3did: a catalog of domain-based interactions of known three-dimensional structure

In Silico Approaches for TRP Channel Modulation

The implication of several TRP ion channels (e.g., TRPV1) in diverse physiological and pathological processes has signaled them as pivotal drug targets. Consequently, the identification of selective and potent ligands for these channels is of great interest in pharmacology and biomedicine. However, a major challenge in the design of modulators is ensuring the specificity for their intended targets. In recent years, the emergence of high-resolution structures of ion channels facilitates the computer-assisted drug design at molecular levels. Here we describe some computational methods and general protocols to discover channel modulators, including homology modelling, docking and virtual screening, and structure-based peptide design.

Methods for Discovering and Targeting Druggable Protein-Protein Interfaces and Their Application to Repurposing

Drug repurposing is a creative and resourceful approach to increase the number of therapies by exploiting available and approved drugs. However, identifying new protein targets for previously approved drugs is challenging. Although new strategies have been developed for drug repurposing, there is broad agreement that there is room for further improvements. In this chapter, we review protein-protein interaction (PPI) interface-targeting strategies for drug repurposing applications. We discuss certain features, such as hot spot residue and hot region prediction and their importance in drug repurposing, and illustrate common methods used in PPI networks to identify drug off-targets. We also collect available online resources for hot spot prediction, binding pocket identification, and interface clustering which are effective resources in polypharmacology. Finally, we provide case studies showing the significance of protein interfaces and hot spots in drug repurposing.

The OncoPPi Portal: an integrative resource to explore and prioritize protein-protein interactions for cancer target discovery

Motivation

As cancer genomics initiatives move toward comprehensive identification of genetic alterations in cancer, attention is now turning to understanding how interactions among these genes lead to the acquisition of tumor hallmarks. Emerging pharmacological and clinical data suggest a highly promising role of cancer-specific protein-protein interactions (PPIs) as druggable cancer targets. However, large-scale experimental identification of cancer-related PPIs remains challenging, and currently available resources to explore oncogenic PPI networks are limited.

Results

Recently, we have developed a PPI high-throughput screening platform to detect PPIs between cancer-associated proteins in the context of cancer cells. Here, we present the OncoPPi Portal, an interactive web resource that allows investigators to access, manipulate and interpret a high-quality cancer-focused network of PPIs experimentally detected in cancer cell lines. To facilitate prioritization of PPIs for further biological studies, this resource combines network connectivity analysis, mutual exclusivity analysis of genomic alterations, cellular co-localization of interacting proteins and domain-domain interactions. Estimates of PPI essentiality allow users to evaluate the functional impact of PPI disruption on cancer cell proliferation. Furthermore, connecting the OncoPPi network with the approved drugs and compounds in clinical trials enables discovery of new tumor dependencies to inform strategies to interrogate undruggable targets like tumor suppressors. The OncoPPi Portal serves as a resource for the cancer research community to facilitate discovery of cancer targets and therapeutic development.

Availability and implementation

The OncoPPi Portal is available at http://oncoppi.emory.edu.

Contact

andrey.ivanov@emory.edu or hfu@emory.edu.

SLiM-Enrich: computational assessment of protein-protein interaction data as a source of domain-motif interactions

Many important cellular processes involve protein–protein interactions (PPIs) mediated by a Short Linear Motif (SLiM) in one protein interacting with a globular domain in another. Despite their significance, these domain-motif interactions (DMIs) are typically low affinity, which makes them challenging to identify by classical experimental approaches, such as affinity pulldown mass spectrometry (AP-MS) and yeast two-hybrid (Y2H). DMIs are generally underrepresented in PPI networks as a result. A number of computational methods now exist to predict SLiMs and/or DMIs from experimental interaction data but it is yet to be established how effective different PPI detection methods are for capturing these low affinity SLiM-mediated interactions. Here, we introduce a new computational pipeline (SLiM-Enrich) to assess how well a given source of PPI data captures DMIs and thus, by inference, how useful that data should be for SLiM discovery. SLiM-Enrich interrogates a PPI network for pairs of interacting proteins in which the first protein is known or predicted to interact with the second protein via a DMI. Permutation tests compare the number of known/predicted DMIs to the expected distribution if the two sets of proteins are randomly associated. This provides an estimate of DMI enrichment within the data and the false positive rate for individual DMIs. As a case study, we detect significant DMI enrichment in a high-throughput Y2H human PPI study. SLiM-Enrich analysis supports Y2H data as a source of DMIs and highlights the high false positive rates associated with naïve DMI prediction. SLiM-Enrich is available as an R Shiny app. The code is open source and available via a GNU GPL v3 license at: https://github.com/slimsuite/SLiMEnrich. A web server is available at: http://shiny.slimsuite.unsw.edu.au/SLiMEnrich/.

Integrating Multifaceted Information to Predict Mycobacterium tuberculosis-Human Protein-Protein Interactions

Tuberculosis (TB) is one of the biggest infectious disease killers caused by Mycobacterium tuberculosis (MTB). Studying the protein-protein interactions (PPIs) between MTB and human can deepen our understanding of the pathogenesis of TB and offer new clues to the treatment against MTB infection, but the experimentally validated interactions are especially scarce in this regard. Herein we proposed an integrated framework that combined template-, domain-domain interaction-, and machine learning-based methods to predict MTB-human PPIs. As a result, we established a network composed of 13 758 PPIs including 451 MTB proteins and 3167 human proteins ( http://liulab.hzau.edu.cn/MTB/ ). Compared to known human targets of various pathogens, our predicted human targets show a similar tendency in terms of the network topological properties and enrichment in important functional genes. Additionally, these human targets largely have longer sequence lengths, more protein domains, more disordered residues, lower evolutionary rates, and older protein ages. Functional analysis demonstrates that these proteins show strong preferences toward the phosphorylation, kinase activity, and signaling transduction processes and the disease and immune related pathways. Dissecting the cross-talk among top-ranked pathways suggests that the cancer pathway may serve as a bridge in MTB infection. Triplet analysis illustrates that the paired targets interacting with the same partner are adjacent to each other in the intraspecies network and tend to share similar expression patterns. Finally, we identified 36 potential anti-MTB human targets by integrating known drug target information and molecular properties of proteins.

Genomic data integration systematically biases interactome mapping

Elucidating the complete network of protein-protein interactions, or interactome, is a fundamental goal of the post-genomic era, yet existing interactome maps are far from complete. To increase the throughput and resolution of interactome mapping, methods for protein-protein interaction discovery by co-migration have been introduced. However, accurate identification of interacting protein pairs within the resulting large-scale proteomic datasets is challenging. Consequently, most computational pipelines for co-migration data analysis incorporate external genomic datasets to distinguish interacting from non-interacting protein pairs. The effect of this procedure on interactome mapping is poorly understood. Here, we conduct a rigorous analysis of genomic data integration for interactome recovery across a large number of co-migration datasets, spanning diverse experimental and computational methods. We find that genomic data integration leads to an increase in the functional coherence of the resulting interactome maps, but this comes at the expense of a decrease in power to discover novel interactions. Importantly, putative novel interactions predicted by genomic data integration are no more likely to later be experimentally discovered than those predicted from co-migration data alone. Our results reveal a widespread and unappreciated limitation in a methodology that has been widely used to map the interactome of humans and model organisms.

