Domain-domain Interaction Network Research Articles

Abstract 6338: Higher-resolution protein interaction networks for precision medicine: A case for drug repurposing in head and neck cancer

Abstract Protein-Protein Interaction (PPI) networks can be useful scaffolds for the integration of whole cell multi-omics datasets that can aid significantly in drug discovery research. This study aims to use domain-domain interaction (DDI) data to produce high resolution protein interaction networks with enhanced topological insights for head and neck squamous cell carcinoma as well as the more well studies, triple negative breast cancer (TNBC). An extensive literature review identified 65 driver genes for HNSCC and 19 genes for TNBC which collectively had 557 and 316 protein domains. Eventually, 343/557 and 191/316 domains had well-annotated somatic mutations reported in COSMIC. PPI networks were created using BioGrid and STRING data and the top two hubs in PPI networks were TP53 for HNSCC and EGFR for TNBC, while the top two hubs in DDI networks were PF12796 (NOTCH) and PF07714 (EGFR) for HNSCC and TNBC networks, respectively. The top nodes in the densest sub-clusters based on module analysis in PPI networks were ERBB4, MET and NOTCH1 for HNSCC, and FGFR1, AR, and EGFR for TNBC. For DDI networks, the top nodes in the densest subcluster for HNSCC were PF06816 (NOTCH1-3), while for TNBC PF07714 domains from FGFR1, ERBB2 and EGFR were the top nodes. Our preliminary results re-emphasize the significance of NOTCH, EGFR and TP53, and their domains, in cancer networks. Next, we attempted to decipher potential candidates for drug repurposing and were able to map 20 out of the 31 FDA approved cancer drugs against proteins within our HNSCC network. EGFR was revealed as a hub gene in both HNSCC and TNBC networks with most of the therapies designed against the PF07714 kinase domain of EGFR. PF07714 is also a common domain in ERBB4, EPHA2, MAP2K1 and MET, and was targeted by different drugs in other cancer types. Next, we attempt to undertake an in vitro drug screen of all candidates against HNSCC cell lines to validate our in silico findings. Citation Format: Maryam Shah, Arsalan Riaz, Faisal Khan. Higher-resolution protein interaction networks for precision medicine: A case for drug repurposing in head and neck cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6338.

Potential Pathogenic Genes Prioritization Based on Protein Domain Interaction Network Analysis.

Pathogenicity-related studies are of great importance in understanding the pathogenesis of complex diseases and improving the level of clinical medicine. This work proposed a bioinformatics scheme to analyze cancer-related gene mutations, and try to figure out potential genes associated with diseases from the protein domain-domain interaction network. Herein, five measures of the principle of centrality lethality had been adopted to implement potential correlation analysis, and prioritize the significance of genes. This method was further applied to KEGG pathway analysis by taking the malignant melanoma as an example. The experimental results show that 25 domains can be found, and 18 of them have high potential to be pathogenically important related to malignant melanoma. Finally, a web-based tool, named Human Cancer Related Domain Interaction Network Analyzer, is developed for potential pathogenic genes prioritization for 26 types of human cancers, and the analysis results can be visualized and downloaded online.

Method for Essential Protein Prediction Based on a Novel Weighted Protein-Domain Interaction Network.

In recent years a number of calculative models based on protein-protein interaction (PPI) networks have been proposed successively. However, due to false positives, false negatives, and the incompleteness of PPI networks, there are still many challenges affecting the design of computational models with satisfactory predictive accuracy when inferring key proteins. This study proposes a prediction model called WPDINM for detecting key proteins based on a novel weighted protein-domain interaction (PDI) network. In WPDINM, a weighted PPI network is constructed first by combining the gene expression data of proteins with topological information extracted from the original PPI network. Simultaneously, a weighted domain-domain interaction (DDI) network is constructed based on the original PDI network. Next, through integrating the newly obtained weighted PPI network and weighted DDI network with the original PDI network, a weighted PDI network is further constructed. Then, based on topological features and biological information, including the subcellular localization and orthologous information of proteins, a novel PageRank-based iterative algorithm is designed and implemented on the newly constructed weighted PDI network to estimate the criticality of proteins. Finally, to assess the prediction performance of WPDINM, we compared it with 12 kinds of competitive measures. Experimental results show that WPDINM can achieve a predictive accuracy rate of 90.19, 81.96, 70.72, 62.04, 55.83, and 51.13% in the top 1%, top 5%, top 10%, top 15%, top 20%, and top 25% separately, which exceeds the prediction accuracy achieved by traditional state-of-the-art competing measures. Owing to the satisfactory identification effect, the WPDINM measure may contribute to the further development of key protein identification.

