Investigation of stable and transient protein-protein interactions: Past, present, and future

Searching for EGF Fragments Recreating the Outer Sphere of the Growth Factor Involved in Receptor Interactions

The aims of this study were to determine whether it is possible to use peptide microarrays obtained using the SPOT technique (immobilized on cellulose) and specific polyclonal antibodies to select fragments that reconstruct the outer sphere of proteins and to ascertain whether the selected peptide fragments can be useful in the study of their protein-protein and/or peptide-protein interactions. Using this approach, epidermal growth factor (EGF) fragments responsible for the interaction with the EGF receptor were searched. A library of EGF fragments immobilized on cellulose was obtained using triazine condensing reagents. Experiments on the interactions with EGFR confirmed the high affinity of the selected peptide fragments. Biological tests on cells showed the lack of cytotoxicity of the EGF fragments. Selected EGF fragments can be used in various areas of medicine.

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.

Field-Flow Fractionation in Molecular Biology and Biotechnology

Field-flow fractionation (FFF) is a family of single-phase separative techniques exploited to gently separate and characterize nano- and microsystems in suspension. These techniques cover an extremely wide dynamic range and are able to separate analytes in an interval between a few nm to 100 µm size-wise (over 15 orders of magnitude mass-wise). They are flexible in terms of mobile phase and can separate the analytes in native conditions, preserving their original structures/properties as much as possible. Molecular biology is the branch of biology that studies the molecular basis of biological activity, while biotechnology deals with the technological applications of biology. The areas where biotechnologies are required include industrial, agri-food, environmental, and pharmaceutical. Many species of biological interest belong to the operational range of FFF techniques, and their application to the analysis of such samples has steadily grown in the last 30 years. This work aims to summarize the main features, milestones, and results provided by the application of FFF in the field of molecular biology and biotechnology, with a focus on the years from 2000 to 2022. After a theoretical background overview of FFF and its methodologies, the results are reported based on the nature of the samples analyzed.

Prediction of transient and permanent protein interactions using AI methods

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

Protein-protein interactions in plant antioxidant defense

The regulation of reactive oxygen species (ROS) levels in plants is ensured by mechanisms preventing their over accumulation, and by diverse antioxidants, including enzymes and nonenzymatic compounds. These are affected by redox conditions, posttranslational modifications, transcriptional and posttranscriptional modifications, Ca²⁺, nitric oxide (NO) and mitogen-activated protein kinase signaling pathways. Recent knowledge about protein-protein interactions (PPIs) of antioxidant enzymes advanced during last decade. The best-known examples are interactions mediated by redox buffering proteins such as thioredoxins and glutaredoxins. This review summarizes interactions of major antioxidant enzymes with regulatory and signaling proteins and their diverse functions. Such interactions are important for stability, degradation and activation of interacting partners. Moreover, PPIs of antioxidant enzymes may connect diverse metabolic processes with ROS scavenging. Proteins like receptor for activated C kinase 1 may ensure coordination of antioxidant enzymes to ensure efficient ROS regulation. Nevertheless, PPIs in antioxidant defense are understudied, and intensive research is required to define their role in complex regulation of ROS scavenging.

Dealing with missing values in proteomics data

Proteomics data are often plagued with missingness issues. These missing values (MVs) threaten the integrity of subsequent statistical analyses by reduction of statistical power, introduction of bias, and failure to represent the true sample. Over the years, several categories of missing value imputation (MVI) methods have been developed and adapted for proteomics data. These MVI methods perform their tasks based on different prior assumptions (e.g., data is normally or independently distributed) and operating principles (e.g., the algorithm is built to address random missingness only), resulting in varying levels of performance even when dealing with the same dataset. Thus, to achieve a satisfactory outcome, a suitable MVI method must be selected. To guide decision making on suitable MVI method, we provide a decision chart which facilitates strategic considerations on datasets presenting different characteristics. We also bring attention to other issues that can impact proper MVI such as the presence of confounders (e.g., batch effects) which can influence MVI performance. Thus, these too, should be considered during or before MVI.

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.

Microscale Thermophoresis as a Tool to Study Protein Interactions and Their Implication in Human Diseases

The review highlights how protein–protein interactions (PPIs) have determining roles in most life processes and how interactions between protein partners are involved in various human diseases. The study of PPIs and binding interactions as well as their understanding, quantification and pharmacological regulation are crucial for therapeutic purposes. Diverse computational and analytical methods, combined with high-throughput screening (HTS), have been extensively used to characterize multiple types of PPIs, but these procedures are generally laborious, long and expensive. Rapid, robust and efficient alternative methods are proposed, including the use of Microscale Thermophoresis (MST), which has emerged as the technology of choice in drug discovery programs in recent years. This review summarizes selected case studies pertaining to the use of MST to detect therapeutically pertinent proteins and highlights the biological importance of binding interactions, implicated in various human diseases. The benefits and limitations of MST to study PPIs and to identify regulators are discussed.

Real-time, single-molecule observation of biomolecular interactions inside nanophotonic zero mode waveguides

Living cells rely on numerous protein-protein, RNA-protein and DNA-protein interactions for processes such as gene expression, biomolecular assembly, protein and RNA degradation. Single-molecule microscopy and spectroscopy are ideal tools for real-time observation and quantification of nucleic acids-protein and protein-protein interactions. One of the major drawbacks of conventional single-molecule imaging methods is low throughput. Methods such as sequencing by synthesis utilizing nanofabrication and single-molecule spectroscopy have brought high throughput into the realm of single-molecule biology. The Pacific Biosciences RS2 sequencer utilizes sequencing by synthesis within nanophotonic zero mode waveguides. A number of years ago this instrument was unlocked by Pacific Biosciences for custom use by researchers allowing them to monitor biological interactions at the single-molecule level with high throughput. In this capability letter we demonstrate the use of the RS2 sequencer for real-time observation of DNA-to-RNA transcription and RNA-protein interactions. We use a relatively complex model-transcription of structured ribosomal RNA fromE. coliand interactions of ribosomal RNA with ribosomal proteins. We also show evidence of observation of transcriptional pausing without the application of an external force (as is required for single-molecule pausing studies using optical traps). Overall, in the unlocked, custom mode, the RS2 sequencer can be used to address a wide variety of biological assembly and interaction questions at the single-molecule level with high throughput. This instrument is available for use at the Center for Integrated Nanotechnologies Gateway located at Los Alamos National Laboratory.

