Computational design of small molecular modulators of protein–protein interactions with a novel thermodynamic cycle: Allosteric inhibitors of HIV‐1 integrase

Targeting protein–protein interactions for therapeutic development involves designing small molecules to either disrupt or enhance a known PPI. For this purpose, it is necessary to compute reliably the effect of chemical modifications of small molecules on the protein–protein association free energy. Here we present results obtained using a novel thermodynamic free energy cycle, for the rational design of allosteric inhibitors of HIV‐1 integrase (ALLINI) that act specifically in the early stage of the infection cycle. The new compounds can serve as molecular probes to dissect the multifunctional mechanisms of ALLINIs to inform the discovery of new allosteric inhibitors. The free energy protocol developed here can be more broadly applied to study quantitatively the effects of small molecules on modulating the strengths of protein–protein interactions.     

Mass spectrometry‐based protein–protein interaction networks for the study of human diseases

A better understanding of the molecular mechanisms underlying disease is key for expediting the development of novel therapeutic interventions. Disease mechanisms are often mediated by interactions between proteins. Insights into the physical rewiring of protein–protein interactions in response to mutations, pathological conditions, or pathogen infection can advance our understanding of disease etiology, progression, and pathogenesis and can lead to the identification of potential druggable targets. Advances in quantitative mass spectrometry (MS)‐based approaches have allowed unbiased mapping of these disease‐mediated changes in protein–protein interactions on a global scale. Here, we review MS techniques that have been instrumental for the identification of protein–protein interactions at a system‐level, and we discuss the challenges associated with these methodologies as well as novel MS advancements that aim to address these challenges. An overview of examples from diverse disease contexts illustrates the potential of MS‐based protein–protein interaction mapping approaches for revealing disease mechanisms, pinpointing new therapeutic targets, and eventually moving toward personalized applications.     

Discarding functional residues from the substitution table improves predictions of active sites within three-dimensional structures

Substitutions of individual amino acids in proteins may be under very different evolutionary restraints depending on their structural and functional roles. The Environment Specific Substitution Table (ESST) describes the pattern of substitutions in terms of amino acid location within elements of secondary structure, solvent accessibility, and the existence of hydrogen bonds between side chains and neighbouring amino acid residues. Clearly amino acids that have very different local environments in their functional state compared to those in the protein analysed will give rise to inconsistencies in the calculation of amino acid substitution tables. Here, we describe how the calculation of ESSTs can be improved by discarding the functional residues from the calculation of substitution tables. Four categories of functions are examined in this study: protein–protein interactions, protein–nucleic acid interactions, protein–ligand interactions, and catalytic activity of enzymes. Their contributions to residue conservation are measured and investigated. We test our new ESSTs using the program CRESCENDO, designed to predict functional residues by exploiting knowledge of amino acid substitutions, and compare the benchmark results with proteins whose functions have been defined experimentally. The new methodology increases the Z-score by 98% at the active site residues and finds 16% more active sites compared with the old ESST. We also find that discarding amino acids responsible for protein–protein interactions helps in the prediction of those residues although they are not as conserved as the residues of active sites. Our methodology can make the substitution tables better reflect and describe the substitution patterns of amino acids that are under structural restraints only.     

Frequent Assembly of Chimeric Complexes in the Protein Interaction Network of an Interspecies Yeast Hybrid

Hybrids between species often show extreme phenotypes, including some that take place at the molecular level. In this study, we investigated the phenotypes of an interspecies diploid hybrid in terms of protein–protein interactions inferred from protein correlation profiling. We used two yeast species, Saccharomyces cerevisiae and Saccharomyces uvarum, which are interfertile, but yet have proteins diverged enough to be differentiated using mass spectrometry. Most of the protein–protein interactions are similar between hybrid and parents, and are consistent with the assembly of chimeric complexes, which we validated using an orthogonal approach for the prefoldin complex. We also identified instances of altered protein–protein interactions in the hybrid, for instance, in complexes related to proteostasis and in mitochondrial protein complexes. Overall, this study uncovers the likely frequent occurrence of chimeric protein complexes with few exceptions, which may result from incompatibilities or imbalances between the parental proteomes.     