Network-Based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis.

High-throughput mRNA sequencing (RNA-Seq) is widely used for transcript quantification of gene isoforms. Since RNA-Seq data alone is often not sufficient to accurately identify the read origins from the isoforms for quantification, we propose to explore protein domain-domain interactions as prior knowledge for integrative analysis with RNA-Seq data. We introduce a Network-based method for RNA-Seq-based Transcript Quantification (Net-RSTQ) to integrate protein domain-domain interaction network with short read alignments for transcript abundance estimation. Based on our observation that the abundances of the neighboring isoforms by domain-domain interactions in the network are positively correlated, Net-RSTQ models the expression of the neighboring transcripts as Dirichlet priors on the likelihood of the observed read alignments against the transcripts in one gene. The transcript abundances of all the genes are then jointly estimated with alternating optimization of multiple EM problems. In simulation Net-RSTQ effectively improved isoform transcript quantifications when isoform co-expressions correlate with their interactions. qRT-PCR results on 25 multi-isoform genes in a stem cell line, an ovarian cancer cell line, and a breast cancer cell line also showed that Net-RSTQ estimated more consistent isoform proportions with RNA-Seq data. In the experiments on the RNA-Seq data in The Cancer Genome Atlas (TCGA), the transcript abundances estimated by Net-RSTQ are more informative for patient sample classification of ovarian cancer, breast cancer and lung cancer. All experimental results collectively support that Net-RSTQ is a promising approach for isoform quantification. Net-RSTQ toolbox is available at http://compbio.cs.umn.edu/Net-RSTQ/.

Structural and Functional Analysis of Multi-Interface Domains

A multi-interface domain is a domain that can shape multiple and distinctive binding sites to contact with many other domains, forming a hub in domain-domain interaction networks. The functions played by the multiple interfaces are usually different, but there is no strict bijection between the functions and interfaces as some subsets of the interfaces play the same function. This work applies graph theory and algorithms to discover fingerprints for the multiple interfaces of a domain and to establish associations between the interfaces and functions, based on a huge set of multi-interface proteins from PDB. We found that about 40% of proteins have the multi-interface property, however the involved multi-interface domains account for only a tiny fraction (1.8%) of the total number of domains. The interfaces of these domains are distinguishable in terms of their fingerprints, indicating the functional specificity of the multiple interfaces in a domain. Furthermore, we observed that both cooperative and distinctive structural patterns, which will be useful for protein engineering, exist in the multiple interfaces of a domain.

Domain-Domain Interactions Underlying Herpesvirus-Human Protein-Protein Interaction Networks

Protein-domains play an important role in mediating protein-protein interactions. Furthermore, the same domain-pairs mediate different interactions in different contexts and in various organisms, and therefore domain-pairs are considered as the building blocks of interactome networks. Here we extend these principles to the host-virus interface and find the domain-pairs that potentially mediate human-herpesvirus interactions. Notably, we find that the same domain-pairs used by other organisms for mediating their interactions underlie statistically significant fractions of human-virus protein inter-interaction networks. Our analysis shows that viral domains tend to interact with human domains that are hubs in the human domain-domain interaction network. This may enable the virus to easily interfere with a variety of mechanisms and processes involving various and different human proteins carrying the relevant hub domain. Comparative genomics analysis provides hints at a molecular mechanism by which the virus acquired some of its interacting domains from its human host.

