Reconstituting protein interaction networks using parameter-dependent domain-domain interactions

Hubs and Bottlenecks in Protein-Protein Interaction Networks

Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes. The two most commonly used centrality measures in networks are degree and betweenness centralities. Degree centrality is the number of connections a node has in the network, and betweenness centrality is the measure of the extent to which a node lies on the shortest paths between pairs of other nodes in the network. In PPINs, proteins with high degree and betweenness centrality are referred to as hubs and bottlenecks respectively. Hubs and bottlenecks are topologically and functionally essential proteins that play crucial roles in maintaining the network's structure and function. This article comprehensively reviews essential literature on hubs and bottlenecks, including their properties and functions.

Fusion proteins mediate alternation of protein interaction networks in cancers

Fusions of two different genes could lead to the production of chimeric RNAs, which could be translated into novel fusion (or chimeric) proteins. Fusion proteins often act as oncoproteins and drive cancer development, particularly in leukemia and lymphomas. Fusion proteins modify the existing protein-protein interaction (PPI) networks, which could eliminate some PPIs by removing protein domains in such fusions. This alternation of protein interaction networks could impact the signaling pathways and switch on the cancer-promoting activity that could drive the generation of cancer phenotypes and/or loss of controlled apoptosis. Thus, knowledge of the fusion proteins and their protein interaction networks could facilitate a deeper molecular understanding of cancer development, which could help to design new approaches for cancer therapies. Here, we discuss the structural features of fusion proteins and how they impact the PPI networks in cancers. Further, we discuss how to analyze the fusion protein-mediated alternation of PPI networks in cancers.

PPIDomainMiner: Inferring domain-domain interactions from multiple sources of protein-protein interactions

Many biological processes are mediated by protein-protein interactions (PPIs). Because protein domains are the building blocks of proteins, PPIs likely rely on domain-domain interactions (DDIs). Several attempts exist to infer DDIs from PPI networks but the produced datasets are heterogeneous and sometimes not accessible, while the PPI interactome data keeps growing.

We describe a new computational approach called “PPIDM” (Protein-Protein Interactions Domain Miner) for inferring DDIs using multiple sources of PPIs. The approach is an extension of our previously described “CODAC” (Computational Discovery of Direct Associations using Common neighbors) method for inferring new edges in a tripartite graph. The PPIDM method has been applied to seven widely used PPI resources, using as “Gold-Standard” a set of DDIs extracted from 3D structural databases. Overall, PPIDM has produced a dataset of 84,552 non-redundant DDIs. Statistical significance (p-value) is calculated for each source of PPI and used to classify the PPIDM DDIs in Gold (9,175 DDIs), Silver (24,934 DDIs) and Bronze (50,443 DDIs) categories. Dataset comparison reveals that PPIDM has inferred from the 2017 releases of PPI sources about 46% of the DDIs present in the 2020 release of the 3did database, not counting the DDIs present in the Gold-Standard. The PPIDM dataset contains 10,229 DDIs that are consistent with more than 13,300 PPIs extracted from the IMEx database, and nearly 23,300 DDIs (27.5%) that are consistent with more than 214,000 human PPIs extracted from the STRING database. Examples of newly inferred DDIs covering more than 10 PPIs in the IMEx database are provided.

Further exploitation of the PPIDM DDI reservoir includes the inventory of possible partners of a protein of interest and characterization of protein interactions at the domain level in combination with other methods. The result is publicly available at http://ppidm.loria.fr/.

We revisit at a large scale the question of inferring DDIs from PPIs. Compared to previous studies, we take a unified approach accross multiple sources of PPIs. This approach is a method for inferring new edges in a tripartite graph setting and can be compared to link prediction approaches in knowledge graphs. Aggregation of several sources is performed using an optimized weighted average of the individual scores calculated in each source. A huge dataset of over 84K DDIs is produced which far exceeds the previous datasets. We show that a significant portion of the PPIDM dataset covers a large number of PPIs from curated (IMEx) or non curated (STRING) databases. Such a reservoir of DDIs deserves further exploration and can be combined with high-throughput methods such as cross-linking mass spectrometry to identify plausible protein partners of proteins of interest.

