An empirical framework for binary interactome mapping

Co-training based prediction of multi-label protein-protein interactions

Prediction of protein-protein interaction (PPI) types enhances the comprehension of the underlying structural characteristics and functions of proteins, which gives rise to a multi-label classification problem. The nominal features describe the physicochemical characteristics of proteins directly, establishing a more robust correlation with the interaction types between proteins than ordered features. Motivated by this, we propose a multi-label PPI prediction model referred to as CoMPPI (Co-training based Multi-Label prediction of Protein-Protein Interaction). This approach aims to maximize the utility of both ordered and nominal features extracted from protein sequences. Specifically, CoMPPI incorporates graph convolutional network (GCN) and 1D convolution operation to process the complementary subsets of features individually, leveraging both local and contextualized information in a more efficient way. In addition, two multi-type PPI datasets were constructed to eliminate the duplication in previous datasets. We compare the performance of CoMPPI with three state-of-the-art methods on three datasets partitioned using distinct schemes (Breadth-first search, Depth-first search, and Random), CoMPPI consistently outperforms the other methods across all cases, demonstrating improvements ranging from 3.81% to 32.40% in Micro-F1. The subsequent ablation experiment confirms the efficacy of employing the co-training framework for multi-label PPI prediction, indicating promising avenues for future advancements in this domain.

α-Hydrazino Acid Insertion Governs Peptide Organization in Solution by Local Structure Ordering

In this work, we have applied the concept of α-hydrazino acid insertion in a peptide sequence as a means of structurally organizing a potential protein-protein interactions (PPI) inhibitor. Hydrazino peptides characterized by the incorporation of an α-hydrazino acid at specific positions introduce an additional nitrogen atom into their backbone. This modification leads to a change in the electrostatic properties of the peptide and induces the restructuring of its hydrogen bonding network, resulting in conformational changes toward more stable structural motifs. Despite the successful use of synthetic hydrazino oligomers in binding to nucleic acids, the structural changes due to the incorporation of α-hydrazino acid into short natural peptides in solution are still poorly understood. Based on NMR data, we report structural models of p53-derived hydrazino peptides with elements of localized peptide structuring in the form of an α-, β-, or γ-turn as a result of hydrazino modification in the peptide backbone. The modifications could potentially lead to the preorganization of a helical secondary peptide structure in a solution that is favorable for binding to a biological receptor. Spectroscopically, we observed that the ensemble averaged rapidly interconverting conformations, including isomerization of the E-Z hydrazide bond. This further increases the adaptability by expanding the conformational space of hydrazine peptides as potential protein-protein interaction antagonists.

PPIscreenML: Structure-based screening for protein-protein interactions using AlphaFold

Protein-protein interactions underlie nearly all cellular processes. With the advent of protein structure prediction methods such as AlphaFold2 (AF2), models of specific protein pairs can be built extremely accurately in most cases. However, determining the relevance of a given protein pair remains an open question. It is presently unclear how to use best structure-based tools to infer whether a pair of candidate proteins indeed interact with one another: ideally, one might even use such information to screen amongst candidate pairings to build up protein interaction networks. Whereas methods for evaluating quality of modeled protein complexes have been co-opted for determining which pairings interact (e.g., pDockQ and iPTM), there have been no rigorously benchmarked methods for this task. Here we introduce PPIscreenML, a classification model trained to distinguish AF2 models of interacting protein pairs from AF2 models of compelling decoy pairings. We find that PPIscreenML out-performs methods such as pDockQ and iPTM for this task, and further that PPIscreenML exhibits impressive performance when identifying which ligand/receptor pairings engage one another across the structurally conserved tumor necrosis factor superfamily (TNFSF). Analysis of benchmark results using complexes not seen in PPIscreenML development strongly suggest that the model generalizes beyond training data, making it broadly applicable for identifying new protein complexes based on structural models built with AF2.

Polyglutamine disease proteins: Commonalities and differences in interaction profiles and pathological effects

Currently, nine polyglutamine (polyQ) expansion diseases are known. They include spinocerebellar ataxias (SCA1, 2, 3, 6, 7, 17), spinal and bulbar muscular atrophy (SBMA), dentatorubral-pallidoluysian atrophy (DRPLA), and Huntington's disease (HD). At the root of these neurodegenerative diseases are trinucleotide repeat mutations in coding regions of different genes, which lead to the production of proteins with elongated polyQ tracts. While the causative proteins differ in structure and molecular mass, the expanded polyQ domains drive pathogenesis in all these diseases. PolyQ tracts mediate the association of proteins leading to the formation of protein complexes involved in gene expression regulation, RNA processing, membrane trafficking, and signal transduction. In this review, we discuss commonalities and differences among the nine polyQ proteins focusing on their structure and function as well as the pathological features of the respective diseases. We present insights from AlphaFold-predicted structural models and discuss the biological roles of polyQ-containing proteins. Lastly, we explore reported protein-protein interaction networks to highlight shared protein interactions and their potential relevance in disease development.

Predictomes: A classifier-curated database of AlphaFold-modeled protein-protein interactions

Protein-protein interactions (PPIs) are ubiquitous in biology, yet a comprehensive structural characterization of the PPIs underlying biochemical processes is lacking. Although AlphaFold-Multimer (AF-M) has the potential to fill this knowledge gap, standard AF-M confidence metrics do not reliably separate relevant PPIs from an abundance of false positive predictions. To address this limitation, we used machine learning on well curated datasets to train a Structure Prediction and Omics informed Classifier called SPOC that shows excellent performance in separating true and false PPIs, including in proteome-wide screens. We applied SPOC to an all-by-all matrix of nearly 300 human genome maintenance proteins, generating ~40,000 predictions that can be viewed at predictomes.org, where users can also score their own predictions with SPOC. High confidence PPIs discovered using our approach suggest novel hypotheses in genome maintenance. Our results provide a framework for interpreting large scale AF-M screens and help lay the foundation for a proteome-wide structural interactome.

AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor

Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays or AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold-Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.

