On the dynamic nature of the transition state for protein-protein association as determined by double-mutant cycle analysis and simulation

The process of protein-protein association starts with their random collision, which may develop into an encounter complex followed by a transition state and final complex formation. Here we aim to experimentally characterize the nature of the transition state of protein-protein association for three different protein-protein interactions; Barnase-Barstar, TEM1-BLIP and IFNalpha2-IFNAR2, and use the data to model the transition state structures. To model the transition state, we determined inter-protein distance-constraints of the activated complex by using double mutant cycles (DMC) assuming that interacting residues are spatially close. Significant DeltaDeltaG(double dagger)(int) values were obtained only between residues on Barnase and Barstar. However, introducing specific mutations that optimize the charge complementarity between BLIP and TEM1 resulted in the introduction of significant DeltaDeltaG(double dagger)(int) values also between residues of these two proteins. While electrostatic interactions make major contributions towards stabilizing the transition state, we show two examples where steric hindrance exerts an effect on the transition state as well. To model the transition-state structures from the experimental DeltaDeltaG(double dagger)(int) values, we introduced a method for structure perturbation, searching for those inter-protein orientations that best support the experimental DeltaDeltaG(double dagger)(int) values. Two types of transition states were found, specific as observed for Barnase-Barstar and the electrostatically optimized TEM1-BLIP mutants, and diffusive as shown for wild-type TEM1-BLIP and IFNalpha2-IFNAR2. The specific transition states are characterized by defined inter-protein orientations, which cannot be modeled for the diffusive transition states. Mutations introduced through rational design can change the transition state from diffusive to specific. Together, these data provide a structural view of the mechanism allowing rates of association to differ by five orders of magnitude between different protein complexes.     

Flexibility and conformational entropy in protein-protein binding

To better understand the interplay between protein-protein binding and protein dynamics, we analyzed molecular dynamics simulations of 17 protein-protein complexes and their unbound components. Complex formation does not restrict the conformational freedom of the partner proteins as a whole, but, rather, it leads to a redistribution of dynamics. We calculate the change in conformational entropy for seven complexes with quasiharmonic analysis. We see significant loss, but also increased or unchanged conformational entropy. Where comparison is possible, the results are consistent with experimental data. However, stringent error estimates based on multiple independent simulations reveal large uncertainties that are usually overlooked. We observe substantial gains of pseudo entropy in individual partner proteins, and we observe that all complexes retain residual stabilizing intermolecular motions. Consequently, protein flexibility has an important influence on the thermodynamics of binding and may disfavor as well as favor association. These results support a recently proposed unified model for flexible protein-protein association.     

A computational tool for identifying minimotifs in protein-protein interactions and improving the accuracy of minimotif predictions

Protein-protein interactions are important to understanding cell functions; however, our theoretical understanding is limited. There is a general discontinuity between the well-accepted physical and chemical forces that drive protein-protein interactions and the large collections of identified protein-protein interactions in various databases. Minimotifs are short functional peptide sequences that provide a basis to bridge this gap in knowledge. However, there is no systematic way to study minimotifs in the context of protein-protein interactions or vice versa. Here we have engineered a set of algorithms that can be used to identify minimotifs in known protein-protein interactions and implemented this for use by scientists in Minimotif Miner. By globally testing these algorithms on verified data and on 100 individual proteins as test cases, we demonstrate the utility of these new computation tools. This tool also can be used to reduce false-positive predictions in the discovery of novel minimotifs. The statistical significance of these algorithms is demonstrated by an ROC analysis (P = 0.001).     

Protein-protein interactions as a tool for site-specific labeling of proteins

Probing structures and dynamics within biomolecules using ensemble and single-molecule fluorescence resonance energy transfer requires the conjugation of fluorophores to proteins in a site-specific and thermodynamically nonperturbative fashion. Using single-molecule fluorescence-aided molecular sorting and the chymotrypsin inhibitor 2-subtilisin BPN' complex as an example, we demonstrate that protein-protein interactions can be exploited to afford site-specific labeling of a recombinant double-cysteine variant of CI2 without the need for extensive and time-consuming chromatography. The use of protein-protein interactions for site-specific labeling of proteins is compatible with and complementary to existing chemistries for selective labeling of N-terminal cysteines, and could be extended to label multiple positions within a given polypeptide chain.     

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.     

