Synchrotron Protein Footprinting: A Technique to Investigate Protein-Protein Interactions

Abstract

Traditional approaches for macromolecular structure elucidation, including NMR, crystallography and cryo-EM have made significant progress in defining the structures of protein-protein complexes. A substantial number of macromolecular structures, however, have not been examined with atomic detail due to sample size and heterogeneity, or resolution limitations of the technique; therefore, the general applicability of each method is greatly reduced. Synchrotron footprinting attempts to bridge the gap in these methods by monitoring changes in accessible surface areas of discrete macromolecular moieties. As evidenced by our previous studies on RNA folding and DNA-protein interactions, the three-dimensional structure is probed by examining the reactions of these moieties with hydroxyl radicals generated by synchrotron X-rays. Here we report the application of synchrotron foot- printing to the investigation of protein-protein interactions, as the novel technique has been utilized to successfully map the contact sites of gelsolin segment-1 in the gelsolin segment 1/actin complex. Footprinting results demonstrate that phenylalanine 104, located on the actin binding helix of gelsolin segment 1, is protected from hydroxyl radical modification in the presence of actin. This change in reactivity results from the specific protection of gel- solin segment-1, consistent with the substantial decrease in solvent accessibility of F104 upon actin binding, as calculated from the crystal structural of the gelsolin segment 1/actin complex. The results presented here establish synchrotron footprinting as a broadly applicable method to probe structural features of macromolecular complexes that are not amenable to conventional approaches.     

RedMDStream: Parameterization and Simulation Toolbox for Coarse-Grained Molecular Dynamics Models

Coarse-grained (CG) models in molecular dynamics (MD) are powerful tools to simulate the dynamics of large biomolecular systems on micro- to millisecond timescales. However, the CG model, potential energy terms, and parameters are typically not transferable between different molecules and problems. So parameterizing CG force fields, which is both tedious and time-consuming, is often necessary. We present RedMDStream, a software for developing, testing, and simulating biomolecules with CG MD models. Development includes an automatic procedure for the optimization of potential energy parameters based on metaheuristic methods. As an example we describe the parameterization of a simple CG MD model of an RNA hairpin.     

Coarse-Grained Molecular Models of Water: A Review

Coarse-grained (CG) models have proven to be very effective tools in the study of phenomena or systems that involve large time- and length-scales. By decreasing the degrees of freedom in the system and using softer interactions than seen in atomistic models, larger timesteps can be used and much longer simulation times can be studied. CG simulations are widely used to study systems of biological importance that are beyond the reach of atomistic simulation, necessitating a computationally efficient and accurate CG model for water. In this review, we discuss the methods used for developing CG water models and the relative advantages and disadvantages of the resulting models. In general, CG water models differ with regards to how many waters each CG group or bead represents, whether analytical or tabular potentials have been used to describe the interactions, and how the model incorporates electrostatic interactions. Finally, how the models are parameterized depends on their application, so, while some are fitted to experimental properties such as surface tension and density, others are fitted to radial distribution functions extracted from atomistic simulations.     

Defining Coarse-Grained Representations of Large Biomolecules and Biomolecular Complexes from Elastic Network Models

Coarse-grained (CG) models of large biomolecular complexes enable simulations of these systems over long timescales that are not accessible for atomistic molecular dynamics (MD) simulations. A systematic methodology, called essential dynamics coarse-graining (ED-CG), has been developed for defining coarse-grained sites in a large biomolecule. The method variationally determines the CG sites so that key dynamic domains in the protein are preserved in the CG representation. The original ED-CG method relies on a principal component analysis (PCA) of a MD trajectory. However, for many large proteins and multi-protein complexes such an analysis may not converge or even be possible. This work develops a new ED-CG scheme using an elastic network model (ENM) of the protein structure. In this procedure, the low-frequency normal modes obtained by ENM are used to define dynamic domains and to define the CG representation accordingly. The method is then applied to several proteins, such as the HIV-1 CA protein dimer, ATP-bound G-actin, and the Arp2/3 complex. Numerical results show that ED-CG with ENM (ENM-ED-CG) is much faster than ED-CG with PCA because no MD is necessary. The ENM-ED-CG models also capture functional essential dynamics of the proteins almost as well as those using full MD with PCA. Therefore, the ENM-ED-CG method may be better suited to coarse-grain a very large biomolecule or biomolecular complex that is too computationally expensive to be simulated by conventional MD, or when a high resolution atomic structure is not even available.     