Direct measurement of protein–protein interactions by FLIM-FRET at UV laser-induced DNA damage sites in living cells

Protein–protein interactions are essential to ensure timely and precise recruitment of chromatin remodellers and repair factors to DNA damage sites. Conventional analyses of protein–protein interactions at a population level may mask the complexity of interaction dynamics, highlighting the need for a method that enables quantification of DNA damage-dependent interactions at a single-cell level. To this end, we integrated a pulsed UV laser on a confocal fluorescence lifetime imaging (FLIM) microscope to induce localized DNA damage. To quantify protein–protein interactions in live cells, we measured Förster resonance energy transfer (FRET) between mEGFP- and mCherry-tagged proteins, based on the fluorescence lifetime reduction of the mEGFP donor protein. The UV-FLIM-FRET system offers a unique combination of real-time and single-cell quantification of DNA damage-dependent interactions, and can distinguish between direct protein–protein interactions, as opposed to those mediated by chromatin proximity. Using the UV-FLIM-FRET system, we show the dynamic changes in the interaction between poly(ADP-ribose) polymerase 1, amplified in liver cancer 1, X-ray repair cross-complementing protein 1 and tripartite motif containing 33 after DNA damage. This new set-up complements the toolset for studying DNA damage response by providing single-cell quantitative and dynamic information about protein–protein interactions at DNA damage sites.     

Coupling Protein Side-Chain and Backbone Flexibility Improves the Re-design of Protein-Ligand Specificity

Interactions between small molecules and proteins play critical roles in regulating and facilitating diverse biological functions, yet our ability to accurately re-engineer the specificity of these interactions using computational approaches has been limited. One main difficulty, in addition to inaccuracies in energy functions, is the exquisite sensitivity of protein–ligand interactions to subtle conformational changes, coupled with the computational problem of sampling the large conformational search space of degrees of freedom of ligands, amino acid side chains, and the protein backbone. Here, we describe two benchmarks for evaluating the accuracy of computational approaches for re-engineering protein-ligand interactions: (i) prediction of enzyme specificity altering mutations and (ii) prediction of sequence tolerance in ligand binding sites. After finding that current state-of-the-art “fixed backbone” design methods perform poorly on these tests, we develop a new “coupled moves” design method in the program Rosetta that couples changes to protein sequence with alterations in both protein side-chain and protein backbone conformations, and allows for changes in ligand rigid-body and torsion degrees of freedom. We show significantly increased accuracy in both predicting ligand specificity altering mutations and binding site sequences. These methodological improvements should be useful for many applications of protein – ligand design. The approach also provides insights into the role of subtle conformational adjustments that enable functional changes not only in engineering applications but also in natural protein evolution.     

Amino acid–base interactions: a three-dimensional analysis of protein–DNA interactions at an atomic level

To assess whether there are universal rules that govern amino acid–base recognition, we investigate hydrogen bonds, van der Waals contacts and water-mediated bonds in 129 protein–DNA complex structures. DNA–backbone interactions are the most numerous, providing stability rather than specificity. For base interactions, there are significant base–amino acid type correlations, which can be rationalised by considering the stereochemistry of protein side chains and the base edges exposed in the DNA structure. Nearly two-thirds of the direct read-out of DNA sequences involves complex networks of hydrogen bonds, which enhance specificity. Two-thirds of all protein–DNA interactions comprise van der Waals contacts, compared to about one-sixth each of hydrogen and water-mediated bonds. This highlights the central importance of these contacts for complex formation, which have previously been relegated to a secondary role. Although common, water-mediated bonds are usually non-specific, acting as space-fillers at the protein–DNA interface. In conclusion, the majority of amino acid–base interactions observed follow general principles that apply across all protein–DNA complexes, although there are individual exceptions. Therefore, we distinguish between interactions whose specificities are ‘universal’ and ‘context-dependent’. An interactive Web-based atlas of side chain–base contacts provides access to the collected data, including analyses and visualisation of the three-dimensional geometry of the interactions.     