Screening of Self-Assembling of Collagen IV Fragments into Stable Structures Potentially Useful in Regenerative Medicine

The aim of the research was to check whether it is possible to use fragments of type IV collagen to obtain, as a result of self-assembling, stable spatial structures that could be used to prepare new materials useful in regenerative medicine. Collagen IV fragments were obtained by using DMT/NMM/TosO- as a coupling reagent. The ability to self-organize and form stable spatial structures was tested by the CD method and microscopic techniques. Biological studies covered: resazurin assay (cytotoxicity assessment) on BJ, BJ-5TA and C2C12 cell lines; an alkaline version of the comet assay (genotoxicity), Biolegend Legendplex human inflammation panel 1 assay (SC cell lines, assessment of the inflammation activity) and MTT test to determine the cytotoxicity of the porous materials based on collagen IV fragments. It was found that out of the pool of 37 fragments (peptides 1-33 and 2.1-2.4) reconstructing the outer sphere of collagen IV, nine fragments (peptides: 2, 4, 5, 6, 14, 15, 25, 26 and 30), as a result of self-assembling, form structures mimicking the structure of the triple helix of native collagens. The stability of spatial structures formed as a result of self-organization at temperatures of 4 °C, 20 °C, and 40 °C was found. The application of the MST method allowed us to determine the Kd of binding of selected fragments of collagen IV to ITGα1β1. The stability of the spatial structures of selected peptides made it possible to obtain porous materials based on their equimolar mixture. The formation of the porous materials was found for cross-linked structures and the material stabilized only by weak interactions. All tested peptides are non-cytotoxic against all tested cell lines. Selected peptides also showed no genotoxicity and no induction of immune system responses. Research on the use of porous materials based on fragments of type IV collagen, able to form stable spatial structures as scaffolds useful in regenerative medicine, will be continued.

Unusual Pseudopeptides: Syntheses and Structural Analyses of Ethylenediprolyl Peptides and Their Metal Complexes with Cu(II) Ion

The synthetic unnatural amino acids and their peptides as peptidomimetics have shown remarkable structural and functional properties. In the repertoire of synthetic peptides, pseudopeptides have emerged as attractive small peptidomimetics that are capable of forming the characteristic secondary structures in the solid/solution phase, as in natural peptides. This report describes the synthesis and structural analyses of novel pseudopeptides as ethylenediprolyl (etpro) tetra/hexapeptides, comprising a chiral diaminedicarboxylate scaffold. Their NMR and CD spectral analyses strongly support the formation of the β-turn-type structures in organic solvents (ACN/MeOH). Further, the single-crystal X-ray studies of tetrapseudopeptide confirm the formation of a unique self-assembly structure as β-strand type in the solid state through hydrogen bonding. Importantly, their diamine moiety influences the formation of Cu-complexes with Cu(II) ions. A tetrapseudopeptide monocarboxylate-Cu(II) complex forms the single crystal that is studied by the single-crystal X-ray diffractometer. The crystal structure of the tetrapseudopeptide-Cu(II) complex confirms the formation of the distorted square planar geometry structure, almost like the amyloid β(Aβ)-peptide-Cu(II) complex structural geometry. Hence, these etpro-pseudopeptides are emerging peptidomimatics that form β-turn types of structures and metal complexes mainly with Cu(II) ions. These molecules could be considered for the development of peptide-based catalysts and peptide-based therapeutic drug candidates.

The Potential Role of Human NME1 in Neuronal Differentiation of Porcine Mesenchymal Stem Cells: Application of NB-hNME1 as a Human NME1 Suppressor

This study aimed to investigate the effects of the human macrophage (MP) secretome in cellular xenograft rejection. The role of human nucleoside diphosphate kinase A (hNME1), from the secretome of MPs involved in the neuronal differentiation of miniature pig adipose tissue-derived mesenchymal stem cells (mp AD-MSCs), was evaluated by proteomic analysis. Herein, we first demonstrate that hNME1 strongly binds to porcine ST8 alpha-N-acetyl-neuraminide alpha-2,8-sialyltransferase 1 (pST8SIA1), which is a ganglioside GD3 synthase. When hNME1 binds with pST8SIA1, it induces degradation of pST8SIA1 in mp AD-MSCs, thereby inhibiting the expression of ganglioside GD3 followed by decreased neuronal differentiation of mp AD-MSCs. Therefore, we produced nanobodies (NBs) named NB-hNME1 that bind to hNME1 specifically, and the inhibitory effect of NB-hNME1 was evaluated for blocking the binding between hNME1 and pST8SIA1. Consequently, NB-hNME1 effectively blocked the binding of hNME1 to pST8SIA1, thereby recovering the expression of ganglioside GD3 and neuronal differentiation of mp AD-MSCs. Our findings suggest that mp AD-MSCs could be a potential candidate for use as an additive, such as an immunosuppressant, in stem cell transplantation.

Recent progress in mass spectrometry-based strategies for elucidating protein-protein interactions

Protein-protein interactions are fundamental to various aspects of cell biology with many protein complexes participating in numerous fundamental biological processes such as transcription, translation and cell cycle. MS-based proteomics techniques are routinely applied for characterising the interactome, such as affinity purification coupled to mass spectrometry that has been used to selectively enrich and identify interacting partners of a bait protein. In recent years, many orthogonal MS-based techniques and approaches have surfaced including proximity-dependent labelling of neighbouring proteins, chemical cross-linking of two interacting proteins, as well as inferring PPIs from the co-behaviour of proteins such as the co-fractionating profiles and the thermal solubility profiles of proteins. This review discusses the underlying principles, advantages, limitations and experimental considerations of these emerging techniques. In addition, a brief account on how MS-based techniques are used to investigate the structural and functional properties of protein complexes, including their topology, stoichiometry, copy number and dynamics, are discussed.

Exploring the Binding Interaction of Raf Kinase Inhibitory Protein With the N-Terminal of C-Raf Through Molecular Docking and Molecular Dynamics Simulation

Protein-protein interactions are indispensable physiological processes regulating several biological functions. Despite the availability of structural information on protein-protein complexes, deciphering their complex topology remains an outstanding challenge. Raf kinase inhibitory protein (RKIP) has gained substantial attention as a favorable molecular target for numerous pathologies including cancer and Alzheimer’s disease. RKIP interferes with the RAF/MEK/ERK signaling cascade by endogenously binding with C-Raf (Raf-1 kinase) and preventing its activation. In the current investigation, the binding of RKIP with C-Raf was explored by knowledge-based protein-protein docking web-servers including HADDOCK and ZDOCK and a consensus binding mode of C-Raf/RKIP structural complex was obtained. Molecular dynamics (MD) simulations were further performed in an explicit solvent to sample the conformations for when RKIP binds to C-Raf. Some of the conserved interface residues were mutated to alanine, phenylalanine and leucine and the impact of mutations was estimated by additional MD simulations and MM/PBSA analysis for the wild-type (WT) and constructed mutant complexes. Substantial decrease in binding free energy was observed for the mutant complexes as compared to the binding free energy of WT C-Raf/RKIP structural complex. Furthermore, a considerable increase in average backbone root mean square deviation and fluctuation was perceived for the mutant complexes. Moreover, per-residue energy contribution analysis of the equilibrated simulation trajectory by HawkDock and ANCHOR web-servers was conducted to characterize the key residues for the complex formation. One residue each from C-Raf (Arg398) and RKIP (Lys80) were identified as the druggable “hot spots” constituting the core of the binding interface and corroborated by additional long-time scale (300 ns) MD simulation of Arg398Ala mutant complex. A notable conformational change in Arg398Ala mutant occurred near the mutation site as compared to the equilibrated C-Raf/RKIP native state conformation and an essential hydrogen bonding interaction was lost. The thirteen binding sites assimilated from the overall analysis were mapped onto the complex as surface and divided into active and allosteric binding sites, depending on their location at the interface. The acquired information on the predicted 3D structural complex and the detected sites aid as promising targets in designing novel inhibitors to block the C-Raf/RKIP interaction.

