Clustering based on multiple biological information: approach for predicting protein complexes.

A novel extended Pareto Optimality Consensus model for predicting essential proteins

BackgroundEssential proteins are distinctly important for an organism’s survival and development and crucial to disease analysis and drug design as well. Large-scale protein-protein interaction (PPI) data sets exist in Saccharomyces cerevisiae, which provides us with a valuable opportunity to predict identify essential proteins from PPI networks. Many network topology-based computational methods have been designed to detect essential proteins. However, these methods are limited by the completeness of available PPI data. To break out of these restraints, some computational methods have been proposed by integrating PPI networks and multi-source biological data. Despite the progress in the research of multiple data fusion, it is still challenging to improve the prediction accuracy of the computational methods.ResultsIn this paper, we design a novel iterative model for essential proteins prediction, named Randomly Walking in the Heterogeneous Network (RWHN). In RWHN, a weighted protein-protein interaction network and a domain-domain association network are constructed according to the original PPI network and the known protein-domain association network, firstly. And then, we establish a new heterogeneous matrix by combining the two constructed networks with the protein-domain association network. Based on the heterogeneous matrix, a transition probability matrix is established by normalized operation. Finally, an improved PageRank algorithm is adopted on the heterogeneous network for essential proteins prediction. In order to eliminate the influence of the false negative, information on orthologous proteins and the subcellular localization information of proteins are integrated to initialize the score vector of proteins. In RWHN, the topology, conservative and functional features of essential proteins are all taken into account in the prediction process. The experimental results show that RWHN obviously exceeds in predicting essential proteins ten other competing methods.ConclusionsWe demonstrated that integrating multi-source data into a heterogeneous network can preserve the complex relationship among multiple biological data and improve the prediction accuracy of essential proteins. RWHN, our proposed method, is effective for the prediction of essential proteins.

BackgroundMost proteins form macromolecular complexes to perform their biological functions. However, experimentally determined protein complex data, especially of those involving more than two protein partners, are relatively limited in the current state-of-the-art high-throughput experimental techniques. Nevertheless, many techniques (such as yeast-two-hybrid) have enabled systematic screening of pairwise protein-protein interactions en masse. Thus computational approaches for detecting protein complexes from protein interaction data are useful complements to the limited experimental methods. They can be used together with the experimental methods for mapping the interactions of proteins to understand how different proteins are organized into higher-level substructures to perform various cellular functions.ResultsGiven the abundance of pairwise protein interaction data from high-throughput genome-wide experimental screenings, a protein interaction network can be constructed from protein interaction data by considering individual proteins as the nodes, and the existence of a physical interaction between a pair of proteins as a link. This binary protein interaction graph can then be used for detecting protein complexes using graph clustering techniques. In this paper, we review and evaluate the state-of-the-art techniques for computational detection of protein complexes, and discuss some promising research directions in this field.ConclusionsExperimental results with yeast protein interaction data show that the interaction subgraphs discovered by various computational methods matched well with actual protein complexes. In addition, the computational approaches have also improved in performance over the years. Further improvements could be achieved if the quality of the underlying protein interaction data can be considered adequately to minimize the undesirable effects from the irrelevant and noisy sources, and the various biological evidences can be better incorporated into the detection process to maximize the exploitation of the increasing wealth of biological knowledge available.

BackgroundAccurate determination of protein complexes has become a key task of system biology for revealing cellular organization and function. Up to now, the protein complex prediction methods are mostly focused on static protein protein interaction (PPI) networks. However, cellular systems are highly dynamic and responsive to cues from the environment. The shift from static PPI networks to dynamic PPI networks is essential to accurately predict protein complex.ResultsThe gene expression data contains crucial dynamic information of proteins and PPIs, along with high-throughput experimental PPI data, are valuable for protein complex prediction. Firstly, we exploit gene expression data to calculate the active time point and the active probability of each protein and PPI. The dynamic active information is integrated into high-throughput PPI data to construct dynamic PPI networks. Secondly, a novel method for predicting protein complexes from the dynamic PPI networks is proposed based on core-attachment structural feature. Our method can effectively exploit not only the dynamic active information but also the topology structure information based on the dynamic PPI networks.ConclusionsWe construct four dynamic PPI networks, and accurately predict many well-characterized protein complexes. The experimental results show that (i) the dynamic active information significantly improves the performance of protein complex prediction; (ii) our method can effectively make good use of both the dynamic active information and the topology structure information of dynamic PPI networks to achieve state-of-the-art protein complex prediction capabilities.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1101-y) contains supplementary material, which is available to authorized users.

Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value <10(-6)), only approximately 20% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value <10(-6) and at least 80% sequence identity) or by actual experimental structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.

Identification of essential proteins is key to understanding the minimal requirements for cellular life and important for drug design. Rapid increasing of available protein-protein interaction data has made it possible to detect protein essentiality on network level. A series of centrality measures have been proposed to discover essential proteins based on network topology. However, most of them tended to focus only on topologies of single proteins, but ignored the relevance between interactions and protein essentiality. In this paper, a new method for identifying essential proteins based on edge clustering coefficient, named as SoECC, is proposed. This method binds characteristics of edges and nodes effectively. The experimental results on yeast protein interaction network show that the number of essential proteins discovered by SoECC universally exceeds that discovered by other six centrality measures. Especially, compared to BC and CC, SoECC is 20% higher in prediction accuracy. Moreover, the essential proteins discovered by SoECC show significant cluster effect.

Many algorithms have been proposed recently to detect protein complexes in protein–protein interaction (PPI) networks. Most proteins form complexes to accomplish biological functions such as transcription of DNA, translation of mRNA and cell growth. Since proteins perform their tasks by interacting with each other, determining these protein–protein interactions is an important task. Traditional clustering approaches for protein complex identification cannot deal with noisy and incomplete PPI data and dependent on information from a single source. Since the noise in the interaction datasets hampers the detection of accurate protein complexes, we propose an ensemble approach for protein complex detection that attempts to combine information from Gene Ontology at the time of complex detection. The PPI data network is taken as input by several baseline complex detection algorithms to generate protein complexes. The protein complexes are then subsequently combined by the proposed ensemble using a consensus building module for the purpose of identifying meaningful complexes. The protein complexes thus predicted by the ensemble are evaluated by comparing them to a set of gold standard protein complexes and their biological relevance established using a co-localization score.

Protein complexes play a critical role in many biological processes. Identifying the component proteins in a protein complex is an important step in understanding the complex as well as the related biological activities. This paper addresses the problem of predicting protein complexes from the protein-protein interaction (PPI) network of one species using a computational approach. Most of the previous methods rely on the assumption that proteins within the same complex would have relatively more interactions. This translates into dense subgraphs in the PPI network. However, the existing software tools have limited success. Recently, Gavin et al. (2006) provided a detailed study on the organization of protein complexes and suggested that a complex consists of two parts: a core and an attachment. Based on this core-attachment concept, we developed a novel approach to identify complexes from the PPI network by identifying their cores and attachments separately. We evaluated the effectiveness of our proposed approach using three different datasets and compared the quality of our predicted complexes with three existing tools. The evaluation results show that we can predict many more complexes and with higher accuracy than these tools with an improvement of over 30%. To verify the cores we identified in each complex, we compared our cores with the mediators produced by Andreopoulos et al. (2007), which were claimed to be the cores, based on the benchmark result produced by Gavin et al. (2006). We found that the cores we produced are of much higher quality ranging from 10- to 30-fold more correctly predicted cores and with better accuracy. (http://alse.cs.hku.hk/complexes/).

Essential proteins are indispensable for cellular life. It is of great significance to identify essential proteins that can help us understand the minimal requirements for cellular life and is also very important for drug design. However, identification of essential proteins based on experimental approaches are always time-consuming and expensive. With the development of high-throughput technology in the post-genomic era, more and more protein-protein interaction data can be obtained, which make us study essential proteins from the network level become possible. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. Most of these topology based essential protein discovery methods were to use network centrality. In this paper, we investigate the essential proteins' topological characters from a completely new perspective. To our knowledge it is the first time that topology potential is used to identify essential proteins from protein-protein interaction network. The basic idea is that each protein in the network can be viewed as a material particle which creates a potential field around itself and the interaction of all proteins forms a topological field over the network. By defining and computing the value of each protein's topology potential, we can obtain a more precise ranking which reflects the importance of proteins from the protein-protein interaction network. The experiment results show that topology potential outperforms traditional topology measures: Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality(SC), Eigenvector Centrality(EC), Information Centrality(IC), and Sum of ECC (NC) for predicting essential proteins. In addition, these centrality measures are improved on their performance for identifying essential proteins in biological network when controlled by topology potential.