Dissecting protein-RNA recognition sites

We analyze the protein–RNA interfaces in 81 transient binary complexes taken from the Protein Data Bank. Those with tRNA or duplex RNA are larger than with single-stranded RNA, and comparable in size to protein–DNA interfaces. The protein side bears a strong positive electrostatic potential and resembles protein–DNA interfaces in its amino acid composition. On the RNA side, the phosphate contributes less, and the sugar much more, to the interaction than in protein–DNA complexes. On average, protein–RNA interfaces contain 20 hydrogen bonds, 7 that involve the phosphates, 5 the sugar 2′OH, and 6 the bases, and 32 water molecules. The average H-bond density per unit buried surface area is less with tRNA or single-stranded RNA than with duplex RNA. The atomic packing is also less compact in interfaces with tRNA. On the protein side, the main chain NH and Arg/Lys side chains account for nearly half of all H-bonds to RNA; the main chain CO and side chain acceptor groups, for a quarter. The 2′OH is a major player in protein–RNA recognition, and shape complementarity an important determinant, whereas electrostatics and direct base–protein interactions play a lesser part than in protein–DNA recognition.     

A rapid and sensitive high-throughput screening method to identify compounds targeting protein–nucleic acids interactions

DNA-binding and RNA-binding proteins are usually considered ‘undruggable’ partly due to the lack of an efficient method to identify inhibitors from existing small molecule repositories. Here we report a rapid and sensitive high-throughput screening approach to identify compounds targeting protein–nucleic acids interactions based on protein–DNA or protein–RNA interaction enzyme-linked immunosorbent assays (PDI-ELISA or PRI-ELISA). We validated the PDI-ELISA method using the mammalian high-mobility-group protein AT-hook 2 (HMGA2) as the protein of interest and netropsin as the inhibitor of HMGA2–DNA interactions. With this method we successfully identified several inhibitors and an activator for HMGA2–DNA interactions from a collection of 29 DNA-binding compounds. Guided by this screening excise, we showed that netropsin, the specific inhibitor of HMGA2–DNA interactions, strongly inhibited the differentiation of the mouse pre-adipocyte 3T3-L1 cells into adipocytes, most likely through a mechanism by which the inhibition is through preventing the binding of HMGA2 to the target DNA sequences. This method should be broadly applicable to identify compounds or proteins modulating many DNA-binding or RNA-binding proteins.     

Non-specific interactions are sufficient to explain the position of heterochromatic chromocenters and nucleoli in interphase nuclei

The organization of the eukaryote nucleus into functional compartments arises by self-organization both through specific protein–protein and protein–DNA interactions and non-specific interactions that lead to entropic effects, such as e.g. depletion attraction. While many specific interactions have so far been demonstrated, the contributions of non-specific interactions are still unclear. We used coarse-grained molecular dynamics simulations of previously published models for Arabidopsis thaliana chromatin organization to show that non-specific interactions can explain the in vivo localization of nucleoli and chromocenters. Also, we quantitatively demonstrate that chromatin looping contributes to the formation of chromosome territories. Our results are consistent with the previously published Rosette model for Arabidopsis chromatin organization and suggest that chromocenter-associated loops play a role in suppressing chromocenter clustering.     