An in vivo Interaction Network of DNA-Repair Proteins: A Snapshot at Double Strand Break Repair in Deinococcus radiodurans

An extremophile Deinococcus radiodurans survives massive DNA damage by efficiently mending hundreds of double strand breaks through homology-dependent DNA repair pathways. Although DNA repair proteins that contribute to its impressive DNA repair capacity are fairly known, interactions among them or with proteins related to other relevant pathways remain unexplored. Here, we report in vivo cross-linking of the interactomes of key DNA repair proteins DdrA, DdrB, RecA, and Ssb (baits) in D. radiodurans cells recovering from gamma irradiation. The protein-protein interactions were systematically investigated through co-immunoprecipitation experiments coupled to mass spectrometry. From a total of 399 proteins co-eluted with the baits, we recovered interactions among diverse biological pathways such as DNA repair, transcription, translation, chromosome partitioning, cell division, antioxidation, protein folding/turnover, metabolism, cell wall architecture, membrane transporters, and uncharacterized proteins. Among these, about 80 proteins were relevant to the DNA damage resistance of the organism based on integration of data on inducible expression following DNA damage, radiation sensitive phenotype of deletion mutant, etc. Further, we cloned ORFs of 23 interactors in heterologous E. coli and expressed corresponding proteins with N-terminal His-tag, which were used for pull-down assays. A total of 95 interactions were assayed, in which we confirmed 25 previously unknown binary interactions between the proteins associated with radiation resistance, and 2 known interactions between DdrB and Ssb or DR_1245. Among these, five interactions were positive even under non-stress conditions. The confirmed interactions cover a wide range of biological processes such as DNA repair, negative regulation of cell division, chromosome partitioning, membrane anchorage, etc., and their functional relevance is discussed from the perspective of DNA repair. Overall, the study substantially advances our understanding on the cross-talk between different homology-dependent DNA repair pathways and other relevant biological processes that essentially contribute to the extraordinary DNA damage repair capability of D. radiodurans. The data sets generated and analyzed in this study have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD021822.

The Mitochondrial Proteome of Tumor Cells: A SnapShot on Methodological Approaches and New Biomarkers

Simple Summary

Mitochondria are central hubs of cellular signaling, energy metabolism, and redox balance. The plasticity of these cellular organelles is an essential requisite for the cells to cope with different stimuli and stress conditions. Cancer cells are characterized by changes in energy metabolism, mitochondrial signaling, and dynamics. These changes are driven by alterations in the mitochondrial proteome. For this reason, in the last years a focus of basic and cancer research has been the implementation and optimization of technologies to investigate changes in the mitochondrial proteome during cancer initiation and progression. This review presents an overview of the most used technologies to investigate the mitochondrial proteome and recent evidence on changes in the expression levels and delocalization of certain proteins in and out the mitochondria for shaping the functional properties of tumor cells.

Abstract

Mitochondria are highly dynamic and regulated organelles implicated in a variety of important functions in the cell, including energy production, fatty acid metabolism, iron homeostasis, programmed cell death, and cell signaling. Changes in mitochondrial metabolism, signaling and dynamics are hallmarks of cancer. Understanding whether these modifications are associated with alterations of the mitochondrial proteome is particularly relevant from a translational point of view because it may contribute to better understanding the molecular bases of cancer development and progression and may provide new potential prognostic and diagnostic biomarkers as well as novel molecular targets for anti-cancer treatment. Making an inventory of the mitochondrial proteins has been particularly challenging given that there is no unique consensus targeting sequence that directs protein import into mitochondria, some proteins are present at very low levels, while other proteins are expressed only in some cell types, in a particular developmental stage or under specific stress conditions. This review aims at providing the state-of-the-art on methodologies used to characterize the mitochondrial proteome in tumors and highlighting the biological relevance of changes in expression and delocalization of proteins in and out the mitochondria in cancer biology.

Membrane Proteocomplexome of Campylobacter jejuni Using 2-D Blue Native/SDS-PAGE Combined to Bioinformatics Analysis

Campylobacter is the leading cause of the human bacterial foodborne infections in the developed countries. The perception cues from biotic or abiotic environments by the bacteria are often related to bacterial surface and membrane proteins that mediate the cellular response for the adaptation of Campylobacter jejuni to the environment. These proteins function rarely as a unique entity, they are often organized in functional complexes. In C. jejuni, these complexes are not fully identified and some of them remain unknown. To identify putative functional multi-subunit entities at the membrane subproteome level of C. jejuni, a holistic non a priori method was addressed using two-dimensional blue native/Sodium dodecyl sulfate (SDS) polyacrylamide gel electrophoresis (PAGE) in strain C. jejuni 81-176. Couples of acrylamide gradient/migration-time, membrane detergent concentration and hand-made strips were optimized to obtain reproducible extraction and separation of intact membrane protein complexes (MPCs). The MPCs were subsequently denatured using SDS-PAGE and each spot from each MPCs was identified by mass spectrometry. Altogether, 21 MPCs could be detected including multi homo-oligomeric and multi hetero-oligomeric complexes distributed in both inner and outer membranes. The function, the conservation and the regulation of the MPCs across C. jejuni strains were inspected by functional and genomic comparison analyses. In this study, relatedness between subunits of two efflux pumps, CmeABC and MacABputC was observed. In addition, a consensus sequence CosR-binding box in promoter regions of MacABputC was present in C. jejuni but not in Campylobacter coli. The MPCs identified in C. jejuni 81-176 membrane are involved in protein folding, molecule trafficking, oxidative phosphorylation, membrane structuration, peptidoglycan biosynthesis, motility and chemotaxis, stress signaling, efflux pumps and virulence.

