Human protein–protein interaction networks and the value for drug discovery

Creative approaches using proximity labeling to gain new biological insights

At its most fundamental level, life is a collection of synchronized cellular processes driven by interactions among biomolecules. Proximity labeling has emerged as a powerful technique to capture these interactions in native settings, revealing previously unexplored elements of biology. This review highlights recent developments in proximity labeling, focusing on methods that push the fundamental technologies beyond the classic bait-prey paradigm, such as RNA-protein interactions, ligand/small-molecule-protein interactions, cell surface protein interactions, and subcellular protein trafficking. The advancement of proximity labeling methods to address different biological problems will accelerate our understanding of the complex biological systems that make up life.

Photocatalytic Activation of Aryl(trifluoromethyl) Diazos to Carbenes for High-Resolution Protein Labeling with Red Light

State-of-the-art methods in photoproximity labeling center on the targeted generation and capture of short-lived reactive intermediates to provide a snapshot of local protein environments. Diazirines are the current gold standard for high-resolution proximity labeling, generating short-lived aryl(trifluoromethyl) carbenes. Here, we present a method to access aryl(trifluoromethyl) carbenes from a stable diazo source via tissue-penetrable, deep red to near-infrared light (600-800 nm). The operative mechanism of this activation involves Dexter energy transfer from photoexcited osmium(II) photocatalysts to the diazo, thus revealing an aryl(trifluoromethyl) carbene. The labeling preferences of the diazo probe with amino acids are studied, showing high reactivity toward heteroatom-H bonds. Upon the synthesis of a biotinylated diazo probe, labeling studies are conducted on native proteins as well as proteins conjugated to the Os photocatalyst. Finally, we demonstrate that the conjugation of a protein inhibitor to the photocatalyst also enables selective protein labeling in the presence of spectator proteins and achieves specific labeling of a membrane protein on the surface of mammalian cells via a two-antibody photocatalytic system.

β-Turn Induction by a Diastereopure Azepane-Derived Quaternary Amino Acid

β-Turns are one of the most common secondary structures found in proteins. In the interest of developing novel β-turn inducers, a diastereopure azepane-derived quaternary amino acid has been incorporated into a library of simplified tetrapeptide models in order to assess the effect of the azepane position and peptide sequence on the stabilization of β-turns. The conformational analysis of these peptides by molecular modeling, NMR spectroscopy, and X-ray crystallography showed that this azepane amino acid is an effective β-turn inducer when incorporated at the i + 1 position. Moreover, the analysis of the supramolecular self-assembly of one of the β-turn-containing peptide models in the solid state reveals that it forms a supramolecular helical arrangement while maintaining the β-turn structure. The results here presented provide the basis for the use of this azepane quaternary amino acid as a strong β-turn inducer in the search for novel peptide-based bioactive molecules, catalysts, and biomaterials.

Transcriptomics-driven drug repositioning for the treatment of diabetic foot ulcer

Diabetic foot ulcers (DFUs) are a common complication of diabetes and can lead to severe disability and even amputation. Despite advances in treatment, there is currently no cure for DFUs and available drugs for treatment are limited. This study aimed to identify new candidate drugs and repurpose existing drugs to treat DFUs based on transcriptomics analysis. A total of 31 differentially expressed genes (DEGs) were identified and used to prioritize the biological risk genes for DFUs. Further investigation using the database DGIdb revealed 12 druggable target genes among 50 biological DFU risk genes, corresponding to 31 drugs. Interestingly, we highlighted that two drugs (urokinase and lidocaine) are under clinical investigation for DFU and 29 drugs are potential candidates to be repurposed for DFU therapy. The top 5 potential biomarkers for DFU from our findings are IL6ST, CXCL9, IL1R1, CXCR2, and IL10. This study highlights IL1R1 as a highly promising biomarker for DFU due to its high systemic score in functional annotations, that can be targeted with an existing drug, Anakinra. Our study proposed that the integration of transcriptomic and bioinformatic-based approaches has the potential to drive drug repurposing for DFUs. Further research will further examine the mechanisms by which targeting IL1R1 can be used to treat DFU.

Tracking chromatin state changes using nanoscale photo-proximity labelling

Interactions between biomolecules underlie all cellular processes and ultimately control cell fate. Perturbation of native interactions through mutation, changes in expression levels or external stimuli leads to altered cellular physiology and can result in either disease or therapeutic effects1,2. Mapping these interactions and determining how they respond to stimulus is the genesis of many drug development efforts, leading to new therapeutic targets and improvements in human health1. However, in the complex environment of the nucleus, it is challenging to determine protein-protein interactions owing to low abundance, transient or multivalent binding and a lack of technologies that are able to interrogate these interactions without disrupting the protein-binding surface under study3. Here, we describe a method for the traceless incorporation of iridium-photosensitizers into the nuclear micro-environment using engineered split inteins. These Ir-catalysts can activate diazirine warheads through Dexter energy transfer to form reactive carbenes within an approximately 10 nm radius, cross-linking with proteins in the immediate micro-environment (a process termed µMap) for analysis using quantitative chemoproteomics4. We show that this nanoscale proximity-labelling method can reveal the critical changes in interactomes in the presence of cancer-associated mutations, as well as treatment with small-molecule inhibitors. µMap improves our fundamental understanding of nuclear protein-protein interactions and, in doing so, is expected to have a significant effect on the field of epigenetic drug discovery in both academia and industry.

