The stress induced caleosin, RD20/CLO3, acts as a negative regulator of GPA1 in Arabidopsis

KEY MESSAGE

A stress induced calcium-binding protein, RD20/CLO3 interacts with the alpha subunit of the heterotrimeric G-protein complex in Arabidopsis and affects etiolation and leaf morphology. Heterotrimeric G proteins and calcium signaling have both been shown to play a role in the response to environmental abiotic stress in plants; however, the interaction between calcium-binding proteins and G-protein signaling molecules remains elusive. We investigated the interaction between the alpha subunit of the heterotrimeric G-protein complex, GPA1, of Arabidopsis thaliana with the calcium-binding protein, the caleosin RD20/CLO3, a gene strongly induced by drought, salt and abscisic acid. The proteins were found to interact in vivo by bimolecular fluorescent complementation (BiFC); the interaction was localized to the endoplasmic reticulum and to oil bodies within the cell. The constitutively GTP-bound GPA1 (GPA1QL) also interacts with RD20/CLO3 as well as its EF-hand mutant variations and these interactions are localized to the plasma membrane. The N-terminal portion of RD20/CLO3 was found to be responsible for the interaction with GPA1 and GPA1QL using both BiFC and yeast two-hybrid assays. RD20/CLO3 contains a single calcium-binding EF-hand in the N-terminal portion of the protein; disruption of the calcium-binding capacity of the protein obliterates interaction with GPA1 in in vivo assays and decreases the interaction between the caleosin and the constitutively active GPA1QL. Analysis of rd20/clo3 mutants shows that RD20/CLO3 plays a key role in the signaling pathway controlling hypocotyl length in dark grown seedlings and in leaf morphology. Our findings indicate a novel role for RD20/CLO3 as a negative regulator of GPA1.

An Ebola, Neisseria and Trypanosoma human protein interaction census reveals a conserved human protein cluster targeted by various human pathogens

Filovirus ebolavirus (ZE; Zaire ebolavirus, Bundibugyo ebolavirus), Neisseria meningitidis (NM), and Trypanosoma brucei (Tb) are serious infectious pathogens, spanning viruses, bacteria and protists and all may target the blood and central nervous system during their life cycle. NM and Tb are extracellular pathogens while ZE is obligatory intracellular, targetting immune privileged sites. By using interactomics and comparative evolutionary analysis we studied whether conserved human proteins are targeted by these pathogens. We examined 2797 unique pathogen-targeted human proteins. The information derived from orthology searches of experimentally validated protein-protein interactions (PPIs) resulted both in unique and shared PPIs for each pathogen. Comparing and analyzing conserved and pathogen-specific infection pathways for NM, TB and ZE, we identified human proteins predicted to be targeted in at least two of the compared host-pathogen networks. However, four proteins were common to all three host-pathogen interactomes: the elongation factor 1-alpha 1 (EEF1A1), the SWI/SNF complex subunit SMARCC2 (matrix-associated actin-dependent regulator of chromatin subfamily C), the dolichyl-diphosphooligosaccharide--protein glycosyltransferase subunit 1 (RPN1), and the tubulin beta-5 chain (TUBB). These four human proteins all are also involved in cytoskeleton and its regulation and are often addressed by various human pathogens. Specifically, we found (i) 56 human pathogenic bacteria and viruses that target these four proteins, (ii) the well researched new pandemic pathogen SARS-CoV-2 targets two of these four human proteins and (iii) nine human pathogenic fungi (yet another evolutionary distant organism group) target three of the conserved proteins by 130 high confidence interactions.

Structure and dynamics of UBA5-UFM1 complex formation showing new insights in the UBA5 activation mechanism

Ubiquitin fold modifier 1 (UFM1) is an ubiquitin-like protein (Ubl) involved especially in endoplasmic stress response. Activation occurs via a three-step mechanism like other Ubls. Data obtained reveal that UFM1 regulates the oligomeric state of ubiquitin activating enzyme 5 (UBA5) to initiate the activation step. Mixtures of homodimers and heterotrimers are observed in solution at the equilibrium state, demonstrating that the UBA5-UFM1 complex undergoes several concentration dependent oligomeric translational states to form a final functional complex. The oligomerization state of unbound UBA5 is also concentration dependent and shifts from the monomeric to the dimeric state. Data describing different oligomeric states are complemented with binding studies that reveal a negative cooperativity for the complex formation and thereby provide more detailed insights into the complex formation mechanism.

Expression and tandem affinity purification of 20S proteasomes and other multisubunit complexes in Haloferax volcanii

Tandem affinity purification is a useful strategy to isolate multisubunit complexes of high yield and purity but can be limited when working with halophilic proteins that are not properly expressed in Escherichia coli. Halophilic proteins are desirable for bioindustrial applications as they are often stable and active in organic solvents; however, these proteins can be difficult to express, fold, and purify by traditional technologies. Haloarchaea provide a useful alternative for expression of halophilic proteins. These microorganisms use a salt-in strategy to maintain homeostasis and express most of their proteins with halophilic properties and low pI. Here, we provide detailed protocols for the genetic modification, expression and tandem affinity purification of "salt-loving" multisubunit complexes from the haloarchaeon Haloferax volcanii. The strategy for isolation of affinity tagged 20S proteasomes that form cylindrical proteolytic nanomachines of α1, α2 and β subunits is described.

Strategies for targeting undruggable targets

INTRODUCTION

Undruggable targets refer to clinically meaningful therapeutic targets that are 'difficult to drug' or 'yet to be drugged' via traditional approaches. Featuring characteristics of lacking defined ligand-binding pockets, non-catalytic protein-protein interaction functional modes and less-investigated 3D structures, these undruggable targets have been targeted with novel therapeutic entities developed with the progress of unconventional drug discovery approaches, such as targeted degradation molecules and display technologies.

AREA COVERED

This review first presents the concept of 'undruggable' exemplified by RAS and other targets. Next, detailed strategies are illustrated in two aspects: innovation of therapeutic entities and development of unconventional drug discovery technologies. Finally, case studies covering typical undruggable targets (Bcl-2, p53, and RAS) are depicted to further demonstrate the feasibility of the strategies and entities above.

EXPERT OPINION

Targeting the undruggable expands the scope of therapeutically reachable targets. Consequently, it represents the drug discovery frontier. Biomedical studies are capable of dissecting disease mechanisms, thus broadening the list of undruggable targets. Encouraged by the recent approval of the KRAS inhibitor Sotorasib, we believe that merging multiple discovery approaches and exploiting various novel therapeutic entities would pave the way for dealing with more 'undruggable' targets in the future.

Potential genes and pathways associated with heterotopic ossification derived from analyses of gene expression profiles

Background

Heterotopic ossification (HO) represents pathological lesions that refer to the development of heterotopic bone in extraskeletal tissues around joints. This study investigates the genetic characteristics of bone marrow mesenchymal stem cells (BMSCs) from HO tissues and explores the potential pathways involved in this ailment.

Methods

Gene expression profiles (GSE94683) were obtained from the Gene Expression Omnibus (GEO), including 9 normal specimens and 7 HO specimens, and differentially expressed genes (DEGs) were identified. Then, protein–protein interaction (PPI) networks and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed for further analysis.

Results

In total, 275 DEGs were differentially expressed, of which 153 were upregulated and 122 were downregulated. In the biological process (BP) category, the majority of DEGs, including EFNB3, UNC5C, TMEFF2, PTH2, KIT, FGF13, and WISP3, were intensively enriched in aspects of cell signal transmission, including axon guidance, negative regulation of cell migration, peptidyl-tyrosine phosphorylation, and cell-cell signaling. Moreover, KEGG analysis indicated that the majority of DEGs, including EFNB3, UNC5C, FGF13, MAPK10, DDIT3, KIT, COL4A4, and DKK2, were primarily involved in the mitogen-activated protein kinase (MAPK) signaling pathway, Ras signaling pathway, phosphatidylinositol-3-kinase/protein kinase B (PI3K/Akt) signaling pathway, and Wnt signaling pathway. Ten hub genes were identified, including CX3CL1, CXCL1, ADAMTS3, ADAMTS16, ADAMTSL2, ADAMTSL3, ADAMTSL5, PENK, GPR18, and CALB2.