Uniting Native Capillary Electrophoresis and Multistage Ultraviolet Photodissociation Mass Spectrometry for Online Separation and Characterization of Escherichia coli Ribosomal Proteins and Protein Complexes

With an overarching goal of characterizing the structure of every protein within a cell, identifying its interacting partners, and quantifying the dynamics of the states in which it exists, key developments are still necessary to achieve comprehensive native proteomics by mass spectrometry (MS). In practice, much work remains to optimize reliable online separation methods that are compatible with native MS and improve tandem MS (MS/MS) approaches with respect to when and how energy is deposited into proteins of interest. Herein, we utilize native capillary zone electrophoresis coupled with MS to characterize the proteoforms in the Escherichia coli 70S ribosome. The capabilities of 193 nm ultraviolet photodissociation (UVPD) to yield informative backbone sequence ions are compared to those of higher-energy collisional dissociation (HCD). To further improve sequence coverage values, a multistage MS/MS approach is implemented involving front-end collisional activation to disassemble protein complexes into constituent subunits that are subsequently individually isolated and activated by HCD or UVPD. In total, 48 of the 55 known E. coli ribosomal proteins are identified as 84 unique proteoforms, including 22 protein-metal complexes and 10 protein-protein complexes. Additionally, mapping metal-bound holo fragment ions resulting from UVPD of protein-metal complexes offers insight into the metal-binding sites.

Protein Proximity Observed Using Fluorogen Activating Protein and Dye Activated by Proximal Anchoring (FAP-DAPA) System

The development and function of tissues, blood, and the immune system is dependent upon proximity for cellular recognition and communication. However, the detection of cell-to-cell contacts is limited due to a lack of reversible, quantitative probes that can function at these dynamic sites of irregular geometry. Described here is a novel chemo-genetic tool developed for fluorescent detection of protein-protein proximity and cell apposition that utilizes the Fluorogen Activating Protein (FAP) in combination with a Dye Activated by Proximal Anchoring (DAPA). The FAP-DAPA system has two protein components, the HaloTag and FAP, expressed on separate protein targets or in separate cells. The proteins function to bind and activate a compound that has the hexyl chloride (HexCl) ligand connected to malachite green (MG), the FAP fluorogen, via a poly(ethylene glycol) spacer spanning up to 28 nm. The dehalogenase protein, HaloTag, covalently binds the HexCl ligand, locally concentrating the attached MG. If the FAP is within range of the anchored fluorogen, it will bind and activate MG specifically when the bath concentration is too low to saturate the FAP receptor. A new FAP variant was isolated with a 1000-fold reduced K_D of ∼10-100 nM so that the fluorogen activation reports proximity without artificially enhancing it. The system was characterized using purified FRB and FKBP fusion proteins and showed a doubling of fluorescence upon rapamycin induced complex formation. In cocultured HEK293 cells (HaloTag and FAP-expressing) fluorescence increased at contact sites across a broad range of labeling conditions, more reliably providing contact-specific fluorescence activation with the lower-affinity FAP variant. When combined with suitable targeting and expression constructs, this labeling system may offer significant improvements in on-demand detection of intercellular contacts, potentially applicable in neurological and immunological synapse measurements and other transient, dynamic biological appositions that can be perturbed using other labeling methods that stabilize these interactions.

Native Cell Membrane Nanoparticles System for Membrane Protein-Protein Interaction Analysis

Protein-protein interactions in cell membrane systems play crucial roles in a wide range of biological processes- from cell-to-cell interactions to signal transduction; from sensing environmental signals to biological response; from metabolic regulation to developmental control. Accurate structural information of protein-protein interactions is crucial for understanding the molecular mechanisms of membrane protein complexes and for the design of highly specific molecules to modulate these proteins. Many in vivo and in vitro approaches have been developed for the detection and analysis of protein-protein interactions. Among them the structural biology approach is unique in that it can provide direct structural information of protein-protein interactions at the atomic level. However, current membrane protein structural biology is still largely limited to detergent-based methods. The major drawback of detergent-based methods is that they often dissociate or denature membrane protein complexes once their native lipid bilayer environment is removed by detergent molecules. We have been developing a native cell membrane nanoparticle system for membrane protein structural biology. Here, we demonstrate the use of this system in the analysis of protein-protein interactions on the cell membrane with a case study of the oligomeric state of AcrB.

Microfluidic approaches for the analysis of protein-protein interactions in solution

Exploration and characterisation of the human proteome is a key objective enabling a heightened understanding of biological function, malfunction and pharmaceutical design. Since proteins typically exhibit their behaviour by binding to other proteins, the challenge of probing protein-protein interactions has been the focus of new and improved experimental approaches. Here, we review recently developed microfluidic techniques for the study and quantification of protein-protein interactions. We focus on methodologies that utilise the inherent strength of microfluidics for the control of mass transport on the micron scale, to facilitate surface and membrane-free interrogation and quantification of interacting proteins. Thus, the microfluidic tools described here provide the capability to yield insights on protein-protein interactions under physiological conditions. We first discuss the defining principles of microfluidics, and methods for the analysis of protein-protein interactions that utilise the diffusion-controlled mixing characteristic of fluids at the microscale. We then describe techniques that employ electrophoretic forces to manipulate and fractionate interacting protein systems for their biophysical characterisation, before discussing strategies that use microdroplet compartmentalisation for the analysis of protein interactions. We conclude by highlighting future directions for the field, such as the integration of microfluidic experiments into high-throughput workflows for the investigation of protein interaction networks.

Local Self-Enhancement of MinD Membrane Binding in Min Protein Pattern Formation

The proteins MinD, MinE and MinC are constitutive for the spatiotemporal organization of cell division in Escherichia coli, in particular, for positioning the division machinery at mid-cell. To achieve this function, the ATPase MinD and the ATPase-activating protein MinE undergo coordinated pole-to-pole oscillations and have thus become a paradigm for protein pattern formation in biology. The exact molecular mechanisms enabling MinDE self-organization, and particularly the role of cooperativity in the membrane binding of MinD, thought to be a key requirement, have remained poorly understood. However, for bottom-up synthetic biology aiming at a de novo design of key cellular features, elucidating these mechanisms is of great relevance. By combining in vitro reconstitution with rationally guided mutagenesis of MinD, we found that when bound to membranes, MinD displays new interfaces for multimerization, which are distinct from the canonical MinD dimerization site. We propose that these additional transient interactions contribute to the local self-enhancement of MinD at the membrane, while their relative lability maintains the structural plasticity required for MinDE wave propagation. This could represent a powerful structural regulation feature not reported so far for self-organizing proteins.