Purification of MAP–kinase protein complexes and identification of candidate components by XL–TAP–MS

The purification of low-abundance protein complexes and detection of in vivo protein–protein interactions in complex biological samples remains a challenging task. Here, we devised crosslinking and tandem affinity purification coupled to mass spectrometry (XL–TAP–MS), a quantitative proteomics approach for analyzing tandem affinity-purified, crosslinked protein complexes from plant tissues. We exemplarily applied XL–TAP–MS to study the MKK2–Mitogen-activated protein kinase (MPK4) signaling module in Arabidopsis thaliana. A tandem affinity tag consisting of an in vivo-biotinylated protein domain flanked by two hexahistidine sequences was adopted to allow for the affinity-based isolation of formaldehyde–crosslinked protein complexes under fully denaturing conditions. Combined with ¹⁵N stable isotopic labeling and tandem MS we captured and identified a total of 107 MKK2–MPK4 module-interacting proteins. Consistent with the role of the MPK signaling module in plant immunity, many of the module-interacting proteins are involved in the biotic and abiotic stress response of Arabidopsis. Validation of binary protein–protein interactions by in planta split-luciferase assays and in vitro kinase assays disclosed several direct phosphorylation targets of MPK4. Together, the XL–TAP–MS approach purifies low abundance protein complexes from biological samples and discovers previously unknown protein–protein interactions.

XL–TAP–MS: a novel technique that allows purification of crosslinked, low abundant protein complexes from plant tissues under denatured conditions and detection of in vivo protein–protein interactions.     

Prediction of yeast protein-protein interaction network: insights from the Gene Ontology and annotations

A map of protein–protein interactions provides valuable insight into the cellular function and machinery of a proteome. By measuring the similarity between two Gene Ontology (GO) terms with a relative specificity semantic relation, here, we proposed a new method of reconstructing a yeast protein–protein interaction map that is solely based on the GO annotations. The method was validated using high-quality interaction datasets for its effectiveness. Based on a Z-score analysis, a positive dataset and a negative dataset for protein–protein interactions were derived. Moreover, a gold standard positive (GSP) dataset with the highest level of confidence that covered 78% of the high-quality interaction dataset and a gold standard negative (GSN) dataset with the lowest level of confidence were derived. In addition, we assessed four high-throughput experimental interaction datasets using the positives and the negatives as well as GSPs and GSNs. Our predicted network reconstructed from GSPs consists of 40753 interactions among 2259 proteins, and forms 16 connected components. We mapped all of the MIPS complexes except for homodimers onto the predicted network. As a result, ~35% of complexes were identified interconnected. For seven complexes, we also identified some nonmember proteins that may be functionally related to the complexes concerned. This analysis is expected to provide a new approach for predicting the protein–protein interaction maps from other completely sequenced genomes with high-quality GO-based annotations.     

Cavities in protein–DNA and protein–RNA interfaces

An analysis of cavities present in protein–DNA and protein–RNA complexes is presented. In terms of the number of cavities and their total volume, the interfaces formed in these complexes are akin to those in transient protein–protein heterocomplexes. With homodimeric proteins protein–DNA interfaces may contain cavities involving both the protein subunits and DNA, and these are more than twice as large as cavities involving a single protein subunit and DNA. A parameter, cavity index, measuring the degree of surface complementarity, indicates that the packing of atoms in protein–protein/DNA/RNA is very similar, but it is about two times less efficient in the permanent interfaces formed between subunits in homodimers. As within the tertiary structure and protein–protein interfaces, protein–DNA interfaces have a higher inclination to be lined by β-sheet residues; from the DNA side, base atoms, in particular those in minor grooves, have a higher tendency to be located in cavities. The larger cavities tend to be less spherical and solvated. A small fraction of water molecules are found to mediate hydrogen-bond interactions with both the components, suggesting their primary role is to fill in the void left due to the local non-complementary nature of the surface patches.     

Rapid antibody selection by mRNA display on a microfluidic chip

In vitro antibody-display technologies are powerful approaches for isolating monoclonal antibodies from recombinant antibody libraries. However, these display techniques require several rounds of affinity selection which is time-consuming. Here, we combined mRNA display with a microfluidic system for in vitro selection and evolution of antibodies and achieved ultrahigh enrichment efficiency of 10⁶- to 10⁸-fold per round. After only one or two rounds of selection, antibodies with high affinity and specificity were obtained from naïve and randomized single-chain Fv libraries of ~10¹² molecules. Furthermore, we confirmed that not only protein–protein (antigen–antibody) interactions, but also protein–DNA and protein–drug interactions were selected with ultrahigh efficiencies. This method will facilitate high-throughput preparation of antibodies and identification of protein interactions in proteomic and therapeutic fields.     