Revolutionizing protein-protein interaction prediction with deep learning

Protein-protein interactions (PPIs) are pivotal for driving diverse biological processes, and any disturbance in these interactions can lead to disease. Thus, the study of PPIs has been a central focus in biology. Recent developments in deep learning methods, coupled with the vast genomic sequence data, have significantly boosted the accuracy of predicting protein structures and modeling protein complexes, approaching levels comparable to experimental techniques. Herein, we review the latest advances in the computational methods for modeling 3D protein complexes and the prediction of protein interaction partners, emphasizing the application of deep learning methods deriving from coevolution analysis. The review also highlights biomedical applications of PPI prediction and outlines challenges in the field.

Selection of collagen IV fragments forming the outer sphere of the native protein: Assessment of biological activity for regenerative medicine

The aim of this research was to select the fragments that make up the outer layer of the collagen IV (COL4A6) protein and to assess their potential usefulness for regenerative medicine. It was expected that because protein-protein interactions take place via contact between external domains, the set of peptides forming the outer sphere of collagen IV will determine its interaction with other proteins. Cellulose-immobilized protein fragment libraries treated with polyclonal anti-collagen IV antibodies were used to select the peptides forming the outer sphere of collagen IV. In the first test, 33 peptides that strongly interacted with the polyclonal anti-collagen IV antibodies were selected from a library of non-overlapping fragments of collagen IV. The selected fragments of collagen IV (cleaved from the cellulose matrix) were tested for their cytotoxicity, their effects on cell viability and proliferation, and their impact on the formation of reactive oxygen species (ROS). The studies used RAW 264.7 mouse macrophage cells and Hs 680.Tr human fibroblasts. PrestoBlue, ToxiLight™, and ToxiLight 100% Lysis Control assays were conducted. The viability of fibroblasts cultured with the addition of increasing concentrations of the peptide mix did not show statistically significant differences from the control. Fragments 161-170, 221-230, 721-730, 1331-1340, 1521-1530, and 1661-1670 of COL4A6 were examined for cytotoxicity against BJ normal human foreskin fibroblasts. None of the collagen fragments were found to be cytotoxic. Further research is underway on the potential uses of collagen IV fragments in regenerative medicine.

Standigm ASK™: knowledge graph and artificial intelligence platform applied to target discovery in idiopathic pulmonary fibrosis

Standigm ASK™ revolutionizes healthcare by addressing the critical challenge of identifying pivotal target genes in disease mechanisms-a fundamental aspect of drug development success. Standigm ASK™ integrates a unique combination of a heterogeneous knowledge graph (KG) database and an attention-based neural network model, providing interpretable subgraph evidence. Empowering users through an interactive interface, Standigm ASK™ facilitates the exploration of predicted results. Applying Standigm ASK™ to idiopathic pulmonary fibrosis (IPF), a complex lung disease, we focused on genes (AMFR, MDFIC and NR5A2) identified through KG evidence. In vitro experiments demonstrated their relevance, as TGFβ treatment induced gene expression changes associated with epithelial-mesenchymal transition characteristics. Gene knockdown reversed these changes, identifying AMFR, MDFIC and NR5A2 as potential therapeutic targets for IPF. In summary, Standigm ASK™ emerges as an innovative KG and artificial intelligence platform driving insights in drug target discovery, exemplified by the identification and validation of therapeutic targets for IPF.

Master regulators of biological systems in higher dimensions

A longstanding goal of biology is to identify the key genes and species that critically impact evolution, ecology, and health. Network analysis has revealed keystone species that regulate ecosystems and master regulators that regulate cellular genetic networks. Yet these studies have focused on pairwise biological interactions, which can be affected by the context of genetic background and other species present, generating higher-order interactions. The important regulators of higher-order interactions are unstudied. To address this, we applied a high-dimensional geometry approach that quantifies epistasis in a fitness landscape to ask how individual genes and species influence the interactions in the rest of the biological network. We then generated and also reanalyzed 5-dimensional datasets (two genetic, two microbiome). We identified key genes (e.g., the rbs locus and pykF) and species (e.g., Lactobacilli) that control the interactions of many other genes and species. These higher-order master regulators can induce or suppress evolutionary and ecological diversification by controlling the topography of the fitness landscape. Thus, we provide a method and mathematical justification for exploration of biological networks in higher dimensions.

Virus-Host Protein Interaction Network of the Hepatitis E Virus ORF2-4 by Mammalian Two-Hybrid Assays

Throughout their life cycle, viruses interact with cellular host factors, thereby influencing propagation, host range, cell tropism and pathogenesis. The hepatitis E virus (HEV) is an underestimated RNA virus in which knowledge of the virus-host interaction network to date is limited. Here, two related high-throughput mammalian two-hybrid approaches (MAPPIT and KISS) were used to screen for HEV-interacting host proteins. Promising hits were examined on protein function, involved pathway(s), and their relation to other viruses. We identified 37 ORF2 hits, 187 for ORF3 and 91 for ORF4. Several hits had functions in the life cycle of distinct viruses. We focused on SHARPIN and RNF5 as candidate hits for ORF3, as they are involved in the RLR-MAVS pathway and interferon (IFN) induction during viral infections. Knocking out (KO) SHARPIN and RNF5 resulted in a different IFN response upon ORF3 transfection, compared to wild-type cells. Moreover, infection was increased in SHARPIN KO cells and decreased in RNF5 KO cells. In conclusion, MAPPIT and KISS are valuable tools to study virus-host interactions, providing insights into the poorly understood HEV life cycle. We further provide evidence for two identified hits as new host factors in the HEV life cycle.