Cell-free Biosynthesis of Peptidomimetics

A wide variety of peptidomimetics (peptide analogs) possessing innovative biological functions have been brought forth as therapeutic candidates through cell-free protein synthesis (CFPS) systems. A key feature of these peptidomimetic drugs is the use of non-canonical amino acid building blocks with diverse biochemical properties that expand functional diversity. Here, we summarize recent technologies leveraging CFPS platforms to expand the reach of peptidomimetics drugs. We also offer perspectives on engineering the translational machinery that may open new opportunities for expanding genetically encoded chemistry to transform drug discovery practice beyond traditional boundaries.

Drug Repurposing for Atopic Dermatitis by Integration of Gene Networking and Genomic Information

Atopic Dermatitis (AD) is a chronic and relapsing skin disease. The medications for treating AD are still limited, most of them are topical corticosteroid creams or antibiotics. The current study attempted to discover potential AD treatments by integrating a gene network and genomic analytic approaches. Herein, the Single Nucleotide Polymorphism (SNPs) associated with AD were extracted from the GWAS catalog. We identified 70 AD-associated loci, and then 94 AD risk genes were found by extending to proximal SNPs based on r2 > 0.8 in Asian populations using HaploReg v4.1. Next, we prioritized the AD risk genes using in silico pipelines of bioinformatic analysis based on six functional annotations to identify biological AD risk genes. Finally, we expanded them according to the molecular interactions using the STRING database to find the drug target genes. Our analysis showed 27 biological AD risk genes, and they were mapped to 76 drug target genes. According to DrugBank and Therapeutic Target Database, 25 drug target genes overlapping with 53 drugs were identified. Importantly, dupilumab, which is approved for AD, was successfully identified in this bioinformatic analysis. Furthermore, ten drugs were found to be potentially useful for AD with clinical or preclinical evidence. In particular, we identified filgotinub and fedratinib, targeting gene JAK1, as potential drugs for AD. Furthermore, four monoclonal antibody drugs (lebrikizumab, tralokinumab, tocilizumab, and canakinumab) were successfully identified as promising for AD repurposing. In sum, the results showed the feasibility of gene networking and genomic information as a potential drug discovery resource.

Protein-Peptide Binding Site Detection Using 3D Convolutional Neural Networks

Peptides and peptide-based molecules represent a promising therapeutic modality targeting intracellular protein-protein interactions, potentially combining the beneficial properties of biologics and small-molecule drugs. Protein-peptide complexes occupy a unique niche of interaction interfaces with respect to protein-protein and protein-small molecule complexes. Protein-peptide binding site identification resembles image object detection, a field that had been revolutionalized with computer vision techniques. We present a new protein-peptide binding site detection method called BiteNet_Pp by harnessing the power of 3D convolutional neural network. Our method employs a tensor-based representation of spatial protein structures, which is fed to 3D convolutional neural network, resulting in probability scores and coordinates of the binding "hot spots" in the input structures. We used the domain adaptation technique to fine-tune model trained on protein-small molecule complexes using a manually curated set of protein-peptide structures. BiteNet_Pp consistently outperforms existing state-of-the-art methods in the independent test benchmark. It takes less than a second to analyze a single-protein structure, making BiteNet_Pp suitable for the large-scale analysis of protein-peptide binding sites.

Co-relation with novel phosphorylation sites of IκBα and necroptosis in breast cancer cells

Background

Phosphorylation of NF-kappaB inhibitor alpha (IκBα) is key to regulation of NF-κB transcription factor activity in the cell. Several sites of IκBα phosphorylation by members of the IκB kinase family have been identified, but phosphorylation of the protein by other kinases remains poorly understood. We investigated a new phosphorylation site on IκBα and identified its biological function in breast cancer cells.

Methods

Previously, we observed that aurora kinase (AURK) binds IκBα in the cell. To identify the domains of IκBα essential for phosphorylation by AURK, we performed kinase assays with a series of IκBα truncation mutants. AURK significantly promoted activation of IκBα at serine 32 but not serine 36; by contrast, IκB kinase (IKK) family proteins activated both of these residues. We also confirmed phosphorylation of IκBα by matrix-assisted laser-desorption/ionization time-of-flight mass spectrometry (MALDI-TOF/TOF MS) and nano-liquid chromatography hybrid quadrupole orbitrap mass spectrometer (nanoLC-MS/MS; Q-Exactive).