Fluorescence resonance energy transfer in revealing protein-protein interactions in living cells

Genes are expressed to proteins for a wide variety of fundamental biological processes at the cellular and organismal levels. However, a protein rarely functions alone, but rather acts through interactions with other proteins to maintain normal cellular and organismal functions. Therefore, it is important to analyze the protein-protein interactions to determine functional mechanisms of proteins, which can also guide to develop therapeutic targets for treatment of diseases caused by altered protein-protein interactions leading to cellular/organismal dysfunctions. There is a large number of methodologies to study protein interactions in vitro, in vivo and in silico, which led to the development of many protein interaction databases, and thus, have enriched our knowledge about protein-protein interactions and functions. However, many of these interactions were identified in vitro, but need to be verified/validated in living cells. Furthermore, it is unclear whether these interactions are direct or mediated via other proteins. Moreover, these interactions are representative of cell- and time-average, but not a single cell in real time. Therefore, it is crucial to detect direct protein-protein interactions in a single cell during biological processes in vivo, towards understanding the functional mechanisms of proteins in living cells. Importantly, a fluorescence resonance energy transfer (FRET)-based methodology has emerged as a powerful technique to decipher direct protein-protein interactions at a single cell resolution in living cells, which is briefly described in a limited available space in this mini-review.

Applications of in Silico Methods for Design and Development of Drugs Targeting Protein-Protein Interactions

BACKGROUND

Identification of Protein-Protein Interactions (PPIs) is a major challenge in modern molecular biology and biochemistry research, due to the unquestionable role of proteins in cells, biological process and pathological states. Over the past decade, the PPIs have evolved from being considered a highly challenging field of research to being investigated and examined as targets for pharmacological intervention.

OBJECTIVE

Comprehension of protein interactions is crucial to known how proteins come together to build signalling pathways, to carry out their functions, or to cause diseases, when deregulated. Multiplicity and great amount of PPIs structures offer a huge number of new and potential targets for the treatment of different diseases.

METHODS

Computational techniques are becoming predominant in PPIs studies for their effectiveness, flexibility, accuracy and cost. As a matter of fact, there are effective in silico approaches which are able to identify PPIs and PPI site. Such methods for computational target prediction have been developed through molecular descriptors and data-mining procedures.

RESULTS

In this review, we present different types of interactions between protein-protein and the application of in silico methods for design and development of drugs targeting PPIs. We described computational approaches for the identification of possible targets on protein surface and to detect of stimulator/ inhibitor molecules.

CONCLUSION

A deeper study of the most recent bioinformatics methodologies for PPIs studies is vital for a better understanding of protein complexes and for discover new potential PPI modulators in therapeutic intervention.

ChiPPI: a novel method for mapping chimeric protein-protein interactions uncovers selection principles of protein fusion events in cancer

Fusion proteins, comprising peptides deriving from the translation of two parental genes, are produced in cancer by chromosomal aberrations. The expressed fusion protein incorporates domains of both parental proteins. Using a methodology that treats discrete protein domains as binding sites for specific domains of interacting proteins, we have cataloged the protein interaction networks for 11 528 cancer fusions (ChiTaRS-3.1). Here, we present our novel method, chimeric protein–protein interactions (ChiPPI) that uses the domain–domain co-occurrence scores in order to identify preserved interactors of chimeric proteins. Mapping the influence of fusion proteins on cell metabolism and pathways reveals that ChiPPI networks often lose tumor suppressor proteins and gain oncoproteins. Furthermore, fusions often induce novel connections between non-interactors skewing interaction networks and signaling pathways. We compared fusion protein PPI networks in leukemia/lymphoma, sarcoma and solid tumors finding distinct enrichment patterns for each disease type. While certain pathways are enriched in all three diseases (Wnt, Notch and TGF β), there are distinct patterns for leukemia (EGFR signaling, DNA replication and CCKR signaling), for sarcoma (p53 pathway and CCKR signaling) and solid tumors (FGFR and EGFR signaling). Thus, the ChiPPI method represents a comprehensive tool for studying the anomaly of skewed cellular networks produced by fusion proteins in cancer.