Mutation effect estimation on protein-protein interactions using deep contextualized representation learning

The functional impact of protein mutations is reflected on the alteration of conformation and thermodynamics of protein–protein interactions (PPIs). Quantifying the changes of two interacting proteins upon mutations is commonly carried out by computational approaches. Hence, extensive research efforts have been put to the extraction of energetic or structural features on proteins, followed by statistical learning methods to estimate the effects of mutations on PPI properties. Nonetheless, such features require extensive human labors and expert knowledge to obtain, and have limited abilities to reflect point mutations. We present an end-to-end deep learning framework, MuPIPR (Mutation Effects in Protein–protein Interaction PRediction Using Contextualized Representations), to estimate the effects of mutations on PPIs. MuPIPR incorporates a contextualized representation mechanism of amino acids to propagate the effects of a point mutation to surrounding amino acid representations, therefore amplifying the subtle change in a long protein sequence. On top of that, MuPIPR leverages a Siamese residual recurrent convolutional neural encoder to encode a wild-type protein pair and its mutation pair. Multi-layer perceptron regressors are applied to the protein pair representations to predict the quantifiable changes of PPI properties upon mutations. Experimental evaluations show that, with only sequence information, MuPIPR outperforms various state-of-the-art systems on estimating the changes of binding affinity for SKEMPI v1, and offers comparable performance on SKEMPI v2. Meanwhile, MuPIPR also demonstrates state-of-the-art performance on estimating the changes of buried surface areas. The software implementation is available at https://github.com/guangyu-zhou/MuPIPR.

TulsiPIN: An Interologous Protein Interactome of Ocimum tenuiflorum

Ocimum tenuiflorum, commonly known as holy basil or tulsi, is globally recognized for its multitude of medicinal properties. However, a comprehensive study revealing the complex interplay among its constituent proteins at subcellular level is still lacking. To bridge this gap, in this work, a genome-scale interologous protein-protein interaction (PPI) network, TulsiPIN, is developed using 36 template plants, which consists of 13 660 nodes and 327 409 binary interactions. A high confidence network, hc-TulsiPIN, consisting of 7719 nodes having 95 532 interactions is inferred using domain-domain interaction information along with interolog-based statistics, and its reliability is assessed using pathway enrichment, functional homogeneity, and protein colocalization of PPIs. Examination of topological features revealed that hc-TulsiPIN possesses conventional properties, like small-world, scale-free, and modular architecture. A total of 1625 vital proteins are predicted by statistically evaluating hc-TulsiPIN with two ensembles of corresponding random networks, each consisting of 10 000 realizations of Erdoős-Rényi and Barabási-Albert models. Also, numerous regulatory proteins like transcription factors, transcription regulators, and protein kinases are profiled. Using 36 guide genes participating in 9 secondary metabolite biosynthetic pathways, a subnetwork consisting of 171 proteins and 612 interactions was constructed, and 127 of these proteins could be successfully characterized. Detailed information of TulsiPIN is available at https://cuhpcbbtulsipin.shinyapps.io/tulsipin_v0/ .

Discovery of Nonpeptide, Environmentally Sensitive Fluorescent Probes for Imaging p53-MDM2 Interactions in Living Cell Lines and Tissue Slice

Based on structural optimization work, probes 9-11 with practical activity and selectivity in tissue as well as living cell lines are well designed and synthesized. All the probes showed potent inhibitory and acceptable cell toxicity compared with the commercially available p53-MDM2 inhibitor Nutlin-3, and can increase the protein expression level of p53 and MDM2 in the A549 cell line; in particular, probes 10 and 11 can increase the protein expression level of p53 better than Nutlin-3. Moreover, their application in imaging and detecting wild-type p53-MDM2 protein-protein interactions have been well demonstrated in at the cell and tissue levels. Overall, these environmentally sensitive fluorescent turn-on probes are affordable and rapid for imaging, which is expected for applications in target drug screening as well as in pathologic diagnosis.

Functionalizing nanoparticles with cancer-targeting antibodies: A comparison of strategies

Standard cancer therapies sometimes fail to deliver chemotherapeutic drugs to tumor cells in a safe and effective manner. Nanotechnology takes the lead in providing new therapeutic options for cancer due to major potential for selective targeting and controlled drug release. Antibodies and antibody fragments are attracting much attention as a source of targeting ligands to bind specific receptors that are overexpressed on cancer cells. Therefore, researchers are devoting time and effort to develop targeting strategies based on nanoparticles functionalized with antibodies, which hold great promise to enhance therapeutic efficacy and circumvent severe side effects. Several methods have been described to immobilize antibodies on the surface of nanoparticles. However, selecting the most appropriate for each application is challenging but also imperative to preserve antigen binding ability and yield stable antibody-conjugated nanoparticles. From this perspective, we aim to provide considerable knowledge on the most widely used methods of functionalization that can be helpful for decision-making and design of conjugation protocols as well. This review summarizes adsorption, covalent conjugation (carbodiimide, maleimide and "click" chemistries) and biotin-avidin interaction, while discussing the advantages, limitations and relevant therapeutic approaches currently under investigation.

The in vivo mechanics of the magnetotactic backbone as revealed by correlative FLIM-FRET and STED microscopy

Protein interaction and protein imaging strongly benefit from the advancements in time-resolved and superresolution fluorescence microscopic techniques. However, the techniques were typically applied separately and ex vivo because of technical challenges and the absence of suitable fluorescent protein pairs. Here, we show correlative in vivo fluorescence lifetime imaging microscopy Förster resonance energy transfer (FLIM-FRET) and stimulated emission depletion (STED) microscopy to unravel protein mechanics and structure in living cells. We use magnetotactic bacteria as a model system where two proteins, MamJ and MamK, are used to assemble magnetic particles called magnetosomes. The filament polymerizes out of MamK and the magnetosomes are connected via the linker MamJ. Our system reveals that bacterial filamentous structures are more fragile than the connection of biomineralized particles to this filament. More importantly, we anticipate the technique to find wide applicability for the study and quantification of biological processes in living cells and at high resolution.

Molecular dynamics simulations for genetic interpretation in protein coding regions: where we are, where to go and when

The increasing ease with which massive genetic information can be obtained from patients or healthy individuals has stimulated the development of interpretive bioinformatics tools as aids in clinical practice. Most such tools analyze evolutionary information and simple physical-chemical properties to predict whether replacement of one amino acid residue with another will be tolerated or cause disease. Those approaches achieve up to 80-85% accuracy as binary classifiers (neutral/pathogenic). As such accuracy is insufficient for medical decision to be based on, and it does not appear to be increasing, more precise methods, such as full-atom molecular dynamics (MD) simulations in explicit solvent, are also discussed. Then, to describe the goal of interpreting human genetic variations at large scale through MD simulations, we restrictively refer to all possible protein variants carrying single-amino-acid substitutions arising from single-nucleotide variations as the human variome. We calculate its size and develop a simple model that allows calculating the simulation time needed to have a 0.99 probability of observing unfolding events of any unstable variant. The knowledge of that time enables performing a binary classification of the variants (stable-potentially neutral/unstable-pathogenic). Our model indicates that the human variome cannot be simulated with present computing capabilities. However, if they continue to increase as per Moore's law, it could be simulated (at 65°C) spending only 3 years in the task if we started in 2031. The simulation of individual protein variomes is achievable in short times starting at present. International coordination seems appropriate to embark upon massive MD simulations of protein variants.

