Combining immunofluorescence with in situ proximity ligation assay: a novel imaging approach to monitor protein–protein interactions in relation to subcellular localization

The in situ Proximity Ligation Assay (PLA) is suited for visualizing protein–protein interactions and post-translational protein modifications in both tissue sections and in vitro cell cultures. Accurate identification and quantification of protein–protein interactions are critical for in vitro cell analysis, especially when studying the dynamic involvement of proteins in various processes, including cell proliferation, differentiation, and apoptosis. Here, we monitored the interactions between protein kinase-Cζ (PKCζ) and Bcl10 protein in untreated and etoposide (VP-16)-treated C4-I cells by means of a new combined morphological approach and validated it by taking stock of our previous proteomic and biochemical work (Chiarini et al. in J Proteome Res 11:3996–4012, 2012). We first analyzed the colocalization of PKCζ and Bcl10 proteins through classical immunofluorescent colocalization analysis. On the basis of these results, we developed a novel imaging approach combining immunofluorescence (IF) techniques with in situ PLA to identify the PKCζ·Bcl10 complexes at the level of a specific subcellular compartment, i.e., the nuclear envelope (NE). By this means, we could show that the amount of PKCζ·Bcl10 complexes localized at the NE of C4-I cells during proliferation or after treatment with VP-16 closely corresponded to our previous purely biochemical results. Hence, the present findings demonstrate that the combination of in situ PLA with classical IF detection is a novel powerful analytical tool allowing to morphologically demonstrate new specific protein–protein interactions at level of subcellular organelles, the complexes functions of which can next be clarified through proteomic/biochemical approaches.     

Intracellular antibody capture: A molecular biology approach to inhibitors of protein–protein interactions

Many proteins of interest in basic biology, translational research studies and for clinical targeting in diseases reside inside the cell and function by interacting with other macromolecules. Protein complexes control basic processes such as development and cell division but also abnormal cell growth when mutations occur such as found in cancer. Interfering with protein–protein interactions is an important aspiration in both basic and disease biology but small molecule inhibitors have been difficult and expensive to isolate. Recently, we have adapted molecular biology techniques to develop a simple set of protocols for isolation of high affinity antibody fragments (in the form of single VH domains) that function within the reducing environment of higher organism cells and can bind to their target molecules. The method called Intracellular Antibody Capture (IAC) has been used to develop inhibitory anti-RAS and anti-LMO2 single domains that have been used for target validation of these antigens in pre-clinical cancer models and illustrate the efficacy of the IAC approach to generation of drug surrogates. Future use of inhibitory VH antibody fragments as drugs in their own right (we term these macrodrugs to distinguish them from small molecule drugs) requires their delivery to target cells in vivo but they can also be templates for small molecule drug development that emulate the binding sites of the antibody fragments. This article is part of a Special Issue entitled: Recent advances in molecular engineering of antibody.     

Molecular glues to stabilise protein–protein interactions

Targeting protein–protein interactions (PPIs) has become a common approach to tackle various diseases whose pathobiology is driven by their mis-regulation in important signalling pathways. Modulating PPIs has tremendous untapped therapeutic potential and different approaches can be used to modulate PPIs. Initially, therapeutic effects were mostly sought by inhibiting PPIs. However, by gaining insight in the mode of action of certain therapeutic compounds, it became clear that stabilising (i.e. enhancing) PPIs can also be useful. The latter strategy is recently gaining a lot of attention, as stabilising physiologic, or even inducing novel interactions of a target protein with E3 ubiquitin ligases forms the basis of the targeted protein degradation (TPD) approach. An emerging additional example for drug discovery based on PPI stabilisation are the 14-3-3 proteins, a family of regulatory proteins, which engages in many protein–protein interactions, some of which might become therapeutical targets.     

An ant colony optimization approach to flexible protein–ligand docking

The prediction of the complex structure of a small ligand with a protein, the so-called protein–ligand docking problem, is a central part of the rational drug design process. For this purpose, we introduce the docking algorithm PLANTS (Protein–Ligand ANT System), which is based on ant colony optimization, one of the most successful swarm intelligence techniques. We study the effectiveness of PLANTS for several parameter settings and present a direct comparison of PLANTS’s performance to a state-of-the-art program called GOLD, which is based on a genetic algorithm and frequently used in the pharmaceutical industry for this task. Last but not least, we also show that PLANTS can make effective use of protein flexibility giving example results on cross-docking and virtual screening experiments for protein kinase A. This article is based on a paper that won the best paper award at ANTS 2006, the 5th International Workshop on Ant Colony Optimization and Swarm Intelligence held in Brussels, Belgium, 2006. This article includes new types of experiments and also the possibility of considering flexibility of protein side-chains.     

Artificial intelligence approaches to human-microbiome protein–protein interactions

Host-microbiome interactions play significant roles in human health and disease. Artificial intelligence approaches have been developed to better understand and predict the molecular interplay between the host and its microbiome. Here, we review recent advancements in computational methods to predict microbial effects on human cells with a special focus on protein–protein interactions. We categorize recent methods from traditional ones to more recent deep learning methods, followed by several challenges and potential solutions in structure-based approaches. This review serves as a brief guide to the current status and future directions in the field.     

Erratum to: An in silico biomechanical analysis of the stent–esophagus interaction

Biomechanics and Modeling in Mechanobiology - In the original publication of the article, Tables 2 and 3 were published with error. The correct tables are provided below (Tables 2, 3). The...     