Results

We identified two novel sites of serine phosphorylation, S63 and S262. Alanine substitution of S63 and S262 (S63A and S262A) of IκBα inhibited proliferation and suppressed p65 transcription activity. In addition, S63A and/or S262A of IκBα regulated apoptotic and necroptotic effects in breast cancer cells.

Conclusions

Phosphorylation of IκBα by AURK at novel sites is related to the apoptosis and necroptosis pathways in breast cancer cells.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08304-7.

Reactive intermediates for interactome mapping

The interactions of biomolecules underpin all cellular processes, and the understanding of their dynamic interplay can lead to significant advances in the treatment of disease through the identification of novel therapeutic strategies. Protein-protein interactions (PPIs) in particular play a vital role within this arena, providing the basis for the majority of cellular signalling pathways. Despite their great importance, the elucidation of weak or transient PPIs that cannot be identified by immunoprecipitation remains a significant challenge, particularly in a disease relevant cellular environment. Recent approaches towards this goal have utilized the in situ generation of high energy intermediates that cross-link with neighboring proteins, providing a snapshot of the biomolecular makeup of the local area or microenvironment, termed the interactome. In this tutorial review, we discuss these reactive intermediates, how they are generated, and the impact they have had on the discovery of new biology. Broadly, we believe this strategy has the potential to significantly accelerate our understanding of PPIs and how they affect cellular physiology.

Integration of genetic variants and gene network for drug repurposing in colorectal cancer

Even though many genetic risk loci for human diseases have been identified and comprehensively cataloged, strategies to guide clinical research by integrating the extensive results of genetic studies and biological resources are still limited. Moreover, integrative analyses that provide novel insights into disease biology are expected to be especially useful for drug discovery. Herein, we used text mining of genetic studies on colorectal cancer (CRC) and assigned biological annotations to identified risk genes in order to discover novel drug targets and potential drugs for repurposing. Risk genes for CRC were obtained from PubMed text mining, and for each gene, six functional and bioinformatic annotations were analyzed. The annotations include missense mutations, cis-expression quantitative trait loci (cis-eQTL), molecular pathway analyses, protein-protein interactions (PPIs), a genetic overlap with knockout mouse phenotypes, and primary immunodeficiency (PID). We then prioritized the biological risk candidate genes according to a scoring system of the six functional annotations. Each functional annotation was assigned one point, and those genes with a score ≥2 were designated "biological CRC risk genes". Using this method, we revealed 82 biological CRC risk genes, which were mapped to 128 genes in an expanded PPI network. Further utilizing DrugBank and the Therapeutic Target Database, we found 21 genes in our list that are targeted by 166 candidate drugs. Based on data from ClinicalTrials.gov and literature review, we found four known target genes with six drugs for clinical treatment in CRC, and three target genes with nine drugs supported by previous preclinical results in CRC. Additionally, 12 genes are targeted by 32 drugs approved for other indications, which can possibly be repurposed for CRC treatment. Finally, analysis from Connectivity Map (CMap) showed that 18 drugs have a high potential for CRC.

Association between ALS and retroviruses: evidence from bioinformatics analysis

BACKGROUND

Emerging evidence suggests retroviruses play a role in the pathophysiology of amyotrophic lateral sclerosis (ALS). Specifically, activation of ancient viral genes embedded in the human genome is theorized to lead to motor neuron degeneration. We explore whether connections exist between ALS and retroviruses through protein interaction networks (PIN) and pathway analysis, and consider the potential roles in drug target discovery. Protein database and pathway/network analytical software including Ingenuity Pathway BioProfiler, STRING, and CytoScape were utilized to identify overlapping protein interaction networks and extract core cluster (s) of retroviruses and ALS.

RESULTS

Topological and statistical analysis of the ALS-PIN and retrovirus-PIN identified a shared, essential protein network and a core cluster with significant connections with both networks. The identified core cluster has three interleukin molecules IL10, Il-6 and IL-1B, a central apoptosis regulator TP53, and several major transcription regulators including MAPK1, ANXA5, SQSTM1, SREBF2, and FADD. Pathway enrichment analysis showed that this core cluster is associated with the glucocorticoid receptor singling and neuroinflammation signaling pathways. For confirmation purposes, we applied the same methodology to the West Nile and Polio virus, which demonstrated trivial connectivity with ALS, supporting the unique connection between ALS and retroviruses.

CONCLUSIONS

Bioinformatics analysis provides evidence to support pathological links between ALS and retroviral activation. The neuroinflammation and apoptotic regulation pathways are specifically implicated. The continuation and further analysis of large scale genome studies may prove useful in exploring genes important in retroviral activation and ALS, which may help discover new drug targets.