Improved prediction of protein-protein interactions by a modified strategy using three conventional docking software in combination

Proteins play a crucial role in many biological processes, where their interaction with other proteins are integral. Abnormal protein-protein interactions (PPIs) have been linked to various diseases including cancer, and thus targeting PPIs holds promise for drug development. However, experimental confirmation of the peculiarities of PPIs is challenging due to their dynamic and transient nature. As a complement to experimental technologies, multiple computational molecular docking (MD) methods have been developed to predict the structures of protein-protein complexes and their dynamics, still requiring further improvements in several issues. Here, we report an improved MD method, namely three-software docking (3SD), by employing three popular protein-peptide docking software (CABS-dock, HPEPDOCK, and HADDOCK) in combination to ensure constant quality for most targets. We validated our 3SD performance in known protein-peptide interactions (PpIs). We also enhanced MD performance in proteins having intrinsically disordered regions (IDRs) by applying the modified 3SD strategy, the three-software docking after removing random coiled IDR (3SD-RR), to the comparable crystal PpI structures. At the end, we applied 3SD-RR to the AlphaFold2-predicted receptors, yielding an efficient prediction of PpI pose with high relevance to the experimental data regardless of the presence of IDRs or the availability of receptor structures. Our study provides an improved solution to the challenges in studying PPIs through computational docking and has the potential to contribute to PPIs-targeted drug discovery. SIGNIFICANCE STATEMENT: Protein-protein interactions (PPIs) are integral to life, and abnormal PPIs are associated with diseases such as cancer. Studying protein-peptide interactions (PpIs) is challenging due to their dynamic and transient nature. Here we developed improved docking methods (3SD and 3SD-RR) to predict the PpI poses, ensuring constant quality in most targets and also addressing issues like intrinsically disordered regions (IDRs) and artificial intelligence-predicted structures. Our study provides an improved solution to the challenges in studying PpIs through computational docking and has the potential to contribute to PPIs-targeted drug discovery.

Simulation of STAT and HP1 interaction by molecular docking

Heterochromatin Protein 1 (HP1) is a major component of heterochromatin. Multiple proteins have been shown to interact with HP1 with the HP1-binding motif PxVxL/I, thereby affecting heterochromatin stability. The HP1-interacting proteins include the signal transducer and activator of transcription (STAT) protein, which can be regulated by phosphorylation on a tyrosine around amino acid 700 in the carboxyl terminus. Previous research has shown that unphosphorylated STAT (uSTAT) binds to HP1 via a PxVxI HP1-binding motif and maintains the stability of heterochromatin, while phosphorylated STAT (pSTAT) dissociates from HP1, resulting in heterochromatin disruption. To understand the theoretical basis of the biochemical observations, we employed computational modeling to investigate STAT-HP1 binding configurations and the effect of STAT phosphorylation on their interaction. Using STAT3 and HP1α protein structures for molecular docking and thermodynamic calculations, our computations predict that uSTAT homodimers have a higher affinity for HP1 and a lower affinity for DNA than pSTAT homodimers, and that phosphorylation induces a conformational change in STAT, shifting its binding preference from HP1 to DNA. The results of our modeling studies support the idea that phosphorylation drives STAT from HP1-binding to DNA-binding, suggesting a potential role for uSTAT in both maintaining and initiating heterochromatin formation.

The protein interactome of the citrus Huanglongbing pathogen Candidatus Liberibacter asiaticus

The bacterium Candidatus Liberibacter asiaticus (CLas) causes citrus Huanglongbing disease. Our understanding of the pathogenicity and biology of this microorganism remains limited because CLas has not yet been cultivated in artificial media. Its genome is relatively small and encodes approximately 1136 proteins, of which 415 have unknown functions. Here, we use a high-throughput yeast-two-hybrid (Y2H) screen to identify interactions between CLas proteins, thus providing insights into their potential functions. We identify 4245 interactions between 542 proteins, after screening 916 bait and 936 prey proteins. The false positive rate of the Y2H assay is estimated to be 2.9%. Pull-down assays for nine protein-protein interactions (PPIs) likely involved in flagellar function support the robustness of the Y2H results. The average number of PPIs per node in the CLas interactome is 15.6, which is higher than the numbers previously reported for interactomes of free-living bacteria, suggesting that CLas genome reduction has been accompanied by increased protein multi-functionality. We propose potential functions for 171 uncharacterized proteins, based on the PPI results, guilt-by-association analyses, and comparison with data from other bacterial species. We identify 40 hub-node proteins, including quinone oxidoreductase and LysR, which are known to protect other bacteria against oxidative stress and might be important for CLas survival in the phloem. We expect our PPI database to facilitate research on CLas biology and pathogenicity mechanisms.

Speos: an ensemble graph representation learning framework to predict core gene candidates for complex diseases

Understanding phenotype-to-genotype relationships is a grand challenge of 21st century biology with translational implications. The recently proposed "omnigenic" model postulates that effects of genetic variation on traits are mediated by core-genes and -proteins whose activities mechanistically influence the phenotype, whereas peripheral genes encode a regulatory network that indirectly affects phenotypes via core gene products. Here, we develop a positive-unlabeled graph representation-learning ensemble-approach based on a nested cross-validation to predict core-like genes for diverse diseases using Mendelian disorder genes for training. Employing mouse knockout phenotypes for external validations, we demonstrate that core-like genes display several key properties of core genes: Mouse knockouts of genes corresponding to our most confident predictions give rise to relevant mouse phenotypes at rates on par with the Mendelian disorder genes, and all candidates exhibit core gene properties like transcriptional deregulation in disease and loss-of-function intolerance. Moreover, as predicted for core genes, our candidates are enriched for drug targets and druggable proteins. In contrast to Mendelian disorder genes the new core-like genes are enriched for druggable yet untargeted gene products, which are therefore attractive targets for drug development. Interpretation of the underlying deep learning model suggests plausible explanations for our core gene predictions in form of molecular mechanisms and physical interactions. Our results demonstrate the potential of graph representation learning for the interpretation of biological complexity and pave the way for studying core gene properties and future drug development.

Drug discovery by targeting the protein-protein interactions involved in autophagy

Autophagy is a cellular process in which proteins and organelles are engulfed in autophagosomal vesicles and transported to the lysosome/vacuole for degradation. Protein-protein interactions (PPIs) play a crucial role at many stages of autophagy, which present formidable but attainable targets for autophagy regulation. Moreover, selective regulation of PPIs tends to have a lower risk in causing undesired off-target effects in the context of a complicated biological network. Thus, small-molecule regulators, including peptides and peptidomimetics, targeting the critical PPIs involved in autophagy provide a new opportunity for innovative drug discovery. This article provides general background knowledge of the critical PPIs involved in autophagy and reviews a range of successful attempts on discovering regulators targeting those PPIs. Successful strategies and existing limitations in this field are also discussed.