Conclusions

This study presented novel insight into the pathogenesis of HO. Ten hub genes and most of the DEGs intensively involved in enrichment analyses may be new candidate targets for the prevention and treatment of HO in the future.

Discovery of the first chemical tools to regulate MKK3-mediated MYC activation in cancer

The transcription master regulator MYC plays an essential role in regulating major cellular programs and is a well-established therapeutic target in cancer. However, MYC targeting for drug discovery is challenging. New therapeutic approaches to control MYC-dependent malignancy are urgently needed. The mitogen-activated protein kinase kinase 3 (MKK3) binds and activates MYC in different cell types, and disruption of MKK3-MYC protein-protein interaction may provide a new strategy to target MYC-driven programs. However, there is no perturbagen available to interrogate and control this signaling arm. In this study, we assessed the drugability of the MKK3-MYC complex and discovered the first chemical tool to regulate MKK3-mediated MYC activation. We have designed a short 44-residue inhibitory peptide and developed a cell lysate-based time-resolved fluorescence resonance energy transfer (TR-FRET) assay to discover the first small molecule MKK3-MYC PPI inhibitor. We have optimized and miniaturized the assay into an ultra-high-throughput screening (uHTS) 1536-well plate format. The pilot screen of ~6,000 compounds of a bioactive chemical library followed by multiple secondary and orthogonal assays revealed a quinoline derivative SGI-1027 as a potent inhibitor of MKK3-MYC PPI. We have shown that SGI-1027 disrupts the MKK3-MYC complex in cells and in vitro and inhibits MYC transcriptional activity in colon and breast cancer cells. In contrast, SGI-1027 does not inhibit MKK3 kinase activity and does not interfere with well-known MKK3-p38 and MYC-MAX complexes. Together, our studies demonstrate the drugability of MKK3-MYC PPI, provide the first chemical tool to interrogate its biological functions, and establish a new uHTS assay to enable future discovery of potent and selective inhibitors to regulate this oncogenic complex.

Computational Phosphorylation Network Reconstruction: An Update on Methods and Resources

Most proteins undergo some form of modification after translation, and phosphorylation is one of the most relevant and ubiquitous post-translational modifications. The succession of protein phosphorylation and dephosphorylation catalyzed by protein kinase and phosphatase, respectively, constitutes a key mechanism of molecular information flow in cellular systems. The protein interactions of kinases, phosphatases, and their regulatory subunits and substrates are the main part of phosphorylation networks. To elucidate the landscape of phosphorylation events has been a central goal pursued by both experimental and computational approaches. Substrate specificity (e.g., sequence, structure) or the phosphoproteome has been utilized in an array of different statistical learning methods to infer phosphorylation networks. In this chapter, different computational phosphorylation network inference-related methods and resources are summarized and discussed.

Improved prediction of protein-protein interaction using a hybrid of functional-link Siamese neural network and gradient boosting machines

In this paper, for accurate prediction of protein-protein interaction (PPI), a novel hybrid classifier is developed by combining the functional-link Siamese neural network (FSNN) with the light gradient boosting machine (LGBM) classifier. The hybrid classifier (FSNN-LGBM) uses the fusion of features derived using pseudo amino acid composition and conjoint triad descriptors. The FSNN extracts the high-level abstraction features from the raw features and LGBM performs the PPI prediction task using these abstraction features. On performing 5-fold cross-validation experiments, the proposed hybrid classifier provides average accuracies of 98.70 and 98.38%, respectively, on the intraspecies PPI data sets of Saccharomyces cerevisiae and Helicobacter pylori. Similarly, the average accuracies for the interspecies PPI data sets of the Human-Bacillus and Human-Yersinia data sets are 98.52 and 97.40%, respectively. Compared with the existing methods, the hybrid classifier achieves higher prediction accuracy on the independent test sets and network data sets. The improved prediction performance obtained by the FSNN-LGBM makes it a flexible and effective PPI prediction model.

PANDA: Predicting the change in proteins binding affinity upon mutations by finding a signal in primary structures

Accurately determining a change in protein binding affinity upon mutations is important to find novel therapeutics and to assist mutagenesis studies. Determination of change in binding affinity upon mutations requires sophisticated, expensive, and time-consuming wet-lab experiments that can be supported with computational methods. Most of the available computational prediction techniques depend upon protein structures that bound their applicability to only protein complexes with recognized 3D structures. In this work, we explore the sequence-based prediction of change in protein binding affinity upon mutation and question the effectiveness of [Formula: see text]-fold cross-validation (CV) across mutations adopted in previous studies to assess the generalization ability of such predictors with no known mutation during training. We have used protein sequence information instead of protein structures along with machine learning techniques to accurately predict the change in protein binding affinity upon mutation. Our proposed sequence-based novel change in protein binding affinity predictor called PANDA performs comparably to the existing methods gauged through an appropriate CV scheme and an external independent test dataset. On an external test dataset, our proposed method gives a maximum Pearson correlation coefficient of 0.52 in comparison to the state-of-the-art existing protein structure-based method called MutaBind which gives a maximum Pearson correlation coefficient of 0.59. Our proposed protein sequence-based method, to predict a change in binding affinity upon mutations, has wide applicability and comparable performance in comparison to existing protein structure-based methods. We made PANDA easily accessible through a cloud-based webserver and python code available at https://sites.google.com/view/wajidarshad/software and https://github.com/wajidarshad/panda, respectively.

Improvement Thermal Stability of D-Lactate Dehydrogenase by Hydrophobin-1 and in Silico Prediction of Protein-Protein Interactions

Hydrophobins are small surface-active proteins. They can connect to hydrophobic or hydrophilic regions and oligomerize in solution to form massive construction. In nature, these proteins are produced by filamentous fungi at different stages of growth. So far, researchers have used them in various fields of biotechnology. In this study, recombinant hydrophobin-1 (rHFB1, 7.5 kDa) was used to stabilize recombinant D-lactate dehydrogenase (rD-LDH, 35 kDa). rD-LDH is a sensitive enzyme deactivated and oxidized by external agents such as O₂ and lights. So, its stabilization with rHFB1 can be the best index to demonstrate the positive effect of rHFB1 on preserving and improving enzyme's activity. The unique ability of rHFB1 for interacting with hydrophobic regions of rD-LDH was predicted by protein-protein docking study with ClusPro and PIC servers and confirmed by fluorescence experiments, and Colorless Native-PAGE. Measurement of thermodynamic parameters allows for authenticating the role of rHFB1 as a thermal stabilizer in the protein-protein complex (rD-LDH@rHFB1). Interaction between rHFB1 and rD-LDH improved half-life of enzyme 2.25-fold at 40 °C. Investigation of the kinetic parameters proved that the presence of rHFB1 along with the rD-LDH enhancement strongly the affinity of the enzyme for pyruvate. Furthermore, an increase of Kcat/Km for complex displayed the effect of rHFB1 for improving the enzyme's catalytic efficiency.

