Methods for protein complex prediction and their contributions towards understanding the organization, function and dynamics of complexes

Complexes of physically interacting proteins constitute fundamental functional units responsible for driving biological processes within cells. A faithful reconstruction of the entire set of complexes is therefore essential to understand the functional organization of cells. In this review, we discuss the key contributions of computational methods developed till date (approximately between 2003 and 2015) for identifying complexes from the network of interacting proteins (PPI network). We evaluate in depth the performance of these methods on PPI datasets from yeast, and highlight challenges faced by these methods, in particular detection of sparse and small or sub- complexes and discerning of overlapping complexes. We describe methods for integrating diverse information including expression profiles and 3D structures of proteins with PPI networks to understand the dynamics of complex formation, for instance, of time-based assembly of complex subunits and formation of fuzzy complexes from intrinsically disordered proteins. Finally, we discuss methods for identifying dysfunctional complexes in human diseases, an application that is proving invaluable to understand disease mechanisms and to discover novel therapeutic targets. We hope this review aptly commemorates a decade of research on computational prediction of complexes and constitutes a valuable reference for further advancements in this exciting area.Comment: 1 Tabl

Network-based approaches to explore complex biological systems towards network medicine

Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs—including long non-coding RNAs (lncRNAs) —competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes—called switch genes—critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes

Mining Biological Networks towards Protein complex Detection and Gene-Disease Association

Large amounts of biological data are continuously generated nowadays, thanks to the advancements of high-throughput experimental techniques. Mining valuable knowledge from such data still motivates the design of suitable computational methods, to complement the experimental work which is often bound by considerable time and cost requirements. Protein complexes or groups of interacting proteins, are key players in most cellular events. The identification of complexes not only allows to better understand normal biological processes but also to uncover Disease-triggering malfunctions. Ultimately, findings in this research branch can highly enhance the design of effective medical treatments. The aim of this research is to detect protein complexes in protein-protein interaction networks and to associate the detected entities to diseases. The work is divided into three main objectives: first, develop a suitable method for the identification of protein complexes in static interaction networks; second, model the dynamic aspect of protein interaction networks and detect complexes accordingly; and third, design a learning model to link proteins, and subsequently protein complexes, to diseases. In response to these objectives, we present, ProRank+, a novel complex-detection approach based on a ranking algorithm and a merging procedure. Then, we introduce DyCluster, which uses gene expression data, to model the dynamics of the interaction networks, and we adapt the detection algorithm accordingly. Finally, we integrate network topology attributes and several biological features of proteins to form a classification model for gene-disease association. The reliability of the proposed methods is supported by various experimental studies conducted to compare them with existing approaches. Pro Rank+ detects more protein complexes than other state-of-the-art methods. DyCluster goes a step further and achieves a better performance than similar techniques. Then, our learning model shows that combining topological and biological features can greatly enhance the gene-disease association process. Finally, we present a comprehensive case study of breast cancer in which we pinpoint disease genes using our learning model; subsequently, we detect favorable groupings of those genes in a protein interaction network using the Pro-rank+ algorithm