Symbiont-host interactome mapping reveals effector-targeted modulation of hormone networks and activation of growth promotion

Plants have benefited from interactions with symbionts for coping with challenging environments since the colonisation of land. The mechanisms of symbiont-mediated beneficial effects and similarities and differences to pathogen strategies are mostly unknown. Here, we use 106 (effector-) proteins, secreted by the symbiont Serendipita indica (Si) to modulate host physiology, to map interactions with Arabidopsis thaliana host proteins. Using integrative network analysis, we show significant convergence on target-proteins shared with pathogens and exclusive targeting of Arabidopsis proteins in the phytohormone signalling network. Functional in planta screening and phenotyping of Si effectors and interacting proteins reveals previously unknown hormone functions of Arabidopsis proteins and direct beneficial activities mediated by effectors in Arabidopsis. Thus, symbionts and pathogens target a shared molecular microbe-host interface. At the same time Si effectors specifically target the plant hormone network and constitute a powerful resource for elucidating the signalling network function and boosting plant productivity.

Prediction of transient and permanent protein interactions using AI methods

Protein-protein interactions (PPIs) can be classified as permanent or transient interactions based on their stability or lifetime. Understanding the precise details of such protein interactions will pave the way for the discovery of inhibitors and for understanding the nature and function of PPIs. In the present work, 43 relevant physicochemical, geometrical and structural features were calculated for a curated dataset from the literature, comprising of 402 protein-protein complexes of permanent and transient categories, and 5 different Supervised Machine Learning models were developed with Scikit-learn to predict transient and permanent PPI. Additionally, deep learning method with Artificial Neural Network was also performed using Tensor Flow and Keras. Predicted models achieved accuracy ranging from 76.54% to 82.71% and k-NN has achieved the highest accuracy. Detailed analysis of these methods revealed that Interface areas such as Percent interface accessible area, Interface accessible area and Total interface area and the parameters defining the shape of the PPI interface such as Planarity, Eccentricity and Circularity are the most discriminating factors between these two categories. The present method could serve as an effective tool to understand the mechanism of protein association and to predict the transient and permanent interactions, which could supplement the costly and time-consuming experimental techniques.

Explainable drug side effect prediction via biologically informed graph neural network

Early detection of potential side effects (SE) is a critical and challenging task for drug discovery and patient care. In-vitro or in-vivo approach to detect potential SEs is not scalable for many drug candidates during the preclinical stage. Recent advances in explainable machine learning may facilitate detecting potential SEs of new drugs before market release and elucidating the critical mechanism of biological actions. Here, we leverage multi-modal interactions among molecules to develop a biologically informed graph-based SE prediction model, called HHAN-DSI. HHAN-DSI predicted frequent and even uncommon SEs of the unseen drug with higher or comparable accuracy against benchmark methods. When applying HHAN-DSI to the central nervous system, the organs with the largest number of SEs, the model revealed diverse psychiatric medications' previously unknown but probable SEs, together with the potential mechanisms of actions through a network of genes, biological functions, drugs, and SEs.

A positive statistical benchmark to assess network agreement

Current computational methods for validating experimental network datasets compare overlap, i.e., shared links, with a reference network using a negative benchmark. However, this fails to quantify the level of agreement between the two networks. To address this, we propose a positive statistical benchmark to determine the maximum possible overlap between networks. Our approach can efficiently generate this benchmark in a maximum entropy framework and provides a way to assess whether the observed overlap is significantly different from the best-case scenario. We introduce a normalized overlap score, Normlap, to enhance comparisons between experimental networks. As an application, we compare molecular and functional networks, resulting in an agreement network of human as well as yeast network datasets. The Normlap score can improve the comparison between experimental networks by providing a computational alternative to network thresholding and validation.

Homo cerevisiae-Leveraging Yeast for Investigating Protein-Protein Interactions and Their Role in Human Disease

Understanding how genetic variation affects phenotypes represents a major challenge, particularly in the context of human disease. Although numerous disease-associated genes have been identified, the clinical significance of most human variants remains unknown. Despite unparalleled advances in genomics, functional assays often lack sufficient throughput, hindering efficient variant functionalization. There is a critical need for the development of more potent, high-throughput methods for characterizing human genetic variants. Here, we review how yeast helps tackle this challenge, both as a valuable model organism and as an experimental tool for investigating the molecular basis of phenotypic perturbation upon genetic variation. In systems biology, yeast has played a pivotal role as a highly scalable platform which has allowed us to gain extensive genetic and molecular knowledge, including the construction of comprehensive interactome maps at the proteome scale for various organisms. By leveraging interactome networks, one can view biology from a systems perspective, unravel the molecular mechanisms underlying genetic diseases, and identify therapeutic targets. The use of yeast to assess the molecular impacts of genetic variants, including those associated with viral interactions, cancer, and rare and complex diseases, has the potential to bridge the gap between genotype and phenotype, opening the door for precision medicine approaches and therapeutic development.

Targeting Protein-Protein Interfaces with Peptides: The Contribution of Chemical Combinatorial Peptide Library Approaches

Protein-protein interfaces play fundamental roles in the molecular mechanisms underlying pathophysiological pathways and are important targets for the design of compounds of therapeutic interest. However, the identification of binding sites on protein surfaces and the development of modulators of protein-protein interactions still represent a major challenge due to their highly dynamic and extensive interfacial areas. Over the years, multiple strategies including structural, computational, and combinatorial approaches have been developed to characterize PPI and to date, several successful examples of small molecules, antibodies, peptides, and aptamers able to modulate these interfaces have been determined. Notably, peptides are a particularly useful tool for inhibiting PPIs due to their exquisite potency, specificity, and selectivity. Here, after an overview of PPIs and of the commonly used approaches to identify and characterize them, we describe and evaluate the impact of chemical peptide libraries in medicinal chemistry with a special focus on the results achieved through recent applications of this methodology. Finally, we also discuss the role that this methodology can have in the framework of the opportunities, and challenges that the application of new predictive approaches based on artificial intelligence is generating in structural biology.