DynBench3D, a Web-Resource to Dynamically Generate Benchmark Sets of Large Heteromeric Protein Complexes

Multi-protein machines are responsible for most cellular tasks, and many efforts have been invested in the systematic identification and characterization of thousands of these macromolecular assemblies. However, unfortunately, the (quasi) atomic details necessary to understand their function are available only for a tiny fraction of the known complexes. The computational biology community is developing strategies to integrate structural data of different nature, from electron microscopy to X-ray crystallography, to model large molecular machines, as it has been done for individual proteins and interactions with remarkable success. However, unlike for binary interactions, there is no reliable gold-standard set of three-dimensional (3D) complexes to benchmark the performance of these methodologies and detect their limitations. Here, we present a strategy to dynamically generate non-redundant sets of 3D heteromeric complexes with three or more components. By changing the values of sequence identity and component overlap between assemblies required to define complex redundancy, we can create sets of representative complexes with known 3D structure (i.e., target complexes). Using an identity threshold of 20% and imposing a fraction of component overlap of <0.5, we identify 495 unique target complexes, which represent a real non-redundant set of heteromeric assemblies with known 3D structure. Moreover, for each target complex, we also identify a set of assemblies, of varying degrees of identity and component overlap, that can be readily used as input in a complex modeling exercise (i.e., template subcomplexes). We hope that resources like this will significantly help the development and progress assessment of novel methodologies, as docking benchmarks and blind prediction contests did. The interactive resource is accessible at https://DynBench3D.irbbarcelona.org.

Proteome-wide analysis of human motif-domain interactions mapped on influenza a virus

Background

The influenza A virus (IAV) is a constant threat for humans worldwide. The understanding of motif-domain protein participation is essential to combat the pathogen.

Results

In this study, a data mining approach was employed to extract influenza-human Protein-Protein interactions (PPI) from VirusMentha,Virus MINT, IntAct, and Pfam databases, to mine motif-domain interactions (MDIs) stored as Regular Expressions (RegExp) in 3DID database. A total of 107 RegExp related to human MDIs were searched on 51,242 protein fragments from H1N1, H1N2, H2N2, H3N2 and H5N1 strains obtained from Virus Variation database. A total 46 MDIs were frequently mapped on the IAV proteins and shared between the different strains. IAV kept host-like MDIs that were associated with the virus survival, which could be related to essential biological process such as microtubule-based processes, regulation of cell cycle check point, regulation of replication and transcription of DNA, etc. in human cells. The amino acid motifs were searched for matches in the immune epitope database and it was found that some motifs are part of experimentally determined epitopes on IAV, implying that such interactions exist.

Conclusion

The directed data-mining method employed could be used to identify functional motifs in other viruses for envisioning new therapies.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2237-8) contains supplementary material, which is available to authorized users.

A Novel Geometry-Based Approach to Infer Protein Interface Similarity

The protein interface is key to understand protein function, providing a vital insight on how proteins interact with each other and with other molecules. Over the years, many computational methods to compare protein structures were developed, yet evaluating interface similarity remains a very difficult task. Here, we present PatchBag – a geometry based method for efficient comparison of protein surfaces and interfaces. PatchBag is a Bag-Of-Words approach, which represents complex objects as vectors, enabling to search interface similarity in a highly efficient manner. Using a novel framework for evaluating interface similarity, we show that PatchBag performance is comparable to state-of-the-art alignment-based structural comparison methods. The great advantage of PatchBag is that it does not rely on sequence or fold information, thus enabling to detect similarities between interfaces in unrelated proteins. We propose that PatchBag can contribute to reveal novel evolutionary and functional relationships between protein interfaces.

PlanNET: homology-based predicted interactome for multiple planarian transcriptomes

Motivation

Planarians are emerging as a model organism to study regeneration in animals. However, the little available data of protein-protein interactions hinders the advances in understanding the mechanisms underlying its regenerating capabilities.

Results

We have developed a protocol to predict protein-protein interactions using sequence homology data and a reference Human interactome. This methodology was applied on 11 Schmidtea mediterranea transcriptomic sequence datasets. Then, using Neo4j as our database manager, we developed PlanNET, a web application to explore the multiplicity of networks and the associated sequence annotations. By mapping RNA-seq expression experiments onto the predicted networks, and allowing a transcript-centric exploration of the planarian interactome, we provide researchers with a useful tool to analyse possible pathways and to design new experiments, as well as a reproducible methodology to predict, store, and explore protein interaction networks for non-model organisms.

Availability and implementation

The web application PlanNET is available at https://compgen.bio.ub.edu/PlanNET. The source code used is available at https://compgen.bio.ub.edu/PlanNET/downloads.

Contact

jabril@ub.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Analyses of Protein Interaction Networks Using Computational Tools

The knowledge of protein-protein interactions (PPIs) and PPI networks (PPINs) is the key to starting to understand the biological processes inside the cell. Many computational tools have been designed to help explore PPIs and PPINs, such as those for interaction detection, reliability assessment and interaction network construction. Here, the application of computational tools is reviewed from three perspectives: PPI database construction, PPI prediction, and interaction network construction and analysis. This overview will provide researchers guidance on choosing appropriate methods for exploring PPIs.

Computational and Experimental Approaches to Predict Host-Parasite Protein-Protein Interactions

In host-parasite systems, protein-protein interactions are key to allow the pathogen to enter the host and persist within the host. The study of host-parasite molecular communication improves the understanding the mechanisms of infection, evasion of the host immune system and tropism across different tissues. Current trends in parasitology focus on unraveling host-parasite protein-protein interactions to aid the development of new strategies to combat pathogenic parasites with better treatments and prevention mechanisms. Due to the complexity of capturing experimentally these interactions, computational approaches integrating data from different sources (mainly "omics" data) become key to complement or support experimental approaches. Here, we focus on the application of experimental and computational methods in the prediction of host-parasite interactions and highlight the potential of each of these methods in specific contexts.

Investigation and identification of functional post-translational modification sites associated with drug binding and protein-protein interactions

Background

Protein post-translational modification (PTM) plays an essential role in various cellular processes that modulates the physical and chemical properties, folding, conformation, stability and activity of proteins, thereby modifying the functions of proteins. The improved throughput of mass spectrometry (MS) or MS/MS technology has not only brought about a surge in proteome-scale studies, but also contributed to a fruitful list of identified PTMs. However, with the increase in the number of identified PTMs, perhaps the more crucial question is what kind of biological mechanisms these PTMs are involved in. This is particularly important in light of the fact that most protein-based pharmaceuticals deliver their therapeutic effects through some form of PTM. Yet, our understanding is still limited with respect to the local effects and frequency of PTM sites near pharmaceutical binding sites and the interfaces of protein-protein interaction (PPI). Understanding PTM’s function is critical to our ability to manipulate the biological mechanisms of protein.

