Current Experimental Methods for Characterizing Protein-Protein Interactions

PPI-Graphomer: enhanced protein-protein affinity prediction using pretrained and graph transformer models

Protein-protein interactions (PPIs) refer to the phenomenon of protein binding through various types of bonds to execute biological functions. These interactions are critical for understanding biological mechanisms and drug research. Among these, the protein binding interface is a critical region involved in protein-protein interactions, particularly the hotspot residues on it that play a key role in protein interactions. Current deep learning methods trained on large-scale data can characterize proteins to a certain extent, but they often struggle to adequately capture information about protein binding interfaces. To address this limitation, we propose the PPI-Graphomer module, which integrates pretrained features from large-scale language models and inverse folding models. This approach enhances the characterization of protein binding interfaces by defining edge relationships and interface masks on the basis of molecular interaction information. Our model outperforms existing methods across multiple benchmark datasets and demonstrates strong generalization capabilities.

Modular Nanoparticle Platform for Solution-Phase Optical Sensing of Protein-Protein Interactions

Protein-protein interactions regulate essentially all cellular processes. Understanding these interactions, including the quantification of binding parameters, is crucial for unraveling the molecular mechanisms underlying cellular pathways and, ultimately, their roles in cellular physiology and pathology. Current methods for measuring protein-protein interactions in vitro generally require amino acid conjugation of fluorescent tags, complex instrumentation, large amounts of purified protein, or measurement at extended surfaces. Here, we present an elegant nanoparticle-based platform for the optical detection of protein-protein interactions in the solution phase. We synthesized gold-coated silver decahedral nanoparticles possessing high chemical stability and exceptional optical sensing properties. The nanoparticle surface is then tailored for specific binding to commonly used polyhistidine tags of recombinant proteins. Sequential addition of proteins to the nanoparticle suspension results in spectral shifts of the localized surface plasmon resonance that can be monitored by conventional UV-vis spectrophotometry. With this approach, we demonstrate both the qualitative detection of specific protein-protein interactions and the quantification of equilibrium and kinetic binding parameters between small globular proteins. Requiring minimal protein quantities and basic laboratory equipment, this technique offers a simple, economical, and modular approach to characterizing protein-protein interactions, holds promise for broad use in future studies, and may serve as a template for future biosensing technologies.

Recent Developments in 14-3-3 Stabilizers for Regulating Protein-Protein Interactions: An Update

14-3-3 proteins play a crucial role in the regulation of protein-protein interactions, impacting various cellular processes and disease mechanisms. Recent advancements have led to the development of stabilizers that enhance the binding of 14-3-3 proteins to clients, presenting promising therapeutic potentials. This perspective provides an updated overview of the latest developments in the field of 14-3-3 stabilizers, with a focus on their design, synthesis, and biological evaluation. We discuss the structural basis for the interaction between 14-3-3 proteins and their ligands, highlighting key modifications that enhance binding affinity and selectivity. Additionally, we explore the therapeutic applications of 14-3-3 stabilizers across major therapeutic areas such as cancer, metabolic disorders, and neurodegenerative diseases. By summarizing recent research findings and technological advancements, this perspective aims to shed light on the current state of 14-3-3 stabilizer developments and outline future directions for optimizing these compounds as effective therapeutic agents.

Structure-Based Approaches for Protein-Protein Interaction Prediction Using Machine Learning and Deep Learning

Protein-Protein Interaction (PPI) prediction plays a pivotal role in understanding cellular processes and uncovering molecular mechanisms underlying health and disease. Structure-based PPI prediction has emerged as a robust alternative to sequence-based methods, offering greater biological accuracy by integrating three-dimensional spatial and biochemical features. This work summarizes the recent advances in computational approaches leveraging protein structure information for PPI prediction, focusing on machine learning (ML) and deep learning (DL) techniques. These methods not only improve predictive accuracy but also provide insights into functional sites, such as binding and catalytic residues. However, challenges such as limited high-resolution structural data and the need for effective negative sampling persist. Through the integration of experimental and computational tools, structure-based prediction paves the way for comprehensive proteomic network analysis, holding promise for advancements in drug discovery, biomarker identification, and personalized medicine. Future directions include enhancing scalability and dataset reliability to expand these approaches across diverse proteomes.

Pil1 Co-tethering Assay to Detect Protein-Protein Interactions in the Fission Yeast Schizosaccharomyces pombe

Protein-protein interactions play critical roles in biological processes. We previously developed the Pil1 co-tethering assay, an imaging-based method to detect protein-protein interactions in living Schizosaccharomyces pombe cells. This assay leverages the distinct localization pattern of the Pil1 protein by fusing a bait protein to Pil1 and examining whether a prey protein co-localize with the Pil1-fused bait. Here, we present an improved protocol of the Pil1 co-tethering assay. In this protocol, modified stable integration vectors (SIVs) with a NotI site as the linearization site are used to express bait and prey proteins. We expect that this protocol will enhance the application of the Pil1 co-tethering assay for studying protein-protein interactions.

ProAffinity-GNN: A Novel Approach to Structure-Based Protein-Protein Binding Affinity Prediction via a Curated Data Set and Graph Neural Networks

Protein-protein interactions (PPIs) are crucial for understanding biological processes and disease mechanisms, contributing significantly to advances in protein engineering and drug discovery. The accurate determination of binding affinities, essential for decoding PPIs, faces challenges due to the substantial time and financial costs involved in experimental and theoretical methods. This situation underscores the urgent need for more effective and precise methodologies for predicting binding affinity. Despite the abundance of research on PPI modeling, the field of quantitative binding affinity prediction remains underexplored, mainly due to a lack of comprehensive data. This study seeks to address these needs by manually curating pairwise interaction labels on available 3D structures of protein complexes, with experimentally determined binding affinities, creating the largest data set for structure-based pairwise protein interaction with binding affinity to date. Subsequently, we introduce ProAffinity-GNN, a novel deep learning framework using protein language model and graph neural network (GNN) to improve the accuracy of prediction of structure-based protein-protein binding affinities. The evaluation results across several benchmark test sets and an additional case study demonstrate that ProAffinity-GNN not only outperforms existing models in terms of accuracy but also shows strong generalization capabilities.

