Protein–protein interaction networks studies and importance of 3D structure knowledge

Protein–protein interaction networks (PPINs) are a powerful tool to study biological processes in living cells. In this review, we present the progress of PPIN studies from abstract to more detailed representations. We will focus on 3D interactome networks, which offer detailed information at the atomic level. This information can be exploited in understanding not only the underlying cellular mechanisms, but also how human variants and disease-causing mutations affect protein functions and complexes’ stability. Recent studies have used structural information on PPINs to also understand the molecular mechanisms of binding partner selection. We will address the challenges in generating 3D PPINs due to the restricted number of solved protein structures. Finally, some of the current use of 3D PPINs will be discussed, highlighting their contribution to the studies in genotype–phenotype relationships and in the optimization of targeted studies to design novel chemical compounds for medical treatments.     

Epitope‐specificity of recombinant antibodies reveals promiscuous peptide‐binding properties

Protein–peptide interactions are a common occurrence and essential for numerous cellular processes, and frequently explored in broad applications within biology, medicine, and proteomics. Therefore, understanding the molecular mechanism(s) of protein–peptide recognition, specificity, and binding interactions will be essential. In this study, we report the first detailed analysis of antibody–peptide interaction characteristics, by combining large‐scale experimental peptide binding data with the structural analysis of eight human recombinant antibodies and numerous peptides, targeting tryptic mammalian and eukaryote proteomes. The results consistently revealed that promiscuous peptide‐binding interactions, that is, both specific and degenerate binding, were exhibited by all antibodies, and the discovery was corroborated by orthogonal data, indicating that this might be a general phenomenon for low‐affinity antibody–peptide interactions. The molecular mechanism for the degenerate peptide‐binding specificity appeared to be executed through the use of 2–3 semi‐conserved anchor residues in the C‐terminal part of the peptides, in analogue to the mechanism utilized by the major histocompatibility complex–peptide complexes. In the long‐term, this knowledge will be instrumental for advancing our fundamental understanding of protein–peptide interactions, as well as for designing, generating, and applying peptide specific antibodies, or peptide‐binding proteins in general, in various biotechnical and medical applications.     

Peptide-based covalent inhibitors of protein–protein interactions

Protein–protein interactions (PPI) are involved in all cellular processes and many represent attractive therapeutic targets. However, the frequently rather flat and large interaction areas render the identification of small molecular PPI inhibitors very challenging. As an alternative, peptide interaction motifs derived from a PPI interface can serve as starting points for the development of inhibitors. However, certain proteins remain challenging targets when applying inhibitors with a competitive mode of action. For that reason, peptide-based ligands with an irreversible binding mode have gained attention in recent years. This review summarizes examples of covalent inhibitors that employ peptidic binders and have been tested in a biological context.     

AraPPISite: a database of fine-grained protein–protein interaction site annotations for Arabidopsis thaliana

Knowledge about protein interaction sites provides detailed information of protein–protein interactions (PPIs). To date, nearly 20,000 of PPIs from Arabidopsis thaliana have been identified. Nevertheless, the interaction site information has been largely missed by previously published PPI databases. Here, AraPPISite, a database that presents fine-grained interaction details for A. thaliana PPIs is established. First, the experimentally determined 3D structures of 27 A. thaliana PPIs are collected from the Protein Data Bank database and the predicted 3D structures of 3023 A. thaliana PPIs are modeled by using two well-established template-based docking methods. For each experimental/predicted complex structure, AraPPISite not only provides an interactive user interface for browsing interaction sites, but also lists detailed evolutionary and physicochemical properties of these sites. Second, AraPPISite assigns domain–domain interactions or domain–motif interactions to 4286 PPIs whose 3D structures cannot be modeled. In this case, users can easily query protein interaction regions at the sequence level. AraPPISite is a free and user-friendly database, which does not require user registration or any configuration on local machines. We anticipate AraPPISite can serve as a helpful database resource for the users with less experience in structural biology or protein bioinformatics to probe the details of PPIs, and thus accelerate the studies of plant genetics and functional genomics. AraPPISite is available at http://systbio.cau.edu.cn/arappisite/index.html.     