Results

In this study, to understand the regulation of protein functions by PTMs, we mapped 25,835 PTM sites to proteins with available three-dimensional (3D) structural information in the Protein Data Bank (PDB), including 1785 modified PTM sites on the 3D structure. Based on the acquired structural PTM sites, we proposed to use five properties for the structural characterization of PTM substrate sites: the spatial composition of amino acids, residues and side-chain orientations surrounding the PTM substrate sites, as well as the secondary structure, division of acidity and alkaline residues, and solvent-accessible surface area. We further mapped the structural PTM sites to the structures of drug binding and PPI sites, identifying a total of 1917 PTM sites that may affect PPI and 3951 PTM sites associated with drug-target binding. An integrated analytical platform (CruxPTM), with a variety of methods and online molecular docking tools for exploring the structural characteristics of PTMs, is presented. In addition, all tertiary structures of PTM sites on proteins can be visualized using the JSmol program.

Conclusion

Resolving the function of PTM sites is important for understanding the role that proteins play in biological mechanisms. Our work attempted to delineate the structural correlation between PTM sites and PPI or drug-target binding. CurxPTM could help scientists narrow the scope of their PTM research and enhance the efficiency of PTM identification in the face of big proteome data. CruxPTM is now available at http://csb.cse.yzu.edu.tw/CruxPTM/.

Electronic supplementary material

The online version of this article (10.1186/s12918-017-0506-1) contains supplementary material, which is available to authorized users.

RedNemo: topology-based PPI network reconstruction via repeated diffusion with neighborhood modifications

Motivation

Analysis of protein-protein interaction (PPI) networks provides invaluable insight into several systems biology problems. High-throughput experimental techniques together with computational methods provide large-scale PPI networks. However, a major issue with these networks is their erroneous nature; they contain false-positive interactions and usually many more false-negatives. Recently, several computational methods have been proposed for network reconstruction based on topology, where given an input PPI network the goal is to reconstruct the network by identifying false-positives/-negatives as correctly as possible.

Results

We observe that the existing topology-based network reconstruction algorithms suffer several shortcomings. An important issue is regarding the scalability of their computational requirements, especially in terms of execution times, with the network sizes. They have only been tested on small-scale networks thus far and when applied on large-scale networks of popular PPI databases, the executions require unreasonable amounts of time, or may even crash without producing any output for some instances even after several months of execution. We provide an algorithm, RedNemo, for the topology-based network reconstruction problem. It provides more accurate networks than the alternatives as far as biological qualities measured in terms of most metrics based on gene ontology annotations. The recovery of a high-confidence network modified via random edge removals and rewirings is also better with RedNemo than with the alternatives under most of the experimented removal/rewiring ratios. Furthermore, through extensive tests on databases of varying sizes, we show that RedNemo achieves these results with much better running time performances.

Availability and Implementation

Supplementary material including source code, useful scripts, experimental data and the results are available at http://webprs.khas.edu.tr/~cesim/RedNemo.tar.gz.

Contact

cesim@khas.edu.tr.

Supplementary information

Supplementary data are available at Bioinformatics online.

In silico structure-based approaches to discover protein-protein interaction-targeting drugs

A core concept behind modern drug discovery is finding a small molecule that modulates a function of a target protein. This concept has been successfully applied since the mid-1970s. However, the efficiency of drug discovery is decreasing because the druggable target space in the human proteome is limited. Recently, protein-protein interaction (PPI) has been identified asan emerging target space for drug discovery. PPI plays a pivotal role in biological pathways including diseases. Current human interactome research suggests that the number of PPIs is between 130,000 and 650,000, and only a small number of them have been targeted as drug targets. For traditional drug targets, in silico structure-based methods have been successful in many cases. However, their performance suffers on PPI interfaces because PPI interfaces are different in five major aspects: From a geometric standpoint, they have relatively large interface regions, flat geometry, and the interface surface shape tends to fluctuate upon binding. Also, their interactions are dominated by hydrophobic atoms, which is different from traditional binding-pocket-targeted drugs. Finally, PPI targets usually lack natural molecules that bind to the target PPI interface. Here, we first summarize characteristics of PPI interfaces and their known binders. Then, we will review existing in silico structure-based approaches for discovering small molecules that bind to PPI interfaces.

Scipion web tools: Easy to use cryo-EM image processing over the web

Macromolecular structural determination by Electron Microscopy under cryogenic conditions is revolutionizing the field of structural biology, interesting a large community of potential users. Still, the path from raw images to density maps is complex, and sophisticated image processing suites are required in this process, often demanding the installation and understanding of different software packages. Here, we present Scipion Web Tools, a web-based set of tools/workflows derived from the Scipion image processing framework, specially tailored to nonexpert users in need of very precise answers at several key stages of the structural elucidation process.

A rapid and accurate approach for prediction of interactomes from co-elution data (PrInCE)

Background

An organism’s protein interactome, or complete network of protein-protein interactions, defines the protein complexes that drive cellular processes. Techniques for studying protein complexes have traditionally applied targeted strategies such as yeast two-hybrid or affinity purification-mass spectrometry to assess protein interactions. However, given the vast number of protein complexes, more scalable methods are necessary to accelerate interaction discovery and to construct whole interactomes. We recently developed a complementary technique based on the use of protein correlation profiling (PCP) and stable isotope labeling in amino acids in cell culture (SILAC) to assess chromatographic co-elution as evidence of interacting proteins. Importantly, PCP-SILAC is also capable of measuring protein interactions simultaneously under multiple biological conditions, allowing the detection of treatment-specific changes to an interactome. Given the uniqueness and high dimensionality of co-elution data, new tools are needed to compare protein elution profiles, control false discovery rates, and construct an accurate interactome.

Results

Here we describe a freely available bioinformatics pipeline, PrInCE, for the analysis of co-elution data. PrInCE is a modular, open-source library that is computationally inexpensive, able to use label and label-free data, and capable of detecting tens of thousands of protein-protein interactions. Using a machine learning approach, PrInCE offers greatly reduced run time, more predicted interactions at the same stringency, prediction of protein complexes, and greater ease of use over previous bioinformatics tools for co-elution data. PrInCE is implemented in Matlab (version R2017a). Source code and standalone executable programs for Windows and Mac OSX are available at https://github.com/fosterlab/PrInCE, where usage instructions can be found. An example dataset and output are also provided for testing purposes.

Conclusions

PrInCE is the first fast and easy-to-use data analysis pipeline that predicts interactomes and protein complexes from co-elution data. PrInCE allows researchers without bioinformatics expertise to analyze high-throughput co-elution datasets.

Electronic supplementary material

The online version of this article (10.1186/s12859-017-1865-8) contains supplementary material, which is available to authorized users.