Hemoglobin subunit beta interacts with the capsid, RdRp and VPg proteins, and antagonizes the replication of rabbit hemorrhagic disease virus

Rabbit hemorrhagic disease virus (RHDV) causes a highly contagious disease in rabbits that is associated with high mortality. Because of the lack of a suitable cell culture system for RHDV, its pathogenic mechanism and replication remain unclear. This study found that the expression level of host protein rabbit hemoglobin subunit beta (HBB) was significantly downregulated in RHDV-infected cells. To investigate the role of HBB in RHDV replication, small interfering RNAs for HBB and HBB eukaryotic expression plasmids were used to change the expression level of HBB in RK-13 cells and the results showed that the RHDV replication level was negatively correlated with the expression level of HBB. It was also verified that HBB inhibited RHDV replication using constructed HBB stable overexpression cell lines and HBB knockout cell lines. The interaction of HBB with viral capsid protein VP60, replicase RdRp, and VPg protein was confirmed, as was the activation of the expression of interferon γ by HBB. The results of this study indicated that HBB may be an important host protein in host resistance to RHDV infection.

Protein-protein interaction and in silico mutagenesis studies on IL17A and its peptide inhibitor

Protein-protein interactions of Interleukin-17 (IL17) play vital role in the autoimmune and inflammatory diseases, such as rheumatoid arthritis, multiple sclerosis, and psoriasis. Potent therapeutics for these diseases could be developed by blocking or modulating these interactions through biologics, peptide inhibitors and small molecule inhibitors. Unlike biologics, peptide inhibitors are cost effective and can be orally available. Peptide inhibitors do not require a binding groove as that of small molecules either. Therefore, crystal structure of IL17A in complex with a high affinity peptide inhibitor (HAP) (1-IHVTIPADLWDWIN-14) is investigated with an aim to find hot spots that could improve its potency. An in silico mutagenesis strategy was implemented using FoldX PSSM to scan for positions tolerant to amino acid substitution. Three positions T4, A7, and N14 showed improved stability when mutated with 'F/M/Y', 'P' and 'F/M/Y', respectively. A set of 31 mutant peptides are designed through combinations of these tolerant mutations using Build Model application of FoldX. Binding affinity and interactions of 31 peptides are assessed through protein-peptide docking and binding free energy calculations. Two peptides namely, P1 ("1-IHVTIPPDLWDWIY-14") and P2 ("1-IHVMIPPDLWDWIF-14") showed better binding affinity to IL17A dimerization site compared to HAP. Interactions of P1, P2 and HAP are also analyzed through 100 ns molecular dynamics simulations using GROMACS v5.0. The results revealed that the P2 peptide likely to offer better potency compared to HAP and P1. Therefore, the P2 peptide can be synthesized to develop oral therapies for autoimmune and inflammatory diseases with further experimental evaluations.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-02856-y.

Enhancement of target toxin neutralization effect in vivo by PEGylation of multifunctionalized lipid nanoparticles

Protein-protein (e.g., antibody-antigen) interactions comprise multiple weak interactions. We have previously reported that lipid nanoparticles (LNPs) bind to and neutralize target toxic peptides after multifunctionalization of the LNP surface (MF-LNPs) with amino acid derivatives that induce weak interactions; however, the MF-LNPs aggregated after target capture and showed short blood circulation times. Here we optimized polyethylene glycol (PEG)-modified MF-LNPs (PEG-MF-LNPs) to inhibit the aggregation and increase the blood circulation time. Melittin was used as a target toxin, and MF-LNPs were prepared with negatively charged, hydrophobic, and neutral amino-acid-derivative-conjugated functional lipids. In this study, MF-LNPs modified with only PEG5k (PEG5k-MF-LNPs) and with both PEG5k and PEG2k (PEGmix-MF-LNPs) were prepared, where PEG5k and PEG2k represent PEG with a molecular weight of 5000 and 2000, respectively. PEGylation of the MF-LNPs did not decrease the melittin neutralization ability of nonPEGylated MF-LNPs, as tested by hemolysis assay. The PEGmix-MF-LNPs showed better blood circulation characteristics than the PEG5k-MF-LNPs. Although the nonPEGylated MF-LNPs immediately aggregated when mixed with melittin, the PEGmix-MF-LNPs did not aggregate. The PEGmix-MF-LNPs dramatically increased the survival rate of melittin-treated mice, whereas the nonPEGylated MF-LNPs increased slightly. These results provide a fundamental strategy to improve the in vivo toxin neutralization ability of MF-LNPs.

Retinoschisis and Norrie disease: a missing link

OBJECTIVE

Retinoschisis and Norrie disease are X-linked recessive retinal disorders caused by mutations in RS1 and NDP genes respectively. Both are likely to be monogenic and no locus heterogeneity has been reported. However, there are reports showing overlapping features of Norrie disease and retinoschisis in a NDP knock-out mouse model and also the involvement of both the genes in retinoschisis patients. Yet, the exact molecular relationships between the two disorders have still not been understood. The study investigated the association between retinoschisin (RS1) and norrin (NDP) using in vitro and in silico approaches. Specific protein-protein interaction between RS1 and NDP was analyzed in human retina by co-immunoprecipitation assay and MALDI-TOF mass spectrometry. STRING database was used to explore the functional relationship.

RESULT

Co-immunoprecipitation demonstrated lack of a direct interaction between RS1 and NDP and was further substantiated by mass spectrometry. However, STRING revealed a potential indirect functional association between the two proteins. Progressively, our analyses indicate that FZD4 protein interactome via PLIN2 as well as the MAP kinase signaling pathway to be a likely link bridging the functional relationship between retinoschisis and Norrie disease.

In silico binding profile characterization of SARS-CoV-2 spike protein and its mutants bound to human ACE2 receptor

Severe acute respiratory syndrome coronavirus (SARS-CoV-2), a novel coronavirus, has brought an unprecedented pandemic to the world and affected over 64 million people. The virus infects human using its spike glycoprotein mediated by a crucial area, receptor-binding domain (RBD), to bind to the human ACE2 (hACE2) receptor. Mutations on RBD have been observed in different countries and classified into nine types: A435S, D364Y, G476S, N354D/D364Y, R408I, V341I, V367F, V483A and W436R. Employing molecular dynamics (MD) simulation, we investigated dynamics and structures of the complexes of the prototype and mutant types of SARS-CoV-2 spike RBDs and hACE2. We then probed binding free energies of the prototype and mutant types of RBD with hACE2 protein by using an end-point molecular mechanics Poisson Boltzmann surface area (MM-PBSA) method. According to the result of MM-PBSA binding free energy calculations, we found that V367F and N354D/D364Y mutant types showed enhanced binding affinities with hACE2 compared to the prototype. Our computational protocols were validated by the successful prediction of relative binding free energies between prototype and three mutants: N354D/D364Y, V367F and W436R. Thus, this study provides a reliable computational protocol to fast assess the existing and emerging RBD mutations. More importantly, the binding hotspots identified by using the molecular mechanics generalized Born surface area (MM-GBSA) free energy decomposition approach can guide the rational design of small molecule drugs or vaccines free of drug resistance, to interfere with or eradicate spike-hACE2 binding.

Yeast proteins do not practice social distancing as species hybridize

With the current COVID-19 pandemic, we all realized how important interactions are. Interactions are everywhere. At the cellular level, protein interactions play a key role and their ensemble, also called interactome, is often referred as the basic building blocks of life. Given its importance, the maintenance of the integrity of the interactome is a real challenge in the cell. Many events during evolution can disrupt interactomes and potentially result in different characteristics for the organisms. However, the molecular underpinnings of changes in interactions at the cellular level are still largely unexplored. Among the perturbations, hybridization puts in contact two different interactomes, which may lead to many changes in the protein interaction network of the hybrid, including gains and losses of interactions. We recently investigated the fate of the interactomes after hybridization between yeast species using a comparative proteomics approach. A large-scale conservation of the interactions was observed in hybrids, but we also noticed the presence of proteostasis-related changes. This suggests that, despite a general robustness, small differences may accumulate in hybrids and perturb their protein physiology. Here, we summarize our work with a broader perspective on the importance of interactions.

