold less recombination than other areas of the genome. Overall, 28% of the mapped loci reside in areas of putatively suppressed recombination. This includes loci corresponding to both random, single copy genomic clones and transcribed genes (detected with cDNA probes). The extreme heterogeneity of crossing over within chromosomes has both practical and evolutionary implications. Currently tomato and potato are among the most thoroughly mapped eukaryotic species and the availability of high density molecular linkage maps should facilitate chromosome walking, quantitative trait mapping, marker-assisted breeding and evolutionary studies in these two important and well studied crop species.     

动态分子网络的构建与分析

分子网络研究是从全局角度揭示生物系统的结构和功能的重要手段,现有的网络分析大部分是基于静态网络.实际上,在不同的环境条件、组织类型和疾病状态以及生长和分化的过程中,分子网络时刻都在发生变化.经过研究人员的努力,人们已经提出了一些可用于分析分子网络动态的生物信息学方法,如节点的动态性分类、动态蛋白质复合物的预测、条件特异子网的构建以及网络动态行为的模拟等.本文综述了动态分子网络的构建与分析方法.可以预见,动态网络分析将成为未来网络研究的标准模式.