Algorithms for matching partially labelled sequence graphs

BACKGROUND

In order to find correlated pairs of positions between proteins, which are useful in predicting interactions, it is necessary to concatenate two large multiple sequence alignments such that the sequences that are joined together belong to those that interact in their species of origin. When each protein is unique then the species name is sufficient to guide this match, however, when there are multiple related sequences (paralogs) in each species then the pairing is more difficult. In bacteria a good guide can be gained from genome co-location as interacting proteins tend to be in a common operon but in eukaryotes this simple principle is not sufficient.

RESULTS

The methods developed in this paper take sets of paralogs for different proteins found in the same species and make a pairing based on their evolutionary distance relative to a set of other proteins that are unique and so have a known relationship (singletons). The former constitute a set of unlabelled nodes in a graph while the latter are labelled. Two variants were tested, one based on a phylogenetic tree of the sequences (the topology-based method) and a simpler, faster variant based only on the inter-sequence distances (the distance-based method). Over a set of test proteins, both gave good results, with the topology method performing slightly better.

CONCLUSIONS

The methods develop here still need refinement and augmentation from constraints other than the sequence data alone, such as known interactions from annotation and databases, or non-trivial relationships in genome location. With the ever growing numbers of eukaryotic genomes, it is hoped that the methods described here will open a route to the use of these data equal to the current success attained with bacterial sequences.

Modeling protein quaternary structure of homo- and hetero-oligomers beyond binary interactions by homology

Cellular processes often depend on interactions between proteins and the formation of macromolecular complexes. The impairment of such interactions can lead to deregulation of pathways resulting in disease states, and it is hence crucial to gain insights into the nature of macromolecular assemblies. Detailed structural knowledge about complexes and protein-protein interactions is growing, but experimentally determined three-dimensional multimeric assemblies are outnumbered by complexes supported by non-structural experimental evidence. Here, we aim to fill this gap by modeling multimeric structures by homology, only using amino acid sequences to infer the stoichiometry and the overall structure of the assembly. We ask which properties of proteins within a family can assist in the prediction of correct quaternary structure. Specifically, we introduce a description of protein-protein interface conservation as a function of evolutionary distance to reduce the noise in deep multiple sequence alignments. We also define a distance measure to structurally compare homologous multimeric protein complexes. This allows us to hierarchically cluster protein structures and quantify the diversity of alternative biological assemblies known today. We find that a combination of conservation scores, structural clustering, and classical interface descriptors, can improve the selection of homologous protein templates leading to reliable models of protein complexes.

Domain-based prediction of the human isoform interactome provides insights into the functional impact of alternative splicing

Alternative splicing is known to remodel protein-protein interaction networks (“interactomes”), yet large-scale determination of isoform-specific interactions remains challenging. We present a domain-based method to predict the isoform interactome from the reference interactome. First, we construct the domain-resolved reference interactome by mapping known domain-domain interactions onto experimentally-determined interactions between reference proteins. Then, we construct the isoform interactome by predicting that an isoform loses an interaction if it loses the domain mediating the interaction. Our prediction framework is of high-quality when assessed by experimental data. The predicted human isoform interactome reveals extensive network remodeling by alternative splicing. Protein pairs interacting with different isoforms of the same gene tend to be more divergent in biological function, tissue expression, and disease phenotype than protein pairs interacting with the same isoforms. Our prediction method complements experimental efforts, and demonstrates that integrating structural domain information with interactomes provides insights into the functional impact of alternative splicing.

Protein-protein interaction networks have been extensively used in systems biology to study the role of proteins in cell function and disease. However, current network biology studies typically assume that one gene encodes one protein isoform, ignoring the effect of alternative splicing. Alternative splicing allows a gene to produce multiple protein isoforms, by alternatively selecting distinct regions in the gene to be translated to protein products. Here, we present a computational method to predict and analyze the large-scale effect of alternative splicing on protein-protein interaction networks. Starting with a reference protein-protein interaction network determined by experiments, our method annotates protein-protein interactions with domain-domain interactions, and predicts that a protein isoform loses an interaction if it loses the domain mediating the interaction as a result of alternative splicing. Our predictions reveal the central role of alternative splicing in extensively remodeling the human protein-protein interaction network, and in increasing the functional complexity of the human cell. Our prediction method complements ongoing experimental efforts by predicting isoform-specific interactions for genes not tested yet by experiments and providing insights into the functional impact of alternative splicing.

Protein-Protein Interactions in Virus-Host Systems

To study virus–host protein interactions, knowledge about viral and host protein architectures and repertoires, their particular evolutionary mechanisms, and information on relevant sources of biological data is essential. The purpose of this review article is to provide a thorough overview about these aspects. Protein domains are basic units defining protein interactions, and the uniqueness of viral domain repertoires, their mode of evolution, and their roles during viral infection make viruses interesting models of study. Mutations at protein interfaces can reduce or increase their binding affinities by changing protein electrostatics and structural properties. During the course of a viral infection, both pathogen and cellular proteins are constantly competing for binding partners. Endogenous interfaces mediating intraspecific interactions—viral–viral or host–host interactions—are constantly targeted and inhibited by exogenous interfaces mediating viral–host interactions. From a biomedical perspective, blocking such interactions is the main mechanism underlying antiviral therapies. Some proteins are able to bind multiple partners, and their modes of interaction define how fast these “hub proteins” evolve. “Party hubs” have multiple interfaces; they establish simultaneous/stable (domain–domain) interactions, and tend to evolve slowly. On the other hand, “date hubs” have few interfaces; they establish transient/weak (domain–motif) interactions by means of short linear peptides (15 or fewer residues), and can evolve faster. Viral infections are mediated by several protein–protein interactions (PPIs), which can be represented as networks (protein interaction networks, PINs), with proteins being depicted as nodes, and their interactions as edges. It has been suggested that viral proteins tend to establish interactions with more central and highly connected host proteins. In an evolutionary arms race, viral and host proteins are constantly changing their interface residues, either to evade or to optimize their binding capabilities. Apart from gaining and losing interactions via rewiring mechanisms, virus–host PINs also evolve via gene duplication (paralogy); conservation (orthology); horizontal gene transfer (HGT) (xenology); and molecular mimicry (convergence). The last sections of this review focus on PPI experimental approaches and their limitations, and provide an overview of sources of biomolecular data for studying virus–host protein interactions.

Genetic and functional characterization of disease associations explains comorbidity

Understanding relationships between diseases, such as comorbidities, has important socio-economic implications, ranging from clinical study design to health care planning. Most studies characterize disease comorbidity using shared genetic origins, ignoring pathway-based commonalities between diseases. In this study, we define the disease pathways using an interactome-based extension of known disease-genes and introduce several measures of functional overlap. The analysis reveals 206 significant links among 94 diseases, giving rise to a highly clustered disease association network. We observe that around 95% of the links in the disease network, though not identified by genetic overlap, are discovered by functional overlap. This disease network portraits rheumatoid arthritis, asthma, atherosclerosis, pulmonary diseases and Crohn's disease as hubs and thus pointing to common inflammatory processes underlying disease pathophysiology. We identify several described associations such as the inverse comorbidity relationship between Alzheimer's disease and neoplasms. Furthermore, we investigate the disruptions in protein interactions by mapping mutations onto the domains involved in the interaction, suggesting hypotheses on the causal link between diseases. Finally, we provide several proof-of-principle examples in which we model the effect of the mutation and the change of the association strength, which could explain the observed comorbidity between diseases caused by the same genetic alterations.