HYST: A Hybrid Set-Based Test for Genome-wide Association Studies, with Application to Protein-Protein Interaction-Based Association Analysis

The extended Simes’ test (known as GATES) and scaled chi-square test were proposed to combine a set of dependent genome-wide association signals at multiple single-nucleotide polymorphisms (SNPs) for assessing the overall significance of association at the gene or pathway levels. The two tests use different strategies to combine association p values and can outperform each other when the number of and linkage disequilibrium between SNPs vary. In this paper, we introduce a hybrid set-based test (HYST) combining the two tests for genome-wide association studies (GWASs). We describe how HYST can be used to evaluate statistical significance for association at the protein-protein interaction (PPI) level in order to increase power for detecting disease-susceptibility genes of moderate effect size. Computer simulations demonstrated that HYST had a reasonable type 1 error rate and was generally more powerful than its parents and other alternative tests to detect a PPI pair where both genes are associated with the disease of interest. We applied the method to three complex disease GWAS data sets in the public domain; the method detected a number of highly connected significant PPI pairs involving multiple confirmed disease-susceptibility genes not found in the SNP- and gene-based association analyses. These results indicate that HYST can be effectively used to examine a collection of predefined SNP sets based on prior biological knowledge for revealing additional disease-predisposing genes of modest effects in GWASs.     

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

Significant efforts were gathered to generate large-scale comprehensive protein-protein interaction network maps. This is instrumental to understand the pathogen-host relationships and was essentially performed by genetic screenings in yeast two-hybrid systems. The recent improvement of protein-protein interaction detection by a Gaussia luciferase-based fragment complementation assay now offers the opportunity to develop integrative comparative interactomic approaches necessary to rigorously compare interaction profiles of proteins from different pathogen strain variants against a common set of cellular factors.This paper specifically focuses on the utility of combining two orthogonal methods to generate protein-protein interaction datasets: yeast two-hybrid (Y2H) and a new assay, high-throughput Gaussia princeps protein complementation assay (HT-GPCA) performed in mammalian cells.A large-scale identification of cellular partners of a pathogen protein is performed by mating-based yeast two-hybrid screenings of cDNA libraries using multiple pathogen strain variants. A subset of interacting partners selected on a high-confidence statistical scoring is further validated in mammalian cells for pair-wise interactions with the whole set of pathogen variants proteins using HT-GPCA. This combination of two complementary methods improves the robustness of the interaction dataset, and allows the performance of a stringent comparative interaction analysis. Such comparative interactomics constitute a reliable and powerful strategy to decipher any pathogen-host interplays.     

Comparative Analysis of Protein-Protein Interactions in Cancer-Associated Genes 25

Protein-protein interactions (PPIs) have been widely studied to understand the bi-ological processes or molecular functions associated with different disease systems like cancer. While focused studies on individual cancers have generated valuable in-formation, global and comparative analysis of datasets from different cancer types has not been done. In this work, we carried out bioinformatic analysis of PPIs corresponding to differentially expressed genes from microarrays of various tumor tissues (belonging to bladder, colon, kidney and thyroid cancers) and compared their associated biological processes and molecular functions (based on Gene On-tology terms). We identified a set of processes or functions that are common to all these cancers, as well as those that are specific to only one or partial cancer types. Similarly, protein interaction networks in nucleic acid metabolism were compared to identify the common/specific clusters of proteins across different cancer types. Our results provide a basis for further experimental investigations to study protein interaction networks associated with cancer. The methodology developed in this work can also be applied to study similar disease systems.     

PPI Finder: A Mining Tool for Human Protein-Protein Interactions

Background

The exponential increase of published biomedical literature prompts the use of text mining tools to manage the information overload automatically. One of the most common applications is to mine protein-protein interactions (PPIs) from PubMed abstracts. Currently, most tools in mining PPIs from literature are using co-occurrence-based approaches or rule-based approaches. Hybrid methods (frame-based approaches) by combining these two methods may have better performance in predicting PPIs. However, the predicted PPIs from these methods are rarely evaluated by known PPI databases and co-occurred terms in Gene Ontology (GO) database.

Methodology/Principal Findings

We here developed a web-based tool, PPI Finder, to mine human PPIs from PubMed abstracts based on their co-occurrences and interaction words, followed by evidences in human PPI databases and shared terms in GO database. Only 28% of the co-occurred pairs in PubMed abstracts appeared in any of the commonly used human PPI databases (HPRD, BioGRID and BIND). On the other hand, of the known PPIs in HPRD, 69% showed co-occurrences in the literature, and 65% shared GO terms.

Conclusions

PPI Finder provides a useful tool for biologists to uncover potential novel PPIs. It is freely accessible at http://liweilab.genetics.ac.cn/tm/.     

Role of Protein Interactions in Defining HIV-1 Viral Capsid Shape and Stability: A Coarse-Grained Analysis

Coarse-grained models of the HIV-1 CA dimer are constructed based on all-atom molecular dynamics simulations. Coarse-grained representations of the capsid shell, which is composed of ∼1500 copies of CA proteins, are constructed and their stability is examined. A key interaction between carboxyl and hexameric amino terminal domains is shown to generate the curvature of the capsid shell. It is demonstrated that variation of the strength of this interaction for different subunits in the lattice can cause formation of asymmetric, conical-shaped closed capsid shells, and it is proposed that variations, in the structure of the additional carboxyl-amino terminal binding interface during self-assembly, are important aspects of capsid cone formation. These results are in agreement with recent structural studies of the capsid hexamer subunit, which suggest that variability in the binding interface is a cause of the differences in subunit environments that exist in a conical structure.     