Protein-protein docking by simulating the process of association subject to biochemical constraints

We present a computational procedure for modeling protein-protein association and predicting the structures of protein-protein complexes. The initial sampling stage is based on an efficient Brownian dynamics algorithm that mimics the physical process of diffusional association. Relevant biochemical data can be directly incorporated as distance constraints at this stage. The docked configurations are then grouped with a hierarchical clustering algorithm into ensembles that represent potential protein-protein encounter complexes. Flexible refinement of selected representative structures is done by molecular dynamics simulation. The protein-protein docking procedure was thoroughly tested on 10 structurally and functionally diverse protein-protein complexes. Starting from X-ray crystal structures of the unbound proteins, in 9 out of 10 cases it yields structures of protein-protein complexes close to those determined experimentally with the percentage of correct contacts >30% and interface backbone RMSD <4 A. Detailed examination of all the docking cases gives insights into important determinants of the performance of the computational approach in modeling protein-protein association and predicting of protein-protein complex structures.     

Detection and localization of single molecular recognition events using atomic force microscopy

Because of its piconewton force sensitivity and nanometer positional accuracy, the atomic force microscope (AFM) has emerged as a powerful tool for exploring the forces and the dynamics of the interaction between individual ligands and receptors, either on isolated molecules or on cellular surfaces. These studies require attaching specific biomolecules or cells on AFM tips and on solid supports and measuring the unbinding forces between the modified surfaces using AFM force spectroscopy. In this review, we describe the current methodology for molecular recognition studies using the AFM, with an emphasis on strategies available for preparing AFM tips and samples, and on procedures for detecting and localizing single molecular recognition events.     

Dissecting protein-protein interactions using directed evolution

Protein-protein interactions are essential for life. They are responsible for most cellular functions and when they go awry often lead to disease. Proteins are inherently complex. They are flexible macromolecules whose constituent amino acid components act in combinatorial and networked ways when they engage one another in binding interactions. It is just this complexity that allows them to conduct such a broad array of biological functions. Despite decades of intense study of the molecular basis of protein-protein interactions, key gaps in our understanding remain, hindering our ability to accurately predict the specificities and affinities of their interactions. Until recently, most protein-protein investigations have been probed experimentally at the single-amino acid level, making them, by definition, incapable of capturing the combinatorial nature of, and networked communications between, the numerous residues within and outside of the protein-protein interface. This aspect of protein-protein interactions, however, is emerging as a major driving force for protein affinity and specificity. Understanding a combinatorial process necessarily requires a combinatorial experimental tool. Much like the organisms in which they reside, proteins naturally evolve over time, through a combinatorial process of mutagenesis and selection, to functionally associate. Elucidating the process by which proteins have evolved may be one of the keys to deciphering the molecular rules that govern their interactions with one another. Directed evolution is a technique performed in the laboratory that mimics natural evolution on a tractable time scale that has been utilized widely to engineer proteins with novel capabilities, including altered binding properties. In this review, we discuss directed evolution as an emerging tool for dissecting protein-protein interactions.     

In silico protein interaction analysis using the global proteome machine database

Experiments to probe for protein-protein interactions are the focus of functional proteomic studies, thus proteomic data repositories are increasingly likely to contain a large cross-section of such information. Here, we use the Global Proteome Machine database (GPMDB), which is the largest curated and publicly available proteomic data repository derived from tandem mass spectrometry, to develop an in silico protein interaction analysis tool. Using a human histone protein for method development, we positively identified an interaction partner from each histone protein family that forms the histone octameric complex. Moreover, this method, applied to the α subunits of the human proteasome, identified all of the subunits in the 20S core particle. Furthermore, we applied this approach to human integrin αIIb and integrin β3, a major receptor involved in the activation of platelets. We identified 28 proteins, including a protein network for integrin and platelet activation. In addition, proteins interacting with integrin β1 obtained using this method were validated by comparing them to those identified in a formaldehyde-supported coimmunoprecipitation experiment, protein-protein interaction databases and the literature. Our results demonstrate that in silico protein interaction analysis is a novel tool for identifying known/candidate protein-protein interactions and proteins with shared functions in a protein network.     