Unravelling the complexity of signalling networks in cancer: A review of the increasing role for computational modelling

Cancer induction is a highly complex process involving hundreds of different inducers but whose eventual outcome is the same. Clearly, it is essential to understand how signalling pathways and networks generated by these inducers interact to regulate cell behaviour and create the cancer phenotype. While enormous strides have been made in identifying key networking profiles, the amount of data generated far exceeds our ability to understand how it all "fits together". The number of potential interactions is astronomically large and requires novel approaches and extreme computation methods to dissect them out. However, such methodologies have high intrinsic mathematical and conceptual content which is difficult to follow. This review explains how computation modelling is progressively finding solutions and also revealing unexpected and unpredictable nano-scale molecular behaviours extremely relevant to how signalling and networking are coherently integrated. It is divided into linked sections illustrated by numerous figures from the literature describing different approaches and offering visual portrayals of networking and major conceptual advances in the field. First, the problem of signalling complexity and data collection is illustrated for only a small selection of known oncogenes. Next, new concepts from biophysics, molecular behaviours, kinetics, organisation at the nano level and predictive models are presented. These areas include: visual representations of networking, Energy Landscapes and energy transfer/dissemination (entropy); diffusion, percolation; molecular crowding; protein allostery; quinary structure and fractal distributions; energy management, metabolism and re-examination of the Warburg effect. The importance of unravelling complex network interactions is then illustrated for some widely-used drugs in cancer therapy whose interactions are very extensive. Finally, use of computational modelling to develop micro- and nano- functional models ("bottom-up" research) is highlighted. The review concludes that computational modelling is an essential part of cancer research and is vital to understanding network formation and molecular behaviours that are associated with it. Its role is increasingly essential because it is unravelling the huge complexity of cancer induction otherwise unattainable by any other approach.

Rewiring of the inferred protein interactome during blood development studied with the tool PPICompare

BACKGROUND

Differential analysis of cellular conditions is a key approach towards understanding the consequences and driving causes behind biological processes such as developmental transitions or diseases. The progress of whole-genome expression profiling enabled to conveniently capture the state of a cell's transcriptome and to detect the characteristic features that distinguish cells in specific conditions. In contrast, mapping the physical protein interactome for many samples is experimentally infeasible at the moment. For the understanding of the whole system, however, it is equally important how the interactions of proteins are rewired between cellular states. To overcome this deficiency, we recently showed how condition-specific protein interaction networks that even consider alternative splicing can be inferred from transcript expression data. Here, we present the differential network analysis tool PPICompare that was specifically designed for isoform-sensitive protein interaction networks.

RESULTS

Besides detecting significant rewiring events between the interactomes of grouped samples, PPICompare infers which alterations to the transcriptome caused each rewiring event and what is the minimal set of alterations necessary to explain all between-group changes. When applied to the development of blood cells, we verified that a reasonable amount of rewiring events were reported by the tool and found that differential gene expression was the major determinant of cellular adjustments to the interactome. Alternative splicing events were consistently necessary in each developmental step to explain all significant alterations and were especially important for rewiring in the context of transcriptional control.

CONCLUSIONS

Applying PPICompare enabled us to investigate the dynamics of the human protein interactome during developmental transitions. A platform-independent implementation of the tool PPICompare is available at https://sourceforge.net/projects/ppicompare/ .

Using structural knowledge in the protein data bank to inform the search for potential host-microbe protein interactions in sequence space: application to Mycobacterium tuberculosis

Background

A comprehensive map of the human-M. tuberculosis (MTB) protein interactome would help fill the gaps in our understanding of the disease, and computational prediction can aid and complement experimental studies towards this end. Several sequence-based in silico approaches tap the existing data on experimentally validated protein-protein interactions (PPIs); these PPIs serve as templates from which novel interactions between pathogen and host are inferred. Such comparative approaches typically make use of local sequence alignment, which, in the absence of structural details about the interfaces mediating the template interactions, could lead to incorrect inferences, particularly when multi-domain proteins are involved.

Results

We propose leveraging the domain-domain interaction (DDI) information in PDB complexes to score and prioritize candidate PPIs between host and pathogen proteomes based on targeted sequence-level comparisons. Our method picks out a small set of human-MTB protein pairs as candidates for physical interactions, and the use of functional meta-data suggests that some of them could contribute to the in vivo molecular cross-talk between pathogen and host that regulates the course of the infection. Further, we present numerical data for Pfam domain families that highlights interaction specificity on the domain level. Not every instance of a pair of domains, for which interaction evidence has been found in a few instances (i.e. structures), is likely to functionally interact. Our sorting approach scores candidates according to how “distant” they are in sequence space from known examples of DDIs (templates). Thus, it provides a natural way to deal with the heterogeneity in domain-level interactions.

Conclusions

Our method represents a more informed application of local alignment to the sequence-based search for potential human-microbial interactions that uses available PPI data as a prior. Our approach is somewhat limited in its sensitivity by the restricted size and diversity of the template dataset, but, given the rapid accumulation of solved protein complex structures, its scope and utility are expected to keep steadily improving.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1550-y) contains supplementary material, which is available to authorized users.

A computational interactome for prioritizing genes associated with complex agronomic traits in rice (Oryza sativa)

Rice (Oryza sativa) is one of the most important staple foods for more than half of the global population. Many rice traits are quantitative, complex and controlled by multiple interacting genes. Thus, a full understanding of genetic relationships will be critical to systematically identify genes controlling agronomic traits. We developed a genome-wide rice protein-protein interaction network (RicePPINet, http://netbio.sjtu.edu.cn/riceppinet) using machine learning with structural relationship and functional information. RicePPINet contained 708 819 predicted interactions for 16 895 non-transposable element related proteins. The power of the network for discovering novel protein interactions was demonstrated through comparison with other publicly available protein-protein interaction (PPI) prediction methods, and by experimentally determined PPI data sets. Furthermore, global analysis of domain-mediated interactions revealed RicePPINet accurately reflects PPIs at the domain level. Our studies showed the efficiency of the RicePPINet-based method in prioritizing candidate genes involved in complex agronomic traits, such as disease resistance and drought tolerance, was approximately 2-11 times better than random prediction. RicePPINet provides an expanded landscape of computational interactome for the genetic dissection of agronomically important traits in rice.

