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

DIGGER 2.0: digging into the functional impact of differential splicing on human and mouse disorders

Changes in alternative splicing between groups or conditions contribute to protein-protein interaction rewiring, a consequence often neglected in data analysis. The web server and database DIGGER overcomes this limitation by augmenting a protein-protein interaction network with domain-domain interactions and splicing information. Here, we present DIGGER 2.0, which now features both experimental and newly added predicted domain-domain interactions. In addition to the human interactome, DIGGER 2.0 adds support for mouse as an important model organism. Additionally, we integrated the splicing analysis tool NEASE, which allows users to perform online splicing- and interactome-informed enrichment analysis on RNA-seq data. In two application cases (multiple sclerosis and mice models of cardiac diseases), we show the utility of DIGGER 2.0 for deeper exploration and functional interpretation of changes in alternative splicing in human and mouse disorders. DIGGER 2.0 is available at https://exbio.wzw.tum.de/digger/.

Studying protein-protein interactions: Latest and most popular approaches

PPIs, or protein-protein interactions, are essential for many biological processes. According to the findings, abnormal PPIs have been linked to several diseases, such as cancer and infectious and neurological disorders. Consequently, focusing on PPIs is a path toward disease treatment and a crucial tool for producing novel medications. Many methods exist to investigate PPIs, including low- and high-throughput studies. Since many PPIs have been discovered using in vitro and in vivo experimental approaches, the use of computational methods to predict PPIs has grown due to the expanding scale of PPI data and the intrinsic complexity of interacting mechanisms. Recognizing PPI networks offers a systematic means of predicting protein functions, and pathways that are included. These investigations can help uncover the underlying molecular mechanisms of complex phenotypes and clarify the biological processes related to health and diseases. Therefore, our goal in this study is to provide an overview of the latest and most popular approaches for investigating PPIs. We also overview some important clinical approaches based on the PPIs and how these interactions can be targeted.

Integration of molecular coarse-grained model into geometric representation learning framework for protein-protein complex property prediction

Structure-based machine learning algorithms have been utilized to predict the properties of protein-protein interaction (PPI) complexes, such as binding affinity, which is critical for understanding biological mechanisms and disease treatments. While most existing algorithms represent PPI complex graph structures at the atom-scale or residue-scale, these representations can be computationally expensive or may not sufficiently integrate finer chemical-plausible interaction details for improving predictions. Here, we introduce MCGLPPI, a geometric representation learning framework that combines graph neural networks (GNNs) with MARTINI molecular coarse-grained (CG) models to predict PPI overall properties accurately and efficiently. Extensive experiments on three types of downstream PPI property prediction tasks demonstrate that at the CG-scale, MCGLPPI achieves competitive performance compared with the counterparts at the atom- and residue-scale, but with only a third of computational resource consumption. Furthermore, CG-scale pre-training on protein domain-domain interaction structures enhances its predictive capabilities for PPI tasks. MCGLPPI offers an effective and efficient solution for PPI overall property predictions, serving as a promising tool for the large-scale analysis of biomolecular interactions.

Mycobacterium tuberculosis-THP-1 like macrophages protein-protein interaction map revealed through dual RNA-seq analysis and a computational approach

Introduction. Infection caused by Mycobacterium tuberculosis (M. tb) is still a leading cause of mortality worldwide with estimated 1.4 million deaths annually.Hypothesis/Gap statement. Despite macrophages' ability to kill bacterium, M. tb can grow inside these innate immune cells and the exploration of the infection has traditionally been characterized by a one-sided relationship, concentrating solely on the host or examining the pathogen in isolation.Aim. Because of only a handful of M. tb-host interactions have been experimentally characterized, our main goal is to predict protein-protein interactions during the early phases of the infection.Methodology. In this work, we performed an integrative computational approach that exploits differentially expressed genes obtained from Dual RNA-seq analysis combined with known domain-domain interactions.Results. A total of 2381 and 7214 genes were identified as differentially expressed in M. tb and in THP-1-like macrophages, respectively, revealing different transcriptional profiles in response to infection. Over 48 h of infection, the host-pathogen network revealed 25 016 PPIs. Analysis of the resulting predicted network based on cellular localization information of M. tb proteins, indicated the implication of interacting nodes including the bacterial PE/PPE/PE_PGRS family. In addition, M. tb proteins interacted with host proteins involved in NF-kB signalling pathway as well as interfering with the host apoptosis ability via the potential interaction of M. tb TB16.3 with human TAB1 and M. tb GroEL2 with host protein kinase C delta, respectively.Conclusion. The prediction of the full range of interactions between M. tb and host will contribute to better understanding of the pathogenesis of this bacterium and may provide advanced approaches to explore new therapeutic targets against tuberculosis.