Protein-protein binding is often associated with changes in protonation state

pK(a) values of ionizable residues have been calculated using the PROPKA method and structures of 75 protein-protein complexes and their corresponding free forms. These pK(a) values were used to compute changes in protonation state of individual residues, net changes in protonation state of the complex relative to the uncomplexed proteins, and the correction to a binding energy calculated assuming standard protonation states at pH 7. For each complex, two different structures for the uncomplexed form of the proteins were used: the X-ray structures determined for the proteins in the absence of the other protein and the individual protein structures taken from the structure of the complex (referred to as unbound and bound structures, respectively). In 28 and 77% of the cases considered here, protein-protein binding is accompanied by a complete (>95%) or significant (>50%) change in protonation state of at least one residue using unbound structures. Furthermore, in 36 and 61% of the cases, protein-protein binding is accompanied by a complete or significant net change in protonation state of the complex relative to the separated monomers. Using bound structures, the corresponding values are 12, 51, 20, and 48%. Comparison to experimental data suggest that using unbound and bound structures lead to over- and underestimation of binding-induced protonation state changes, respectively. Thus, we conclude that protein-protein binding is often associated with changes in protonation state of amino acid residues and with changes in the net protonation state of the proteins. The pH-dependent correction to the binding energy contributes at least one order of magnitude to the binding constant in 45 and 23%, using unbound and bound structures, respectively.     

Crenarchaeal chromatin proteins Cren7 and Sul7 compact DNA by inducing rigid bends

Archaeal chromatin proteins share molecular and functional similarities with both bacterial and eukaryotic chromatin proteins. These proteins play an important role in functionally organizing the genomic DNA into a compact nucleoid. Cren7 and Sul7 are two crenarchaeal nucleoid-associated proteins, which are structurally homologous, but not conserved at the sequence level. Co-crystal structures have shown that these two proteins induce a sharp bend on binding to DNA. In this study, we have investigated the architectural properties of these proteins using atomic force microscopy, molecular dynamics simulations and magnetic tweezers. We demonstrate that Cren7 and Sul7 both compact DNA molecules to a similar extent. Using a theoretical model, we quantify the number of individual proteins bound to the DNA as a function of protein concentration and show that forces up to 3.5 pN do not affect this binding. Moreover, we investigate the flexibility of the bending angle induced by Cren7 and Sul7 and show that the protein–DNA complexes differ in flexibility from analogous bacterial and eukaryotic DNA-bending proteins.     

Crowding in extremophiles: linkage between solvation and weak protein-protein interactions, stability and dynamics, provides insight into molecular adaptation

The study of the molecular adaptation of microorganisms to extreme environments (solvent, temperature, etc.) has provided tools to investigate the complex relationships between protein-solvent and protein-protein interactions, protein stability and protein dynamics, and how they are modulated by the crowded environment of the cell. We have evaluated protein-solvent and protein-protein interactions by solution experiments (analytical ultracentrifugation, small angle neutron and X-ray scattering, density) and crystallography, and protein dynamics by energy resolved neutron scattering. This review concerns work from our laboratory on (i) proteins from extreme halophilic Archaea, and (ii) psychrophile, mesophile, thermophile and hyperthermophile bacterial cells.     

A Disulfide Bond Is Required for the Transmission of Forces through SUN-KASH Complexes

Numerous biological functions of a cell, including polarization, differentiation, division, and migration, rely on its ability to endure mechanical forces generated by the cytoskeleton on the nucleus. Coupling of the cytoskeleton and nucleoskeleton is ultimately mediated by LINC complexes that are formed via a strong interaction between SUN- and KASH-domain-containing proteins in the nuclear envelope. These complexes are mechanosensitive and essential for the transmission of forces between the cytoskeleton and nucleoskeleton, and the progression of cellular mechanotransduction. Herein, using molecular dynamics, we examine the effect of tension on the human SUN2-KASH2 complex and show that it is remarkably stable under physiologically relevant tensile forces and large strains. However, a covalent disulfide bond between two highly conserved cysteine residues of SUN2 and KASH2 is crucial for the stability of this interaction and the transmission of forces through the complex.     

An introduction to biomolecular simulations and docking

The biomolecules in and around a living cell – proteins, nucleic acids, lipids and carbohydrates – continuously sample myriad conformational states that are thermally accessible at physiological temperatures. Simultaneously, a given biomolecule also samples (and is sampled by) a rapidly fluctuating local environment comprising other biopolymers, small molecules, water, ions, etc. that diffuse to within a few nanometres, leading to inter-molecular contacts that stitch together large supramolecular assemblies. Indeed, all biological systems can be viewed as dynamic networks of molecular interactions. As a complement to experimentation, molecular simulation offers a uniquely powerful approach to analyse biomolecular structure, mechanism and dynamics; this is possible because the molecular contacts that define a complicated biomolecular system are governed by the same physical principles (forces and energetics) that characterise individual small molecules, and these simpler systems are relatively well-understood. With modern algorithms and computing capabilities, simulations are now an indispensable tool for examining biomolecular assemblies in atomic detail, from the conformational motion in an individual protein to the diffusional dynamics and inter-molecular collisions in the early stages of formation of cellular-scale assemblies such as the ribosome. This text introduces the physicochemical foundations of molecular simulations and docking, largely from the perspective of biomolecular interactions.     