In-Silico Identification of Drug Lead Molecule Against Pesticide Exposed-neurodevelopmental Disorders Through Network-Based Computational Model Approach

Background:

Neurodevelopmental Disorders (NDDs) are impairment of the growth and development of the brain or central nervous system, which occurs at the developmental stage. This can include developmental brain dysfunction, which can manifest as neuropsychiatric problems or impaired motor function, learning, language or non-verbal communication. These include the array of disorder, including: Autism Spectrum Disorders (ASD), Attention Deficit Hyperactivity Disorders (ADHD) etc. There is no particular diagnosis and cure for NDDs. These disorders seem to be result from a combination of genetic, biological, psychosocial and environmental risk factors. Diverse scientific literature reveals the adverse effect of environmental factors specifically, exposure of pesticides, which leads to growing number of human pathological conditions; among these, neurodevelopmental disorder is an emerging issue nowadays.

Objective:

The current study focused on in silico identification of potential drug targets for pesticides induced neurodevelopmental disorder including Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) and to design potential drug molecule for the target through drug discovery approaches.

Methods:

We identified 139 candidate genes for ADHD and 206 candidate genes for ASD from the NCBI database for detailed study. Protein-protein interaction network analysis was performed to identify key genes/proteins in the network by using STRING 10.0 database and Cytoscape 3.3.0 software. The 3D structure of target protein was built and validated. Molecular docking was performed against twenty seven possible phytochemicals i.e. beta amyrin, ajmaline, serpentine, urosolic, huperzine A etc. having neuroprotective activity. The best-docked compound was identified by the lowest Binding Energy (BE). Further, the prediction of drug-likeness and bioactivity analysis of leads were performed by using molinspiration cheminformatics software.

Result & Conclusion:

Based on betweenness centrality and node degree as a network topological parameter, solute carrier family 6 member 4 (SLC6A4) was identified as a common key protein in both the networks. 3-D structure of SLC6A4 protein was designed and validated respectively. Based on the lowest binding energy, beta amyrin (B.E = -8.54 kcal/mol) was selected as a potential drug candidate against SLC6A4 protein. Prediction of drug-likeness and bioactivity analysis of leads showed drug candidate as a potential inhibitor. Beta amyrin (CID: 73145) was obtained as the most potential therapeutic inhibitor for ASD & ADHD in human.

Stapled Peptides Inhibitors: A New Window for Target Drug Discovery

Protein-protein interaction (PPI) is a hot topic in clinical research as protein networking has a major impact in human disease. Such PPIs are potential drugs targets, leading to the need to inhibit/block specific PPIs. While small molecule inhibitors have had some success and reached clinical trials, they have generally failed to address the flat and large nature of PPI surfaces. As a result, larger biologics were developed for PPI surfaces and they have successfully targeted PPIs located outside the cell. However, biologics have low bioavailability and cannot reach intracellular targets. A novel class -hydrocarbon-stapled α-helical peptides that are synthetic mini-proteins locked into their bioactive structure through site-specific introduction of a chemical linker- has shown promise. Stapled peptides show an ability to inhibit intracellular PPIs that previously have been intractable with traditional small molecule or biologics, suggesting that they offer a novel therapeutic modality. In this review, we highlight what stapling adds to natural-mimicking peptides, describe the revolution of synthetic chemistry techniques and how current drug discovery approaches have been adapted to stabilize active peptide conformations, including ring-closing metathesis (RCM), lactamisation, cycloadditions and reversible reactions. We provide an overview on the available stapled peptide high-resolution structures in the protein data bank, with four selected structures discussed in details due to remarkable interactions of their staple with the target surface. We believe that stapled peptides are promising drug candidates and open the doors for peptide therapeutics to reach currently "undruggable" space.

Distinct control of PERIOD2 degradation and circadian rhythms by the oncoprotein and ubiquitin ligase MDM2

The circadian clock relies on posttranslational modifications to set the timing for degradation of core regulatory components, which drives clock progression. Ubiquitin-modifying enzymes that target clock components for degradation mainly recognize phosphorylated substrates. Degradation of the circadian clock component PERIOD 2 (PER2) is mediated by its phospho-specific recognition by β-transducin repeat-containing proteins (β-TrCPs), which are F-box-containing proteins that function as substrate recognition subunits of the SCF^β-TRCP ubiquitin ligase complex. However, this mode of regulating PER2 stability falls short of explaining the persistent oscillatory phenotypes reported in biological systems lacking functional elements of the phospho-dependent PER2 degradation machinery. We identified PER2 as a previously uncharacterized substrate for the ubiquitin ligase mouse double minute 2 homolog (MDM2) and found that MDM2 targeted PER2 for degradation in a manner independent of PER2 phosphorylation. Deregulation of MDM2 plays a major role in oncogenesis by contributing to the accumulation of genomic and epigenomic alterations that favor tumor development. MDM2-mediated PER2 turnover was important for defining the circadian period length in mammalian cells, a finding that emphasizes the connection between the circadian clock and cancer. Our results not only broaden the range of specific substrates of MDM2 beyond the cell cycle to include circadian components but also identify a previously unknown regulator of the clock as a druggable node that is often found to be deregulated during tumorigenesis.