Conservation of binding properties in protein models

We study the models submitted to round 12 of the Critical Assessment of protein Structure Prediction (CASP) experiment to assess how well the binding properties are conserved when the X-ray structures of the target proteins are replaced by their models. To explore small molecule binding we generate distributions of molecular probes - which are fragment-sized organic molecules of varying size, shape, and polarity - around the protein, and count the number of interactions between each residue and the probes, resulting in a vector of interactions we call a binding fingerprint. The similarity between two fingerprints, one for the X-ray structure and the other for a model of the protein, is determined by calculating the correlation coefficient between the two vectors. The resulting correlation coefficients are shown to correlate with global measures of accuracy established in CASP, and the relationship yields an accuracy threshold that has to be reached for meaningful binding surface conservation. The clusters formed by the probe molecules reliably predict binding hot spots and ligand binding sites in both X-ray structures and reasonably accurate models of the target, but ensembles of models may be needed for assessing the availability of proper binding pockets. We explored ligand docking to the few targets that had bound ligands in the X-ray structure. More targets were available to assess the ability of the models to reproduce protein-protein interactions by docking both the X-ray structures and models to their interaction partners in complexes. It was shown that this application is more difficult than finding small ligand binding sites, and the success rates heavily depend on the local structure in the potential interface. In particular, predicted conformations of flexible loops are frequently incorrect in otherwise highly accurate models, and may prevent predicting correct protein-protein interactions.

Isothermal Titration Calorimetry of Membrane Proteins

The ability to quantify protein-protein interactions without adding labels to protein has made isothermal titration calorimetry (ITC) a preferred technique to study proteins in aqueous solution. Here, we describe the application of ITC to the study of protein-protein interactions in membrane mimics using the association of integrin αIIb and β3 transmembrane domains in phospholipid bicelles as an example. A higher conceptual and experimental effort compared to water-soluble proteins is required for membrane proteins and rewarded with rare thermodynamic insight into this central class of proteins.

Identification of genes and pathways involved in malignant pleural mesothelioma using bioinformatics methods

Background

Malignant pleural mesothelioma (MPM) is a rare tumor in the pleura. This study was carried out to identify key genes and pathways that may be involved in MPM.

Methods

Microarray datasets GSE51024 and GSE2549 were analyzed for differentially expressed genes (DEGs) between normal and MPM tissues. The identified DEGs were subjected to functional analyses using bioinformatics tools.

Results

A total of 276 DEGs were identified, consisting of 187 downregulated and 79 upregulated genes. Gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analysis indicated that the DEGs were enriched in extracellular structure organization, extracellular matrix, and ECM−receptor interaction. Due to high degree of connectivity among 24 hub genes, EZH2 and HMMR are likely to play roles in the carcinogenesis and progression of MPM. The two genes were found over-expressed in MPM tissues. Patients with elevated EZH2 and HMMR expressions had poor overall survival.

Conclusions

EZH2 and HMMR are identified to be the hub genes for MPM and they may be further characterized to better understand the molecular mechanisms underlying the carcinogenesis of MPM.

Targeting Son of Sevenless 1: The pacemaker of KRAS

Son of Sevenless (SOS) is a guanine nucleotide exchange factor that activates the important cell signaling switch KRAS. SOS acts as a pacemaker for KRAS, the beating heart of cancer, by catalyzing the "beating" from the KRAS(off) to the KRAS(on) conformation. Activating mutations in SOS1 are common in Noonan syndrome and oncogenic alterations in KRAS drive 1 in seven human cancers. Promising clinical efficacy has been observed for selective KRAS^G12C inhibitors, but the vast majority of oncogenic KRAS alterations remain undrugged. The discovery of a druggable pocket on SOS1 has led to potent SOS1 inhibitors such as BI-3406. SOS1 inhibition leads to antiproliferative effects against all major KRAS mutants. The first SOS1 inhibitor has entered clinical trials for KRAS-mutated cancers. In this review, we provide an overview of SOS1 function, its association with cancer and RASopathies, known SOS1 activators and inhibitors, and a future perspective is provided.

Multi-schema computational prediction of the comprehensive SARS-CoV-2 vs. human interactome

BACKGROUND

Understanding the disease pathogenesis of the novel coronavirus, denoted SARS-CoV-2, is critical to the development of anti-SARS-CoV-2 therapeutics. The global propagation of the viral disease, denoted COVID-19 ("coronavirus disease 2019"), has unified the scientific community in searching for possible inhibitory small molecules or polypeptides. A holistic understanding of the SARS-CoV-2 vs. human inter-species interactome promises to identify putative protein-protein interactions (PPI) that may be considered targets for the development of inhibitory therapeutics.

METHODS

We leverage two state-of-the-art, sequence-based PPI predictors (PIPE4 & SPRINT) capable of generating the comprehensive SARS-CoV-2 vs. human interactome, comprising approximately 285,000 pairwise predictions. Three prediction schemas (all, proximal, RP-PPI) are leveraged to obtain our highest-confidence subset of PPIs and human proteins predicted to interact with each of the 14 SARS-CoV-2 proteins considered in this study. Notably, the use of the Reciprocal Perspective (RP) framework demonstrates improved predictive performance in multiple cross-validation experiments.

RESULTS

The all schema identified 279 high-confidence putative interactions involving 225 human proteins, the proximal schema identified 129 high-confidence putative interactions involving 126 human proteins, and the RP-PPI schema identified 539 high-confidence putative interactions involving 494 human proteins. The intersection of the three sets of predictions comprise the seven highest-confidence PPIs. Notably, the Spike-ACE2 interaction was the highest ranked for both the PIPE4 and SPRINT predictors with the all and proximal schemas, corroborating existing evidence for this PPI. Several other predicted PPIs are biologically relevant within the context of the original SARS-CoV virus. Furthermore, the PIPE-Sites algorithm was used to identify the putative subsequence that might mediate each interaction and thereby inform the design of inhibitory polypeptides intended to disrupt the corresponding host-pathogen interactions.

CONCLUSION

We publicly released the comprehensive sets of PPI predictions and their corresponding PIPE-Sites landscapes in the following DataVerse repository: https://www.doi.org/10.5683/SP2/JZ77XA. The information provided represents theoretical modeling only and caution should be exercised in its use. It is intended as a resource for the scientific community at large in furthering our understanding of SARS-CoV-2.

Isolation of Artificial Binding Proteins (Affimer Reagents) for Use in Molecular and Cellular Biology

Artificial binding proteins have been validated as alternatives to antibodies in their use as research reagents in molecular and cellular biology. For example, they have been used as inhibitors of protein-protein interactions to modulate activity, to facilitate crystallization, and as probes for cellular imaging.Phage display is a widely used approach for isolating target-specific binding reagents, and it has even been used to isolate isoform-specific binding proteins and binders that can distinguish between highly homologous protein domains.Here, we describe methods that have been employed in isolating highly specific artificial binding proteins against a wide range of target proteins.

Identification of blood-derived candidate gene markers and a new 7-gene diagnostic model for multiple sclerosis

Background

Multiple sclerosis (MS) is a central nervous system disease with a high disability rate. Modern molecular biology techniques have identified a number of key genes and diagnostic markers to MS, but the etiology and pathogenesis of MS remain unknown.

Results

In this study, the integration of three peripheral blood mononuclear cell (PBMC) microarray datasets and one peripheral blood T cells microarray dataset allowed comprehensive network and pathway analyses of the biological functions of MS-related genes. Differential expression analysis identified 78 significantly aberrantly expressed genes in MS, and further functional enrichment analysis showed that these genes were associated with innate immune response-activating signal transduction (p = 0.0017), neutrophil mediated immunity (p = 0.002), positive regulation of innate immune response (p = 0.004), IL-17 signaling pathway (p < 0.035) and other immune-related signaling pathways. In addition, a network of MS-specific protein–protein interactions (PPI) was constructed based on differential genes. Subsequent analysis of network topology properties identified the up-regulated CXCR4, ITGAM, ACTB, RHOA, RPS27A, UBA52, and RPL8 genes as the hub genes of the network, and they were also potential biomarkers of MS through Rap1 signaling pathway or leukocyte transendothelial migration. RT-qPCR results demonstrated that CXCR4 was obviously up-regulated, while ACTB, RHOA, and ITGAM were down-regulated in MS patient PBMC in comparison with normal samples. Finally, support vector machine was employed to establish a diagnostic model of MS with a high prediction performance in internal and external datasets (mean AUC = 0.97) and in different chip platform datasets (AUC = (0.93).