Identifying protein complexes and disease genes from biomolecular networks

With advances in high-throughput measurement techniques, large-scale biological data, such as protein-protein interaction (PPI) data, gene expression data, gene-disease association data, cellular pathway data, and so on, have been and will continue to be produced. Those data contain insightful information for understanding the mechanisms of biological systems and have been proved useful for developing new methods in disease diagnosis, disease treatment and drug design. This study focuses on two main research topics: (1) identifying protein complexes and (2) identifying disease genes from biomolecular networks. Firstly, protein complexes are groups of proteins that interact with each other at the same time and place within living cells. They are molecular entities that carry out cellular processes. The identification of protein complexes plays a primary role for understanding the organization of proteins and the mechanisms of biological systems. Many previous algorithms are designed based on the assumption that protein complexes are densely connected sub-graphs in PPI networks. In this research, a dense sub-graph detection algorithm is first developed following this assumption by using clique seeds and graph entropy. Although the proposed algorithm generates a large number of reasonable predictions and its f-score is better than many previous algorithms, it still cannot identify many known protein complexes. After that, we analyze characteristics of known yeast protein complexes and find that not all of the complexes exhibit dense structures in PPI networks. Many of them have a star-like structure, which is a very special case of the core-attachment structure and it cannot be identified by many previous core-attachment-structure-based algorithms. To increase the prediction accuracy of protein complex identification, a multiple-topological-structure-based algorithm is proposed to identify protein complexes from PPI networks. Four single-topological-structure-based algorithms are first employed to detect raw predictions with clique, dense, core-attachment and star-like structures, respectively. A merging and trimming step is then adopted to generate final predictions based on topological information or GO annotations of predictions. A comprehensive review about the identification of protein complexes from static PPI networks to dynamic PPI networks is also given in this study. Secondly, genetic diseases often involve the dysfunction of multiple genes. Various types of evidence have shown that similar disease genes tend to lie close to one another in various biomolecular networks. The identification of disease genes via multiple data integration is indispensable towards the understanding of the genetic mechanisms of many genetic diseases. However, the number of known disease genes related to similar genetic diseases is often small. It is not easy to capture the intricate gene-disease associations from such a small number of known samples. Moreover, different kinds of biological data are heterogeneous and no widely acceptable criterion is available to standardize them to the same scale. In this study, a flexible and reliable multiple data integration algorithm is first proposed to identify disease genes based on the theory of Markov random fields (MRF) and the method of Bayesian analysis. A novel global-characteristic-based parameter estimation method and an improved Gibbs sampling strategy are introduced, such that the proposed algorithm has the capability to tune parameters of different data sources automatically. However, the Markovianity characteristic of the proposed algorithm means it only considers information of direct neighbors to formulate the relationship among genes, ignoring the contribution of indirect neighbors in biomolecular networks. To overcome this drawback, a kernel-based MRF algorithm is further proposed to take advantage of the global characteristics of biological data via graph kernels. The kernel-based MRF algorithm generates predictions better than many previous disease gene identification algorithms in terms of the area under the receiver operating characteristic curve (AUC score). However, it is very time-consuming, since the Gibbs sampling process of the algorithm has to maintain a long Markov chain for every single gene. Finally, to reduce the computational time of the MRF-based algorithm, a fast and high performance logistic-regression-based algorithm is developed for identifying disease genes from biomolecular networks. Numerical experiments show that the proposed algorithm outperforms many existing methods in terms of the AUC score and running time. To summarize, this study has developed several computational algorithms for identifying protein complexes and disease genes from biomolecular networks, respectively. These proposed algorithms are better than many other existing algorithms in the literature

Noise reduction in protein-protein interaction graphs by the implementation of a novel weighting scheme

<p>Abstract</p> <p>Background</p> <p>Recent technological advances applied to biology such as yeast-two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of protein interaction networks. These interaction networks represent a rich, yet noisy, source of data that could be used to extract meaningful information, such as protein complexes. Several interaction network weighting schemes have been proposed so far in the literature in order to eliminate the noise inherent in interactome data. In this paper, we propose a novel weighting scheme and apply it to the <it>S. cerevisiae </it>interactome. Complex prediction rates are improved by up to 39%, depending on the clustering algorithm applied.</p> <p>Results</p> <p>We adopt a two step procedure. During the first step, by applying both novel and well established protein-protein interaction (PPI) weighting methods, weights are introduced to the original interactome graph based on the confidence level that a given interaction is a true-positive one. The second step applies clustering using established algorithms in the field of graph theory, as well as two variations of Spectral clustering. The clustered interactome networks are also cross-validated against the confirmed protein complexes present in the MIPS database.</p> <p>Conclusions</p> <p>The results of our experimental work demonstrate that interactome graph weighting methods clearly improve the clustering results of several clustering algorithms. Moreover, our proposed weighting scheme outperforms other approaches of PPI graph weighting.</p