Binding affinity data of DNA aptamers for therapeutic anthracyclines from microscale thermophoresis and surface plasmon resonance spectroscopy

Anthracyclines like daunorubicin (DRN) and doxorubicin (DOX) play an undisputed key role in cancer treatment, but their chronic administration can cause severe side effects. For precise anthracycline analytical systems, aptamers are preferable recognition elements. Here, we describe the detailed characterisation of a single-stranded DNA aptamer DRN-10 and its truncated versions for DOX and DRN detection. Binding affinities were determined from surface plasmon resonance (SPR) and microscale thermophoresis (MST) and combined with conformational data from circular dichroism (CD). Both aptamers displayed similar nanomolar binding affinities to DRN and DOX, even though their rate constants differed as shown by SPR recordings. SPR kinetic data unravelled a two-state reaction model including a 1 : 1 binding and a subsequent conformational change of the binding complex. This model was supported by CD spectra. In addition, the dissociation constants determined with MST were always lower than that from SPR, and especially for the truncated aptamer they differed by two orders of magnitude. This most probably reflects the methodological difference, namely labelling for MST vs. immobilisation for SPR. From CD recordings, we suggested a specific G-quadruplex as structural basis for anthracycline binding. We concluded that the aptamer DRN-10 is a promising recognition element for anthracycline detection systems and further selected aptamers can be also characterised with the combined methodological approach presented here.

Applications of Protein Fragment Complementation Assays for Analyzing Biomolecular Interactions and Biochemical Networks in Living Cells

Protein-protein interactions (PPIs) are indispensable for the dynamic assembly of multiprotein complexes that are central players of nearly all of the intracellular biological processes, such as signaling pathways, metabolic pathways, formation of intracellular organelles, establishment of cytoplasmic skeletons, etc. Numerous approaches have been invented to study PPIs both in vivo and in vitro, including the protein-fragment complementation assay (PCA), which is a widely applied technology to study PPIs and biomolecular interactions. PCA is a technology based on the expression of the bait and prey proteins in fusion with two complementary reporter protein fragments, respectively, that will reassemble when in close proximity. The reporter protein can be the enzymes or fluorescent proteins. Recovery of the enzymatic activity or fluorescent signal can be the indicator of PPI between the bait and prey proteins. Significant effort has been invested in developing many derivatives of PCA, along with various applications, in order to address specific questions. Therefore, a prompt review of these applications is important. In this review, we will categorize these applications according to the scenarios that the PCAs were applied and expect to provide a reference guideline for the future selection of PCA methods in solving a specific problem.

Computational identification of protein-protein interactions in model plant proteomes

Protein-protein interactions (PPIs) play essential roles in many biological processes. A PPI network provides crucial information on how biological pathways are structured and coordinated from individual protein functions. In the past two decades, large-scale PPI networks of a handful of organisms were determined by experimental techniques. However, these experimental methods are time-consuming, expensive, and are not easy to perform on new target organisms. Large-scale PPI data is particularly sparse in plant organisms. Here, we developed a computational approach for detecting PPIs trained and tested on known PPIs of Arabidopsis thaliana and applied to three plants, Arabidopsis thaliana, Glycine max (soybean), and Zea mays (maize) to discover new PPIs on a genome-scale. Our method considers a variety of features including protein sequences, gene co-expression, functional association, and phylogenetic profiles. This is the first work where a PPI prediction method was developed for is the first PPI prediction method applied on benchmark datasets of Arabidopsis. The method showed a high prediction accuracy of over 90% and very high precision of close to 1.0. We predicted 50,220 PPIs in Arabidopsis thaliana, 13,175,414 PPIs in corn, and 13,527,834 PPIs in soybean. Newly predicted PPIs were classified into three confidence levels according to the availability of existing supporting evidence and discussed. Predicted PPIs in the three plant genomes are made available for future reference.

The Methods Employed in Mass Spectrometric Analysis of Posttranslational Modifications (PTMs) and Protein-Protein Interactions (PPIs)

Mass Spectrometry (MS) has revolutionized the way we study biomolecules, especially proteins, their interactions and posttranslational modifications (PTM). As such MS has established itself as the leading tool for the analysis of PTMs mainly because this approach is highly sensitive, amenable to high throughput and is capable of assigning PTMs to specific sites in the amino acid sequence of proteins and peptides. Along with the advances in MS methodology there have been improvements in biochemical, genetic and cell biological approaches to mapping the interactome which are discussed with consideration for both the practical and technical considerations of these techniques. The interactome of a species is generally understood to represent the sum of all potential protein-protein interactions. There are still a number of barriers to the elucidation of the human interactome or any other species as physical contact between protein pairs that occur by selective molecular docking in a particular spatiotemporal biological context are not easily captured and measured.PTMs massively increase the complexity of organismal proteomes and play a role in almost all aspects of cell biology, allowing for fine-tuning of protein structure, function and localization. There are an estimated 300 PTMS with a predicted 5% of the eukaryotic genome coding for enzymes involved in protein modification, however we have not yet been able to reliably map PTM proteomes due to limitations in sample preparation, analytical techniques, data analysis, and the substoichiometric and transient nature of some PTMs. Improvements in proteomic and mass spectrometry methods, as well as sample preparation, have been exploited in a large number of proteome-wide surveys of PTMs in many different organisms. Here we focus on previously published global PTM proteome studies in the Apicomplexan parasites T. gondii and P. falciparum which offer numerous insights into the abundance and function of each of the studied PTM in the Apicomplexa. Integration of these datasets provide a more complete picture of the relative importance of PTM and crosstalk between them and how together PTM globally change the cellular biology of the Apicomplexan protozoa. A multitude of techniques used to investigate PTMs, mostly techniques in MS-based proteomics, are discussed for their ability to uncover relevant biological function.