State-of-the-art computational methods to predict protein–protein interactions with high accuracy and coverage

Prediction of protein–protein interactions (PPIs) commonly involves a significant computational component. Rapid recent advances in the power of computational methods for protein interaction prediction motivate a review of the state-of-the-art. We review the major approaches, organized according to the primary source of data utilized: protein sequence, protein structure, and protein co-abundance. The advent of deep learning (DL) has brought with it significant advances in interaction prediction, and we show how DL is used for each source data type. We review the literature taxonomically, present example case studies in each category, and conclude with observations about the strengths and weaknesses of machine learning methods in the context of the principal sources of data for protein interaction prediction.     

Plucking the high hanging fruit: A systematic approach for targeting protein–protein interactions

Development of specific ligands for protein targets that help decode the complexities of protein–protein interaction networks is a key goal for the field of chemical biology. Despite the emergence of powerful in silico and experimental high-throughput screening strategies, the discovery of synthetic ligands that selectively modulate protein–protein interactions remains a challenge for bioorganic and medicinal chemists. This Perspective discusses emerging principles for the rational design of PPI inhibitors. Fundamentally, the approach seeks to adapt nature’s protein recognition principles for the design of suitable secondary structure mimetics.     

Targeting of extracellular protein–protein interactions with macrocyclic peptides

Targeting of extracellular protein–protein interactions (PPI) is emerging as a major application for de novo discovered macrocyclic peptides. Modern discovery platforms can routinely identify macrocyclic peptide ligands capable of highly selective modulation of extracellular signaling pathways; amenability to chemical synthesis and natural modularity of peptides additionally provides an avenue for their further structural elaboration, while the challenge of cell internalization can be minimized. Here, we discuss the recent progress in targeting extracellular PPIs with macrocyclic peptides by focusing on a number of recent case studies. We analyze the scope and potential limitations of the discovery systems in identifying functional macrocyclic ligands. We also highlight the recent technical advancements allowing for a more streamlined discovery pipeline and our brief perspective in this field.     

Mimicking direct protein–protein and solvent-mediated interactions in the CDP-methylerythritol kinase homodimer: a pharmacophore-directed virtual screening approach

The 2C-methylerythritol 4-phosphate (MEP) pathway for the biosynthesis of isopentenyl pyrophosphate and its isomer dimethylallyl pyrophosphate, which are the precursors of isoprenoids, is present in plants, in the malaria parasite Plasmodium falciparum and in most eubacteria, including pathogenic agents. However, the MEP pathway is absent from fungi and animals, which have exclusively the mevalonic acid pathway. Given the characteristics of the MEP pathway, its enzymes represent potential targets for the generation of selective antibacterial, antimalarial and herbicidal molecules. We have focussed on the enzyme 4-(cytidine 5′-diphospho)-2-C-methyl-d-erythritol kinase (CMK), which catalyses the fourth reaction step of the MEP pathway. A molecular dynamics simulation was carried out on the CMK dimer complex, and protein–protein interactions analysed, considering also water-mediated interactions between monomers. In order to find small molecules that bind to CMK and disrupt dimer formation, interactions observed in the dynamics trajectory were used to model a pharmacophore used in database searches. Using an intensity-fading matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry approach, one compound was found to interact with CMK. The data presented here indicate that a virtual screening approach can be used to identify candidate molecules that disrupt the CMK–CMK complex. This strategy can contribute to speeding up the discovery of new antimalarial, antibacterial, and herbicidal compounds.     

'Oming in on RNA–protein interactions

A Report on the Plant Genomes & Biotechnology: From Genes to Networks meeting, held at the Cold Spring Harbor Laboratories, USA, December 4–7, 2013.     

An in silico approach to understand the structure–function properties of a serine protease (Bacifrinase) from Bacillus cereus and experimental evidence to support the interaction of Bacifrinase with fibrinogen and thrombin

Microbial fibrinogenolytic serine proteases find therapeutic applications in the treatment of thrombosis- and hyperfibrinogenemia-associated disorders. However, analysis of structure–function properties of an enzyme is utmost important before its commercial application. In this study, an attempt has been made to understand the structure of a fibrinogenolytic protease enzyme, “Bacifrinase” from Bacillus cereus strain AB01. From the molecular dynamics trajectory analysis, the modelled three-dimensional structure of the protease was found to be stable and the presence of a catalytic triad made up of Asp102, His83 and Ser195 suggests that it is a serine protease. To understand the mechanism of enzyme–substrate and enzyme–inhibitor interactions, the equilibrated protein was docked with human fibrinogen (the physiological substrate of this enzyme), human thrombin and with ten selective protease inhibitors. The Bacifrinase–chymostatin interaction was the strongest among the selected protease inhibitors. The serine protease inhibitor phenyl methane sulphonyl fluoride was found to interact with the Ser134 residue of Bacifrinase. Furthermore, protein–protein docking study revealed the fibrinogenolytic property of Bacifrinase and its interaction with Aα-, Bβ- and Cγ-chains human fibrinogen to a different extent. However, biochemical analysis showed that Bacifrinase did not hydrolyse the γ-chain of fibrinogen. The in silico and spectrofluorometric studies also showed interaction of Bacifrinase with thrombin as well as fibrinogen with a Kd value of 16.5 and .81 nM, respectively. Our findings have shed light on the salient structural features of Bacifrinase and confirm that it is a fibrinogenolytic serine protease.