Coarse-grained simulations of protein-protein association: an energy landscape perspective

Understanding protein-protein association is crucial in revealing the molecular basis of many biological processes. Here, we describe a theoretical simulation pipeline to study protein-protein association from an energy landscape perspective. First, a coarse-grained model is implemented and its applications are demonstrated via molecular dynamics simulations for several protein complexes. Second, an enhanced search method is used to efficiently sample a broad range of protein conformations. Third, multiple conformations are identified and clustered from simulation data and further projected on a three-dimensional globe specifying protein orientations and interacting energies. Results from several complexes indicate that the crystal-like conformation is favorable on the energy landscape even if the landscape is relatively rugged with metastable conformations. A closer examination on molecular forces shows that the formation of associated protein complexes can be primarily electrostatics-driven, hydrophobics-driven, or a combination of both in stabilizing specific binding interfaces. Taken together, these results suggest that the coarse-grained simulations and analyses provide an alternative toolset to study protein-protein association occurring in functional biomolecular complexes.     

Quantitative analysis of dynamic protein-protein interactions in planta by a floated-leaf luciferase complementation imaging (FLuCI) assay using binary Gateway vectors

Dynamic protein-protein interactions are essential in all cellular and developmental processes. Protein-fragment complementation assays allow such protein-protein interactions to be investigated in vivo. In contrast to other protein-fragment complementation assays, the split-luciferase (split-LUC) complementation approach facilitates dynamic and quantitative in vivo analysis of protein interactions, as the restoration of luciferase activity upon protein-protein interaction of investigated proteins is reversible. Here, we describe the development of a floated-leaf luciferase complementation imaging (FLuCI) assay that enables rapid and quantitative in vivo analyses of protein interactions in leaf discs floating on a luciferin infiltration solution after transient expression of split-LUC-labelled interacting proteins in Nicotiana benthamiana. We generated a set of eight Gateway-compatible split-LUC destination vectors, enabling fast, and almost fail-safe cloning of candidate proteins to the LUC termini in all possible constellations. We demonstrate their functionality by visualizing the well-established homodimerization of the 14-3-3 regulator proteins. Quantitative interaction analyses of the molybdenum co-factor biosynthesis proteins CNX6 and CNX7 show that the luciferase-based protein-fragment complementation assay allows direct real-time monitoring of absolute values of protein complex assembly. Furthermore, the split-LUC assay is established as valuable tool to investigate the dynamics of protein interactions by monitoring the disassembly of actin filaments in planta. The new Gateway-compatible split-LUC destination vector system, in combination with the FLuCI assay, provides a useful means to facilitate quantitative analyses of interactions between large numbers of proteins constituting interaction networks in plant cells.     

Atomic force microscope-based single-molecule force spectroscopy of RNA unfolding

Single-molecule force spectroscopy (SMFS) using the atomic force microscope (AFM) has emerged as an important tool for probing biomolecular interaction and exploring the forces, dynamics, and energy landscapes that underlie function and specificity of molecular interaction. These studies require attaching biomolecules on solid supports and AFM tips to measure unbinding forces between individual binding partners. Herein we describe efficient and robust protocols for probing RNA interaction by AFM and show their value on two well-known RNA regulators, the Rev-responsive element (RRE) from the HIV-1 genome and an adenine-sensing riboswitch. The results show the great potential of AFM–SMFS in the investigation of RNA molecular interactions, which will contribute to the development of bionanodevices sensing single RNA molecules.     

Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data

MOTIVATION: Biological processes in cells are properly performed by gene regulations, signal transductions and interactions between proteins. To understand such molecular networks, we propose a statistical method to estimate gene regulatory networks and protein-protein interaction networks simultaneously from DNA microarray data, protein-protein interaction data and other genome-wide data. RESULTS: We unify Bayesian networks and Markov networks for estimating gene regulatory networks and protein-protein interaction networks according to the reliability of each biological information source. Through the simultaneous construction of gene regulatory networks and protein-protein interaction networks of Saccharomyces cerevisiae cell cycle, we predict the role of several genes whose functions are currently unknown. By using our probabilistic model, we can detect false positives of high-throughput data, such as yeast two-hybrid data. In a genome-wide experiment, we find possible gene regulatory relationships and protein-protein interactions between large protein complexes that underlie complex regulatory mechanisms of biological processes.