Prediction of GCRV virus-host protein interactome based on structural motif-domain interactions

BACKGROUND

Grass carp hemorrhagic disease, caused by grass carp reovirus (GCRV), is the most fatal causative agent in grass carp aquaculture. Protein-protein interactions between virus and host are one avenue through which GCRV can trigger infection and induce disease. Experimental approaches for the detection of host-virus interactome have many inherent limitations, and studies on protein-protein interactions between GCRV and its host remain rare.

RESULTS

In this study, based on known motif-domain interaction information, we systematically predicted the GCRV virus-host protein interactome by using motif-domain interaction pair searching strategy. These proteins derived from different domain families and were predicted to interact with different motif patterns in GCRV. JAM-A protein was successfully predicted to interact with motifs of GCRV Sigma1-like protein, and shared the similar binding mode compared with orthoreovirus. Differentially expressed genes during GCRV infection process were extracted and mapped to our predicted interactome, the overlapped genes displayed different tissue expression distributions on the whole, the overall expression level in intestinal is higher than that of other three tissues, which may suggest that the functions of these genes are more active in intestinal. Function annotation and pathway enrichment analysis revealed that the host targets were largely involved in signaling pathway and immune pathway, such as interferon-gamma signaling pathway, VEGF signaling pathway, EGF receptor signaling pathway, B cell activation, and T cell activation.

CONCLUSIONS

Although the predicted PPIs may contain some false positives due to limited data resource and poor research background in non-model species, the computational method still provide reasonable amount of interactions, which can be further validated by high throughput experiments. The findings of this work will contribute to the development of system biology for GCRV infectious diseases, and help guide the identification of novel receptors of GCRV in its host.

The OncoPPi network of cancer-focused protein-protein interactions to inform biological insights and therapeutic strategies

As genomics advances reveal the cancer gene landscape, a daunting task is to understand how these genes contribute to dysregulated oncogenic pathways. Integration of cancer genes into networks offers opportunities to reveal protein-protein interactions (PPIs) with functional and therapeutic significance. Here, we report the generation of a cancer-focused PPI network, termed OncoPPi, and identification of >260 cancer-associated PPIs not in other large-scale interactomes. PPI hubs reveal new regulatory mechanisms for cancer genes like MYC, STK11, RASSF1 and CDK4. As example, the NSD3 (WHSC1L1)-MYC interaction suggests a new mechanism for NSD3/BRD4 chromatin complex regulation of MYC-driven tumours. Association of undruggable tumour suppressors with drug targets informs therapeutic options. Based on OncoPPi-derived STK11-CDK4 connectivity, we observe enhanced sensitivity of STK11-silenced lung cancer cells to the FDA-approved CDK4 inhibitor palbociclib. OncoPPi is a focused PPI resource that links cancer genes into a signalling network for discovery of PPI targets and network-implicated tumour vulnerabilities for therapeutic interrogation.

Pathogenicity Genes in Ustilaginoidea virens Revealed by a Predicted Protein-Protein Interaction Network

Rice false smut, caused by Ustilaginoidea virens, produces significant losses in rice yield and grain quality and has recently emerged as one of the most important rice diseases worldwide. Despite its importance in rice production, relatively few studies have been conducted to illustrate the complex interactome and the pathogenicity gene interactions. Here a protein-protein interaction network of U. virens was built through two well-recognized approaches, interolog- and domain-domain interaction-based methods. A total of 20 217 interactions associated with 3305 proteins were predicted after strict filtering. The reliability of the network was assessed computationally and experimentally. The topology of the interactome network revealed highly connected proteins. A pathogenicity-related subnetwork involving up-regulated genes during early U. virens infection was also constructed, and many novel pathogenicity proteins were predicted in the subnetwork. In addition, we built an interspecies PPI network between U. virens and Oryza sativa, providing new insights for molecular interactions of this host-pathogen pathosystem. A web-based publicly available interactive database based on these interaction networks has also been released. In summary, a proteome-scale map of the PPI network was described for U. virens, which will provide new perspectives for finely dissecting interactions of genes related to its pathogenicity.

Conservation of coevolving protein interfaces bridges prokaryote-eukaryote homologies in the twilight zone

Protein-protein interactions are fundamental for the proper functioning of the cell. As a result, protein interaction surfaces are subject to strong evolutionary constraints. Recent developments have shown that residue coevolution provides accurate predictions of heterodimeric protein interfaces from sequence information. So far these approaches have been limited to the analysis of families of prokaryotic complexes for which large multiple sequence alignments of homologous sequences can be compiled. We explore the hypothesis that coevolution points to structurally conserved contacts at protein-protein interfaces, which can be reliably projected to homologous complexes with distantly related sequences. We introduce a domain-centered protocol to study the interplay between residue coevolution and structural conservation of protein-protein interfaces. We show that sequence-based coevolutionary analysis systematically identifies residue contacts at prokaryotic interfaces that are structurally conserved at the interface of their eukaryotic counterparts. In turn, this allows the prediction of conserved contacts at eukaryotic protein-protein interfaces with high confidence using solely mutational patterns extracted from prokaryotic genomes. Even in the context of high divergence in sequence (the twilight zone), where standard homology modeling of protein complexes is unreliable, our approach provides sequence-based accurate information about specific details of protein interactions at the residue level. Selected examples of the application of prokaryotic coevolutionary analysis to the prediction of eukaryotic interfaces further illustrate the potential of this approach.

Prediction of Protein-Protein Interactions by Evidence Combining Methods

Most cellular functions involve proteins' features based on their physical interactions with other partner proteins. Sketching a map of protein-protein interactions (PPIs) is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy.

AraPPISite: a database of fine-grained protein-protein interaction site annotations for Arabidopsis thaliana

Knowledge about protein interaction sites provides detailed information of protein-protein interactions (PPIs). To date, nearly 20,000 of PPIs from Arabidopsis thaliana have been identified. Nevertheless, the interaction site information has been largely missed by previously published PPI databases. Here, AraPPISite, a database that presents fine-grained interaction details for A. thaliana PPIs is established. First, the experimentally determined 3D structures of 27 A. thaliana PPIs are collected from the Protein Data Bank database and the predicted 3D structures of 3023 A. thaliana PPIs are modeled by using two well-established template-based docking methods. For each experimental/predicted complex structure, AraPPISite not only provides an interactive user interface for browsing interaction sites, but also lists detailed evolutionary and physicochemical properties of these sites. Second, AraPPISite assigns domain-domain interactions or domain-motif interactions to 4286 PPIs whose 3D structures cannot be modeled. In this case, users can easily query protein interaction regions at the sequence level. AraPPISite is a free and user-friendly database, which does not require user registration or any configuration on local machines. We anticipate AraPPISite can serve as a helpful database resource for the users with less experience in structural biology or protein bioinformatics to probe the details of PPIs, and thus accelerate the studies of plant genetics and functional genomics. AraPPISite is available at http://systbio.cau.edu.cn/arappisite/index.html .