Essential protein discovery based on a combination of modularity and conservatism

Essential proteins are indispensable for the survival of a living organism and play important roles in the emerging field of synthetic biology. Many computational methods have been proposed to identify essential proteins by using the topological features of interactome networks. However, most of these methods ignored intrinsic biological meaning of proteins. Researches show that essentiality is tied not only to the protein or gene itself, but also to the molecular modules to which that protein belongs. The results of this study reveal the modularity of essential proteins. On the other hand, essential proteins are more evolutionarily conserved than nonessential proteins and frequently bind each other. That is to say, conservatism is another important feature of essential proteins. Multiple networks are constructed by integrating protein-protein interaction (PPI) networks, time course gene expression data and protein domain information. Based on these networks, a new essential protein identification method is proposed based on a combination of modularity and conservatism of proteins. Experimental results show that the proposed method outperforms other essential protein identification methods in terms of a number essential protein out of top ranked candidates.

Integration of multiple biological features yields high confidence human protein interactome

The biological function of a protein is usually determined by its physical interaction with other proteins. Protein-protein interactions (PPIs) are identified through various experimental methods and are stored in curated databases. The noisiness of the existing PPI data is evident, and it is essential that a more reliable data is generated. Furthermore, the selection of a set of PPIs at different confidence levels might be necessary for many studies. Although different methodologies were introduced to evaluate the confidence scores for binary interactions, a highly reliable, almost complete PPI network of Homo sapiens is not proposed yet. The quality and coverage of human protein interactome need to be improved to be used in various disciplines, especially in biomedicine. In the present work, we propose an unsupervised statistical approach to assign confidence scores to PPIs of H. sapiens. To achieve this goal PPI data from six different databases were collected and a total of 295,288 non-redundant interactions between 15,950 proteins were acquired. The present scoring system included the context information that was assigned to PPIs derived from eight biological attributes. A high confidence network, which included 147,923 binary interactions between 13,213 proteins, had scores greater than the cutoff value of 0.80, for which sensitivity, specificity, and coverage were 94.5%, 80.9%, and 82.8%, respectively. We compared the present scoring method with others for evaluation. Reducing the noise inherent in experimental PPIs via our scoring scheme increased the accuracy significantly. As it was demonstrated through the assessment of process and cancer subnetworks, this study allows researchers to construct and analyze context-specific networks via valid PPI sets and one can easily achieve subnetworks around proteins of interest at a specified confidence level.

Prediction of Intra-Species Protein-Protein Interactions in Enteropathogens Facilitating Systems Biology Study

Protein-protein interactions in Escherichia coli (E. coli) has been studied extensively using high throughput methods such as tandem affinity purification followed by mass spectrometry and yeast two-hybrid method. This can in turn be used to understand the mechanisms of bacterial cellular processes. However, experimental characterization of such huge amount of interactions data is not available for other important enteropathogens. Here, we propose a support vector machine (SVM)-based prediction model using the known PPIs data of E. coli that can be used to predict PPIs in other enteropathogens, such as Vibrio cholerae, Salmonella Typhi, Shigella flexneri and Yersinia entrocolitica. Different features such as domain-domain association (DDA), network topology, and sequence information were used in developing the SVM model. The proposed model using DDA, degree and amino acid composition features has achieved an accuracy of 82% and 62% on 5-fold cross validation and blind E. coli datasets, respectively. The predicted interactions were validated by Gene Ontology (GO) semantic similarity measure and String PPIs database (experimental PPIs only). Finally, we have developed a user-friendly webserver named EnPPIpred to predict intra-species PPIs in enteropathogens, which will be of great help for the experimental biologists. The webserver EnPPIpred is freely available at http://bicresources.jcbose.ac.in/ssaha4/EnPPIpred/.

Prediction of interactions between viral and host proteins using supervised machine learning methods

BACKGROUND

Viral-host protein-protein interaction plays a vital role in pathogenesis, since it defines viral infection of the host and regulation of the host proteins. Identification of key viral-host protein-protein interactions (PPIs) has great implication for therapeutics.