Next-generation large-scale binary protein interaction network for Drosophila melanogaster

Generating reference maps of interactome networks illuminates genetic studies by providing a protein-centric approach to finding new components of existing pathways, complexes, and processes. We apply state-of-the-art methods to identify binary protein-protein interactions (PPIs) for Drosophila melanogaster. Four all-by-all yeast two-hybrid (Y2H) screens of > 10,000 Drosophila proteins result in the 'FlyBi' dataset of 8723 PPIs among 2939 proteins. Testing subsets of data from FlyBi and previous PPI studies using an orthogonal assay allows for normalization of data quality; subsequent integration of FlyBi and previous data results in an expanded binary Drosophila reference interaction network, DroRI, comprising 17,232 interactions among 6511 proteins. We use FlyBi data to generate an autophagy network, then validate in vivo using autophagy-related assays. The deformed wings (dwg) gene encodes a protein that is both a regulator and a target of autophagy. Altogether, these resources provide a foundation for building new hypotheses regarding protein networks and function.

Assessment of community efforts to advance network-based prediction of protein-protein interactions

Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.

Computational approaches for the design of modulators targeting protein-protein interactions

BACKGROUND

Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery.

AREAS COVERED

In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes.

EXPERT OPINION

Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.

A binary interaction map between turnip mosaic virus and Arabidopsis thaliana proteomes

Viruses are obligate intracellular parasites that have co-evolved with their hosts to establish an intricate network of protein-protein interactions. Here, we followed a high-throughput yeast two-hybrid screening to identify 378 novel protein-protein interactions between turnip mosaic virus (TuMV) and its natural host Arabidopsis thaliana. We identified the RNA-dependent RNA polymerase NIb as the viral protein with the largest number of contacts, including key salicylic acid-dependent transcription regulators. We verified a subset of 25 interactions in planta by bimolecular fluorescence complementation assays. We then constructed and analyzed a network comprising 399 TuMV-A. thaliana interactions together with intravirus and intrahost connections. In particular, we found that the host proteins targeted by TuMV are enriched in different aspects of plant responses to infections, are more connected and have an increased capacity to spread information throughout the cell proteome, display higher expression levels, and have been subject to stronger purifying selection than expected by chance. The proviral or antiviral role of ten host proteins was validated by characterizing the infection dynamics in the corresponding mutant plants, supporting a proviral role for the transcriptional regulator TGA1. Comparison with similar studies with animal viruses, highlights shared fundamental features in their mode of action.

Predicting the Likelihood of Molecules to Act as Modulators of Protein-Protein Interactions

Targeting protein-protein interactions (PPIs) by small molecule modulators (iPPIs) is an attractive strategy for drug therapy, and some iPPIs have already been introduced into the clinic. Blocking PPIs is however considered to be a more difficult task than inhibiting enzymes or antagonizing receptor activity. In this paper, we examine whether it is possible to predict the likelihood of molecules to act as iPPIs. Using our in-house iterative stochastic elimination (ISE) algorithm, we constructed two classification models that successfully distinguish between iPPIs from the iPPI-DB database and decoy molecules from either the Enamine HTS collection (ISE 1) or the ZINC database (ISE 2). External test sets of iPPIs taken from the TIMBAL database and decoys from Enamine HTS or ZINC were screened by the models: the area under the curve for the receiver operating characteristic curve was 0.85-0.89, and the Enrichment Factor increased from an initial 1 to as much as 66 for ISE 1 and 57 for ISE 2. Screening of the Enamine HTS and ZINC data sets through both models results in a library of ∼1.3 million molecules that pass either one of the models. This library is enriched with iPPI candidates that are structurally different from known iPPIs, and thus, it is useful for target-specific screenings and should accelerate the discovery of iPPI drug candidates. The entire library is available in Table S6.

High-Throughput Protein-Protein Interactions Screening Using Pool-Based Liquid Yeast Two-Hybrid Pipeline

Because of its adaptability to high-throughput approaches and a low operating cost, the yeast two-hybrid (Y2H) assay remains the most widely used one for high-throughput protein-protein interactions (PPI) mapping experiments. Here we provide a detailed protocol for a liquid culture-based high-throughput binary protein-protein Y2H screen pipeline of a pool of 50 proteins used as baits against a collection of ~12,000 Arabidopsis proteins encoded by sequence-verified open reading frames (ORFs).

Computational Methods and Deep Learning for Elucidating Protein Interaction Networks

Protein interactions play a critical role in all biological processes, but experimental identification of protein interactions is a time- and resource-intensive process. The advances in next-generation sequencing and multi-omics technologies have greatly benefited large-scale predictions of protein interactions using machine learning methods. A wide range of tools have been developed to predict protein-protein, protein-nucleic acid, and protein-drug interactions. Here, we discuss the applications, methods, and challenges faced when employing the various prediction methods. We also briefly describe ways to overcome the challenges and prospective future developments in the field of protein interaction biology.

TRIP13 Participates in Immediate-Early Sensing of DNA Strand Breaks and ATM Signaling Amplification through MRE11

Thyroid hormone receptor-interacting protein 13 (TRIP13) participates in various regulatory steps related to the cell cycle, such as the mitotic spindle assembly checkpoint and meiotic recombination, possibly by interacting with members of the HORMA domain protein family. Recently, it was reported that TRIP13 could regulate the choice of the DNA repair pathway, i.e., homologous recombination (HR) or nonhomologous end-joining (NHEJ). However, TRIP13 is recruited to DNA damage sites within a few seconds after damage and may therefore have another function in DNA repair other than regulation of the pathway choice. Furthermore, the depletion of TRIP13 inhibited both HR and NHEJ, suggesting that TRIP13 plays other roles besides regulation of choice between HR and NHEJ. To explore the unidentified functions of TRIP13 in the DNA damage response, we investigated its genome-wide interaction partners in the context of DNA damage using quantitative proteomics with proximity labeling. We identified MRE11 as a novel interacting partner of TRIP13. TRIP13 controlled the recruitment of MDC1 to DNA damage sites by regulating the interaction between MDC1 and the MRN complex. Consistently, TRIP13 was involved in ATM signaling amplification. Our study provides new insight into the function of TRIP13 in immediate-early DNA damage sensing and ATM signaling activation.