Multi-OMICs and Genome Editing Perspectives on Liver Cancer Signaling Networks

The advent of the human genome sequence and the resulting ~20,000 genes provide a crucial framework for a transition from traditional biology to an integrative “OMICs” arena (Lander et al., 2001; Venter et al., 2001; Kitano, 2002). This brings in a revolution for cancer research, which now enters a big data era. In the past decade, with the facilitation by next-generation sequencing, there have been a huge number of large-scale sequencing efforts, such as The Cancer Genome Atlas (TCGA), the HapMap, and the 1000 genomes project. As a result, a deluge of genomic information becomes available from patients stricken by a variety of cancer types. The list of cancer-associated genes is ever expanding. New discoveries are made on how frequent and highly penetrant mutations, such as those in the telomerase reverse transcriptase (TERT) and TP53, function in cancer initiation, progression, and metastasis. Most genes with relatively frequent but weakly penetrant cancer mutations still remain to be characterized. In addition, genes that harbor rare but highly penetrant cancer-associated mutations continue to emerge. Here, we review recent advances related to cancer genomics, proteomics, and systems biology and suggest new perspectives in targeted therapy and precision medicine.

K-shell Analysis Reveals Distinct Functional Parts in an Electron Transfer Network and Its Implications for Extracellular Electron Transfer

Shewanella oneidensis MR-1 is capable of extracellular electron transfer (EET) and hence has attracted considerable attention. The EET pathways mainly consist of c-type cytochromes, along with some other proteins involved in electron transfer processes. By whole genome study and protein interactions inquisition, we constructed a large-scale electron transfer network containing 2276 interactions among 454 electron transfer related proteins in S. oneidensis MR-1. Using the k-shell decomposition method, we identified and analyzed distinct parts of the electron transfer network. We found that there was a negative correlation between the k s (k-shell values) and the average DR_100 (disordered regions per 100 amino acids) in every shell, which suggested that disordered regions of proteins played an important role during the formation and extension of the electron transfer network. Furthermore, proteins in the top three shells of the network are mainly located in the cytoplasm and inner membrane; these proteins can be responsible for transfer of electrons into the quinone pool in a wide variety of environmental conditions. In most of the other shells, proteins are broadly located throughout the five cellular compartments (cytoplasm, inner membrane, periplasm, outer membrane, and extracellular), which ensures the important EET ability of S. oneidensis MR-1. Specifically, the fourth shell was responsible for EET and the c-type cytochromes in the remaining shells of the electron transfer network were involved in aiding EET. Taken together, these results show that there are distinct functional parts in the electron transfer network of S. oneidensis MR-1, and the EET processes could achieve high efficiency through cooperation through such an electron transfer network.

SPAR: a random forest-based predictor for self-interacting proteins with fine-grained domain information

Protein self-interaction, i.e. the interaction between two or more identical proteins expressed by one gene, plays an important role in the regulation of cellular functions. Considering the limitations of experimental self-interaction identification, it is necessary to design specific bioinformatics tools for self-interacting protein (SIP) prediction from protein sequence information. In this study, we proposed an improved computational approach for SIP prediction, termed SPAR (Self-interacting Protein Analysis serveR). Firstly, we developed an improved encoding scheme named critical residues substitution (CRS), in which the fine-grained domain-domain interaction information was taken into account. Then, by employing the Random Forest algorithm, the performance of CRS was evaluated and compared with several other encoding schemes commonly used for sequence-based protein-protein interaction prediction. Through the tenfold cross-validation tests on a balanced training dataset, CRS performed the best, with the average accuracy up to 72.01 %. We further integrated CRS with other encoding schemes and identified the most important features using the mRMR (the minimum redundancy maximum relevance) feature selection method. Our SPAR model with selected features achieved an average accuracy of 92.09 % on the human-independent test set (the ratio of positives to negatives was about 1:11). Besides, we also evaluated the performance of SPAR on an independent yeast test set (the ratio of positives to negatives was about 1:8) and obtained an average accuracy of 76.96 %. The results demonstrate that SPAR is capable of achieving a reasonable performance in cross-species application. The SPAR server is freely available for academic use at http://systbio.cau.edu.cn/zzdlab/spar/ .

Maximum margin classifier working in a set of strings

Numbers and numerical vectors account for a large portion of data. However, recently, the amount of string data generated has increased dramatically. Consequently, classifying string data is a common problem in many fields. The most widely used approach to this problem is to convert strings into numerical vectors using string kernels and subsequently apply a support vector machine that works in a numerical vector space. However, this non-one-to-one conversion involves a loss of information and makes it impossible to evaluate, using probability theory, the generalization error of a learning machine, considering that the given data to train and test the machine are strings generated according to probability laws. In this study, we approach this classification problem by constructing a classifier that works in a set of strings. To evaluate the generalization error of such a classifier theoretically, probability theory for strings is required. Therefore, we first extend a limit theorem for a consensus sequence of strings demonstrated by one of the authors and co-workers in a previous study. Using the obtained result, we then demonstrate that our learning machine classifies strings in an asymptotically optimal manner. Furthermore, we demonstrate the usefulness of our machine in practical data analysis by applying it to predicting protein-protein interactions using amino acid sequences and classifying RNAs by the secondary structure using nucleotide sequences.

Widespread Expansion of Protein Interaction Capabilities by Alternative Splicing

While alternative splicing is known to diversify the functional characteristics of some genes, the extent to which protein isoforms globally contribute to functional complexity on a proteomic scale remains unknown. To address this systematically, we cloned full-length open reading frames of alternatively spliced transcripts for a large number of human genes and used protein-protein interaction profiling to functionally compare hundreds of protein isoform pairs. The majority of isoform pairs share less than 50% of their interactions. In the global context of interactome network maps, alternative isoforms tend to behave like distinct proteins rather than minor variants of each other. Interaction partners specific to alternative isoforms tend to be expressed in a highly tissue-specific manner and belong to distinct functional modules. Our strategy, applicable to other functional characteristics, reveals a widespread expansion of protein interaction capabilities through alternative splicing and suggests that many alternative "isoforms" are functionally divergent (i.e., "functional alloforms").

Association analysis of the perturbation of interactions in biological pathways and anticancer drug activity

Understanding how different genomic mutational landscapes in patients with cancer lead to different responses to anticancer drugs is an important challenge for realizing precision medicine for cancer. Many studies have analyzed the comprehensive anticancer drug-response profiles and genomic profiles of cancer cell lines to identify the relationship between the anticancer drug response and genomic alternations. However, few studies have focused on interpreting these profiles with a network perspective. In this work, we analyzed genomic alterations in cancer cell lines by considering which interactions in the signaling pathway were perturbed by mutations. With our interaction-centric approach, we identified novel interaction/drug response associations for two drugs (afatinib and ixabepilone) for which no gene-centric association could be found. When we compared the performance of classifiers for predicting the responses to 164 drugs, the classifiers trained with interaction-centric features outperformed the classifiers trained with gene-centric features, despite the smaller number of features (p-value = 2.0 × 10(-3)). By incorporating the interaction information from signaling pathways, we revealed associations between genomic alterations and drug responses that could be missed when using a gene-centric approach.