Conclusion

This study provides new understanding for the etiology/pathogenesis of MS, facilitating an early identification and prediction of MS.

Analyzing the Integrin Adhesome by In Situ Proximity Ligation Assay

The in situ proximity ligation assay (PLA) is capable of detecting single protein events such as protein protein-interactions and posttranslational modifications (e.g., protein phosphorylation) in tissue and cell samples prepared for analysis by immunofluorescent or immunohistochemical microscopy. The targets are detected using two primary antibodies which must be from different host species. A pair of secondary antibodies (PLA probes) conjugated to complementary oligonucleotides is applied to the sample, and a signal is generated only when the two PLA probes are in close proximity by their binding to the two primary antibodies that have bound to their targets in close proximity. The signal from each pair of PLA probes is visualized as an individual fluorescent spot. These PLA signals can be quantified (counted) using image analysis software (ImageJ), and also assigned to a specific subcellular location based on microscopy image overlays. In principle, in situ PLA offers a relatively simple and sensitive technique to analyze interactions among any proteins for which suitable antibodies are available. Integrin-mediated focal adhesions (FAs) are large multiprotein complexes consisting of more than 150 proteins, also known as the integrin adhesome, which link the extracellular matrix (ECM) to the actin cytoskeleton and regulate the functioning of mechanosignaling pathways. The in situ PLA approach is well suited for examining the spatiotemporal aspects of protein posttranslational modifications and protein interactions occurring in dynamic multiprotein complexes such as integrin mediated focal adhesions.

Mechanisms Activating Latent Functions of PIP Aquaporin Water Channels via the Interaction between PIP1 and PIP2 Proteins

Plant plasma membrane-type plasma membrane intrinsic protein (PIP) aquaporins are classified into two groups, PIP1s and PIP2s. In this study, we focused on HvPIP1;2, a PIP1 in barley (Hordeum vulgare), to dissect the molecular mechanisms that evoke HvPIP1-mediated water transport. No HvPIP1;2 protein was localized to the plasma membrane when expressed alone in Xenopus laevis oocytes. By contrast, a chimeric HvPIP1;2 protein (HvPIP1;2_24NC), in which the N- and C-terminal regions were replaced with the corresponding regions from HvPIP2;4, was found to localize to the plasma membrane of oocytes. However, HvPIP1;2_24NC showed no water transport activity in swelling assays. These results suggested that the terminal regions of PIP2 proteins direct PIP proteins to the plasma membrane, but the relocalization of PIP1 proteins was not sufficient to PIP1s functionality as a water channel in a membrane. A single amino acid replacement of threonine by methionine in HvPIP2;4 (HvPIP2;4T229M) abolished water transport activity. Co-expression of HvPIP1;2_24NC either with HvPIP2;4_12NC or with HvPIP2;4TM_12NC, in which the N- and C-terminal regions were replaced with the corresponding regions of HvPIP1;2, increased the water transport activity in oocytes. These data provided evidence that the HvPIP1;2 molecule has own water transport activity and an interaction with the middle part of the HvPIP2;4 protein (except for the N- and C-termini) is required for HvPIP1;2 functionality as a water channel. This molecular mechanism could be applied to other PIP1s and PIP2s in addition to the known mechanism that the terminal regions of some PIP2s lead some PIP1s to the plasma membrane.

Constitutive androstane receptor induced-hepatomegaly and liver regeneration is partially via yes-associated protein activation

The constitutive androstane receptor (CAR, NR3I1) belongs to nuclear receptor superfamily. It was reported that CAR agonist TCPOBOP induces hepatomegaly but the underlying mechanism remains largely unknown. Yes-associated protein (YAP) is a potent regulator of organ size. The aim of this study is to explore the role of YAP in CAR activation-induced hepatomegaly and liver regeneration. TCPOBOP-induced CAR activation on hepatomegaly and liver regeneration was evaluated in wild-type (WT) mice, liver-specific YAP-deficient mice, and partial hepatectomy (PHx) mice. The results demonstrate that TCPOBOP can increase the liver-to-body weight ratio in wild-type mice and PHx mice. Hepatocytes enlargement around central vein (CV) area was observed, meanwhile hepatocytes proliferation was promoted as evidenced by the increased number of KI67⁺ cells around portal vein (PV) area. The protein levels of YAP and its downstream targets were upregulated in TCPOBOP-treated mice and YAP translocation can be induced by CAR activation. Co-immunoprecipitation results suggested a potential protein–protein interaction of CAR and YAP. However, CAR activation-induced hepatomegaly can still be observed in liver-specific YAP-deficient (Yap^–/–) mice. In summary, CAR activation promotes hepatomegaly and liver regeneration partially by inducing YAP translocation and interaction with YAP signaling pathway, which provides new insights to further understand the physiological functions of CAR.

Identification of novel EED-EZH2 PPI inhibitors using an in silico fragment mapping method

Enhancer of zeste homolog 2 (EZH2) is a histone lysine methyltransferase that is overexpressed in many cancers. Numerous EZH2 inhibitors have been developed as anticancer agents, but recent studies have also focused on protein-protein interaction (PPI) between embryonic ectoderm development (EED) and EZH2 as a novel drug discovery target. Because EED indirectly enhances EZH2 enzymatic activity, EED-EZH2 PPI inhibitors suppress the methyltransferase activity and inhibit cancer growth. By contrast to the numerous promising EZH2 inhibitors, there are a paucity of EED-EZH2 PPI inhibitors reported in the literature. Here, we aimed to discover novel EED-EZH2 PPI inhibitors by first identifying possible binders of EED using an in-house knowledge-based in silico fragment mapping method. Next, 3D pharmacophore models were constructed from the arrangement pattern of the potential binders mapped onto the EED surface. In all, 16 compounds were selected by 3D pharmacophore-based virtual screening followed by docking-based virtual screening. In vitro evaluation revealed that five of these compounds exhibited inhibitory activities. This study has provided structural insights into the discovery and the molecular design of novel EED-EZH2 PPI inhibitors using an in silico fragment mapping method.

Computational Screening of Phase-separating Proteins

Phase separation is an important mechanism that mediates the compartmentalization of proteins in cells. Proteins that can undergo phase separation in cells share certain typical sequence features, like intrinsically disordered regions (IDRs) and multiple modular domains. Sequence-based analysis tools are commonly used in the screening of these proteins. However, current phase separation predictors are mostly designed for IDR-containing proteins, thus inevitably overlook the phase-separating proteins with relatively low IDR content. Features other than amino acid sequence could provide crucial information for identifying possible phase-separating proteins: protein–protein interaction (PPI) networks show multivalent interactions that underlie phase separation process; post-translational modifications (PTMs) are crucial in the regulation of phase separation behavior; spherical structures revealed in immunofluorescence (IF)images indicate condensed droplets formed by phase-separating proteins, distinguishing these proteins from non-phase-separating proteins. Here, we summarize the sequence-based tools for predicting phase-separating proteins and highlight the importance of incorporating PPIs, PTMs, and IF images into phase separation prediction in future studies.

Protein-protein interaction analysis reveals a novel cancer stem cell related target TMEM17 in colorectal cancer

BACKGROUND

Cancer stem cells (CSCs) are a small subpopulation of cells within tumors with stem cell property. Increased evidence suggest that CSCs could be responsible for chemoresistance and recurrence in colorectal cancer (CRC). However, a reliable therapeutic target on CSCs is still lacking.