Intersection of network medicine and machine learning towards investigating the key biomarkers and pathways underlying amyotrophic lateral sclerosis: a systematic review

BACKGROUND

Network medicine is an emerging area of research that focuses on delving into the molecular complexity of the disease, leading to the discovery of network biomarkers and therapeutic target discovery. Amyotrophic lateral sclerosis (ALS) is a complicated rare disease with unknown pathogenesis and no available treatment. In ALS, network properties appear to be potential biomarkers that can be beneficial in disease-related applications when explored independently or in tandem with machine learning (ML) techniques.

OBJECTIVE

This systematic literature review explores recent trends in network medicine and implementations of network-based ML algorithms in ALS. We aim to provide an overview of the identified primary studies and gather details on identifying the potential biomarkers and delineated pathways.

METHODS

The current study consists of searching for and investigating primary studies from PubMed and Dimensions.ai, published between 2018 and 2022 that reported network medicine perspectives and the coupling of ML techniques. Each abstract and full-text study was individually evaluated, and the relevant studies were finally included in the review for discussion once they met the inclusion and exclusion criteria.

RESULTS

We identified 109 eligible publications from primary studies representing this systematic review. The data coalesced into two themes: application of network science to identify disease modules and promising biomarkers in ALS, along with network-based ML approaches. Conclusion This systematic review gives an overview of the network medicine approaches and implementations of network-based ML algorithms in ALS to determine new disease genes, and identify critical pathways and therapeutic target discovery for personalized treatment.

A Hybrid Docking and Machine Learning Approach to Enhance the Performance of Virtual Screening Carried out on Protein-Protein Interfaces

The modulation of protein-protein interactions (PPIs) by small chemical compounds is challenging. PPIs play a critical role in most cellular processes and are involved in numerous disease pathways. As such, novel strategies that assist the design of PPI inhibitors are of major importance. We previously reported that the knowledge-based DLIGAND2 scoring tool was the best-rescoring function for improving receptor-based virtual screening (VS) performed with the Surflex docking engine applied to several PPI targets with experimentally known active and inactive compounds. Here, we extend our investigation by assessing the vs. potential of other types of scoring functions with an emphasis on docking-pose derived solvent accessible surface area (SASA) descriptors, with or without the use of machine learning (ML) classifiers. First, we explored rescoring strategies of Surflex-generated docking poses with five GOLD scoring functions (GoldScore, ChemScore, ASP, ChemPLP, ChemScore with Receptor Depth Scaling) and with consensus scoring. The top-ranked poses were post-processed to derive a set of protein and ligand SASA descriptors in the bound and unbound states, which were combined to derive descriptors of the docked protein-ligand complexes. Further, eight ML models (tree, bagged forest, random forest, Bayesian, support vector machine, logistic regression, neural network, and neural network with bagging) were trained using the derivatized SASA descriptors and validated on test sets. The results show that many SASA descriptors are better than Surflex and GOLD scoring functions in terms of overall performance and early recovery success on the used dataset. The ML models were superior to all scoring functions and rescoring approaches for most targets yielding up to a seven-fold increase in enrichment factors at 1% of the screened collections. In particular, the neural networks and random forest-based ML emerged as the best techniques for this PPI dataset, making them robust and attractive vs. tools for hit-finding efforts. The presented results suggest that exploring further docking-pose derived SASA descriptors could be valuable for structure-based virtual screening projects, and in the present case, to assist the rational design of small-molecule PPI inhibitors.

Large-Scale Crystallographic Fragment Screening Expedites Compound Optimization and Identifies Putative Protein-Protein Interaction Sites

The identification of starting points for compound development is one of the key steps in early-stage drug discovery. Information-rich techniques such as crystallographic fragment screening can potentially increase the efficiency of this step by providing the structural information of the binding mode of the ligands in addition to the mere binding information. Here, we present the crystallographic screening of our 1000-plus-compound F2X-Universal Library against the complex of the yeast spliceosomal Prp8 RNaseH-like domain and the snRNP assembly factor Aar2. The observed 269 hits are distributed over 10 distinct binding sites on the surface of the protein-protein complex. Our work shows that hit clusters from large-scale crystallographic fragment screening campaigns identify known interaction sites with other proteins and suggest putative additional interaction sites. Furthermore, the inherent binding pose validation within the hit clusters may accelerate downstream compound optimization.

Exploiting breakdown in nonhost effector-target interactions to boost host disease resistance

Plant nonhost resistance (NHR) prevents infection by all members of most microbial species, but its molecular mechanisms are not well understood. We found that effector proteins from the potato blight pathogen Phytophthora infestans, which enhance infection in host plants, fail to enhance susceptibility in nonhost Arabidopsis. These P. infestans effectors often failed to interact with Arabidopsis orthologs of their potato target proteins, whereas many interactions were detected between these Arabidopsis orthologs and effectors from its adapted pathogen Hyaloperonospora arabidopsidis. Thus, breakdown in effector–target interactions in distantly related nonhost plants is likely a key component of NHR. Importantly, we demonstrate that exploiting this breakdown and expressing nonhost target orthologs in host plants provide a strategy to prevent crop disease.

Plants are resistant to most microbial species due to nonhost resistance (NHR), providing broad-spectrum and durable immunity. However, the molecular components contributing to NHR are poorly characterised. We address the question of whether failure of pathogen effectors to manipulate nonhost plants plays a critical role in NHR. RxLR (Arg-any amino acid-Leu-Arg) effectors from two oomycete pathogens, Phytophthora infestans and Hyaloperonospora arabidopsidis, enhanced pathogen infection when expressed in host plants (Nicotiana benthamiana and Arabidopsis, respectively) but the same effectors performed poorly in distantly related nonhost pathosystems. Putative target proteins in the host plant potato were identified for 64 P. infestans RxLR effectors using yeast 2-hybrid (Y2H) screens. Candidate orthologues of these target proteins in the distantly related non-host plant Arabidopsis were identified and screened using matrix Y2H for interaction with RxLR effectors from both P. infestans and H. arabidopsidis. Few P. infestans effector-target protein interactions were conserved from potato to candidate Arabidopsis target orthologues (cAtOrths). However, there was an enrichment of H. arabidopsidis RxLR effectors interacting with cAtOrths. We expressed the cAtOrth AtPUB33, which unlike its potato orthologue did not interact with P. infestans effector PiSFI3, in potato and Nicotiana benthamiana. Expression of AtPUB33 significantly reduced P. infestans colonization in both host plants. Our results provide evidence that failure of pathogen effectors to interact with and/or correctly manipulate target proteins in distantly related non-host plants contributes to NHR. Moreover, exploiting this breakdown in effector-nonhost target interaction, transferring effector target orthologues from non-host to host plants is a strategy to reduce disease.