Protein-protein interaction (PPI) data is essential to elucidate the complex molecular relationships in living systems, and thus understand the biological functions at cellular and systems levels. The complete map of PPIs that can occur in a living organism is called the interactome. For animals, PPI data is stored in multiple databases (e.g., BioGRID, CCSB, DroID, FlyBase, HIPPIE, HitPredict, HomoMINT, INstruct, Interactome3D, mentha, MINT, and PINA2) with different formats. This makes PPI comparisons difficult to perform, especially between species, since orthologous proteins may have different names. Moreover, there is only a partial overlap between databases, even when considering a single species. The EvoPPI ( http://evoppi.i3s.up.pt ) web application presented in this paper allows comparison of data from the different databases at the species level, or between species using a BLAST approach. We show its usefulness by performing a comparative study of the interactome of the nine polyglutamine (polyQ) disease proteins, namely androgen receptor (AR), atrophin-1 (ATN1), ataxin 1 (ATXN1), ataxin 2 (ATXN2), ataxin 3 (ATXN3), ataxin 7 (ATXN7), calcium voltage-gated channel subunit alpha1 A (CACNA1A), Huntingtin (HTT), and TATA-binding protein (TBP). Here we show that none of the human interactors of these proteins is common to all nine interactomes. Only 15 proteins are common to at least 4 of these polyQ disease proteins, and 40% of these are involved in ubiquitin protein ligase-binding function. The results obtained in this study suggest that polyQ disease proteins are involved in different functional networks. Comparisons with Mus musculus PPIs are also made for AR and TBP, using EvoPPI BLAST search approach (a unique feature of EvoPPI), with the goal of understanding why there is a significant excess of common interactors for these proteins in humans.

Motivations. Available biomolecular annotations are very valuable, but dispersed and far from being complete. High quality integration of scattered annotation data and reliable identification of new annotations can greatly support unveiling new biomedical knowledge. Biomolecular interactions are one of the main objectives of biomolecular studies, due to the understanding that biological processes are mainly driven by interactions among biomolecular entities, such as proteins and DNA. New powerful high-throughput experimental techniques are providing numerous protein-protein interaction data; they are being collected, together with computational results, in several different databases, which include IntAct, BioGrid, BIND, DIP, HPRD and MINT. Yet, they generally do not include phenotypic or even functional or structural information about the interactors, which in many cases are available in other databases. In particular, no information is available about the association of protein-protein interactions with genetic disorders. This creates the need to integrate the sparsely available data in order to enrich the identified interactions with additional evidence, support their biological interpretation and identify their involvement in inherited pathologies. In addition, integration of gene and protein annotations and protein-protein interaction data provides the base to infer new gene annotations and interaction networks. Methods. We developed an automatic association inference method, based on the transitive closure concept, and applied it on the data from several distributed sources integrated in our Genomic and Proteomic Data Warehouse (GPDW). In particular, by leveraging protein-protein interaction data, provided by the IntAct and MINT databases, and protein encoding gene data form the Entrez Gene database, we inferred gene interaction networks. In addition, by taking advantage of genetic disorder and phenotype data provided by the OMIM database, we inferred associations between proteins and genetic disorders and their phenotypes. Then, in order to identify genetic disorders possibly associated with protein-protein interactions, we looked for those interacting proteins that resulted associated with the same genetic disorder. Results. Our GPDW currently includes 46,154 human protein-protein interactions (out of the 254,048 protein-protein interactions contained), which involve 12,178 different human proteins (out of the 326,766 human proteins in the GPDW) that are encoded by 11,232 different human genes. By applying the above described method, we identified 1,130 gene networks and found 1,136 human protein-protein interactions associated with 628 genetic disorders (6% of all genetic disorders in the GPDW), which are related to 86 clinical synopses (87% of all clinical synopses in the GPDW) and 3,481 phenotypes (10% of the total phenotypes in the GPDW). Among others, we found four interacting proteins (AHSA1_HUMAN, CFTR_HUMAN, DERL1_HUMAN, and RNF5_HUMAN) whose encoding genes are known to be associated with cystic fibrosis, as well as other 43 genes. Mutations of the CFTR human gene are known to be directly involved in different grades and manifestations of cystic fibrosis. The found associations of the AHSA1_HUMAN, CFTR_HUMAN, DERL1_HUMAN, and RNF5_HUMAN interacting proteins with cystic fibrosis could suggest that some types of this multi-variant disorder may be associated with defects in the interactions between these proteins. Possibly, different CFTR_HUMAN protein mutations could alter its functional interaction with one or more of the AHSA1_HUMAN, DERL1_HUMAN and RNF5_HUMAN proteins. If this would be proven, such finding would also suggest, as a possible disease treatment strategy, the engineering of a synthetic protein interacting with the mutated CFTR_HUMAN protein and similar in function to the one of the AHSA1_HUMAN, DERL1_HUMAN or RNF5_HUMAN proteins whose interactions with the mutated CFTR_HUMAN protein result altered. The above discussed findings demonstrate the importance of the transitive closure based inference method developed, as well as of the data integration approach implemented in the GPDW and the relevance of the comprehensive data there integrated. Availability The GPDW constitutes the backend of a Genomic and Proteomic Knowledge Base (GPKB) that is publicly available at http://www.bioinformatics.dei.polimi.it/GPKB/ through a prototype easy-to-use and efficient Web interface.