Prediction and characterization of protein-protein interaction network in Bacillus licheniformis WX-02

In this study, we constructed a protein-protein interaction (PPI) network of B. licheniformis strain WX-02 with interolog method and domain-based method, which contained 15,864 edges and 2,448 nodes. Although computationally predicted networks have relatively low coverage and high false-positive rate, our prediction was confirmed from three perspectives: local structural features, functional similarities and transcriptional correlations. Further analysis of the COG heat map showed that protein interactions in B. licheniformis WX-02 mainly occurred in the same functional categories. By incorporating the transcriptome data, we found that the topological properties of the PPI network were robust under normal and high salt conditions. In addition, 267 different protein complexes were identified and 117 poorly characterized proteins were annotated with certain functions based on the PPI network. Furthermore, the sub-network showed that a hub protein CcpA jointed directly or indirectly many proteins related to γ-PGA synthesis and regulation, such as PgsB, GltA, GltB, ProB, ProJ, YcgM and two signal transduction systems ComP-ComA and DegS-DegU. Thus, CcpA might play an important role in the regulation of γ-PGA synthesis. This study therefore will facilitate the understanding of the complex cellular behaviors and mechanisms of γ-PGA synthesis in B. licheniformis WX-02.

3DIANA: 3D Domain Interaction Analysis: A Toolbox for Quaternary Structure Modeling

Electron microscopy (EM) is experiencing a revolution with the advent of a new generation of Direct Electron Detectors, enabling a broad range of large and flexible structures to be resolved well below 1 nm resolution. Although EM techniques are evolving to the point of directly obtaining structural data at near-atomic resolution, for many molecules the attainable resolution might not be enough to propose high-resolution structural models. However, accessing information on atomic coordinates is a necessary step toward a deeper understanding of the molecular mechanisms that allow proteins to perform specific tasks. For that reason, methods for the integration of EM three-dimensional maps with x-ray and NMR structural data are being developed, a modeling task that is normally referred to as fitting, resulting in the so called hybrid models. In this work, we present a novel application—3DIANA—specially targeted to those cases in which the EM map resolution is medium or low and additional experimental structural information is scarce or even lacking. In this way, 3DIANA statistically evaluates proposed/potential contacts between protein domains, presents a complete catalog of both structurally resolved and predicted interacting regions involving these domains and, finally, suggests structural templates to model the interaction between them. The evaluation of the proposed interactions is computed with DIMERO, a new method that scores physical binding sites based on the topology of protein interaction networks, which has recently shown the capability to increase by 200% the number of domain-domain interactions predicted in interactomes as compared to previous approaches. The new application displays the information at a sequence and structural level and is accessible through a web browser or as a Chimera plugin at http://3diana.cnb.csic.es.

HitPredict version 4: comprehensive reliability scoring of physical protein-protein interactions from more than 100 species

HitPredict is a consolidated resource of experimentally identified, physical protein–protein interactions with confidence scores to indicate their reliability. The study of genes and their inter-relationships using methods such as network and pathway analysis requires high quality protein–protein interaction information. Extracting reliable interactions from most of the existing databases is challenging because they either contain only a subset of the available interactions, or a mixture of physical, genetic and predicted interactions. Automated integration of interactions is further complicated by varying levels of accuracy of database content and lack of adherence to standard formats. To address these issues, the latest version of HitPredict provides a manually curated dataset of 398 696 physical associations between 70 808 proteins from 105 species. Manual confirmation was used to resolve all issues encountered during data integration. For improved reliability assessment, this version combines a new score derived from the experimental information of the interactions with the original score based on the features of the interacting proteins. The combined interaction score performs better than either of the individual scores in HitPredict as well as the reliability score of another similar database. HitPredict provides a web interface to search proteins and visualize their interactions, and the data can be downloaded for offline analysis. Data usability has been enhanced by mapping protein identifiers across multiple reference databases. Thus, the latest version of HitPredict provides a significantly larger, more reliable and usable dataset of protein–protein interactions from several species for the study of gene groups.

Database URL: http://hintdb.hgc.jp/htp

Deciphering Supramolecular Structures with Protein-Protein Interaction Network Modeling

Many biological molecules are assembled into supramolecules that are essential to perform complicated functions in the cell. However, experimental information about the structures of supramolecules is not sufficient at this point. We developed a method of predicting and modeling the structures of supramolecules in a biological network by combining structural data of the Protein Data Bank (PDB) and interaction data in IntAct databases. Templates for binary complexes in IntAct were extracted from PDB. Modeling was attempted by assembling binary complexes with superposed shared subunits. A total of 3,197 models were constructed, and 1,306 (41% of the total) contained at least one subunit absent from experimental structures. The models also suggested 970 (25% of the total) experimentally undetected subunit interfaces, and 41 human disease-related amino acid variants were mapped onto these model-suggested interfaces. The models demonstrated that protein-protein interaction network modeling is useful to fill the information gap between biological networks and structures.

PPIXpress: construction of condition-specific protein interaction networks based on transcript expression

Protein-protein interaction networks are an important component of modern systems biology. Yet, comparatively few efforts have been made to tailor their topology to the actual cellular condition being studied. Here, we present a network construction method that exploits expression data at the transcript-level and thus reveals alterations in protein connectivity not only caused by differential gene expression but also by alternative splicing. We achieved this by establishing a direct correspondence between individual protein interactions and underlying domain interactions in a complete but condition-unspecific protein interaction network. This knowledge was then used to infer the condition-specific presence of interactions from the dominant protein isoforms. When we compared contextualized interaction networks of matched normal and tumor samples in breast cancer, our transcript-based construction identified more significant alterations that affected proteins associated with cancerogenesis than a method that only uses gene expression data. The approach is provided as the user-friendly tool PPIXpress.

AVAILABILITY AND IMPLEMENTATION

PPIXpress is available at https://sourceforge.net/projects/ppixpress/.

FARE-CAFE: a database of functional and regulatory elements of cancer-associated fusion events

Chromosomal translocation (CT) is of enormous clinical interest because this disorder is associated with various major solid tumors and leukemia. A tumor-specific fusion gene event may occur when a translocation joins two separate genes. Currently, various CT databases provide information about fusion genes and their genomic elements. However, no database of the roles of fusion genes, in terms of essential functional and regulatory elements in oncogenesis, is available. FARE-CAFE is a unique combination of CTs, fusion proteins, protein domains, domain–domain interactions, protein–protein interactions, transcription factors and microRNAs, with subsequent experimental information, which cannot be found in any other CT database. Genomic DNA information including, for example, manually collected exact locations of the first and second break points, sequences and karyotypes of fusion genes are included. FARE-CAFE will substantially facilitate the cancer biologist’s mission of elucidating the pathogenesis of various types of cancer. This database will ultimately help to develop ‘novel’ therapeutic approaches.

Database URL:http://ppi.bioinfo.asia.edu.tw/FARE-CAFE