Functional diversity of apolipoprotein E: from subcellular localization to mitochondrial function

Human apolipoprotein E (APOE), originally known for its role in lipid metabolism, is polymorphic with three major allele forms, namely, APOEε2, APOEε3, and APOEε4, leading to three different human APOE isoforms. The ε4 allele is a genetic risk factor for Alzheimer's disease (AD); therefore, the vast majority of APOE research focuses on its role in AD pathology. However, there is increasing evidence for other functions of APOE through the involvement in other biological processes such as transcriptional regulation, mitochondrial metabolism, immune response, and responsiveness to dietary factors. Therefore, the aim of this review is to provide an overview of the potential novel functions of APOE and their characterization. The detection of APOE in various cell organelles points to previously unrecognized roles in mitochondria and others, although it is actually considered a secretory protein. Furthermore, numerous interactions of APOE with other proteins have been detected, providing indications for new metabolic pathways involving APOE. The present review summarizes the current evidence on APOE beyond its original role in lipid metabolism, to change the perspective and encourage novel approaches to future research on APOE and its isoform-dependent role in the cellular metabolism.

Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism

Simple Summary

The influence of data incompleteness on the correctness of conclusions about the structure and functions of the objects under study is widely discussed in the literature. It was noted that even a small percentage of missing data can lead to incorrect conclusions and imperfect knowledge. In particular, incompleteness can lead to critical errors in the qualitative and quantitative assessments of interactions in biological systems and a distorted understanding of the functioning mechanisms of living systems. In this brief review, we attempt to demonstrate the extent of this incompleteness in functional information about living systems using the best-studied examples. We suggest that this incompleteness may form seemingly insurmountable barriers in deciphering the mechanisms of the functioning of complex systems with unpredictable properties arising from the interaction of the system components.

Abstract

In this brief review, we attempt to demonstrate that the incompleteness of data, as well as the intrinsic heterogeneity of biological systems, may form very strong and possibly insurmountable barriers for researchers trying to decipher the mechanisms of the functioning of live systems. We illustrate this challenge using the two most studied organisms: E. coli, with 34.6% genes lacking experimental evidence of function, and C. elegans, with identified proteins for approximately 50% of its genes. Another striking example is an artificial unicellular entity named JCVI-syn3.0, with a minimal set of genes. A total of 31.5% of the genes of JCVI-syn3.0 cannot be ascribed a specific biological function. The human interactome mapping project identified only 5–10% of all protein interactions in humans. In addition, most of the available data are static snapshots, and it is barely possible to generate realistic models of the dynamic processes within cells. Moreover, the existing interactomes reflect the de facto interaction but not its functional result, which is an unpredictable emerging property. Perhaps the completeness of molecular data on any living organism is beyond our reach and represents an unsolvable problem in biology.

The Fra-1: Novel role in regulating extensive immune cell states and affecting inflammatory diseases

Fra-1(Fos-related antigen1), a member of transcription factor activator protein (AP-1), plays an important role in cell proliferation, apoptosis, differentiation, inflammation, oncogenesis and tumor metastasis. Accumulating evidence suggest that the malignancy and invasive ability of tumors can be significantly changed by directly targeting Fra-1. Besides, the effects of Fra-1 are gradually revealed in immune and inflammatory settings, such as arthritis, pneumonia, psoriasis and cardiovascular disease. These regulatory mechanisms that orchestrate immune and non-immune cells underlie Fra-1 as a potential therapeutic target for a variety of human diseases. In this review, we focus on the current knowledge of Fra-1 in immune system, highlighting its unique importance in regulating tissue homeostasis. In addition, we also discuss the possible critical intervention strategy in diseases, which also outline future research and development avenues.

Application of nanopore sensors for biomolecular interactions and drug discovery

Biomolecular interactions, including protein-protein, protein-nucleic acid, and protein/nucleic acid-ligand interactions, play crucial roles in various cellular signaling and biological processes, and offer attractive therapeutic targets in numerous human diseases. Currently, drug discovery is limited by the low efficiency and high cost of conventional ensemble-averaging-based techniques for biomolecular interaction analysis and high-throughput drug screening. Nanopores are an emerging technology for single-molecule sensing of biomolecules. Owing to the robust advantages of single-molecule sensing, nanopore sensors have contributed tremendously to nucleic acid sequencing and disease diagnostics. In this minireview, we summarize the recent developments and outlooks in single-molecule sensing of various biomolecular interactions for drug discovery applications using biological and solid-state nanopore sensors.

A Yeast BiFC-seq Method for Genome-wide Interactome Mapping

Genome-wide physical protein-protein interaction (PPI) mapping remains a major challenge for current technologies. Here, we reported a high-efficiency BiFC-seq method, yeast-enhanced green fluorescent protein-based bimolecular fluorescence complementation (yEGFP-BiFC) coupled with next-generation DNA sequencing, for interactome mapping. We first applied yEGFP-BiFC method to systematically investigate an intraviral network of the Ebola virus. Two-thirds (9/14) of known interactions of EBOV were recaptured, and five novel interactions were discovered. Next, we used the BiFC-seq method to map the interactome of the tumor protein p53. We identified 97 interactors of p53, more than three-quarters of which were novel. Furthermore, in a more complex background, we screened potential interactors by pooling two BiFC libraries together and revealed a network of 229 interactions among 205 proteins. These results show that BiFC-seq is a highly sensitive, rapid, and economical method for genome-wide interactome mapping.