A Large Scale Analysis of Protein-Protein Interactions in the Nitrogen-fixing Bacterium Mesorhizobium loti

Global viewing of protein–protein interactions (PPIs)is a useful way to assign biological roles to large numbersof proteins predicted by complete genome sequence. Here, wesystematically analyzed PPIs in the nitrogen-fixing soil bacteriumMesorhizobium loti using a modified high-throughput yeast two-hybridsystem. The aims of this study are primarily on the providingfunctional clues to M. loti proteins that are relevant to symbioticnitrogen fixation and conserved in other rhizobium species,especially proteins with regulatory functions and unannotatedproteins. By the screening of 1542 genes as bait, 3121 independentinteractions involving 1804 proteins (24% of the total proteincoding genes) were identified and each interaction was evaluatedusing an interaction generality (IG) measure and the generalfeatures of the interacting partners. Most PPIs detected inthis study are novel interactions revealing potential functionalrelationships between genes for symbiotic nitrogen fixationand signal transduction. Furthermore, we have predicted theputative functions of unannotated proteins through their interactionswith known proteins. The results described here represent newinsight into protein network of M. loti and provide useful experimentalclues to elucidate the biological function of rhizobial genesthat can not be assigned directly from their genomic sequence.     

双杂交和质谱技术在蛋白质-蛋白质相互作用研究中的应用

基因的功能是由蛋白质来执行的,而蛋白质要通过与其他生物分子相互作用来完成其各种生物功能。因此,如果能够快速做出蛋白质在不同时间、空间和不同环境中的相互作用图谱,就会帮助我们了解这些蛋白质的功能,进而了解许多生命活动的机制。目前,用于大规模研究蛋白质间相互作用的方法主要有酵母双杂交系统及其衍生系统、亲和纯化与质谱分析联用技术,前者用于研究蛋白分子间的两两相互作用,后者用于研究蛋白质复合物间的相互作用。本文主要阐述了酵母双杂交、细菌双杂交、哺乳动物细胞双杂交、亲和纯化与质谱联用技术在大规模蛋白质相互作用研究中的应用。     

A Flow Cytometry-Based FRET Assay to Identify and Analyse Protein-Protein Interactions in Living Cells

Background

Försters resonance energy transfer (FRET) microscopy is widely used for the analysis of protein interactions in intact cells. However, FRET microscopy is technically challenging and does not allow assessing interactions in large cell numbers. To overcome these limitations we developed a flow cytometry-based FRET assay and analysed interactions of human and simian immunodeficiency virus (HIV and SIV) Nef and Vpu proteins with cellular factors, as well as HIV Rev multimer-formation.

Results

Amongst others, we characterize the interaction of Vpu with CD317 (also termed Bst-2 or tetherin), a host restriction factor that inhibits HIV release from infected cells and demonstrate that the direct binding of both is mediated by the Vpu membrane-spanning region. Furthermore, we adapted our assay to allow the identification of novel protein interaction partners in a high-throughput format.

Conclusion

The presented combination of FRET and FACS offers the precious possibility to discover and define protein interactions in living cells and is expected to contribute to the identification of novel therapeutic targets for treatment of human diseases.     

Wiki-Pi: A Web-Server of Annotated Human Protein-Protein Interactions to Aid in Discovery of Protein Function

Protein-protein interactions (PPIs) are the basis of biological functions. Knowledge of the interactions of a protein can help understand its molecular function and its association with different biological processes and pathways. Several publicly available databases provide comprehensive information about individual proteins, such as their sequence, structure, and function. There also exist databases that are built exclusively to provide PPIs by curating them from published literature. The information provided in these web resources is protein-centric, and not PPI-centric. The PPIs are typically provided as lists of interactions of a given gene with links to interacting partners; they do not present a comprehensive view of the nature of both the proteins involved in the interactions. A web database that allows search and retrieval based on biomedical characteristics of PPIs is lacking, and is needed. We present Wiki-Pi (read Wiki-π), a web-based interface to a database of human PPIs, which allows users to retrieve interactions by their biomedical attributes such as their association to diseases, pathways, drugs and biological functions. Each retrieved PPI is shown with annotations of both of the participant proteins side-by-side, creating a basis to hypothesize the biological function facilitated by the interaction. Conceptually, it is a search engine for PPIs analogous to PubMed for scientific literature. Its usefulness in generating novel scientific hypotheses is demonstrated through the study of IGSF21, a little-known gene that was recently identified to be associated with diabetic retinopathy. Using Wiki-Pi, we infer that its association to diabetic retinopathy may be mediated through its interactions with the genes HSPB1, KRAS, TMSB4X and DGKD, and that it may be involved in cellular response to external stimuli, cytoskeletal organization and regulation of molecular activity. The website also provides a wiki-like capability allowing users to describe or discuss an interaction. Wiki-Pi is available publicly and freely at http://severus.dbmi.pitt.edu/wiki-pi/.     