Predicting Affinity Through Homology (PATH): Interpretable Binding Affinity Prediction with Persistent Homology

Accurate binding affinity prediction is crucial to structure-based drug design. Recent work used computational topology to obtain an effective representation of protein-ligand interactions. While algorithms using algebraic topology have proven useful in predicting properties of biomolecules, previous algorithms employed uninterpretable machine learning models which failed to explain the underlying geometric and topological features that drive accurate binding affinity prediction. Moreover, they had high computational complexity which made them intractable for large proteins. We present the fastest known algorithm to compute persistent homology features for protein-ligand complexes using opposition distance, with a runtime that is independent of the protein size. Then, we exploit these features in a novel, interpretable algorithm to predict protein-ligand binding affinity. Our algorithm achieves interpretability through an effective embedding of distances across bipartite matchings of the protein and ligand atoms into real-valued functions by summing Gaussians centered at features constructed by persistent homology. We name these functions internuclear persistent contours (IPCs) . Next, we introduce persistence fingerprints , a vector with 10 components that sketches the distances of different bipartite matching between protein and ligand atoms, refined from IPCs. Let the number of protein atoms in the protein-ligand complex be n , number of ligand atoms be m , and ω ≈ 2.4 be the matrix multiplication exponent. We show that for any 0 < ε < 1, after an 𝒪 ( mn log( mn )) preprocessing procedure, we can compute an ε -accurate approximation to the persistence fingerprint in 𝒪 ( m log 6 ω ( m/ε )) time, independent of protein size. This is an improvement in time complexity by a factor of 𝒪 (( m + n ) 3 ) over any previous binding affinity prediction that uses persistent homology. We show that the representational power of persistence fingerprint generalizes to protein-ligand binding datasets beyond the training dataset. Then, we introduce PATH , Predicting Affinity Through Homology, a two-part algorithm consisting of PATH + and PATH - . PATH + is an interpretable, small ensemble of shallow regression trees for binding affinity prediction from persistence fingerprints. We show that despite using 1,400-fold fewer features, PATH + has comparable performance to a previous state-of-the-art binding affinity prediction algorithm that uses persistent homology. Moreover, PATH + has the advantage of being interpretable. We visualize the features captured by persistence fingerprint for variant HIV-1 protease complexes and show that persistence fingerprint captures binding-relevant structural mutations. PATH - , in turn, uses regression trees over IPCs to differentiate between binding and decoy complexes. Finally, we benchmarked PATH versus established binding affinity prediction algorithms spanning physics-based, knowledge-based, and deep learning methods, revealing that PATH has comparable or better performance with less overfitting, compared to these state-of-the-art methods. The source code for PATH is released open-source as part of the osprey protein design software package.

MERS-CoV-nsp5 expression in human epithelial BEAS 2b cells attenuates type I interferon production by inhibiting IRF3 nuclear translocation

Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is an enveloped, positive-sense RNA virus that emerged in 2012, causing sporadic cases and localized outbreaks of severe respiratory illness with high fatality rates. A characteristic feature of the immune response to MERS-CoV infection is low type I IFN induction, despite its importance in viral clearance. The non-structural proteins (nsps) of other coronaviruses have been shown to block IFN production. However, the role of nsp5 from MERS-CoV in IFN induction of human respiratory cells is unclear. In this study, we elucidated the role of MERS-CoV-nsp5, the viral main protease, in modulating the host's antiviral responses in human bronchial epithelial BEAS 2b cells. We found that overexpression of MERS-CoV-nsp5 had a dose-dependent inhibitory effect on IFN-β promoter activation and cytokine production induced by HMW-poly(I:C). It also suppressed IFN-β promoter activation triggered by overexpression of key components in the RIG-I-like receptor (RLR) pathway, including RIG-I, MAVS, IKK-ε and IRF3. Moreover, the overexpression of MERS-CoV-nsp5 did not impair expression or phosphorylation of IRF3, but suppressed the nuclear translocation of IRF3. Further investigation revealed that MERS-CoV-nsp5 specifically interacted with IRF3. Using docking and molecular dynamic (MD) simulations, we also found that amino acids on MERS-CoV-nsp5, IRF3, and KPNA4 may participate in protein-protein interactions. Additionally, we uncovered protein conformations that mask the nuclear localization signal (NLS) regions of IRF3 and KPNA4 when interacting with MERS-CoV-nsp5, suggesting a mechanism by which this viral protein blocks IRF3 nuclear translocation. Of note, the IFN-β expression was restored after administration of protease inhibitors targeting nsp5, indicating this suppression of IFN-β production was dependent on the enzyme activity of nsp5. Collectively, our findings elucidate a mechanism by which MERS-CoV-nsp5 disrupts the host's innate antiviral immunity and thus provides insights into viral pathogenesis.

The power of computational proteomics platforms to decipher protein-protein interactions

Adopting computational tools for analyzing extensive biological datasets has profoundly transformed our understanding and interpretation of biological phenomena. Innovative platforms have emerged, providing automated analysis to unravel essential insights about proteins and the complexities of their interactions. These computational advancements align with traditional studies, which employ experimental techniques to discern and quantify physical and functional protein-protein interactions (PPIs). Among these techniques, tandem mass spectrometry is notably recognized for its precision and sensitivity in identifying PPIs. These approaches might serve as important information enabling the identification of PPIs with potential pharmacological significance. This review aims to convey our experience using computational tools for detecting PPI networks and offer an analysis of platforms that facilitate predictions derived from experimental data.

Genetically Encoded Microtubule Binders for Single-Cell Interrogation of Cytoskeleton Dynamics and Protein Activity

Microtubule (MT) dynamics is tightly regulated by microtubule-associated proteins (MAPs) and various post-translational modifications (PTMs) of tubulin. Here, we introduce OligoMT and OligoTIP as genetically encoded oligomeric MT binders designed for real-time visualization and manipulation of MT behaviors within living cells. OligoMT acts as a reliable marker to label the MT cytoskeleton, while OligoTIP allows for live monitoring of the growing MT plus-ends. These engineered MT binders have been successfully utilized to label the MT network, monitor cell division, track MT plus-ends, and assess the effect of tubulin acetylation on the MT stability at the single-cell level. Moreover, OligoMT and OligoTIP can be repurposed as biosensors for quantitative assessment of drug actions and for reporting enzymatic activity. Overall, these engineered MT binders hold promise for advancing the mechanistic dissection of MT biology and have translational applications in cell-based high-throughput drug discovery efforts.