Protein-protein interactions: detection, reliability assessment and applications

Protein-protein interactions (PPIs) participate in all important biological processes in living organisms, such as catalyzing metabolic reactions, DNA replication, DNA transcription, responding to stimuli and transporting molecules from one location to another. To reveal the function mechanisms in cells, it is important to identify PPIs that take place in the living organism. A large number of PPIs have been discovered by high-throughput experiments and computational methods. However, false-positive PPIs have been introduced too. Therefore, to obtain reliable PPIs, many computational methods have been proposed. Generally, these methods can be classified into two categories. One category includes the methods that are designed to determine new reliable PPIs. The other one is designed to assess the reliability of existing PPIs and filter out the unreliable ones. In this article, we review the two kinds of methods for detecting reliable PPIs, and then focus on evaluating the performance of some of these typical methods. Later on, we also enumerate several PPI network-based applications with taking a reliability assessment of the PPI data into consideration. Finally, we will discuss the challenges for obtaining reliable PPIs and future directions of the construction of reliable PPI networks. Our research will provide readers some guidance for choosing appropriate methods and features for obtaining reliable PPIs.

Generation of specific inhibitors of SUMO-1- and SUMO-2/3-mediated protein-protein interactions using Affimer (Adhiron) technology

Because protein-protein interactions underpin most biological processes, developing tools that target them to understand their function or to inform the development of therapeutics is an important task. SUMOylation is the posttranslational covalent attachment of proteins in the SUMO family (SUMO-1, SUMO-2, or SUMO-3), and it regulates numerous cellular pathways. SUMOylated proteins are recognized by proteins with SUMO-interaction motifs (SIMs) that facilitate noncovalent interactions with SUMO. We describe the use of the Affimer system of peptide display for the rapid isolation of synthetic binding proteins that inhibit SUMO-dependent protein-protein interactions mediated by SIMs both in vitro and in cells. Crucially, these synthetic proteins did not prevent SUMO conjugation either in vitro or in cell-based systems, enabling the specific analysis of SUMO-mediated protein-protein interactions. Furthermore, through structural analysis and molecular modeling, we explored the molecular mechanisms that may underlie their specificity in interfering with either SUMO-1-mediated interactions or interactions mediated by either SUMO-2 or SUMO-3. Not only will these reagents enable investigation of the biological roles of SUMOylation, but the Affimer technology used to generate these synthetic binding proteins could also be exploited to design or validate reagents or therapeutics that target other protein-protein interactions.

Peptide-Directed Binding for the Discovery of Modulators of α-Helix-Mediated Protein-Protein Interactions: Proof-of-Concept Studies with the Apoptosis Regulator Mcl-1

Targeting PPIs with small molecules can be challenging owing to large, hydrophobic binding surfaces. Herein, we describe a strategy that exploits selective α-helical PPIs, transferring these characteristics to small molecules. The proof of concept is demonstrated with the apoptosis regulator Mcl-1, commonly exploited by cancers to avoid cell death. Peptide-directed binding uses few synthetic transformations, requires the production of a small number of compounds, and generates a high percentage of hits. In this example, about 50 % of the small molecules prepared showed an IC50 value of less than 100 μm, and approximately 25 % had IC50 values below 1 μm to Mcl-1. Compounds show selectivity for Mcl-1 over other anti-apoptotic proteins, possess cytotoxicity to cancer cell lines, and induce hallmarks of apoptosis. This approach represents a novel and economic process for the rapid discovery of new α-helical PPI modulators.

Discovery of Novel Biomarkers for Alzheimer's Disease from Blood

Blood-based biomarkers for Alzheimer's disease would be very valuable because blood is a more accessible biofluid and is suitable for repeated sampling. However, currently there are no robust and reliable blood-based biomarkers for practical diagnosis. In this study we used a knowledge-based protein feature pool and two novel support vector machine embedded feature selection methods to find panels consisting of two and three biomarkers. We validated these biomarker sets using another serum cohort and an RNA profile cohort from the brain. Our panels included the proteins ECH1, NHLRC2, HOXB7, FN1, ERBB2, and SLC6A13 and demonstrated promising sensitivity (>87%), specificity (>91%), and accuracy (>89%).