METHODS

Here we describe a two-step strategy to generate CSC targets with high selectivity for colon stem cell markers, specific proteins that are interacted with CSC markers were selected and subsequently validated in a survival analysis. TMEM17 protein was found and its biological functions in CRC cells were further examined. Finally, we utilized the Gene Set Enrichment Analysis (GSEA) to investigate the potential mechanisms of TMEM17 in CRC.

RESULTS

By combining protein-protein interaction (PPI) database and high-throughput gene profiles, network analysis revealed a cluster of colon CSCs related genes. In the cluster, TMEM17 was identified as a novel CSCs related gene. The results of in-vitro functional study demonstrated that TMEM17 depletion can suppress the proliferation of CRC cells and sensitize CRC cells to chemotherapy drugs. Enrichment analysis revealed that the expression of TMEM17 is associated with the magnitude of activation of the Wnt/β-catenin pathway. Further validation in clinical samples demonstrated that the TMEM17 expression was much higher in tumor than normal tissue and was associated with poor survival in CRC patients.

CONCLUSION

Collectively, our finding unveils the critical role of TMEM17 in CRC and TMEM17 could be a potential effective therapeutic target for tumor recurrence and chemoresistance in the colorectal cancer (CRC).

An Integrated Systems Biology Approach Identifies the Proteasome as A Critical Host Machinery for ZIKV and DENV Replication

The Zika virus (ZIKV) and dengue virus (DENV) flaviviruses exhibit similar replicative processes but have distinct clinical outcomes. A systematic understanding of virus-host protein-protein interaction networks can reveal cellular pathways critical to viral replication and disease pathogenesis. Here we employed three independent systems biology approaches toward this goal. First, protein array analysis of direct interactions between individual ZIKV/DENV viral proteins and 20,240 human proteins revealed multiple conserved cellular pathways and protein complexes, including proteasome complexes. Second, an RNAi screen of 10,415 druggable genes identified the host proteins required for ZIKV infection and uncovered that proteasome proteins were crucial in this process. Third, high-throughput screening of 6016 bioactive compounds for ZIKV inhibition yielded 134 effective compounds, including six proteasome inhibitors that suppress both ZIKV and DENV replication. Integrative analyses of these orthogonal datasets pinpoint proteasomes as critical host machinery for ZIKV/DENV replication. Our study provides multi-omics datasets for further studies of flavivirus-host interactions, disease pathogenesis, and new drug targets.

GTB-PPI: Predict Protein-protein Interactions Based on L1-regularized Logistic Regression and Gradient Tree Boosting

Protein–protein interactions (PPIs) are of great importance to understand genetic mechanisms, delineate disease pathogenesis, and guide drug design. With the increase of PPI data and development of machine learning technologies, prediction and identification of PPIs have become a research hotspot in proteomics. In this study, we propose a new prediction pipeline for PPIs based on gradient tree boosting (GTB). First, the initial feature vector is extracted by fusing pseudo amino acid composition (PseAAC), pseudo position-specific scoring matrix (PsePSSM), reduced sequence and index-vectors (RSIV), and autocorrelation descriptor (AD). Second, to remove redundancy and noise, we employ L1-regularized logistic regression (L1-RLR) to select an optimal feature subset. Finally, GTB-PPI model is constructed. Five-fold cross-validation showed that GTB-PPI achieved the accuracies of 95.15% and 90.47% on Saccharomyces cerevisiae and Helicobacter pylori datasets, respectively. In addition, GTB-PPI could be applied to predict the independent test datasets for Caenorhabditis elegans, Escherichia coli, Homo sapiens, and Mus musculus, the one-core PPI network for CD9, and the crossover PPI network for the Wnt-related signaling pathways. The results show that GTB-PPI can significantly improve accuracy of PPI prediction. The code and datasets of GTB-PPI can be downloaded from https://github.com/QUST-AIBBDRC/GTB-PPI/.

Visualized heterooligomeric subunit structures of 817 human cellular proteins by correlating native protein 2D maps with protein interaction databases

Previously, we have reported on the reconstruction of 4323 native protein 2D maps of human bronchial smooth muscle cells (HBSMC) by combining nondenaturing 2DE, grid gel-cutting, and quantitative LC-MS/MS. In this work, we report on the visualization of the heterooligomeric subunit structures of HBSMC proteins by correlating the native protein 2D maps with the information in protein interaction databases. Image analysis of the 2D maps was employed as the first approach. Each of the native protein 2D maps of 2327 proteins, which had three or more detected squares in the native protein 2D maps, was compared with the 2D maps of the remaining 2326 proteins scoring the degree of overlap in an area around the quantity peak and the protein partner which showed the best score was decided. The protein pairs were examined on their reported interactions referring to protein interaction databases. In order to consider the cases where a protein has multiple functions and the heterooligomeric subunit structures might not be detected from the image analysis, prior database search was employed as the second approach. Each of the 1689 HBSMC proteins, which had five or more detected squares in the native protein 2D maps, was examined on its interactor proteins described in the databases, then the native 2D map was compared with the maps of the interactor proteins to find the overlap which reasonably supported the interaction. Summarizing these examinations, 215 heterooligomeric subunit structures of 817 human cellular proteins could be visualized on the native protein 2D maps.

Using BioID for the Identification of Interacting and Proximal Proteins in Subcellular Compartments in Toxoplasma gondii

BioID is an in vivo biotinylation system developed to examine the proximal and interacting proteins of a bait protein within a subcellular compartment. This approach has been exploited in Toxoplasma for protein-protein interaction studies and proteomic characterizations of intracellular compartments. The BioID method requires constructing a translational fusion between a protein of interest and the promiscuous biotin ligase BirA∗ (a mutant of the E. coli protein BirA) which enables trafficking of the protein to the correct intracellular compartment and association with its partners. Proximity labelling occurs upon addition of biotin to the media and the biotinylated target proteins are then be purified using stringent conditions via streptavidin chromatography. In this chapter, we describe the methodology to fuse BirA∗ (or the newer variant BioID2) to a bait protein using endogenous gene tagging in Toxoplasma and then identify the proximal and interacting proteins using in vivo biotinylation, streptavidin purification and mass spectrometric analysis.

SPServer: split-statistical potentials for the analysis of protein structures and protein-protein interactions

BACKGROUND

Statistical potentials, also named knowledge-based potentials, are scoring functions derived from empirical data that can be used to evaluate the quality of protein folds and protein-protein interaction (PPI) structures. In previous works we decomposed the statistical potentials in different terms, named Split-Statistical Potentials, accounting for the type of amino acid pairs, their hydrophobicity, solvent accessibility and type of secondary structure. These potentials have been successfully used to identify near-native structures in protein structure prediction, rank protein docking poses, and predict PPI binding affinities.

RESULTS

Here, we present the SPServer, a web server that applies the Split-Statistical Potentials to analyze protein folds and protein interfaces. SPServer provides global scores as well as residue/residue-pair profiles presented as score plots and maps. This level of detail allows users to: (1) identify potentially problematic regions on protein structures; (2) identify disrupting amino acid pairs in protein interfaces; and (3) compare and analyze the quality of tertiary and quaternary structural models.

CONCLUSIONS

While there are many web servers that provide scoring functions to assess the quality of either protein folds or PPI structures, SPServer integrates both aspects in a unique easy-to-use web server. Moreover, the server permits to locally assess the quality of the structures and interfaces at a residue level and provides tools to compare the local assessment between structures. SERVER ADDRESS: https://sbi.upf.edu/spserver/ .

Enhancing the activity of platinum-based drugs by improved inhibitors of ERCC1-XPF-mediated DNA repair

PURPOSE

The ERCC1-XPF 5'-3' DNA endonuclease complex is involved in the nucleotide excision repair pathway and in the DNA inter-strand crosslink repair pathway, two key mechanisms modulating the activity of chemotherapeutic alkylating agents in cancer cells. Inhibitors of the interaction between ERCC1 and XPF can be used to sensitize cancer cells to such drugs.