Rapid quantitative analysis of soybean protein isolates secondary structure by two-dimensional correlation infrared spectroscopy through pH perturbation

The infrared spectroscopy (IR) signal of protein is prone to being covered by impurity signals, and the accuracy of the secondary structure content calculated using spectral data is poor. To tackle this challenge, a rapid high-precision quantitative model for protein secondary structure was proposed. Firstly, a two-dimensional correlation calculation was performed based on 60 groups of soybean protein isolates (SPI) infrared spectroscopy data, resulting in a two-dimensional correlation infrared spectroscopy (2DCOS-IR). Subsequently, the optimal characteristic bands of the four secondary structures were extracted from the 2DCOS-IR. Ultimately, partial least squares (PLS), long short-term memory (LSTM), and bidirectional long short-term memory (BILSTM) algorithms were used to model the extracted characteristic bands and predict the content of SPI secondary structure. The findings suggested that BILSTM combined with 2DCOS-IR model (2DCOS-BILSTM) exhibited superior predictive performance. The prediction sets for α-helix, β-sheet, β-turn, and random coil were designated as 0.9257, 0.9077, 0.9476, and 0.8443, respectively, and their corresponding RMSEP values were 0.26, 0.48, 0.20, and 0.15. This strategy enhances the precision of IR and facilitates the rapid identification of secondary structure components within SPI, which is vital for the advancement of protein industrial production.

Epitope mapping of SARS-CoV-2 RBDs by hydroxyl radical protein footprinting reveals the importance of including negative antibody controls

Understanding protein-protein interactions is crucial for drug design and investigating biological processes. Various techniques, such as CryoEM, X-ray spectroscopy, linear epitope mapping, and mass spectrometry-based methods, can be employed to map binding regions on proteins. Commonly used mass spectrometry-based techniques are cross-linking and hydrogen‑deuterium exchange (HDX). Another approach, hydroxyl radical protein footprinting (HRPF), identifies binding residues on proteins but faces challenges due to high initial costs and complex setups. This study introduces a generally applicable method using Fenton chemistry for epitope mapping in a standard mass spectrometry laboratory. It emphasizes the importance of controls, particularly the inclusion of a negative antibody control, not widely utilized in HRPF epitope mapping. Quantification by TMT labelling is introduced to reduce false positives, enabling direct comparison between sample conditions and biological triplicates. Additionally, six technical replicates were incorporated to enhance the depth of analysis. Observations on the receptor-binding domain (RBD) of SARS-CoV-2 Spike Protein, Alpha and Delta variants, revealed both binding and opening regions. Significantly changed peptides upon mixing with a negative control antibody suggested structural alterations or nonspecific binding induced by the antibody alone. Integration of negative control antibody experiments and high overlap between biological triplicates led to the exclusion of 40% of significantly changed regions. The final identified binding region correlated with existing literature on neutralizing antibodies against RBD. The presented method offers a straightforward implementation for HRPF analysis in a generic mass spectrometry-based laboratory. Enhanced data reliability was achieved through increased technical and biological replicates alongside negative antibody controls.

Nano-bio convergence unveiled: Systematic review on quantum dots-protein interaction, their implications, and applications

Quantum dots (QDs) are semiconductor nanocrystals (2-10 nm) with unique optical and electronic properties due to quantum confinement effects. They offer high photostability, narrow emission spectra, broad absorption spectrum, and high quantum yields, making them versatile in various applications. Due to their highly reactive surfaces, QDs can conjugate with biomolecules while being used, produced, or unintentionally released into the environment. This systematic review delves into intricate relationship between QDs and proteins, examining their interactions that influence their physicochemical properties, enzymatic activity, ligand binding affinity, and stability. The research utilized electronic databases like PubMed, WOS, and Proquest, along with manual reviews from 2013 to 2023 using relevant keywords, to identify suitable literature. After screening titles and abstracts, only articles meeting inclusion criteria were selected for full text readings. This systematic review of 395 articles identifies 125 articles meeting the inclusion criteria, categorized into five overarching themes, encompassing various mechanisms of QDs and proteins interactions, including adsorption to covalent binding, contingent on physicochemical properties of QDs. Through a meticulous analysis of existing literature, it unravels intricate nature of interaction, significant influence on nanomaterials and biological entities, and potential for synergistic applications harnessing both specific and nonspecific interactions across various fields.

Molecular glues and induced proximity: An evolution of tools and discovery

Small molecule molecular glues can nucleate protein complexes and rewire interactomes. Molecular glues are widely used as probes for understanding functional proximity at a systems level, and the potential to instigate event-driven pharmacology has motivated their application as therapeutics. Despite advantages such as cell permeability and the potential for low off-target activity, glues are still rare when compared to canonical inhibitors in therapeutic development. Their often simple structure and specific ability to reshape protein-protein interactions pose several challenges for widespread, designer applications. Molecular glue discovery and design campaigns can find inspiration from the fields of synthetic biology and biophysics to mine chemical libraries for glue-like molecules.

Microbiome modeling: a beginner's guide

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

A fluorescence-based binding assay for proteins using the cell surface as a sensing platform

Protein-protein interaction (PPI) analysis is very important for elucidating the functions of proteins because many proteins execute their functions in living cells by interacting with one another. In PPI analysis, methods using the sensor chips are widely employed to obtain quantitative data. However, these methods require that the target proteins be immobilized on the sensor chips, and the immobilization processes can affect the binding of the target proteins to their binding partners. In the present work, we propose a PPI analysis system in which the surface of the living cells is utilized as a sensing platform. In our approach, the target protein is displayed on the cell surface by expressing it as a fusion protein with a membrane protein, and the PPI analysis is then conducted by applying its binding partner labeled with a fluorescent dye to the cell surface. We have constructed a model of this binding analysis system using the interaction between biotin protein ligase (BPL) and biotin carboxyl carrier protein (BCCP), where BCCP was displayed on the cell surface and BPL labeled with fluorescein was applied to the cell surface. Here, a red fluorescent protein, mApple, was attached to the C-terminus of the fusion protein of BCCP with a membrane protein. We evaluated the binding level of the labeled BPL by using the intensity ratios of fluorescence from fluorescein to that from mApple. We found that the binding level of the labeled BPL was stably evaluated at least across 60 min observation period and estimated the binding dissociation constant between BPL and BCCP by equilibrium analysis to be 0.33 ± 0.05 μM.

A generic approach based on long-lifetime fluorophores for the assessment of protein binding to polymer nanoparticles by fluorescence anisotropy

Quantitation of protein-nanoparticle interactions is essential for the investigation of the protein corona around NPs in vivo and when using synthetic polymer nanoparticles as affinity reagents for selective protein recognition in vitro. Here, a method based on steady-state fluorescence anisotropy measurement is presented as a novel, separation-free tool for the assessment of protein-nanoparticle interactions. For this purpose, a long-lifetime luminescent Ru-complex is used for protein labelling, which exhibits low anisotropy when conjugated to the protein but displays high anisotropy when the proteins are bound to the much larger polymer nanoparticles. As a proof of concept, the interaction of lysozyme with poly(N-isopropylacrylamide-co-N-tert-butylacrylamide-co-acrylic acid) nanoparticles is studied, and fluorescence anisotropy measurements are used to establish the binding kinetics, binding isotherm and a competitive binding assay.