A Dual Ranking Algorithm Based on the Multiplex Network for Heterogeneous Complex Disease Analysis

Identifying biomarkers of heterogeneous complex diseases has always been one of the focuses in medical research. In previous studies, the powerful network propagation methods have been applied to finding marker genes related to specific diseases, but existing methods are mostly based on a single network, which may be greatly affected by the incompleteness of the network and the ignorance of a large amount of information about physical and functional interactions between biological components. Other methods that directly integrate multiple types of interactions into an aggregate network have the risks that different types of data may conflict with each other and the characteristics and topologies of each individual network are lost. Meanwhile, biomarkers used in clinical trials should have the characteristics of small quantity and strong discriminate ability. In this study, we developed a multiplex network-based dual ranking framework (DualRank) for heterogeneous complex disease analysis. We applied the proposed method to heterogeneous complex diseases for diagnosis, prognosis, and classification. The results showed that DualRank outperformed competing methods and could identify biomarkers with the small quantity, great prediction performance (average AUC = 0.818) and biological interpretability.

Overcoming Sparseness of Biomedical Networks to Identify Drug Repositioning Candidates

Modeling complex biological systems is necessary to understand biochemical interactions behind pharmacological effects of drugs. Successful in silico drug repurposing relies on exploration of diverse biochemical concepts and their relationships, including drug's adverse reactions, drug targets, disease symptoms, as well as disease associated genes and their pathways, to name a few. We present a computational method for inferring drug-disease associations from complex but incomplete and biased biological networks. Our method employs matrix completion to overcome the sparseness of biomedical data and to enrich the set of relationships between different biomedical entities. We present a strategy for identifying network paths supportive of drug efficacy as well as a computational procedure capable of combining different network patterns to better distinguish treatments from non-treatments. The algorithms is available at http://bioinfo.cs.uni.edu/AEONET.html.

The Intricacy of the Viral-Human Protein Interaction Networks: Resources, Data, and Analyses

Viral infections are one of the major causes of human diseases that cause yearly millions of deaths and seriously threaten global health, as we have experienced with the COVID-19 pandemic. Numerous approaches have been adopted to understand viral diseases and develop pharmacological treatments. Among them, the study of virus-host protein-protein interactions is a powerful strategy to comprehend the molecular mechanisms employed by the virus to infect the host cells and to interact with their components. Experimental protein-protein interactions described in the scientific literature have been systematically captured into several molecular interaction databases. These data are organized in structured formats and can be easily downloaded by users to perform further bioinformatic and network studies. Network analysis of available virus-host interactomes allow us to understand how the host interactome is perturbed upon viral infection and what are the key host proteins targeted by the virus and the main cellular pathways that are subverted. In this review, we give an overview of publicly available viral-human protein-protein interactions resources and the community standards, curation rules and adopted ontologies. A description of the main virus-human interactome available is provided, together with the main network analyses that have been performed. We finally discuss the main limitations and future challenges to assess the quality and reliability of protein-protein interaction datasets and resources.

Recent advances in predicting protein-protein interactions with the aid of artificial intelligence algorithms

Protein-protein interactions (PPIs) are essential in the regulation of biological functions and cell events, therefore understanding PPIs have become a key issue to understanding the molecular mechanism and investigating the design of drugs. Here we highlight the major developments in computational methods developed for predicting PPIs by using types of artificial intelligence algorithms. The first part introduces the source of experimental PPI data. The second part is devoted to the PPI prediction methods based on sequential information. The third part covers representative methods using structural information as the input feature. The last part is methods designed by combining different types of features. For each part, the state-of-the-art computational PPI prediction methods are reviewed in an inclusive view. Finally, we discuss the flaws existing in this area and future directions of next-generation algorithms.

Experimental evidence for long-distance electrodynamic intermolecular forces

Both classical and quantum electrodynamics predict the existence of dipole-dipole long-range electrodynamic intermolecular forces; however, these have never been hitherto experimentally observed. The discovery of completely new and unanticipated forces acting between biomolecules could have considerable impact on our understanding of the dynamics and functioning of the molecular machines at work in living organisms. Here, using two independent experiments, on the basis of different physical effects detected by fluorescence correlation spectroscopy and terahertz spectroscopy, respectively, we demonstrate experimentally the activation of resonant electrodynamic intermolecular forces. This is an unprecedented experimental proof of principle of a physical phenomenon that, having been observed for biomacromolecules and with long-range action (up to 1000 Å), could be of importance for biology. In addition to thermal fluctuations that drive molecular motion randomly, these resonant (and thus selective) electrodynamic forces may contribute to molecular encounters in the crowded cellular space.

Splicing Variants, Protein-Protein Interactions, and Drug Targeting in Hutchinson-Gilford Progeria Syndrome and Small Cell Lung Cancer

Alternative splicing (AS) is a biological operation that enables a messenger RNA to encode protein variants (isoforms) that give one gene several functions or properties. This process provides one of the major sources of use for understanding the proteomic diversity of multicellular organisms. In combination with post-translational modifications, it contributes to generating a variety of protein–protein interactions (PPIs) that are essential to cellular homeostasis or proteostasis. However, cells exposed to many kinds of stresses (aging, genetic changes, carcinogens, etc.) sometimes derive cancer or disease onset from aberrant PPIs caused by DNA mutations. In this review, we summarize how splicing variants may form a neomorphic protein complex and cause diseases such as Hutchinson-Gilford progeria syndrome (HGPS) and small cell lung cancer (SCLC), and we discuss how protein–protein interfaces obtained from the variants may represent efficient therapeutic target sites to treat HGPS and SCLC.

Probing GPCR Dimerization Using Peptides

G protein-coupled receptors (GPCRs) are the largest class of membrane proteins and the most common and extensively studied pharmacological target. Numerous studies over the last decade have confirmed that GPCRs do not only exist and function in their monomeric form but in fact, have the ability to form dimers or higher order oligomers with other GPCRs, as well as other classes of receptors. GPCR oligomers have become increasingly attractive to investigate as they have the ability to modulate the pharmacological responses of the receptors which in turn, could have important functional roles in diseases, such as cancer and several neurological & neuropsychiatric disorders. Despite the growing evidence in the field of GPCR oligomerisation, the lack of structural information, as well as targeting the 'undruggable' protein-protein interactions (PPIs) involved in these complexes, has presented difficulties. Outside the field of GPCRs, targeting PPIs has been widely studied, with a variety of techniques being investigated; from small-molecule inhibitors to disrupting peptides. In this review, we will demonstrate several physiologically relevant GPCR dimers and discuss an array of strategies and techniques that can be employed when targeting these complexes, as well as provide ideas for future development.