Quantifying Protein-Protein Interactions of Peripheral Membrane Proteins by Fluorescence Brightness Analysis

Fluorescently labeled proteins that are found both in the cytoplasm and at the plasma membrane, such as peripheral membrane proteins, create stratified fluorescent layers that present a challenging environment for brightness studies with fluorescence fluctuation spectroscopy. The geometry of each layer along with fluorescence and brightness contributions from adjacent layers generates a convoluted raw brightness that conceals the underlying brightness of each individual layer. Because the brightness at a layer establishes the oligomeric state of the fluorescently labeled protein at said layer, we developed a method that connects the experimental raw brightness with the physical brightness at each layered compartment. The technique determines the oligomerization in each compartment from an axial intensity scan through the sample, followed by a fluorescence fluctuation spectroscopy measurement at each layer. We experimentally verify the technique with H-Ras-EGFP as a model system and determine its oligomeric state at both the plasma membrane and in the cytoplasm. Furthermore, we study the oligomerization of the Gag matrix domain of Human T-lymphotropic virus Type 1. The matrix domain targets the Gag polyprotein to the plasma membrane where, subsequently, viral assembly occurs. We determine the oligomerization of matrix in the cytoplasm and observe the onset of protein-protein interactions at the membrane. These observations shed light on the early assembly steps of the retrovirus.     

Quantifying Protein-Protein Interactions of Peripheral Membrane Proteins by Fluorescence Brightness Analysis

Fluorescently labeled proteins that are found both in the cytoplasm and at the plasma membrane, such as peripheral membrane proteins, create stratified fluorescent layers that present a challenging environment for brightness studies with fluorescence fluctuation spectroscopy. The geometry of each layer along with fluorescence and brightness contributions from adjacent layers generates a convoluted raw brightness that conceals the underlying brightness of each individual layer. Because the brightness at a layer establishes the oligomeric state of the fluorescently labeled protein at said layer, we developed a method that connects the experimental raw brightness with the physical brightness at each layered compartment. The technique determines the oligomerization in each compartment from an axial intensity scan through the sample, followed by a fluorescence fluctuation spectroscopy measurement at each layer. We experimentally verify the technique with H-Ras-EGFP as a model system and determine its oligomeric state at both the plasma membrane and in the cytoplasm. Furthermore, we study the oligomerization of the Gag matrix domain of Human T-lymphotropic virus Type 1. The matrix domain targets the Gag polyprotein to the plasma membrane where, subsequently, viral assembly occurs. We determine the oligomerization of matrix in the cytoplasm and observe the onset of protein-protein interactions at the membrane. These observations shed light on the early assembly steps of the retrovirus.     

Protein-Protein Interactions in a Crowded Environment: An Analysis via Cross-Docking Simulations and Evolutionary Information

Large-scale analyses of protein-protein interactions based on coarse-grain molecular docking simulations and binding site predictions resulting from evolutionary sequence analysis, are possible and realizable on hundreds of proteins with variate structures and interfaces. We demonstrated this on the 168 proteins of the Mintseris Benchmark 2.0. On the one hand, we evaluated the quality of the interaction signal and the contribution of docking information compared to evolutionary information showing that the combination of the two improves partner identification. On the other hand, since protein interactions usually occur in crowded environments with several competing partners, we realized a thorough analysis of the interactions of proteins with true partners but also with non-partners to evaluate whether proteins in the environment, competing with the true partner, affect its identification. We found three populations of proteins: strongly competing, never competing, and interacting with different levels of strength. Populations and levels of strength are numerically characterized and provide a signature for the behavior of a protein in the crowded environment. We showed that partner identification, to some extent, does not depend on the competing partners present in the environment, that certain biochemical classes of proteins are intrinsically easier to analyze than others, and that small proteins are not more promiscuous than large ones. Our approach brings to light that the knowledge of the binding site can be used to reduce the high computational cost of docking simulations with no consequence in the quality of the results, demonstrating the possibility to apply coarse-grain docking to datasets made of thousands of proteins. Comparison with all available large-scale analyses aimed to partner predictions is realized. We release the complete decoys set issued by coarse-grain docking simulations of both true and false interacting partners, and their evolutionary sequence analysis leading to binding site predictions. Download site: http://www.lgm.upmc.fr/CCDMintseris/