Applications of machine learning in time-domain fluorescence lifetime imaging: a review

Many medical imaging modalities have benefited from recent advances in Machine Learning (ML), specifically in deep learning, such as neural networks. Computers can be trained to investigate and enhance medical imaging methods without using valuable human resources. In recent years, Fluorescence Lifetime Imaging (FLIm) has received increasing attention from the ML community. FLIm goes beyond conventional spectral imaging, providing additional lifetime information, and could lead to optical histopathology supporting real-time diagnostics. However, most current studies do not use the full potential of machine/deep learning models. As a developing image modality, FLIm data are not easily obtainable, which, coupled with an absence of standardisation, is pushing back the research to develop models which could advance automated diagnosis and help promote FLIm. In this paper, we describe recent developments that improve FLIm image quality, specifically time-domain systems, and we summarise sensing, signal-to-noise analysis and the advances in registration and low-level tracking. We review the two main applications of ML for FLIm: lifetime estimation and image analysis through classification and segmentation. We suggest a course of action to improve the quality of ML studies applied to FLIm. Our final goal is to promote FLIm and attract more ML practitioners to explore the potential of lifetime imaging.

State of the art in protein-protein interactions within the fungi kingdom

Proteins rarely exert their function by themselves. Protein-protein interactions (PPIs) regulate virtually every biological process that takes place in a cell. Such interactions are targets for new therapeutic agents against all sorts of diseases, through the screening and design of a variety of inhibitors. Here we discuss several aspects of PPIs that contribute to prediction of protein function and drug discovery. As the high-throughput techniques continue to release biological data, targets for fungal therapeutics that rely on PPIs are being proposed worldwide. Computational approaches have reduced the time taken to develop new therapeutic approaches. The near future brings the possibility of developing new PPI and interaction network inhibitors and a revolution in the way we treat fungal diseases.

Deep learning-based method for predicting and classifying the binding affinity of protein-protein complexes

Protein-protein interactions (PPIs) play a critical role in various biological processes. Accurately estimating the binding affinity of PPIs is essential for understanding the underlying molecular recognition mechanisms. In this study, we employed a deep learning approach to predict the binding affinity (ΔG) of protein-protein complexes. To this end, we compiled a dataset of 903 protein-protein complexes, each with its corresponding experimental binding affinity, which belong to six functional classes. We extracted 8 to 20 non-redundant features from the sequence information as well as the predicted three-dimensional structures using feature selection methods for each protein functional class. Our method showed an overall mean absolute error of 1.05 kcal/mol and a correlation of 0.79 between experimental and predicted ΔG values. Additionally, we evaluated our model for discriminating high and low affinity protein-protein complexes and it achieved an accuracy of 87% with an F1 score of 0.86 using 10-fold cross-validation on the selected features. Our approach presents an efficient tool for studying PPIs and provides crucial insights into the underlying mechanisms of the molecular recognition process. The web server can be freely accessed at https://web.iitm.ac.in/bioinfo2/DeepPPAPred/index.html.

Identification of binding sites for Tartary buckwheat protein-phenols covalent complex and alterations in protein structure and antioxidant properties

To investigate the effects of structure, multiple binding sites and antioxidant property of Tartary buckwheat protein-phenols covalent complex, protein was combined with different concentrations of phenolic extract. Four kinds of phenols were identified by UPLC-Q/TOF-MS, which were rutin, quercetin, kaempferol and myricetin. UV-vis absorption spectroscopy and X-ray diffraction showed that the phenols can successfully bind to BPI. Fourier-transform infrared, circular dichroism and fluorescence emission spectroscopy showed that the binding of phenol can change the secondary/tertiary structure of protein. The particle distribution indicated that the binding of phenols could reduce the particle size (from 304.70 to 205.55 nm), but cross-linking occurred (435.35 nm) when the bound phenol content was too high. Proteomics showed that only rutin, quercetin and myricetin can covalently bind to BPI. Meanwhile, 4 peptides covalently bound to phenols were identified. The DPPH· scavenging capacity of complexes were from 8.38 to 33.76 %, and the ABTS·⁺ binding activity of complexes were from 19.35 to 63.99 %. The antioxidant activity of the complex was significantly higher than that of the pure protein. These results indicated that protein-phenol covalent complexes had great potential as functional components in the food field.

Medicinal Chemistry Strategies for the Development of Inhibitors Disrupting β-Catenin's Interactions with Its Nuclear Partners

Dysregulation of the Wnt/β-catenin signaling pathway is strongly associated with various aspects of cancer, including tumor initiation, proliferation, and metastasis as well as antitumor immunity, and presents a promising opportunity for cancer therapy. Wnt/β-catenin signaling activation increases nuclear dephosphorylated β-catenin levels, resulting in β-catenin binding to TCF and additional cotranscription factors, such as BCL9, CBP, and p300. Therefore, directly disrupting β-catenin's interactions with these nuclear partners holds promise for the effective and selective suppression of the aberrant activation of Wnt/β-catenin signaling. Herein, we summarize recent advances in biochemical techniques and medicinal chemistry strategies used to identify potent peptide-based and small-molecule inhibitors that directly disrupt β-catenin's interactions with its nuclear binding partners. We discuss the challenges involved in developing drug-like inhibitors that target the interactions of β-catenin and its nuclear binding partner into therapeutic agents.

Applications of fluorescence spectroscopy in protein conformational changes and intermolecular contacts

Emission fluorescence is one of the most versatile and powerful biophysical techniques used in several scientific subjects. It is extensively applied in the studies of proteins, their conformations, and intermolecular contacts, such as in protein-ligand and protein-protein interactions, allowing qualitative, quantitative, and structural data elucidation. This review, aimed to outline some of the most widely used fluorescence techniques in this area, illustrate their applications and display a few examples. At first, the data on the intrinsic fluorescence of proteins is disclosed, mainly on the tryptophan side chain. Predominantly, research to study protein conformational changes, protein interactions, and changes in intensities and shifts of the fluorescence emission maximums were discussed. Fluorescence anisotropy or fluorescence polarization is a measurement of the changing orientation of a molecule in space, concerning the time between the absorption and emission events. Absorption and emission indicate the spatial alignment of the molecule's dipoles relative to the electric vector of the electromagnetic wave of excitation and emitted light, respectively. In other words, if the fluorophore population is excited with vertically polarized light, the emitted light will retain some polarization based on how fast it rotates in solution. Therefore, fluorescence anisotropy can be successfully used in protein-protein interaction investigations. Then, green fluorescent proteins (GFPs), photo-transformable fluorescent proteins (FPs) such as photoswitchable and photoconvertible FPs, and those with Large Stokes Shift (LSS) are disclosed in more detail. FPs are potent tools for the study of biological systems. Their versatility and wide range of colours and properties allow many applications. Finally, the application of fluorescence in life sciences is exposed, especially the application of FPs in fluorescence microscopy techniques with super-resolution that enables precise in vivo photolabeling to monitor the movement and interactions of target proteins.