Non‐interacting surface solvation and dynamics in protein–protein interactions

Protein–protein interactions control a plethora of cellular processes, including cell proliferation, differentiation, apoptosis, and signal transduction. Understanding how and why proteins interact will inevitably lead to novel structure‐based drug design methods, as well as design of de novo binders with preferred interaction properties. At a structural and molecular level, interface and rim regions are not enough to fully account for the energetics of protein–protein binding, even for simple lock‐and‐key rigid binders. As we have recently shown, properties of the global surface might also play a role in protein–protein interactions. Here, we report on molecular dynamics simulations performed to understand solvent effects on protein–protein surfaces. We compare properties of the interface, rim, and non‐interacting surface regions for five different complexes and their free components. Interface and rim residues become, as expected, less mobile upon complexation. However, non‐interacting surface appears more flexible in the complex. Fluctuations of polar residues are always lower compared with charged ones, independent of the protein state. Further, stable water molecules are often observed around polar residues, in contrast to charged ones. Our analysis reveals that (a) upon complexation, the non‐interacting surface can have a direct entropic compensation for the lower interface and rim entropy and (b) the mobility of the first hydration layer, which is linked to the stability of the protein–protein complex, is influenced by the local chemical properties of the surface. These findings corroborate previous hypotheses on the role of the hydration layer in shielding protein–protein complexes from unintended protein–protein interactions. Proteins 2015; 83:445–458. © 2014 Wiley Periodicals, Inc.     

Binding of small molecules at interface of protein–protein complex – A newer approach to rational drug design

Protein–protein interaction is a vital process which drives many important physiological processes in the cell and has also been implicated in several diseases. Though the protein–protein interaction network is quite complex but understanding its interacting partners using both in silico as well as molecular biology techniques can provide better insights for targeting such interactions. Targeting protein–protein interaction with small molecules is a challenging task because of druggability issues. Nevertheless, several studies on the kinetics as well as thermodynamic properties of protein–protein interactions have immensely contributed toward better understanding of the affinity of these complexes. But, more recent studies on hot spots and interface residues have opened up new avenues in the drug discovery process. This approach has been used in the design of hot spot based modulators targeting protein–protein interaction with the objective of normalizing such interactions.     

Geometrical and electro-static determinants of protein-protein interactions

Protein-protein interactions (PPI) are pivotal to the numerous processes in the cell. Therefore, it is of interest to document the analysis of these interactions in terms of binding sites, topology of the interacting structures and physiochemical properties of interacting interfaces and the of forces interactions. The interaction interface of obligatory protein-protein complexes differs from that of the transient interactions. We have created a large database of protein-protein interactions containing over100 thousand interfaces. The structural redundancy was eliminated to obtain a non-redundant database of over 2,265 interaction interfaces. Therefore, it is of interest to document the analysis of these interactions in terms of binding sites, topology of the interacting structures and physiochemical properties of interacting interfaces and the offorces interactions. The residue interaction propensity and all of the rest of the parametric scores converged to a statistical indistinguishable common sub-range and followed the similar distribution trends for all three classes of sequence-based classifications PPInS. This indicates that the principles of molecular recognition are dependent on the preciseness of the fit in the interaction interfaces. Thus, it reinforces the importance of geometrical and electrostatic complementarity as the main determinants for PPIs.     

Recognition pliability is coupled to structural heterogeneity: a calmodulin intrinsically disordered binding region complex

Protein interactions within regulatory networks should adapt in a spatiotemporal-dependent dynamic environment, in order to process and respond to diverse and versatile cellular signals. However, the principles governing recognition pliability in protein complexes are not well understood. We have investigated a region of the intrinsically disordered protein myelin basic protein (MBP(145-165)) that interacts with calmodulin, but that also promiscuously binds other biomolecules (membranes, modifying enzymes). To characterize this interaction, we implemented an NMR spectroscopic approach that calculates, for each conformation of the complex, the maximum occurrence based on recorded pseudocontact shifts and residual dipolar couplings. We found that the MBP(145-165)-calmodulin interaction is characterized by structural heterogeneity. Quantitative comparative analysis indicated that distinct conformational landscapes of structural heterogeneity are sampled for different calmodulin-target complexes. Such structural heterogeneity in protein complexes could potentially explain the way that transient and promiscuous protein interactions are optimized and tuned in complex regulatory networks.     

LEVELNET to visualize,explore, and compare protein–protein interaction networks

Physical interactions between proteins are central to all biological processes. Yet, the current knowledge of who interacts with whom in the cell and in what manner relies on partial, noisy, and highly heterogeneous data. Thus, there is a need for methods comprehensively describing and organizing such data. LEVELNET is a versatile and interactive tool for visualizing, exploring, and comparing protein–protein interaction (PPI) networks inferred from different types of evidence. LEVELNET helps to break down the complexity of PPI networks by representing them as multi-layered graphs and by facilitating the direct comparison of their subnetworks toward biological interpretation. It focuses primarily on the protein chains whose 3D structures are available in the Protein Data Bank. We showcase some potential applications, such as investigating the structural evidence supporting PPIs associated to specific biological processes, assessing the co-localization of interaction partners, comparing the PPI networks obtained through computational experiments versus homology transfer, and creating PPI benchmarks with desired properties.     