Mass Spectrometry- and Computational Structural Biology-Based Investigation of Proteins and Peptides

Recent developments of mass spectrometry (MS) allow us to identify, estimate, and characterize proteins and protein complexes. At the same time, structural biology helps to determine the protein structure and its structure-function relationship. Together, they aid to understand the protein structure, property, function, protein-complex assembly, protein-protein interaction, and dynamics. The present chapter is organized with illustrative results to demonstrate how experimental mass spectrometry can be combined with computational structural biology for detailed studies of protein's structures. We have used tumor differentiation factor protein/peptide as ligand and Hsp70/Hsp90 as receptor protein as examples to study ligand-protein interaction. To investigate possible protein conformation, we will describe two proteins-lysozyme and myoglobin. As an application of MS-based assignment of disulfide bridges, the case of the spider venom polypeptide Phα1β will also be discussed.

A Pilot Exploratory Proteomics Investigation of Mental Fatigue and Mental Energy

Fatigue is a common and poorly understood problem that impacts approximately 45% of the United States (US) population. Fatigue has also been associated with fatigue-related driving accidents, school absences, decline in school performance and negative health outcomes. Fatigue has been linked to many diseases and is consistently underreported in medical care. Despite these high financial and societal costs, fatigue is a poorly understood problem and there is no consensus on how to measure fatigue. Proteomics is one of the most unbiased approach to measure differences in the protein levels from various biological fluids in two conditions, i.e. before and after mental exercise, aka fatigue. There are, however, challenges associated with such analyses: proteomics experiments are usually expensive and time consuming and also require a large number of participants. Here, we performed a proteomics experiment of three (pre- and post-fatigue) samples and also three matched controls (pre- and post-non-fatigue). We found no particular protein that has significant changes in fatigue sample upon treatment. We did note a potential association between changes in mental energy and Annexin A1. However, the study has value simply because it is an extra study in the field of fatigue, but also allows other to correlate our results with their results.

BioID: A Proximity-Dependent Labeling Approach in Proteomics Study

Biological activities are mainly executed by proteins and in most of the occasions these activities are accomplished by protein complexes or through protein-protein interactions (PPI). So it is critical to reveal how the protein complexes are organized and demonstrate the PPIs involved in the biological processes. In addition to the traditional biochemical approaches, proximity-dependent labeling (PDL) has recently been proposed to identify the interacting partners of a given protein. PDL requires the fusion expression of the target protein with an enzyme which catalyzes the attachment of a reactive molecule to the interacting partners in a distance-dependent manner. Further analysis of all the proteins that are modified by the reactive molecule discloses the identity of these proteins which are presumed to be interacting partners of the target protein. BioID is one of those representative PDL methods with the most widely applications. The enzyme used in BioID is a biotin ligase BirA which catalyzes the biotinylation of target protein with the presence of biotin. Through streptavidin-mediated pull-down and mass spectrometry analysis, the interacting protein candidates of a given protein can be obtained.

Mitoproteomics: Tackling Mitochondrial Dysfunction in Human Disease

Mitochondria are highly dynamic and regulated organelles that historically have been defined based on their crucial role in cell metabolism. However, they are implicated in a variety of other important functions, making mitochondrial dysfunction an important axis in several pathological contexts. Despite that conventional biochemical and molecular biology approaches have provided significant insight into mitochondrial functionality, innovative techniques that provide a global view of the mitochondrion are still necessary. Proteomics fulfils this need by enabling accurate, systems-wide quantitative analysis of protein abundance. More importantly, redox proteomics approaches offer unique opportunities to tackle oxidative stress, a phenomenon that is intimately linked to aging, cardiovascular disease, and cancer. In addition, cutting-edge proteomics approaches reveal how proteins exert their functions in complex interaction networks where even subtle alterations stemming from early pathological states can be monitored. Here, we describe the proteomics approaches that will help to deepen the role of mitochondria in health and disease by assessing not only changes to mitochondrial protein composition but also alterations to their redox state and how protein interaction networks regulate mitochondrial function and dynamics. This review is aimed at showing the reader how the application of proteomics approaches during the last 20 years has revealed crucial mitochondrial roles in the context of aging, neurodegenerative disorders, metabolic disease, and cancer.

Chromatographic separation strategies for precision mass spectrometry to study protein-protein interactions and protein phosphorylation

Investigating protein-protein interactions and protein phosphorylation can be of great significance when studying biological processes and human diseases at the molecular level. However, sample complexity, presence of low abundance proteins, and dynamic nature of the proteins often impede in achieving sufficient analytical depth in proteomics research. In this regard, chromatographic separation methodologies have played a vital role in the identification and quantification of proteins in complex sample mixtures. The combination of peptide and protein fractionation techniques with advanced high-performance mass spectrometry has allowed the researchers to successfully study the protein-protein interactions and protein phosphorylation. Several new fractionation strategies for large scale analysis of proteins and peptides have been developed to study protein-protein interactions and protein phosphorylation. These emerging chromatography methodologies have enabled the identification of several hundred protein complexes and even thousands of phosphorylation sites in a single study. In this review, we focus on current workflow strategies and chromatographic tools, highlighting their advantages and disadvantages, and examining their associated challenges and future potential.

Optimization of a Bioluminescence Resonance Energy Transfer-Based Assay for Screening of Trypanosoma cruzi Protein/Protein Interaction Inhibitors

Chagas disease, a parasitic disease caused by Trypanosoma cruzi, is a major public health burden in poor rural populations of Central and South America and a serious emerging threat outside the endemic region, since the number of infections in non-endemic countries continues to rise. In order to develop more efficient anti-trypanosomal treatments to replace the outdated therapies, new molecular targets need to be explored and new drugs discovered. Trypanosoma cruzi has distinctive structural and functional characteristics with respect to the human host. These exclusive features could emerge as interesting drug targets. In this work, essential and differential protein-protein interactions for the parasite, including the ribosomal P proteins and proteins involved in mRNA processing, were evaluated in a bioluminescence resonance energy transfer-based assay as a starting point for drug screening. Suitable conditions to consider using this simple and robust methodology to screening compounds and natural extracts able to inhibit protein-protein interactions were set in living cells and lysates.

Probing Protein-Protein Interactions Using Asymmetric Labeling and Carbonyl-Carbon Selective Heteronuclear NMR Spectroscopy

Protein-protein interactions (PPIs) regulate a plethora of cellular processes and NMR spectroscopy has been a leading technique for characterizing them at the atomic resolution. Technically, however, PPIs characterization has been challenging due to multiple samples required to characterize the hot spots at the protein interface. In this paper, we review our recently developed methods that greatly simplify PPI studies, which minimize the number of samples required to fully characterize residues involved in the protein-protein binding interface. This original strategy combines asymmetric labeling of two binding partners and the carbonyl-carbon label selective (CCLS) pulse sequence element implemented into the heteronuclear single quantum correlation (¹H-15N HSQC) spectra. The CCLS scheme removes signals of the J-coupled 15N⁻13C resonances and records simultaneously two individual amide fingerprints for each binding partner. We show the application to the measurements of chemical shift correlations, residual dipolar couplings (RDCs), and paramagnetic relaxation enhancements (PRE). These experiments open an avenue for further modifications of existing experiments facilitating the NMR analysis of PPIs.