METHODS

In this study, a systematic attempt has been made to predict viral-host PPIs by integrating different features, including domain-domain association, network topology and sequence information using viral-host PPIs from VirusMINT. The three well-known supervised machine learning methods, such as SVM, Naïve Bayes and Random Forest, which are commonly used in the prediction of PPIs, were employed to evaluate the performance measure based on five-fold cross validation techniques.

RESULTS

Out of 44 descriptors, best features were found to be domain-domain association and methionine, serine and valine amino acid composition of viral proteins. In this study, SVM-based method achieved better sensitivity of 67% over Naïve Bayes (37.49%) and Random Forest (55.66%). However the specificity of Naïve Bayes was the highest (99.52%) as compared with SVM (74%) and Random Forest (89.08%). Overall, the SVM and Random Forest achieved accuracy of 71% and 72.41%, respectively. The proposed SVM-based method was evaluated on blind dataset and attained a sensitivity of 64%, specificity of 83%, and accuracy of 74%. In addition, unknown potential targets of hepatitis B virus-human and hepatitis E virus-human PPIs have been predicted through proposed SVM model and validated by gene ontology enrichment analysis. Our proposed model shows that, hepatitis B virus "C protein" binds to membrane docking protein, while "X protein" and "P protein" interacts with cell-killing and metabolic process proteins, respectively.

CONCLUSION

The proposed method can predict large scale interspecies viral-human PPIs. The nature and function of unknown viral proteins (HBV and HEV), interacting partners of host protein were identified using optimised SVM model.

Improved multi-level protein-protein interaction prediction with semantic-based regularization

Background

Protein–protein interactions can be seen as a hierarchical process occurring at three related levels: proteins bind by means of specific domains, which in turn form interfaces through patches of residues. Detailed knowledge about which domains and residues are involved in a given interaction has extensive applications to biology, including better understanding of the binding process and more efficient drug/enzyme design. Alas, most current interaction prediction methods do not identify which parts of a protein actually instantiate an interaction. Furthermore, they also fail to leverage the hierarchical nature of the problem, ignoring otherwise useful information available at the lower levels; when they do, they do not generate predictions that are guaranteed to be consistent between levels.

Results

Inspired by earlier ideas of Yip et al. (BMC Bioinformatics 10:241, 2009), in the present paper we view the problem as a multi-level learning task, with one task per level (proteins, domains and residues), and propose a machine learning method that collectively infers the binding state of all object pairs. Our method is based on Semantic Based Regularization (SBR), a flexible and theoretically sound machine learning framework that uses First Order Logic constraints to tie the learning tasks together. We introduce a set of biologically motivated rules that enforce consistent predictions between the hierarchy levels.

Conclusions

We study the empirical performance of our method using a standard validation procedure, and compare its performance against the only other existing multi-level prediction technique. We present results showing that our method substantially outperforms the competitor in several experimental settings, indicating that exploiting the hierarchical nature of the problem can lead to better predictions. In addition, our method is also guaranteed to produce interactions that are consistent with respect to the protein–domain–residue hierarchy.

Protein-protein interaction detection: methods and analysis

Protein-protein interaction plays key role in predicting the protein function of target protein and drug ability of molecules. The majority of genes and proteins realize resulting phenotype functions as a set of interactions. The in vitro and in vivo methods like affinity purification, Y2H (yeast 2 hybrid), TAP (tandem affinity purification), and so forth have their own limitations like cost, time, and so forth, and the resultant data sets are noisy and have more false positives to annotate the function of drug molecules. Thus, in silico methods which include sequence-based approaches, structure-based approaches, chromosome proximity, gene fusion, in silico 2 hybrid, phylogenetic tree, phylogenetic profile, and gene expression-based approaches were developed. Elucidation of protein interaction networks also contributes greatly to the analysis of signal transduction pathways. Recent developments have also led to the construction of networks having all the protein-protein interactions using computational methods for signaling pathways and protein complex identification in specific diseases.