DomainRBF: a Bayesian regression approach to the prioritization of candidate domains for complex diseases

BackgroundDomains are basic units of proteins, and thus exploring associations between protein domains and human inherited diseases will greatly improve our understanding of the pathogenesis of human complex diseases and further benefit the medical prevention, diagnosis and treatment of these diseases. Within a given domain-domain interaction network, we make the assumption that similarities of disease phenotypes can be explained using proximities of domains associated with such diseases. Based on this assumption, we propose a Bayesian regression approach named "domainRBF" (domain Rank with Bayes Factor) to prioritize candidate domains for human complex diseases.ResultsUsing a compiled dataset containing 1,614 associations between 671 domains and 1,145 disease phenotypes, we demonstrate the effectiveness of the proposed approach through three large-scale leave-one-out cross-validation experiments (random control, simulated linkage interval, and genome-wide scan), and we do so in terms of three criteria (precision, mean rank ratio, and AUC score). We further show that the proposed approach is robust to the parameters involved and the underlying domain-domain interaction network through a series of permutation tests. Once having assessed the validity of this approach, we show the possibility of ab initio inference of domain-disease associations and gene-disease associations, and we illustrate the strong agreement between our inferences and the evidences from genome-wide association studies for four common diseases (type 1 diabetes, type 2 diabetes, Crohn's disease, and breast cancer). Finally, we provide a pre-calculated genome-wide landscape of associations between 5,490 protein domains and 5,080 human diseases and offer free access to this resource.ConclusionsThe proposed approach effectively ranks susceptible domains among the top of the candidates, and it is robust to the parameters involved. The ab initio inference of domain-disease associations shows strong agreement with the evidence provided by genome-wide association studies. The predicted landscape provides a comprehensive understanding of associations between domains and human diseases.

Comparative study of network-based prioritization of protein domains associated with human complex diseases

Domains are basic structural and functional unit of proteins, and, thus, exploring associations between protein domains and human inherited diseases will greatly improve our understanding of the pathogenesis of human complex diseases and further benefit the medical prevention, diagnosis and treatment of these diseases. Based on the assumption that deleterious nonsynonymous single nucleotide polymorphisms (nsSNPs) underlying human complex diseases may actually change structures of protein domains, affect functions of corresponding proteins, and finally result in these diseases, we compile a dataset that contains 1174 associations between 433 protein domains and 848 human disease phenotypes. With this dataset, we compare two approaches (guilt-by-association and correlation coefficient) that use a domain-domain interaction network and a phenotype similarity network to prioritize associations between candidate domains and human disease phenotypes. We implement these methods with three distance measures (direct neighbor, shortest path with Gaussian kernel, and diffusion kernel), demonstrate the effectiveness of these methods using three large-scale leave-one-out cross-validation experiments (random control, simulated linkage interval, and whole-genome scan), and evaluate the performance of these methods in terms of three criteria (mean rank ratio, precision, and AUC score). Results show that both methods can effectively prioritize domains that are associated with human diseases at the top of the candidate list, while the correlation coefficient approach can achieve slightly higher performance in most cases. Finally, taking the advantage that the correlation coefficient method does not require known disease-domain associations, we calculate a genome-wide landscape of associations between 4036 protein domains and 5080 human disease phenotypes using this method and offer a freely accessible web interface for this landscape.

Prioritisation of associations between protein domains and complex diseases using domain–domain interaction networks

It is of vital importance to find genetic variants that underlie human complex diseases and locate genes that are responsible for these diseases. Since proteins are typically composed of several structural domains, it is reasonable to assume that harmful genetic variants may alter structures of protein domains, affect functions of proteins and eventually cause disorders. With this understanding, the authors explore the possibility of recovering associations between protein domains and complex diseases. The authors define associations between protein domains and disease families on the basis of associations between non-synonymous single nucleotide polymorphisms (nsSNPs) and complex diseases, similarities between diseases, and relations between proteins and domains. Based on a domain-domain interaction network, the authors propose a 'guilt-by-proximity' principle to rank candidate domains according to their average distance to a set of seed domains in the domain-domain interaction network. The authors validate the method through large-scale cross-validation experiments on simulated linkage intervals, random controls and the whole genome. Results show that areas under receiver operating characteristic curves (AUC scores) can be as high as 77.90%, and the mean rank ratios can be as low as 21.82%. The authors further offer a freely accessible web interface for a genome-wide landscape of associations between domains and disease families.

Protein Domain Annotation with Predicted Domain-Domain Interaction Networks

This paper presents a framework for annotating protein domains with predicted domain-domain interaction networks. Specially, domain annotation is formalized as a multi-class classification problem in this work. The numerical experiments on InterPro domains show promising results, which proves the efficiency of our proposed methods.