Machine learning methods for protein-protein binding affinity prediction in protein design

Protein-protein interactions govern a wide range of biological activity. A proper estimation of the protein-protein binding affinity is vital to design proteins with high specificity and binding affinity toward a target protein, which has a variety of applications including antibody design in immunotherapy, enzyme engineering for reaction optimization, and construction of biosensors. However, experimental and theoretical modelling methods are time-consuming, hinder the exploration of the entire protein space, and deter the identification of optimal proteins that meet the requirements of practical applications. In recent years, the rapid development in machine learning methods for protein-protein binding affinity prediction has revealed the potential of a paradigm shift in protein design. Here, we review the prediction methods and associated datasets and discuss the requirements and construction methods of binding affinity prediction models for protein design.

HPIPred: Host-pathogen interactome prediction with phenotypic scoring

Protein-protein interactions (PPIs) are involved in most cellular processes. Unfortunately, current knowledge of host-pathogen interactomes is still very limited. Experimental methods used to detect PPIs have several limitations, including increasing complexity and economic cost in large-scale screenings. Hence, computational methods are commonly used to support experimental data, although they generally suffer from high false-positive rates. To address this issue, we have created HPIPred, a host-pathogen PPI prediction tool based on numerical encoding of physicochemical properties. Unlike other available methods, HPIPred integrates phenotypic data to prioritize biologically meaningful results. We used HPIPred to screen the entire Homo sapiens and Pseudomonas aeruginosa PAO1 proteomes to generate a host-pathogen interactome with 763 interactions displaying a highly connected network topology. Our predictive model can be used to prioritize protein-protein interactions as potential targets for antibacterial drug development. Available at: https://github.com/SysBioUAB/hpi_predictor.

Native Mass Spectrometry-Guided Screening Identifies Hit Fragments for HOP-HSP90 PPI Inhibition

Contemporary medicinal chemistry considers fragment-based drug discovery (FBDD) and inhibition of protein-protein interactions (PPI) as important means of expanding the volume of druggable chemical space. However, the ability to robustly identify valid fragments and PPI inhibitors is an enormous challenge, requiring the application of sensitive biophysical methodology. Accordingly, in this study, we exploited the speed and sensitivity of nanoelectrospray (nano-ESI) native mass spectrometry to identify a small collection of fragments which bind to the TPR2AB domain of HOP. Follow-up biophysical assessment of a small selection of binding fragments confirmed binding to the single TPR2A domain, and that this binding translated into PPI inhibitory activity between TPR2A and the HSP90 C-terminal domain. An in-silico assessment of binding fragments at the PPI interfacial region, provided valuable structural insight for future fragment elaboration strategies, including the identification of losartan as a weak, albeit dose-dependent inhibitor of the target PPI.

Development and application of a novel aldehyde nanoparticle-based amplified luminescent proximity homogeneous assay for rapid quantitation of pancreatic stone protein

BACKGROUND

Timely diagnosis of bacterial infections is important to prevent sepsis. Classical infection biomarkers have some flaws, and common detection methods are time-consuming. Thus, we aimed to establish an efficient detection method that precisely detects pancreatic stone protein (PSP) in human plasma for the timely diagnosis of bacterial infections.

METHODS

Based on the novel amplified luminescent proximity homogeneous assay (AlphaLISA) method, donor and acceptor beads modified with aldehyde groups were directly coupled to the anti-PSP antibodies. PSP was quickly detected by a double-antibody sandwich method. Plasma samples from healthy individuals, bacterially infected patients, and acute-phase response patients were tested.

RESULTS

The detection time of the developed method is only 5 min. The results of PSP-AlphaLISA and time-resolved fluorescence were consistent (ρ = 0.9722). The plasma PSP levels of patients with bacterial infection were significantly higher than those of acute-phase response patients and healthy individuals (P < 0.05). PSP levels in patients with bacterial infection with sepsis were significantly higher than those in patients with bacterial infection without sepsis (P < 0.05).

CONCLUSIONS

The PSP-AlphaLISA exhibited excellent performance and may be applied to the differential diagnosis between bacterial infection and sepsis in patients without interference from patients with acute-phase response.

Protein-protein interaction prediction with deep learning: A comprehensive review

Most proteins perform their biological function by interacting with themselves or other molecules. Thus, one may obtain biological insights into protein functions, disease prevalence, and therapy development by identifying protein-protein interactions (PPI). However, finding the interacting and non-interacting protein pairs through experimental approaches is labour-intensive and time-consuming, owing to the variety of proteins. Hence, protein-protein interaction and protein-ligand binding problems have drawn attention in the fields of bioinformatics and computer-aided drug discovery. Deep learning methods paved the way for scientists to predict the 3-D structure of proteins from genomes, predict the functions and attributes of a protein, and modify and design new proteins to provide desired functions. This review focuses on recent deep learning methods applied to problems including predicting protein functions, protein-protein interaction and their sites, protein-ligand binding, and protein design.

Heterocomplexes between the atypical chemokine MIF and the CXC-motif chemokine CXCL4L1 regulate inflammation and thrombus formation