Virulence network of interacting domains of influenza a and mouse proteins

There exist several databases that provide virus-host protein interactions. While most provide curated records of interacting virus-host protein pairs, information on the strain-specific virulence factors or protein domains involved, is lacking. Some databases offer incomplete coverage of influenza strains because of the need to sift through vast amounts of literature (including those of major viruses including HIV and Dengue, besides others). None have offered complete, strain specific protein-protein interaction records for the influenza A group of viruses. In this paper, we present a comprehensive network of predicted domain-domain interaction(s) (DDI) between influenza A virus (IAV) and mouse host proteins, that will allow the systematic study of disease factors by taking the virulence information (lethal dose) into account. From a previously published dataset of lethal dose studies of IAV infection in mice, we constructed an interacting domain network of mouse and viral protein domains as nodes with weighted edges. The edges were scored with the Domain Interaction Statistical Potential (DISPOT) to indicate putative DDI. The virulence network can be easily navigated via a web browser, with the associated virulence information (LD50 values) prominently displayed. The network will aid influenza A disease modeling by providing strain-specific virulence levels with interacting protein domains. It can possibly contribute to computational methods for uncovering influenza infection mechanisms mediated through protein domain interactions between viral and host proteins. It is available at https://iav-ppi.onrender.com/home.

Beneficial bacterial-Auricularia cornea interactions fostering growth enhancement identified from microbiota present in spent mushroom substrate

Complex dynamic bacterial-fungal interactions play key roles during mushroom growth, ranging from mutualism to antagonism. These interactions convey a large influence on mushroom's mycelial and fruiting body formation during mushroom cultivation. In this study, high-throughput amplicon sequencing was conducted to investigate the structure of bacterial communities in spent mushroom substrates obtained from cultivation of two different groups of Auricularia cornea with (A) high yield and (B) low yield of fruiting body production. It was found that species richness and diversity of microbiota in group (A) samples were significantly higher than in group (B) samples. Among the identified 765 bacterial OTUs, 5 bacterial species found to exhibit high differential abundance between group (A) and group (B) were Pseudonocardia mangrovi, Luteimonas composti, Paracoccus pantotrophus, Sphingobium jiangsuense, and Microvirga massiliensis. The co-cultivation with selected bacterial strains showed that A. cornea TBRC 12900 co-cultivated with P. mangrovi TBRC-BCC 42794 promoted a high level of mycelial growth. Proteomics analysis was performed to elucidate the biological activities involved in the mutualistic association between A. cornea TBRC 12900 and P. mangrovi TBRC-BCC 42794. After co-cultivation of A. cornea TBRC 12900 and P. mangrovi TBRC-BCC 42794, 1,616 proteins were detected including 578 proteins of A. cornea origin and 1,038 proteins of P. mangrovi origin. Functional analysis and PPI network construction revealed that the high level of mycelial growth in the co-culture condition most likely resulted from concerted actions of (a) carbohydrate-active enzymes including hydrolases, glycosyltransferases, and carbohydrate esterases important for carbohydrate metabolism and cell wall generation/remodeling, (b) peptidases including cysteine-, metallo-, and serine-peptidases, (c) transporters including the ABC-type transporter superfamily, the FAT transporter family, and the VGP family, and (d) proteins with proposed roles in formation of metabolites that can act as growth-promoting molecules or those normally contain antimicrobial activity (e.g., indoles, terpenes, β-lactones, lanthipeptides, iturins, and ectoines). The findings will provide novel insights into bacterial-fungal interactions during mycelial growth and fruiting body formation. Our results can be utilized for the selection of growth-promoting bacteria to improve the cultivation process of A. cornea with a high production yield, thus conveying potentially high socio-economic impact to mushroom agriculture.