Small-Molecule and Peptide Inhibitors of the Pro-Survival Protein Mcl-1

The ability of protein-protein interactions to regulate cellular processes in both beneficial and detrimental ways has made them obvious drug targets. The Bcl-2 family of proteins undergo a series of protein-protein interactions which regulate the intrinsic cell-death pathway. The pro-survival members of the Bcl-2 family, including Bcl-2, Bcl-xL , and Mcl-1, are commonly overexpressed in a number of human cancers. Effective modulators of members of the Bcl-2 family have been developed and are undergoing clinical trials, but the efficient modulation of Mcl-1 is still not represented in the clinic. In addition, Mcl-1 is a major cause of resistance to radio- and chemotherapies, including inhibitors that target other Bcl-2 family members. Subsequently, the inhibition of Mcl-1 has become of significant interest to the scientific community. This review covers the progress made to date in modulating the activity of Mcl-1, by both stapled peptides and small molecules. The development of peptides as drug candidates, and the advancement of experimental and computational techniques used to discover small molecules are also highlighted.

Current Experimental Methods for Characterizing Protein-Protein Interactions

Protein molecules often interact with other partner protein molecules in order to execute their vital functions in living organisms. Characterization of protein-protein interactions thus plays a central role in understanding the molecular mechanism of relevant protein molecules, elucidating the cellular processes and pathways relevant to health or disease for drug discovery, and charting large-scale interaction networks in systems biology research. A whole spectrum of methods, based on biophysical, biochemical, or genetic principles, have been developed to detect the time, space, and functional relevance of protein-protein interactions at various degrees of affinity and specificity. This article presents an overview of these experimental methods, outlining the principles, strengths and limitations, and recent developments of each type of method.

Current Experimental Methods for Characterizing Protein-Protein Interactions

Protein molecules often interact with other partner protein molecules in order to execute their vital functions in living organisms. Characterization of protein-protein interactions thus plays a central role in understanding the molecular mechanism of relevant protein molecules, elucidating the cellular processes and pathways relevant to health or disease for drug discovery, and charting large-scale interaction networks in systems biology research. A whole spectrum of methods, based on biophysical, biochemical, or genetic principles, have been developed to detect the time, space, and functional relevance of protein-protein interactions at various degrees of affinity and specificity. This article presents an overview of these experimental methods, outlining the principles, strengths and limitations, and recent developments of each type of method.

Sequence context and crosslinking mechanism affect the efficiency of in vivo capture of a protein-protein interaction

Protein-protein interactions (PPIs) are essential for implementing cellular processes and thus methods for the discovery and study of PPIs are highly desirable. An emerging method for capturing PPIs in their native cellular environment is in vivo covalent chemical capture, a method that uses nonsense suppression to site specifically incorporate photoactivable unnatural amino acids (UAAs) in living cells. However, in one study we found that this method did not capture a PPI for which there was abundant functional evidence, a complex formed between the transcriptional activator Gal4 and its repressor protein Gal80. Here we describe the factors that influence the success of covalent chemical capture and show that the innate reactivity of the two UAAs utilized, (p-benzoylphenylalanine (pBpa) and p-azidophenylalanine (pAzpa)), plays a profound role in the capture of Gal80 by Gal4. Based upon these data, guidelines are outlined for the successful use of in vivo photo-crosslinking to capture novel PPIs and to characterize the interfaces.

The power of the yeast two-hybrid system in the identification of novel drug targets: building and modulating PPP1 interactomes

Since the description of the yeast two-hybrid (Y2H) method, it has become more and more evident that it is the most commonly used method to identify protein-protein interactions (PPIs). The improvements in the original Y2H methodology in parallel with the idea that PPIs are promising drug targets, offer an excellent opportunity to apply the principles of this molecular biology technique to the pharmaceutical field. Additionally, the theoretical developments in the networks field make PPI networks very useful frameworks that facilitate many discoveries in biomedicine. This review highlights the relevance of Y2H in the determination of PPIs, specifically phosphoprotein phosphatase 1 interactions, and its possible outcomes in pharmaceutical research.

Ubiquitin ligase Siah2 regulates RevErbα degradation and the mammalian circadian clock

Regulated degradation of proteins by the proteasome is often critical to their function in dynamic cellular pathways. The molecular clock underlying mammalian circadian rhythms relies on the rhythmic expression and degradation of its core components. However, because the tools available for identifying the mechanisms underlying the degradation of a specific protein are limited, the mechanisms regulating clock protein degradation are only beginning to be elucidated. Here we describe a cell-based functional screening approach designed to quickly identify the ubiquitin E3 ligases that induce the degradation of potentially any protein of interest. We screened the nuclear hormone receptor RevErbα (Nr1d1), a key constituent of the mammalian circadian clock, for E3 ligases that regulate its stability and found Seven in absentia2 (Siah2) to be a key regulator of RevErbα stability. Previously implicated in hypoxia signaling, Siah2 overexpression destabilizes RevErbα/β, and siRNA depletion of Siah2 stabilizes endogenous RevErbα. Moreover, Siah2 depletion delays circadian degradation of RevErbα and lengthens period length. These results demonstrate the utility of functional screening approaches for identifying regulators of protein stability and reveal Siah2 as a previously unidentified circadian clockwork regulator that mediates circadian RevErbα turnover.