To fulfil its orchestration of immune cell trafficking, a network of chemokines and receptors developed that capitalizes on specificity, redundancy, and functional selectivity. The discovery of heteromeric interactions in the chemokine interactome has expanded the complexity within this network. Moreover, some inflammatory mediators, not structurally linked to classical chemokines, bind to chemokine receptors and behave as atypical chemokines (ACKs). We identified macrophage migration inhibitory factor (MIF) as an ACK that binds to chemokine receptors CXCR2 and CXCR4 to promote atherogenic leukocyte recruitment. Here, we hypothesized that chemokine–chemokine interactions extend to ACKs and that MIF forms heterocomplexes with classical chemokines. We tested this hypothesis by using an unbiased chemokine protein array. Platelet chemokine CXCL4L1 (but not its variant CXCL4 or the CXCR2/CXCR4 ligands CXCL8 or CXCL12) was identified as a candidate interactor. MIF/CXCL4L1 complexation was verified by co-immunoprecipitation, surface plasmon-resonance analysis, and microscale thermophoresis, also establishing high-affinity binding. We next determined whether heterocomplex formation modulates inflammatory/atherogenic activities of MIF. Complex formation was observed to inhibit MIF-elicited T-cell chemotaxis as assessed by transwell migration assay and in a 3D-matrix-based live cell-imaging set-up. Heterocomplexation also blocked MIF-triggered migration of microglia in cortical cultures in situ, as well as MIF-mediated monocyte adhesion on aortic endothelial cell monolayers under flow stress conditions. Of note, CXCL4L1 blocked binding of Alexa-MIF to a soluble surrogate of CXCR4 and co-incubation with CXCL4L1 attenuated MIF responses in HEK293-CXCR4 transfectants, indicating that complex formation interferes with MIF/CXCR4 pathways. Because MIF and CXCL4L1 are platelet-derived products, we finally tested their role in platelet activation. Multi-photon microscopy, FLIM-FRET, and proximity-ligation assay visualized heterocomplexes in platelet aggregates and in clinical human thrombus sections obtained from peripheral artery disease (PAD) in patients undergoing thrombectomy. Moreover, heterocomplexes inhibited MIF-stimulated thrombus formation under flow and skewed the lamellipodia phenotype of adhering platelets. Our study establishes a novel molecular interaction that adds to the complexity of the chemokine interactome and chemokine/receptor-network. MIF/CXCL4L1, or more generally, ACK/CXC-motif chemokine heterocomplexes may be target structures that can be exploited to modulate inflammation and thrombosis.

Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context

At the heart of the cellular machinery through the regulation of cellular functions, protein-protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.

Advances and Trends in Omics Technology Development

The human history has witnessed the rapid development of technologies such as high-throughput sequencing and mass spectrometry that led to the concept of “omics” and methodological advancement in systematically interrogating a cellular system. Yet, the ever-growing types of molecules and regulatory mechanisms being discovered have been persistently transforming our understandings on the cellular machinery. This renders cell omics seemingly, like the universe, expand with no limit and our goal toward the complete harness of the cellular system merely impossible. Therefore, it is imperative to review what has been done and is being done to predict what can be done toward the translation of omics information to disease control with minimal cell perturbation. With a focus on the “four big omics,” i.e., genomics, transcriptomics, proteomics, metabolomics, we delineate hierarchies of these omics together with their epiomics and interactomics, and review technologies developed for interrogation. We predict, among others, redoxomics as an emerging omics layer that views cell decision toward the physiological or pathological state as a fine-tuned redox balance.

Protein-Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation

Here, we present a Gaussian-based method for estimation of protein-protein binding entropy to augment the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) method for computational prediction of binding free energy (ΔG). The method is termed f5-MM/PBSA/E, where "E" stands for entropy and f5 for five adjustable parameters. The enthalpy components of ΔG (molecular mechanics, polar and non-polar solvation energies) are computed from a single implicit solvent generalized Born (GB) energy minimized structure of a protein-protein complex, while the binding entropy is computed using independently GB energy minimized unbound and bound structures. It should be emphasized that the f5-MM/PBSA/E method does not use snapshots, just energy minimized structures, and is thus very fast and computationally efficient. The method is trained and benchmarked in 5-fold validation test over a data set consisting of 46 protein-protein binding cases with experimentally determined dissociation constant K _d values. This data set has been used for benchmarking in recently published protein-protein binding studies that apply conventional MM/PBSA and MM/PBSA with an enhanced sampling method. The f5-MM/PBSA/E tested on the same data set achieves similar or better performance than these computationally demanding approaches, making it an excellent choice for high throughput protein-protein binding affinity prediction studies.

Modeling the Dynamics of Protein-Protein Interfaces, How and Why?

Protein-protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein-protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein-protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein-protein interface.

TRScore: a three-dimensional RepVGG-based scoring method for ranking protein docking models

MOTIVATION

Protein-protein interactions (PPI) play important roles in cellular activities. Due to the technical difficulty and high cost of experimental methods, there are considerable interests towards the development of computational approaches, such as protein docking, to decipher PPI patterns. One of the important and difficult aspects in protein docking is recognizing near-native conformations from a set of decoys, but unfortunately traditional scoring functions still suffer from limited accuracy. Therefore, new scoring methods are pressingly needed in methodological and/or practical implications.

RESULTS

We present a new deep learning-based scoring method for ranking protein-protein docking models based on a three-dimensional (3D) RepVGG network, named TRScore. To recognize near-native conformations from a set of decoys, TRScore voxelizes the protein-protein interface into a 3D grid labeled by the number of atoms in different physicochemical classes. Benefiting from the deep convolutional RepVGG architecture, TRScore can effectively capture the subtle differences between energetically favorable near-native models and unfavorable non-native decoys without needing extra information. TRScore was extensively evaluated on diverse test sets including protein-protein docking benchmark 5.0 update set, DockGround decoy set, as well as realistic CAPRI decoy set, and overall obtained a significant improvement over existing methods in cross validation and independent evaluations.

AVAILABILITY

Codes available at: https://github.com/BioinformaticsCSU/TRScore.

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

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

Insights into ultra-low affinity lipase-antibody noncovalent complex binding mechanisms

Detection of host cell protein (HCP) impurities is critical to ensuring that recombinant drug products, including monoclonal antibodies (mAbs), are safe. Mechanistic characterization as to how HCPs persist in drug products is important to refining downstream processing. It has been hypothesized that weak lipase-mAb interactions enable HCP lipases to evade drug purification processes. Here, we apply state-of-the-art methods to establish lipase-mAb binding mechanisms. First, the mass spectrometry (MS) approach of fast photochemical oxidation of proteins was used to elucidate putative binding regions. The CH1 domain was identified as a conserved interaction site for IgG1 and IgG4 mAbs against the HCPs phospholipase B-like protein (PLBL2) and lysosomal phospholipase A2 (LPLA2). Rationally designed mutations in the CH1 domain of the IgG4 mAb caused a 3- to 70-fold K_D reduction against PLBL2 by surface plasmon resonance (SPR). LPLA2-IgG4 mutant complexes, undetected by SPR and studied using native MS collisional dissociation experiments, also showed significant complex disruption, from 16% to 100%. Native MS and ion mobility (IM) determined complex stoichiometries for four lipase-IgG4 complexes and directly interrogated the enrichment of specific lipase glycoforms. Confirmed with time-course and exoglycosidase experiments, deglycosylated lipases prevented binding, and low-molecular-weight glycoforms promoted binding, to mAbs. This work demonstrates the value of integrated biophysical approaches to characterize micromolar affinity complexes. It is the first in-depth structural report of lipase-mAb binding, finding roles for the CH1 domain and lipase glycosylation in mediating binding. The structural insights gained offer new approaches for the bioengineering of cells or mAbs to reduce HCP impurity levels.Abbreviations: CAN, Acetonitrile; AMAC, Ammonium acetate; BFGS, Broyden-Fletcher-Goldfarb-Shanno; CHO, Chinese Hamster Ovary; K_D, Dissociation constant; DTT, Dithiothreitol; ELISA, Enzyme-linked immunosorbent assay; FPOP, Fast photochemical oxidation of proteins; FA, Formic acid; F(ab'), Fragment antibodies; HCP, Host cell protein; IgG, Immunoglobulin; IM, Ion mobility; LOD, Lower limit of detection; LPLA2, Lysosomal phospholipase A2; Man, Mannose; MS, Mass spectrometry; MeOH, Methanol; MST, Microscale thermophoresis; mAbs, Monoclonal antibodies; PPT1, Palmitoyl protein thioesterase; ppm, Parts per million; PLBL2, Phospholipase B-like protein; PLD3, Phospholipase D3; PS-20, Polysorbate-20; SP, Sphingomyelin phosphodiesterase; SPR, Surface plasmon resonance; TFA, Trifluoroacetic acid.