Ligand Clouds around Protein Clouds: A Scenario of Ligand Binding with Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) were found to be widely associated with human diseases and may serve as potential drug design targets. However, drug design targeting IDPs is still in the very early stages. Progress in drug design is usually achieved using experimental screening; however, the structural disorder of IDPs makes it difficult to characterize their interaction with ligands using experiments alone. To better understand the structure of IDPs and their interactions with small molecule ligands, we performed extensive simulations on the c-Myc_370–409 peptide and its binding to a reported small molecule inhibitor, ligand 10074-A4. We found that the conformational space of the apo c-Myc_370–409 peptide was rather dispersed and that the conformations of the peptide were stabilized mainly by charge interactions and hydrogen bonds. Under the binding of the ligand, c-Myc_370–409 remained disordered. The ligand was found to bind to c-Myc_370–409 at different sites along the chain and behaved like a ‘ligand cloud’. In contrast to ligand binding to more rigid target proteins that usually results in a dominant bound structure, ligand binding to IDPs may better be described as ligand clouds around protein clouds. Nevertheless, the binding of the ligand and a non-ligand to the c-Myc_370–409 target could be clearly distinguished. The present study provides insights that will help improve rational drug design that targets IDPs.     

In vivo imaging of protein–protein and RNA–protein interactions using novel far-red fluorescence complementation systems

Imaging of protein–protein and RNA–protein interactions in vivo, especially in live animals, is still challenging. Here we developed far-red mNeptune-based bimolecular fluorescence complementation (BiFC) and trimolecular fluorescence complementation (TriFC) systems with excitation and emission above 600 nm in the ‘tissue optical window’ for imaging of protein–protein and RNA–protein interactions in live cells and mice. The far-red mNeptune BiFC was first built by selecting appropriate split mNeptune fragments, and then the mNeptune-TriFC system was built based on the mNeptune-BiFC system. The newly constructed mNeptune BiFC and TriFC systems were verified as useful tools for imaging protein–protein and mRNA–protein interactions, respectively, in live cells and mice. We then used the new mNeptune-TriFC system to investigate the interactions between human polypyrimidine-tract-binding protein (PTB) and HIV-1 mRNA elements as PTB may participate in HIV mRNA processing in HIV activation from latency. An interaction between PTB and the 3′long terminal repeat region of HIV-1 mRNAs was found and imaged in live cells and mice, implying a role for PTB in regulating HIV-1 mRNA processing. The study provides new tools for in vivo imaging of RNA–protein and protein–protein interactions, and adds new insight into the mechanism of HIV-1 mRNA processing.     

Surface plasmon resonance: a useful technique for cell biologists to characterize biomolecular interactions

Surface plasmon resonance (SPR) is a powerful technique for monitoring the affinity and selectivity of biomolecular interactions. SPR allows for analysis of association and dissociation rate constants and modeling of biomolecular interaction kinetics, as well as for equilibrium binding analysis and ligand specificity studies. SPR has received much use and improved precision in classifying protein–protein interactions, as well as in studying small-molecule ligand binding to receptors; however, lipid–protein interactions have been underserved in this regard. With the field of lipids perhaps the next frontier in cellular research, SPR is a highly advantageous technique for cell biologists, as newly identified proteins that associate with cellular membranes can be screened rapidly and robustly for lipid specificity and membrane affinity. This technical perspective discusses the conditions needed to achieve success with lipid–protein interactions and highlights the unique lipid–protein interaction mechanisms that have been elucidated using SPR. It is intended to provide the reader a framework for quantitative and confident conclusions from SPR analysis of lipid–protein interactions.