Overcoming Chemical, Biological, and Computational Challenges in the Development of Inhibitors Targeting Protein-Protein Interactions

Protein-protein interactions (PPIs) underlie the majority of biological processes, signaling, and disease. Approaches to modulate PPIs with small molecules have therefore attracted increasing interest over the past decade. However, there are a number of challenges inherent in developing small-molecule PPI inhibitors that have prevented these approaches from reaching their full potential. From target validation to small-molecule screening and lead optimization, identifying therapeutically relevant PPIs that can be successfully modulated by small molecules is not a simple task. Following the recent review by Arkin et al., which summarized the lessons learnt from prior successes, we focus in this article on the specific challenges of developing PPI inhibitors and detail the recent advances in chemistry, biology, and computation that facilitate overcoming them. We conclude by providing a perspective on the field and outlining four innovations that we see as key enabling steps for successful development of small-molecule inhibitors targeting PPIs.

Network representations of immune system complexity

The mammalian immune system is a dynamic multiscale system composed of a hierarchically organized set of molecular, cellular, and organismal networks that act in concert to promote effective host defense. These networks range from those involving gene regulatory and protein–protein interactions underlying intracellular signaling pathways and single‐cell responses to increasingly complex networks of in vivo cellular interaction, positioning, and migration that determine the overall immune response of an organism. Immunity is thus not the product of simple signaling events but rather nonlinear behaviors arising from dynamic, feedback‐regulated interactions among many components. One of the major goals of systems immunology is to quantitatively measure these complex multiscale spatial and temporal interactions, permitting development of computational models that can be used to predict responses to perturbation. Recent technological advances permit collection of comprehensive datasets at multiple molecular and cellular levels, while advances in network biology support representation of the relationships of components at each level as physical or functional interaction networks. The latter facilitate effective visualization of patterns and recognition of emergent properties arising from the many interactions of genes, molecules, and cells of the immune system. We illustrate the power of integrating ‘omics’ and network modeling approaches for unbiased reconstruction of signaling and transcriptional networks with a focus on applications involving the innate immune system. We further discuss future possibilities for reconstruction of increasingly complex cellular‐ and organism‐level networks and development of sophisticated computational tools for prediction of emergent immune behavior arising from the concerted action of these networks. WIREs Syst Biol Med 2015, 7:13–38. doi: 10.1002/wsbm.1288

This article is categorized under: 1

Analytical and Computational Methods > Computational Methods

2

Laboratory Methods and Technologies > Macromolecular Interactions, Methods

Integration strategy is a key step in network-based analysis and dramatically affects network topological properties and inferring outcomes

An increasing number of experiments have been designed to detect intracellular and intercellular molecular interactions. Based on these molecular interactions (especially protein interactions), molecular networks have been built for using in several typical applications, such as the discovery of new disease genes and the identification of drug targets and molecular complexes. Because the data are incomplete and a considerable number of false-positive interactions exist, protein interactions from different sources are commonly integrated in network analyses to build a stable molecular network. Although various types of integration strategies are being applied in current studies, the topological properties of the networks from these different integration strategies, especially typical applications based on these network integration strategies, have not been rigorously evaluated. In this paper, systematic analyses were performed to evaluate 11 frequently used methods using two types of integration strategies: empirical and machine learning methods. The topological properties of the networks of these different integration strategies were found to significantly differ. Moreover, these networks were found to dramatically affect the outcomes of typical applications, such as disease gene predictions, drug target detections, and molecular complex identifications. The analysis presented in this paper could provide an important basis for future network-based biological researches.

The human plasma membrane peripherome: visualization and analysis of interactions

A major part of membrane function is conducted by proteins, both integral and peripheral. Peripheral membrane proteins temporarily adhere to biological membranes, either to the lipid bilayer or to integral membrane proteins with noncovalent interactions. The aim of this study was to construct and analyze the interactions of the human plasma membrane peripheral proteins (peripherome hereinafter). For this purpose, we collected a dataset of peripheral proteins of the human plasma membrane. We also collected a dataset of experimentally verified interactions for these proteins. The interaction network created from this dataset has been visualized using Cytoscape. We grouped the proteins based on their subcellular location and clustered them using the MCL algorithm in order to detect functional modules. Moreover, functional and graph theory based analyses have been performed to assess biological features of the network. Interaction data with drug molecules show that ~10% of peripheral membrane proteins are targets for approved drugs, suggesting their potential implications in disease. In conclusion, we reveal novel features and properties regarding the protein-protein interaction network created by peripheral proteins of the human plasma membrane.