Epitope alteration by small molecules and applications in drug discovery

Small molecules and antibodies are normally considered separately in drug discovery, except in the case of covalent conjugates. We unexpectedly discovered several small molecules that could inhibit or enhance antibody–epitope interactions which opens new possibilities in drug discovery and therapeutic modulation of auto-antibodies. We first discovered a small molecule, CRANAD-17, that enhanced the binding of an antibody to amyloid beta (Aβ), one of the major hallmarks of Alzheimer's disease, by stable triplex formation. Next, we found several small molecules that altered antibody–epitope interactions of tau and PD-L1 proteins, demonstrating the generality of this phenomenon. We report a new screening technology for ligand discovery, screening platform based on epitope alteration for drug discovery (SPEED), which is label-free for both the antibody and small molecule. SPEED, applied to an Aβ antibody, led to the discovery of a small molecule, GNF5837, that inhibits Aβ aggregation and another, obatoclax, that binds Aβ plaques and can serve as a fluorescent reporter in brain slices of AD mice. We also found a small molecule that altered the binding between Aβ and auto-antibodies from AD patient serum. SPEED reveals the sensitivity of antibody–epitope interactions to perturbation by small molecules and will have multiple applications in biotechnology and drug discovery.

A screening platform based on epitope alteration for drug discovery (SPEED).

Homodimeric and Heterodimeric Interactions among Vertebrate Basic Helix-Loop-Helix Transcription Factors

The basic helix–loop–helix transcription factor (bHLH TF) family is involved in tissue development, cell differentiation, and disease. These factors have transcriptionally positive, negative, and inactive functions by combining dimeric interactions among family members. The best known bHLH TFs are the E-protein homodimers and heterodimers with the tissue-specific TFs or ID proteins. These cooperative and dynamic interactions result in a complex transcriptional network that helps define the cell’s fate. Here, the reported dimeric interactions of 67 vertebrate bHLH TFs with other family members are summarized in tables, including specifications of the experimental techniques that defined the dimers. The compilation of these extensive data underscores homodimers of tissue-specific bHLH TFs as a central part of the bHLH regulatory network, with relevant positive and negative transcriptional regulatory roles. Furthermore, some sequence-specific TFs can also form transcriptionally inactive heterodimers with each other. The function, classification, and developmental role for all vertebrate bHLH TFs in four major classes are detailed.

Ablation of mitochondrial DNA results in widespread remodeling of the mitochondrial complexome

So-called ρ0 cells lack mitochondrial DNA and are therefore incapable of aerobic ATP synthesis. How cells adapt to survive ablation of oxidative phosphorylation remains poorly understood. Complexome profiling analysis of ρ0 cells covered 1,002 mitochondrial proteins and revealed changes in abundance and organization of numerous multiprotein complexes including previously not described assemblies. Beyond multiple subassemblies of complexes that would normally contain components encoded by mitochondrial DNA, we observed widespread reorganization of the complexome. This included distinct changes in the expression pattern of adenine nucleotide carrier isoforms, other mitochondrial transporters, and components of the protein import machinery. Remarkably, ablation of mitochondrial DNA hardly affected the complexes organizing cristae junctions indicating that the altered cristae morphology in ρ0 mitochondria predominantly resulted from the loss of complex V dimers required to impose narrow curvatures to the inner membrane. Our data provide a comprehensive resource for in-depth analysis of remodeling of the mitochondrial complexome in response to respiratory deficiency.

Quantitative FRET (qFRET) Technology for the Determination of Protein-Protein Interaction Affinity in Solution

Protein-protein interactions play pivotal roles in life, and the protein interaction affinity confers specific protein interaction events in physiology or pathology. Förster resonance energy transfer (FRET) has been widely used in biological and biomedical research to detect molecular interactions in vitro and in vivo. The FRET assay provides very high sensitivity and efficiency. Several attempts have been made to develop the FRET assay into a quantitative measurement for protein-protein interaction affinity in the past. However, the progress has been slow due to complicated procedures or because of challenges in differentiating the FRET signal from other direct emission signals from donor and receptor. This review focuses on recent developments of the quantitative FRET analysis and its application in the determination of protein-protein interaction affinity (K_D), either through FRET acceptor emission or donor quenching methods. This paper mainly reviews novel theatrical developments and experimental procedures rather than specific experimental results. The FRET-based approach for protein interaction affinity determination provides several advantages, including high sensitivity, high accuracy, low cost, and high-throughput assay. The FRET-based methodology holds excellent potential for those difficult-to-be expressed proteins and for protein interactions in living cells.

Stable and Temporary Enzyme Complexes and Metabolons Involved in Energy and Redox Metabolism

Significance: Alongside well-characterized permanent multimeric enzymes and multienzyme complexes, relatively unstable transient enzyme-enzyme assemblies, including metabolons, provide an important mechanism for the regulation of energy and redox metabolism. Critical Issues: Despite the fact that enzyme-enzyme assemblies have been proposed for many decades and experimentally analyzed for at least 40 years, there are very few pathways for which unequivocal evidence for the presence of metabolite channeling, the most frequently evoked reason for their formation, has been provided. Further, in contrast to the stronger, permanent interactions for which a deep understanding of the subunit interface exists, the mechanism(s) underlying transient enzyme-enzyme interactions remain poorly studied. Recent Advances: The widespread adoption of proteomic and cell biological approaches to characterize protein-protein interaction is defining an ever-increasing number of enzyme-enzyme assemblies as well as enzyme-protein interactions that likely identify factors which stabilize such complexes. Moreover, the use of microfluidic technologies provided compelling support of a role for substrate-specific chemotaxis in complex assemblies. Future Directions: Embracing current and developing technologies should render the delineation of metabolons from other enzyme-enzyme complexes more facile. In parallel, attempts to confirm that the findings reported in microfluidic systems are, indeed, representative of the cellular situation will be critical to understanding the physiological circumstances requiring and evoking dynamic changes in the levels of the various transient enzyme-enzyme assemblies of the cell. Antioxid. Redox Signal. 35, 788-807.