Previsão e análise da estrutura e dinâmica de redes biológicas

Increasing knowledge about the biological processes that govern the dynamics of living organisms has fostered a better understanding of the origin of many diseases as well as the identification of potential therapeutic targets. Biological systems can be modeled through biological networks, allowing to apply and explore methods of graph theory in their investigation and characterization. This work had as main motivation the inference of patterns and rules that underlie the organization of biological networks. Through the integration of different types of data, such as gene expression, interaction between proteins and other biomedical concepts, computational methods have been developed so that they can be used to predict and study diseases. The first contribution, was the characterization a subsystem of the human protein interactome through the topological properties of the networks that model it. As a second contribution, an unsupervised method using biological criteria and network topology was used to improve the understanding of the genetic mechanisms and risk factors of a disease through co-expression networks. As a third contribution, a methodology was developed to remove noise (denoise) in protein networks, to obtain more accurate models, using the network topology. As a fourth contribution, a supervised methodology was proposed to model the protein interactome dynamics, using exclusively the topology of protein interactions networks that are part of the dynamic model of the system. The proposed methodologies contribute to the creation of more precise, static and dynamic biological models through the identification and use of topological patterns of protein interaction networks, which can be used to predict and study diseases.O conhecimento crescente sobre os processos biológicos que regem a dinâmica dos organismos vivos tem potenciado uma melhor compreensão da origem de muitas doenças, assim como a identificação de potenciais alvos terapêuticos. Os sistemas biológicos podem ser modelados através de redes biológicas, permitindo aplicar e explorar métodos da teoria de grafos na sua investigação e caracterização. Este trabalho teve como principal motivação a inferência de padrões e de regras que estão subjacentes à organização de redes biológicas. Através da integração de diferentes tipos de dados, como a expressão de genes, interação entre proteínas e outros conceitos biomédicos, foram desenvolvidos métodos computacionais, para que possam ser usados na previsão e no estudo de doenças. Como primeira contribuição, foi proposto um método de caracterização de um subsistema do interactoma de proteínas humano através das propriedades topológicas das redes que o modelam. Como segunda contribuição, foi utilizado um método não supervisionado que utiliza critérios biológicos e topologia de redes para, através de redes de co-expressão, melhorar a compreensão dos mecanismos genéticos e dos fatores de risco de uma doença. Como terceira contribuição, foi desenvolvida uma metodologia para remover ruído (denoise) em redes de proteínas, para obter modelos mais precisos, utilizando a topologia das redes. Como quarta contribuição, propôs-se uma metodologia supervisionada para modelar a dinâmica do interactoma de proteínas, usando exclusivamente a topologia das redes de interação de proteínas que fazem parte do modelo dinâmico do sistema. As metodologias propostas contribuem para a criação de modelos biológicos, estáticos e dinâmicos, mais precisos, através da identificação e uso de padrões topológicos das redes de interação de proteínas, que podem ser usados na previsão e no estudo doenças.Programa Doutoral em Engenharia Informátic

Detecting modules in biological networks by edge weights clustering and entropy significance

Detection of the modular structure of biological networks is of interest to researchers adopting a systems perspective for the analysis of omics data. Computational systems biology has provided a rich array of methods for network clustering. To date, the majority of approaches address this task through a network node classification based on topological or external quantifiable properties of network nodes. Conversely, numerical properties of network edges are underused, even though the information content which can be associated with network edges has augmented due to steady advances in molecular biology technology over the last decade. Properly accounting for network edges in the development of clustering approaches can become crucial to improve quantitative interpretation of omics data, finally resulting in more biologically plausible models. In this study, we present a novel technique for network module detection, named WG-Cluster (Weighted Graph CLUSTERing). WG-Cluster's notable features, compared to current approaches, lie in: (1) the simultaneous exploitation of network node and edge weights to improve the biological interpretability of the connected components detected, (2) the assessment of their statistical significance, and (3) the identification of emerging topological properties in the detected connected components. WG-Cluster utilizes three major steps: (i) an unsupervised version of k-means edge-based algorithm detects sub-graphs with similar edge weights, (ii) a fast-greedy algorithm detects connected components which are then scored and selected according to the statistical significance of their scores, and (iii) an analysis of the convolution between sub-graph mean edge weight and connected component score provides a summarizing view of the connected components. WG-Cluster can be applied to directed and undirected networks of different types of interacting entities and scales up to large omics data sets. Here, we show that WG-Cluster can be successfully used in the differential analysis of physical protein-protein interaction (PPI) networks. Specifically, applying WG-Cluster to a PPI network weighted by measurements of differential gene expression permits to explore the changes in network topology under two distinct (normal vs. tumor) conditions. WG-Cluster code is available at https://sites.google.com/site/paolaleccapersonalpage/