Feature selection and classification of protein–protein complexes based on their binding affinities using machine learning approaches

Protein–protein interactions are intrinsic to virtually every cellular process. Predicting the binding affinity of protein–protein complexes is one of the challenging problems in computational and molecular biology. In this work, we related sequence features of protein–protein complexes with their binding affinities using machine learning approaches. We set up a database of 185 protein–protein complexes for which the interacting pairs are heterodimers and their experimental binding affinities are available. On the other hand, we have developed a set of 610 features from the sequences of protein complexes and utilized Ranker search method, which is the combination of Attribute evaluator and Ranker method for selecting specific features. We have analyzed several machine learning algorithms to discriminate protein‐protein complexes into high and low affinity groups based on their K_d values. Our results showed a 10‐fold cross‐validation accuracy of 76.1% with the combination of nine features using support vector machines. Further, we observed accuracy of 83.3% on an independent test set of 30 complexes. We suggest that our method would serve as an effective tool for identifying the interacting partners in protein–protein interaction networks and human–pathogen interactions based on the strength of interactions. Proteins 2014; 82:2088–2096. © 2014 Wiley Periodicals, Inc.     

LEC–BiFC: a new method for rapid assay of protein interaction

Abstract

Protein–protein interactions play fundamental roles in most biological processes. Bimolecular fluorescence complementation (BiFC) is a promising method for its simplicity and direct visualization of protein–protein interactions in cells. This method, however, is limited by background fluorescence that appears without specific interaction between the proteins. We report here a point mutation (V150L) in one Venus BiFC fragment that efficiently decreases background fluorescence of BiFC assay. Furthermore, by combining this modified BiFC and linear expression cassette (LEC), we develop a simple and rapid method (LEC–BiFC) for protein interaction analysis that is demonstrated by a case study of the interaction between Bcl–X_L and Bak BH3 peptide. The total analysis procedure can be completed in two days for screening tens of mutants. LEC–BiFC can be applied easily in any lab equipped with a fluorescence microscope.     

Unraveling the dynamics of protein interactions with quantitative mass spectrometry

Knowledge of structure and dynamics of proteins and protein complexes is important to unveil the molecular basis and mechanisms involved in most biological processes. Protein complex dynamics can be defined as the changes in the composition of a protein complex during a cellular process. Protein dynamics can be defined as conformational changes in a protein during enzyme activation, for example, when a protein binds to a ligand or when a protein binds to another protein. Mass spectrometry (MS) combined with affinity purification has become the analytical tool of choice for mapping protein–protein interaction networks and the recent developments in the quantitative proteomics field has made it possible to identify dynamically interacting proteins. Furthermore, hydrogen/deuterium exchange MS is emerging as a powerful technique to study structure and conformational dynamics of proteins or protein assemblies in solution. Methods have been developed and applied for the identification of transient and/or weak dynamic interaction partners and for the analysis of conformational dynamics of proteins or protein complexes. This review is an overview of existing and recent developments in studying the overall dynamics of in vivo protein interaction networks and protein complexes using MS-based methods.     

OpenPIP: An Open-source Platform for Hosting,Visualizing and Analyzing Protein Interaction Data

Knowing which proteins interact with each other is essential information for understanding how most biological processes at the cellular and organismal level operate and how their perturbation can cause disease. Continuous technical and methodological advances over the last two decades have led to many genome-wide systematically-generated protein–protein interaction (PPI) maps. To help store, visualize, analyze and disseminate these specialized experimental datasets via the web, we developed the freely-available Open-source Protein Interaction Platform (openPIP) as a customizable web portal designed to host experimental PPI maps. Such a portal is often required to accompany a paper describing the experimental data set, in addition to depositing the data in a standard repository. No coding skills are required to set up and customize the database and web portal. OpenPIP has been used to build the databases and web portals of two major protein interactome maps, the Human and Yeast Reference Protein Interactome maps (HuRI and YeRI, respectively). OpenPIP is freely available as a ready-to-use Docker container for hosting and sharing PPI data with the scientific community at http://openpip.baderlab.org/ and the source code can be downloaded from https://github.com/BaderLab/openPIP/.