A bimolecular fluorescence complementation flow cytometry screen for membrane protein interactions

Interactions between membrane proteins within a cellular environment are crucial for all living cells. Robust methods to screen and analyse membrane protein complexes are essential to shed light on the molecular mechanism of membrane protein interactions. Most methods for detecting protein:protein interactions (PPIs) have been developed to target the interactions of soluble proteins. Bimolecular fluorescence complementation (BiFC) assays allow the formation of complexes involving PPI partners to be visualized in vivo, irrespective of whether or not these interactions are between soluble or membrane proteins. In this study, we report the development of a screening approach which utilizes BiFC and applies flow cytometry to characterize membrane protein interaction partners in the host Saccharomyces cerevisiae. These data allow constructive complexes to be discriminated with statistical confidence from random interactions and potentially allows an efficient screen for PPIs in vivo within a high-throughput setup.

EndoBind detects endogenous protein-protein interactions in real time

We present two high-throughput compatible methods to detect the interaction of ectopically expressed (RT-Bind) or endogenously tagged (EndoBind) proteins of interest. Both approaches provide temporal evaluation of dimer formation over an extended duration. Using examples of the Nrf2-KEAP1 and the CRAF-KRAS-G12V interaction, we demonstrate that our method allows for the detection of signal for more than 2 days after substrate addition, allowing for continuous monitoring of endogenous protein-protein interactions in real time.

Bill et al describe two high-throughput methods to detect protein-protein interactions in cells in real-time using the split-NanoLuciferase-complementation system. They demonstrate the methods can detect exogenously (RT-bind) or endogenously (EndoBind) expressed proteins, respectively.

Label-free methods for optical in vitro characterization of protein-protein interactions

Protein-protein interactions are involved in the regulation and function of the majority of cellular processes. As a result, much effort has been aimed at the development of methodologies capable of quantifying protein-protein interactions, with label-free methods being of particular interest due to the associated simplified workflows and minimisation of label-induced perturbations. Here, we review recent advances in optical technologies providing label-free in vitro measurements of affinities and kinetics. We provide an overview and comparison of existing techniques and their principles, discussing advantages, limitations, and recent applications.

Assembling Disease Networks From Causal Interaction Resources

The development of high-throughput high-content technologies and the increased ease in their application in clinical settings has raised the expectation of an important impact of these technologies on diagnosis and personalized therapy. Patient genomic and expression profiles yield lists of genes that are mutated or whose expression is modulated in specific disease conditions. The challenge remains of extracting from these lists functional information that may help to shed light on the mechanisms that are perturbed in the disease, thus setting a rational framework that may help clinical decisions. Network approaches are playing an increasing role in the organization and interpretation of patients' data. Biological networks are generated by connecting genes or gene products according to experimental evidence that demonstrates their interactions. Till recently most approaches have relied on networks based on physical interactions between proteins. Such networks miss an important piece of information as they lack details on the functional consequences of the interactions. Over the past few years, a number of resources have started collecting causal information of the type protein A activates/inactivates protein B, in a structured format. This information may be represented as signed directed graphs where physiological and pathological signaling can be conveniently inspected. In this review we will (i) present and compare these resources and discuss the different scope in comparison with pathway resources; (ii) compare resources that explicitly capture causality in terms of data content and proteome coverage (iii) review how causal-graphs can be used to extract disease-specific Boolean networks.

Evaluating the Efficiency of the Martini Force Field to Study Protein Dimerization in Aqueous and Membrane Environments

Protein-protein complex assembly is one of the major drivers of biological response. Understanding the mechanisms of protein oligomerization/dimerization would allow one to elucidate how these complexes participate in biological activities and could ultimately lead to new approaches in designing novel therapeutic agents. However, determining the exact association pathways and structures of such complexes remains a challenge. Here, we use parallel tempering metadynamics simulations in the well-tempered ensemble to evaluate the performance of Martini 2.2P and Martini open-beta 3 (Martini 3) force fields in reproducing the structure and energetics of the dimerization process of membrane proteins and proteins in an aqueous solution in reasonable accuracy and throughput. We find that Martini 2.2P systematically overestimates the free energy of association by estimating large barriers in distinct areas, which likely leads to overaggregation when multiple monomers are present. In comparison, the less viscous Martini 3 results in a systematic underestimation of the free energy of association for proteins in solution, while it performs well in describing the association of membrane proteins. In all cases, the near-native dimer complexes are identified as minima in the free energy surface albeit not always as the lowest minima. In the case of Martini 3, we find that the spurious supramolecular protein aggregation present in Martini 2.2P multimer simulations is alleviated and thus this force field may be more suitable for the study of protein oligomerization. We propose that the use of enhanced sampling simulations with a refined coarse-grained force field and appropriately defined collective variables is a robust approach for studying the protein dimerization process, although one should be cautious of the ranking of energy minima.

Interfaces with Structure Dynamics of the Workhorses from Cells Revealed through Cross-Linking Mass Spectrometry (CLMS)

The fundamentals of how protein-protein/RNA/DNA interactions influence the structures and functions of the workhorses from the cells have been well documented in the 20th century. A diverse set of methods exist to determine such interactions between different components, particularly, the mass spectrometry (MS) methods, with its advanced instrumentation, has become a significant approach to analyze a diverse range of biomolecules, as well as bring insights to their biomolecular processes. This review highlights the principal role of chemistry in MS-based structural proteomics approaches, with a particular focus on the chemical cross-linking of protein-protein/DNA/RNA complexes. In addition, we discuss different methods to prepare the cross-linked samples for MS analysis and tools to identify cross-linked peptides. Cross-linking mass spectrometry (CLMS) holds promise to identify interaction sites in larger and more complex biological systems. The typical CLMS workflow allows for the measurement of the proximity in three-dimensional space of amino acids, identifying proteins in direct contact with DNA or RNA, and it provides information on the folds of proteins as well as their topology in the complexes. Principal CLMS applications, its notable successes, as well as common pipelines that bridge proteomics, molecular biology, structural systems biology, and interactomics are outlined.