Truncated FGFR2 is a clinically actionable oncogene in multiple cancers

Somatic hotspot mutations and structural amplifications and fusions that affect fibroblast growth factor receptor 2 (encoded by FGFR2) occur in multiple types of cancer¹. However, clinical responses to FGFR inhibitors have remained variable^1–9, emphasizing the need to better understand which FGFR2 alterations are oncogenic and therapeutically targetable. Here we apply transposon-based screening^10,11 and tumour modelling in mice^12,13, and find that the truncation of exon 18 (E18) of Fgfr2 is a potent driver mutation. Human oncogenomic datasets revealed a diverse set of FGFR2 alterations, including rearrangements, E1–E17 partial amplifications, and E18 nonsense and frameshift mutations, each causing the transcription of E18-truncated FGFR2 (FGFR2^ΔE18). Functional in vitro and in vivo examination of a compendium of FGFR2^ΔE18 and full-length variants pinpointed FGFR2-E18 truncation as single-driver alteration in cancer. By contrast, the oncogenic competence of FGFR2 full-length amplifications depended on a distinct landscape of cooperating driver genes. This suggests that genomic alterations that generate stable FGFR2^ΔE18 variants are actionable therapeutic targets, which we confirmed in preclinical mouse and human tumour models, and in a clinical trial. We propose that cancers containing any FGFR2 variant with a truncated E18 should be considered for FGFR-targeted therapies.

Truncation of exon 18 of FGFR2 (FGFR2^ΔE18) is a potent driver mutation in mice and humans, and FGFR-targeted therapy should be considered for patients with cancer expressing stable FGFR2^ΔE18 variants.

Protein-protein interaction networks as miners of biological discovery

Protein-protein interactions (PPIs) form the basis of a myriad of biological pathways and mechanism, such as the formation of protein-complexes or the components of signaling cascades. Here, we reviewed experimental methods for identifying PPI pairs, including yeast two-hybrid, mass spectrometry, co-localization, and co-immunoprecipitation. Furthermore, a range of computational methods leveraging biochemical properties, evolution history, protein structures and more have enabled identification of additional PPIs. Given the wealth of known PPIs, we reviewed important network methods to construct and analyze networks of PPIs. These methods aid biological discovery through identifying hub genes and dynamic changes in the network, and have been thoroughly applied in various fields of biological research. Lastly, we discussed the challenges and future direction of research utilizing the power of PPI networks. This article is protected by copyright. All rights reserved.

Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient

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Provided a family of efficient network algorithms for protein complex identification.

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The parameter-free family outperforms existing approaches on different networks.

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It exactly recovered ~ 35% of protein complexes in a pan-plant PPI network.

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We examined of network perturbations on predicted protein complexes.

Identification of protein complexes from protein-protein interaction (PPI) networks is a key problem in PPI mining, solved by parameter-dependent approaches that suffer from small recall rates. Here we introduce GCC-v, a family of efficient, parameter-free algorithms to accurately predict protein complexes using the (weighted) clustering coefficient of proteins in PPI networks. Through comparative analyses with gold standards and PPI networks from Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens, we demonstrate that GCC-v outperforms twelve state-of-the-art approaches for identification of protein complexes with respect to twelve performance measures in at least 85.71% of scenarios. We also show that GCC-v results in the exact recovery of ∼35% of protein complexes in a pan-plant PPI network and discover 144 new protein complexes in Arabidopsis thaliana, with high support from GO semantic similarity. Our results indicate that findings from GCC-v are robust to network perturbations, which has direct implications to assess the impact of the PPI network quality on the predicted protein complexes.