METHODS

We tested recently synthesized new generation inhibitors of this interaction and evaluated their capacity to sensitize cancer cells to the genotoxic activity of agents in synergy studies, as well as their capacity to inhibit the protein-protein interaction in cancer cells using proximity ligation assay.

RESULTS

Compound B9 showed the best activity being synergistic with cisplatin and mitomycin C in both colon and lung cancer cells. Also, B9 abolished the interaction between ERCC1 and XPF in cancer cells as shown by proximity ligation assay. Results of different compounds correlated with values from our previously obtained in silico predictions.

CONCLUSION

Our results confirm the feasibility of the approach of targeting the protein-protein interaction between ERCC1 and XPF to sensitize cancer cells to alkylating agents, thanks to the improved binding affinity of the newly synthesized compounds.

Biotin-Based Proximity Labeling of Protein Complexes in Planta

Proteome networks are a crucial facet of biological systems that mediate cellular functions and responses to the environment. However, a main limitation of traditional approaches to study protein interactions, such as yeast-2-hybrid and affinity purification-coupled with mass spectrometry (AP-MS), is their restricted ability to identify interactions for membrane-bound and/or insoluble protein complexes. These types of interactions include many of the protein complexes that mediate the perception and response to cellular stimuli and are therefore of great research interest. Proximity-dependent biotinylation (PDB) coupled to mass spectrometry provides a powerful approach to survey proximal protein interactions in living cells, including membrane bound and insoluble complexes. One PDB method, BioID, translationally fuses a promiscuous biotin ligase to a bait protein of interest, allowing covalent biotinylation of proximal proteins (within ~10 nm). Modified proteins can be purified from cells without the need to maintain protein interactions, and subsequently identified by mass spectrometry. Although BioID has revolutionized the study of proteomes in numerous organisms, its application to plant systems has only recently been realized. In this chapter, we outline a protocol for BioID in tissues of the model plant Arabidopsis thaliana.

Investigations of Lipid Binding to Acyl-CoA-Binding Proteins (ACBP) Using Isothermal Titration Calorimetry (ITC)

Isothermal titration calorimetry (ITC) is a quantitative, biophysical method to investigate intermolecular binding between biomolecules by directly measuring the heat exchange in the binding reaction. The assay is carried out in solution when the molecules interact in vitro. This allows to determine values for binding affinity (K_d), binding stoichiometry (n), as well as changes in Gibbs free energy (ΔG), entropy (ΔS), and enthalpy (ΔH). This method also addresses the kinetics of enzymatic reactions for a substrate during conversion to a product. ITC has been used to study the interactions between proteins and ligands such as those of acyl-CoA-binding proteins (ACBPs) and acyl-CoA thioesters or ACBPs with protein partners. ITC has also been used in investigating interactions between antiserum and antigen, as well as those involving RNA and DNA and other macromolecules. We describe the methods used to isolate and purify a recombinant rice ACBP (OsACBP) for ITC. To study OsACBP binding to long-chain acyl-CoA thioesters, a microcalorimeter was used at 30 °C, and the ligand (acyl-CoA thioesters or a protein partner in the first cell), was mixed with the ACBP protein solution in a second cell, for more than 40 min comprising 20 injections. Subsequently, the binding parameters including the heat-release data were analyzed and various thermodynamic parameters were calculated.

Determination of the Rab27-Effector Binding Affinity Using a High-Throughput FRET-Based Assay

Thus far, two Rab27 isoforms (Rab27a and Rab27b) have been identified that interact with their eleven downstream effectors proteins, preferentially in their GTP-bound state. In recent years, a number of studies has suggested roles for Rab27-effector protein interactions in the development of cancer cell invasion and metastasis, and immune and inflammatory responses. Here we develop an in vitro fluorescence resonance energy transfer (FRET)-based protein-protein interaction assay to report Rab27 protein interactions with their effectors. We particularly focus on determining the interaction of mouse (m) Synaptotagmin-like protein (Slp)1 and mSlp2 effector proteins with human (h)Rab27. Green fluorescent protein (GFP)-N-terminus Rab27 binding domains (m-Slp1 and m-Slp2) recombinant proteins were used as donor fluorophores, whereas mCherry-hRab27a/b recombinant proteins were used as acceptor fluorophores. The conditions of this assay were validated and optimized, and the specificity of the assay was confirmed. Accordingly, this assay can be used to assess and identify key determinants and/or candidate inhibitors of Rab27-effector interactions.

Identification of PDZ Interactions by Yeast Two-Hybrid Technique

The yeast two-hybrid technique is a powerful method to detect direct protein-protein interactions. Due to its accessibility, speed, and versatility, this technique is easy to set up in any laboratory and suitable for small and large scale screenings. Here we describe the implementation of an array-based screening that allows for the probing of the entire human PDZ ORFeome (or hPDZome) by yeast two-hybrid technique. With this approach, one can rapidly identify the PDZ domains that are able to interact (up to K_D in the high μmolar range) with any candidate protein among a panel of 266 individual clones, thereby comprehensively identifying its PDZ interactome.

Detecting Blue Light-Dependent Protein-Protein Interactions by LexA-Based Yeast Two-Hybrid Assay

The LexA-based yeast two-hybrid system is one of the most powerful techniques used to detect blue light-dependent protein-protein interactions. In Arabidopsis, many protein-protein interactions in blue light signaling pathway were identified using this system. Here we present an easy and efficient method of the LexA-based yeast two-hybrid assay for testing protein-protein interactions in a blue light-dependent manner.

Biotin Proximity Labeling for Protein-Protein Interaction Discovery: The BioID Method

Biotinylation identification (BioID) is a method designed to provide new cellular location and functional knowledge of the protein of interest through the identification of those proteins surrounding and in direct contact. A biotin ligase is fused onto the protein of interest and expressed in cells where it can biotinylate even short-lived transient protein complexes. In addition, due to the proximity labeling nature of the experiment, cellular localization and functional enrichment information can also be obtained. Since labeling occurs only after the addition of biotin, temporal relationships and localization changes (e.g., cytoplasmic to nuclear) can also be identified. Labeled proteins are easily purified, and contaminants minimized, using the strong interaction between biotin and streptavidin. Mass spectrometry analysis of the purified proteins allows for the identification of potential interactors for further validation and characterization.

In Vivo Imaging of Protein Interactions in the Germplasm with Bimolecular Fluorescent Complementation

Protein-protein interactions (PPIs) play a central role in all cellular processes. The discovery of green fluorescent protein (GFP) and split varieties, which are functionally reconstituted by complementation, led to the development of the bimolecular fluorescence complementation (BiFC) assay for the investigation of PPI in vivo. BiFC became a popular tool, as it is a convenient and quick technology to directly visualize PPI in a wide variety of living cells. In combination with the transparency of the early zebrafish embryo, it also permits detection of PPI in the context of an entire living organism, which performs all spatial and temporal regulations missing in in vitro systems like tissue culture. However, the application of BiFC in some research areas including the study of zebrafish is limited due to the lack of efficient and convenient BiFC expression vectors. Here, we describe the engineering of a novel set of Gateway^®-adapted BiFC destination vectors to investigate PPI with all possible permutations for BiFC experiments. Moreover, we demonstrate the versatility of these destination vectors by confirming the interaction between zebrafish Bucky ball and RNA helicase Vasa in living embryos.