Computational Methods for Predicting Protein-Protein Interactions Using Various Protein Features

Understanding protein-protein interactions (PPIs) in a cell is essential for learning protein functions, pathways, and mechanism of diseases. PPIs are also important targets for developing drugs. Experimental methods, both small-scale and large-scale, have identified PPIs in several model organisms. However, results cover only a part of PPIs of organisms; moreover, there are many organisms whose PPIs have not yet been investigated. To complement experimental methods, many computational methods have been developed that predict PPIs from various characteristics of proteins. Here we provide an overview of literature reports to classify computational PPI prediction methods that consider different features of proteins, including protein sequence, genomes, protein structure, function, PPI network topology, and those which integrate multiple methods. © 2018 by John Wiley & Sons, Inc.

Network inference reveals novel connections in pathways regulating growth and defense in the yeast salt response

Cells respond to stressful conditions by coordinating a complex, multi-faceted response that spans many levels of physiology. Much of the response is coordinated by changes in protein phosphorylation. Although the regulators of transcriptome changes during stress are well characterized in Saccharomyces cerevisiae, the upstream regulatory network controlling protein phosphorylation is less well dissected. Here, we developed a computational approach to infer the signaling network that regulates phosphorylation changes in response to salt stress. We developed an approach to link predicted regulators to groups of likely co-regulated phospho-peptides responding to stress, thereby creating new edges in a background protein interaction network. We then use integer linear programming (ILP) to integrate wild type and mutant phospho-proteomic data and predict the network controlling stress-activated phospho-proteomic changes. The network we inferred predicted new regulatory connections between stress-activated and growth-regulating pathways and suggested mechanisms coordinating metabolism, cell-cycle progression, and growth during stress. We confirmed several network predictions with co-immunoprecipitations coupled with mass-spectrometry protein identification and mutant phospho-proteomic analysis. Results show that the cAMP-phosphodiesterase Pde2 physically interacts with many stress-regulated transcription factors targeted by PKA, and that reduced phosphorylation of those factors during stress requires the Rck2 kinase that we show physically interacts with Pde2. Together, our work shows how a high-quality computational network model can facilitate discovery of new pathway interactions during osmotic stress.

Cells sense and respond to stressful environments by utilizing complex signaling networks that integrate diverse signals to coordinate a multi-faceted physiological response. Much of this response is controlled by post-translational protein phosphorylation. Although many regulators that mediate changes in protein phosphorylation are known, how these regulators inter-connect in a single regulatory network that can transmit cellular signals is not known. It is also unclear how regulators that promote growth and regulators that activate the stress response interconnect to reorganize resource allocation during stress. Here, we developed an integrated experimental and computational workflow to infer the signaling network that regulates phosphorylation changes during osmotic stress in the budding yeast Saccharomyces cerevisiae. The workflow integrates data measuring protein phosphorylation changes in response to osmotic stress with known physical interactions between yeast proteins from large-scale datasets, along with other information about how regulators recognize their targets. The resulting network suggested new signaling connections between regulators and pathways, including those involved in regulating growth and defense, and predicted new regulators involved in stress defense. Our work highlights the power of using network inference to deliver new insight on how cells coordinate a diverse adaptive strategy to stress.

What factors determine the stability of a weak protein-protein interaction in a charged aqueous droplet?

Maintaining the interface of a weak transient protein complex transferred from bulk solution to the gaseous state via evaporating droplets is a critical question in the detection of the complex association (dissociation) constant by using electrospray ionization mass spectrometry (ESI-MS). Here we explore the factors that may affect the stability of a protein-protein interaction (PPI) using atomistic molecular dynamics (MD) modelling of a complex of ubiquitin (Ub) and the ubiquitin-associated domain (UbA) (RCSB PDB code ) and a non-covalent complex of diubiquitin (RCSB PDB code ) in aqueous droplets. A general method is presented to determine the protonation states of the complexes we investigate in particular, and that of a protein in general, under various pH conditions that an evaporating droplet acquires due to its change in size. We find that the combination of high temperature and high charge states of the protein complexes may destabilize the interface by creating new interfaces instead of a direct rupture of the initial stable interface. We provide evidence that highly charged protein complexes are found in droplets that form conical extrusions of the solvent on the surface due to charge-induced instability. This distinct droplet morphology leads to a higher solvent evaporation rate that assists in transferring the complex in the gaseous state without dissociation. The conical solvent protrusions expose on the droplet surface certain amino acids that otherwise would be solvated in a droplet with the protein complex of low charge states. The new vapor-protein interface does not have a direct effect on the stability of the PPI. A common way in experiments to stabilize the protein complexes in droplets is to reduce the protonation state of the proteins. Here we find that weakly bound protein complexes even at high protonation states can be stabilized by the presence of a small number of counterions, without affecting the protonation state of the protein. Our findings may provide guiding principles in ESI-MS experiments to stabilize weak transient PPIs.

The distinctive cell division interactome of Neisseria gonorrhoeae

BACKGROUND

Bacterial cell division is an essential process driven by the formation of a Z-ring structure, as a cytoskeletal scaffold at the mid-cell, followed by the recruitment of various proteins which form the divisome. The cell division interactome reflects the complement of different interactions between all divisome proteins. To date, only two cell division interactomes have been characterized, in Escherichia coli and in Streptococcus pneumoniae. The cell divison proteins encoded by Neisseria gonorrhoeae include FtsZ, FtsA, ZipA, FtsK, FtsQ, FtsI, FtsW, and FtsN. The purpose of the present study was to characterize the cell division interactome of N. gonorrhoeae using several different methods to identify protein-protein interactions. We also characterized the specific subdomains of FtsA implicated in interactions with FtsZ, FtsQ, FtsN and FtsW.

RESULTS

Using a combination of bacterial two-hybrid (B2H), glutathione S-transferase (GST) pull-down assays, and surface plasmon resonance (SPR), nine interactions were observed among the eight gonococcal cell division proteins tested. ZipA did not interact with any other cell division proteins. Comparisons of the N. gonorrhoeae cell division interactome with the published interactomes from E. coli and S. pneumoniae indicated that FtsA-FtsZ and FtsZ-FtsK interactions were common to all three species. FtsA-FtsW and FtsK-FtsN interactions were only present in N. gonorrhoeae. The 2A and 2B subdomains of FtsA_Ng were involved in interactions with FtsQ, FtsZ, and FtsN, and the 2A subdomain was involved in interaction with FtsW.

CONCLUSIONS

Results from this research indicate that N. gonorrhoeae has a distinctive cell division interactome as compared with other microorganisms.