Drug-Like Protein-Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology

[Formula: see text] Fundamental processes in living cells are largely controlled by macromolecular interactions and among them, protein-protein interactions (PPIs) have a critical role while their dysregulations can contribute to the pathogenesis of numerous diseases. Although PPIs were considered as attractive pharmaceutical targets already some years ago, they have been thus far largely unexploited for therapeutic interventions with low molecular weight compounds. Several limiting factors, from technological hurdles to conceptual barriers, are known, which, taken together, explain why research in this area has been relatively slow. However, this last decade, the scientific community has challenged the dogma and became more enthusiastic about the modulation of PPIs with small drug-like molecules. In fact, several success stories were reported both, at the preclinical and clinical stages. In this review article, written for the 2014 International Summer School in Chemoinformatics (Strasbourg, France), we discuss in silico tools (essentially post 2012) and databases that can assist the design of low molecular weight PPI modulators (these tools can be found at www.vls3d.com). We first introduce the field of protein-protein interaction research, discuss key challenges and comment recently reported in silico packages, protocols and databases dedicated to PPIs. Then, we illustrate how in silico methods can be used and combined with experimental work to identify PPI modulators.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

A network perspective on unraveling the role of TRP channels in biology and disease

Transient receptor potential (TRP) channels are a large family of non-selective cation channels that mediate numerous physiological and pathophysiological processes; however, still largely unknown are the underlying molecular mechanisms. With data generated on an unprecedented scale, network-based approaches have been revolutionizing the way in which we understand biology and disease, discover disease genes, and develop therapeutic strategies. These circumstances have created opportunities to encounter TRP channel research to data-intensive science. In this review, we provide an introduction of network-based approaches in biomedical science, describe the current state of TRP channel network biology, and discuss the future direction of TRP channel research. Network perspective will facilitate the discovery of latent roles and underlying mechanisms of TRP channels in biology and disease.

Integrative proteomic profiling of protein activity and interactions using protein arrays

Proteomic studies based on abundance, activity, or interactions have been used to investigate protein functions in normal and pathological processes, but their combinatory approach has not been attempted. We present an integrative proteomic profiling method to measure protein activity and interaction using fluorescence-based protein arrays. We used an on-chip assay to simultaneously monitor the transamidating activity and binding affinity of transglutaminase 2 (TG2) for 16 TG2-related proteins. The results of this assay were compared with confidential scores provided by the STRING database to analyze the functional interactions of TG2 with these proteins. We further created a quantitative activity-interaction map of TG2 with these 16 proteins, categorizing them into seven groups based upon TG2 activity and interaction. This integrative proteomic profiling method can be applied to quantitative validation of previously known protein interactions, and in understanding the functions and regulation of target proteins in biological processes of interest.

Large-scale protein interactome mapping: strategies and opportunities

Interactions between proteins are central to any cellular process, and mapping these into a protein network is informative both for the function of individual proteins and the functional organization of the cell as a whole. Many strategies have been developed that are up to this task, and the last 10 years have seen the high-throughput application of a number of those in large-scale, sometimes proteome-wide, interactome mapping efforts. Although initially the quality of the data produced in these screening campaigns has been questioned, quality standards and empirical validation schemes are now in place to ensure high-quality data generation. Through their integration with other 'omics' data, interactomics datasets have proven highly valuable towards applications in different areas of clinical importance.

A novel paradigm for potential drug-targets discovery: quantifying relationships of enzymes and cascade interactions of neighboring biological processes to identify drug-targets

Target discovery is the most crucial step in a modern drug discovery development. Our objective in this study is to propose a novel paradigm for a better discrimination of drug-targets and non-drug-targets with minimum disruptive side-effects under a biological pathway context. We introduce a novel metric, namely, "pathway closeness centrality", for each gene that jointly considers the relationships of its neighboring enzymes and cross-talks of biological processes, to evaluate its probability of being a drug-target. This metric could distinguish drug-targets with non-drug-targets. Genes with lower pathway closeness centrality values are prone to play marginal roles in biological processes and have less lethality risk, but appear to have tissue-specific expressions. Compared with traditional metrics, our method outperforms degree, betweenness and bridging centrality under the human pathway context. Analysis of the existing top 20 drugs with the most disruptive side-effects indicates that pathway closeness centrality is an appropriate index to predict the probability of the occurrence of adverse pharmacological effects. Case studies in prostate cancer and type 2 diabetes mellitus indicate that the pathway closeness centrality metric could distinguish likely drug-targets well from human pathways. Thus, our method is a promising tool to aid target identification in drug discovery.