Co-evolution has an important function in the evolution of species and it is clearly manifested in certain scenarios such as host–parasite and predator–prey interactions, symbiosis and mutualism. The extrapolation of the concepts and methodologies developed for the study of species co-evolution at the molecular level has prompted the development of a variety of computational methods able to predict protein interactions through the characteristics of co-evolution. Particularly successful have been those methods that predict interactions at the genomic level based on the detection of pairs of protein families with similar evolutionary histories (similarity of phylogenetic trees: mirrortree). Future advances in this field will require a better understanding of the molecular basis of the co-evolution of protein families. Thus, it will be important to decipher the molecular mechanisms underlying the similarity observed in phylogenetic trees of interacting proteins, distinguishing direct specific molecular interactions from other general functional constraints. In particular, it will be important to separate the effects of physical interactions within protein complexes (‘co-adaptation') from other forces that, in a less specific way, can also create general patterns of co-evolution.

Detecting protein complexes from available protein-protein interaction (PPI) data will help to deeply understand the mechanism of the biological activities. In recent years, various computational methods have been developed for identifying protein complexes from PPI networks. Almost all the basic computational methods mainly depend on the association of topological analysis of PPI networks. However, most of them fail to satisfactorily capture the global and local topological structures of the PPI networks, as well as the diversity of connectivity patterns between individual nodes at the same time. To solve this problem, in this work we propose a node embedding based alias sampling extension method to detect protein complexes. More specifically, for a given set of seed nodes, it first uses the alias sampling strategy based on protein node embedding similarities to select potential addable nodes. Then it makes use of a new conductance measure, which could better quantify the likelihood of a subgraph being a protein complex, to decide whether to extend the current candidate subgraph in order to find protein complexes. Evaluated on six real yeast PPI networks, our method outperforms state-of-the-art methods in detecting protein complexes. Furthermore, the experimental results demonstrate the protein complexes predicted by our method have higher biological significance.

BackgroundEssential Proteins are demonstrated to exert vital functions on cellular processes and are indispensable for the survival and reproduction of the organism. Traditional centrality methods perform poorly on complex protein–protein interaction (PPI) networks. Machine learning approaches based on high-throughput data lack the exploitation of the temporal and spatial dimensions of biological information.ResultsWe put forward a deep learning framework to predict essential proteins by integrating features obtained from the PPI network, subcellular localization, and gene expression profiles. In our model, the node2vec method is applied to learn continuous feature representations for proteins in the PPI network, which capture the diversity of connectivity patterns in the network. The concept of depthwise separable convolution is employed on gene expression profiles to extract properties and observe the trends of gene expression over time under different experimental conditions. Subcellular localization information is mapped into a long one-dimensional vector to capture its characteristics. Additionally, we use a sampling method to mitigate the impact of imbalanced learning when training the model. With experiments carried out on the data of Saccharomyces cerevisiae, results show that our model outperforms traditional centrality methods and machine learning methods. Likewise, the comparative experiments have manifested that our process of various biological information is preferable.ConclusionsOur proposed deep learning framework effectively identifies essential proteins by integrating multiple biological data, proving a broader selection of subcellular localization information significantly improves the results of prediction and depthwise separable convolution implemented on gene expression profiles enhances the performance.

The study of proteins and the interactions between them, known as Protein-Protein Interactions (PPI), is extremely important in interpreting all biological cellular functions. In this article, a new web tool called Superclusteroid is presented which can analyse PPI data, in order to detect protein complexes or characterise the functionality of unknown proteins. The tool is essentially an intuitive PPI data processing pipeline. It supports various input file formats and provides services such as clustering, PPI network visualisation and protein cluster function prediction. Each Superclusteroid service can be used in a sequential manner or on an individual basis. In order to assess the reliability of our tool to infer PPIs, the results of the tool were compared to already known MIPS database complexes and a case scenario is presented where a known protein complex is predicted and the functionality of some of its proteins is revealed. Availability: Superclusteroid is freely available online at http://superclusteroid.uio.no/ .