Ligand Binding in Allosteric Flavoproteins: Part 2. Quantitative Analysis of the Redox-Dependent Interaction of the Apoptosis-Inducing Factor (AIF) with Its Protein Partner

To perform their action usually flavoproteins interact transiently with a variety of molecular partners, whose binding is reciprocally affected and often controlled by the redox state of the bound flavin cofactor. As a case study, here we describe an approach for the quantitative characterization of the redox-controlled interaction of the mammalian apoptosis inducing factor (AIF) with one of its known protein partners, namely, the mitochondrial coiled-coil-helix-coiled-coil-helix domain-containing protein 4 (CHCHD4). In particular, we report a protocol for the titration of the flavoprotein in both in its oxidized and reduced states with CHCHD4, using an implementation of the MicroScale Thermophoresis (MST) technique.

Bimolecular Fluorescence Complementation (BiFC) and Multiplexed Imaging of Protein-Protein Interactions in Human Living Cells

Deciphering protein-protein interactions (PPIs) in vivo is crucial to understand protein function. Bimolecular fluorescence complementation (BiFC) makes applicable the analysis of PPIs in many different native contexts, including human live cells. It relies on the property of monomeric fluorescent proteins to be reconstituted from two separate subfragments upon spatial proximity. Candidate partners fused to such complementary subfragments can form a fluorescent protein complex upon interaction, allowing visualization of weak and transient PPIs. It can also be applied for investigation of distinct PPIs at the same time using a multicolor setup. In this chapter, we provide a detailed protocol for analyzing PPIs by doing BiFC in cultured cells. Proof-of-principle experiments rely on the complementation property between the N-terminal fragment of mVenus (designated VN173) and the C-terminal fragment of mCerulean (designated CC155) and the partnership between HOXA7 and PBX1 proteins. This protocol is compatible with any other fluorescent complementation pair fragments and any type of candidate interacting proteins.

Deep Learning for Protein-Protein Interaction Site Prediction

Protein-protein interactions (PPIs) are central to cellular functions. Experimental methods for predicting PPIs are well developed but are time and resource expensive and suffer from high false-positive error rates at scale. Computational prediction of PPIs is highly desirable for a mechanistic understanding of cellular processes and offers the potential to identify highly selective drug targets. In this chapter, details of developing a deep learning approach to predicting which residues in a protein are involved in forming a PPI-a task known as PPI site prediction-are outlined. The key decisions to be made in defining a supervised machine learning project in this domain are here highlighted. Alternative training regimes for deep learning models to address shortcomings in existing approaches and provide starting points for further research are discussed. This chapter is written to serve as a companion to developing deep learning approaches to protein-protein interaction site prediction, and an introduction to developing geometric deep learning projects operating on protein structure graphs.

Confocal Laser Scanning Microscopy and Fluorescence Correlation Methods for the Evaluation of Molecular Interactions

Confocal laser scanning microscopy (CLSM) and related microscopic techniques allow a unique and versatile approach to image and analyze living cells due to their specificity and high sensitivity. Among confocal related techniques, fluorescence correlation methods, such as fluorescence correlation spectroscopy (FCS) and dual-color fluorescence cross-correlation spectroscopy (FCCS), are highly sensitive biophysical methods for analyzing the complex dynamic events of molecular diffusion and interaction change in live cells as well as in solution by exploiting the characteristics of fluorescence signals. Analytical and quantitative information from FCS and FCCS coupled with fluorescence images obtained from CLSM can now be applied in convergence science such as drug delivery and nanomedicine, as well as in basic cell biology. In this chapter, a brief introduction into the physical parameters that can be obtained from FCS and FCCS is first provided. Secondly, experimental examples of the methods for evaluating the parameters is presented. Finally, two potential FCS and FCCS applications for convergence science are introduced in more detail.

Dynamic Control of Signaling by Phosphorylation of PDZ Binding Motifs

The dynamic regulation of protein-protein interactions (PPIs) involves phosphorylation of short liner motifs in disordered protein regions modulating binding affinities. The ribosomal-S6-kinase 1 is capable of binding to scaffold proteins containing PDZ domains through a PDZ-binding motif (PBM) located at the disordered C-terminus of the kinase. Phosphorylation of the PBM dramatically changes the interactome of RSK1 with PDZ domains exerting a fine-tuning mechanism to regulate PPIs. Here we present in detail highly effective biophysical (fluorescence polarization, isothermal calorimetry) and cellular (protein-fragment complementation) methods to study the effect of phosphorylation on RSK1-PDZ interactions that can be also applied to investigate phosphoregulation of other PPIs in signaling pathways.

Inter-protein residue covariation information unravels physically interacting protein dimers

BACKGROUND

Predicting physical interaction between proteins is one of the greatest challenges in computational biology. There are considerable various protein interactions and a huge number of protein sequences and synthetic peptides with unknown interacting counterparts. Most of co-evolutionary methods discover a combination of physical interplays and functional associations. However, there are only a handful of approaches which specifically infer physical interactions. Hybrid co-evolutionary methods exploit inter-protein residue coevolution to unravel specific physical interacting proteins. In this study, we introduce a hybrid co-evolutionary-based approach to predict physical interplays between pairs of protein families, starting from protein sequences only.

RESULTS

In the present analysis, pairs of multiple sequence alignments are constructed for each dimer and the covariation between residues in those pairs are calculated by CCMpred (Contacts from Correlated Mutations predicted) and three mutual information based approaches for ten accessible surface area threshold groups. Then, whole residue couplings between proteins of each dimer are unified into a single Frobenius norm value. Norms of residue contact matrices of all dimers in different accessible surface area thresholds are fed into support vector machine as single or multiple feature models. The results of training the classifiers by single features show no apparent different accuracies in distinct methods for different accessible surface area thresholds. Nevertheless, mutual information product and context likelihood of relatedness procedures may roughly have an overall higher and lower performances than other two methods for different accessible surface area cut-offs, respectively. The results also demonstrate that training support vector machine with multiple norm features for several accessible surface area thresholds leads to a considerable improvement of prediction performance. In this context, CCMpred roughly achieves an overall better performance than mutual information based approaches. The best accuracy, sensitivity, specificity, precision and negative predictive value for that method are 0.98, 1, 0.962, 0.96, and 0.962, respectively.

CONCLUSIONS

In this paper, by feeding norm values of protein dimers into support vector machines in different accessible surface area thresholds, we demonstrate that even small number of proteins in pairs of multiple alignments could allow one to accurately discriminate between positive and negative dimers.

Identification of biomarkers associated with clinical severity of chronic obstructive pulmonary disease

We sought to identify the biomarkers related to the clinical severity of stage I to stage IV chronic obstructive pulmonary disease (COPD). Gene expression profiles from the blood samples of COPD patients at each of the four stages were acquired from the Gene Expression Omnibus Database (GEO, accession number: GSE54837). Genes showing expression changes among the different stages were sorted by soft clustering. We performed functional enrichment, protein-protein interaction (PPI), and miRNA regulatory network analyses for the differentially expressed genes. The biomarkers associated with the clinical classification of COPD were selected from logistic regression models and the relationships between TLR2 and inflammatory factors were verified in clinical blood samples by qPCR and ELISA. Gene clusters demonstrating continuously rising or falling changes in expression (clusters 1, 2, and 7 and clusters 5, 6, and 8, respectively) from stage I to IV were defined as upregulated and downregulated genes, respectively, and further analyzed. The upregulated genes were enriched in functions associated with defense, inflammatory, or immune responses. The downregulated genes were associated with lymphocyte activation and cell activation. TLR2, HMOX1, and CD79A were hub proteins in the integrated network of PPI and miRNA regulatory networks. TLR2 and CD79A were significantly correlated with clinical classifications. TLR2 was closely associated with inflammatory responses during COPD progression. Functions associated with inflammatory and immune responses as well as lymphocyte activation may play important roles in the progression of COPD from stage I to IV. TLR2 and CD79A may serve as potential biomarkers for the clinical severity of COPD. TLR2 and CD79A may also serve as independent biomarkers in the clinical classification in COPD. TLR2 may play an important role in the inflammatory responses of COPD.