Characterisation of Protein-Ligand Interfaces: Separating Surfaces

A new method for characterising protein-protein complexes is presented wherein the interface is modelled as a separating surface. This surface is defined by a set of points located halfway on the shortest distance vectors between surface points of the two molecular partners. The surface is generated using a grid-based algorithm. The distance to the nearest atom is stored on the grid points and an isosurface is generated forming the separating surface. Size and shape of the surface characterises the complex interface. Distances, forces, and other physicochemical properties can be mapped onto the surface and are used to study the intermolecular interactions. This is demonstrated with the systems lysozym-antibody, p53-DNA and trypsin-BPTI.Electronic Supplementary Material available.     

Predicting where Small Molecules Bind at Protein-Protein Interfaces

Small molecules that bind at protein-protein interfaces may either block or stabilize protein-protein interactions in cells. Thus, some of these binding interfaces may turn into prospective targets for drug design. Here, we collected 175 pairs of protein-protein (PP) complexes and protein-ligand (PL) complexes with known three-dimensional structures for which (1) one protein from the PP complex shares at least 40% sequence identity with the protein from the PL complex, and (2) the interface regions of these proteins overlap at least partially with each other. We found that those residues of the interfaces that may bind the other protein as well as the small molecule are evolutionary more conserved on average, have a higher tendency of being located in pockets and expose a smaller fraction of their surface area to the solvent than the remaining protein-protein interface region. Based on these findings we derived a statistical classifier that predicts patches at binding interfaces that have a higher tendency to bind small molecules. We applied this new prediction method to more than 10 000 interfaces from the protein data bank. For several complexes related to apoptosis the predicted binding patches were in direct contact to co-crystallized small molecules.     

Exploring the Composition of Protein-Ligand Binding Sites on a Large Scale

The residue composition of a ligand binding site determines the interactions available for diffusion-mediated ligand binding, and understanding general composition of these sites is of great importance if we are to gain insight into the functional diversity of the proteome. Many structure-based drug design methods utilize such heuristic information for improving prediction or characterization of ligand-binding sites in proteins of unknown function. The Binding MOAD database if one of the largest curated sets of protein-ligand complexes, and provides a source of diverse, high-quality data for establishing general trends of residue composition from currently available protein structures. We present an analysis of 3,295 non-redundant proteins with 9,114 non-redundant binding sites to identify residues over-represented in binding regions versus the rest of the protein surface. The Binding MOAD database delineates biologically-relevant “valid” ligands from “invalid” small-molecule ligands bound to the protein. Invalids are present in the crystallization medium and serve no known biological function. Contacts are found to differ between these classes of ligands, indicating that residue composition of biologically relevant binding sites is distinct not only from the rest of the protein surface, but also from surface regions capable of opportunistic binding of non-functional small molecules. To confirm these trends, we perform a rigorous analysis of the variation of residue propensity with respect to the size of the dataset and the content bias inherent in structure sets obtained from a large protein structure database. The optimal size of the dataset for establishing general trends of residue propensities, as well as strategies for assessing the significance of such trends, are suggested for future studies of binding-site composition.     

Exploiting Amino Acid Composition for Predicting Protein-Protein Interactions

Background

Computational prediction of protein interactions typically use protein domains as classifier features because they capture conserved information of interaction surfaces. However, approaches relying on domains as features cannot be applied to proteins without any domain information. In this paper, we explore the contribution of pure amino acid composition (AAC) for protein interaction prediction. This simple feature, which is based on normalized counts of single or pairs of amino acids, is applicable to proteins from any sequenced organism and can be used to compensate for the lack of domain information.

Results

AAC performed at par with protein interaction prediction based on domains on three yeast protein interaction datasets. Similar behavior was obtained using different classifiers, indicating that our results are a function of features and not of classifiers. In addition to yeast datasets, AAC performed comparably on worm and fly datasets. Prediction of interactions for the entire yeast proteome identified a large number of novel interactions, the majority of which co-localized or participated in the same processes. Our high confidence interaction network included both well-studied and uncharacterized proteins. Proteins with known function were involved in actin assembly and cell budding. Uncharacterized proteins interacted with proteins involved in reproduction and cell budding, thus providing putative biological roles for the uncharacterized proteins.

Conclusion

AAC is a simple, yet powerful feature for predicting protein interactions, and can be used alone or in conjunction with protein domains to predict new and validate existing interactions. More importantly, AAC alone performs at par with existing, but more complex, features indicating the presence of sequence-level information that is predictive of interaction, but which is not necessarily restricted to domains.     

Evidence of Conformational Selection Driving the Formation of Ligand Binding Sites in Protein-Protein Interfaces

Many protein-protein interactions (PPIs) are compelling targets for drug discovery, and in a number of cases can be disrupted by small molecules. The main goal of this study is to examine the mechanism of binding site formation in the interface region of proteins that are PPI targets by comparing ligand-free and ligand-bound structures. To avoid any potential bias, we focus on ensembles of ligand-free protein conformations obtained by nuclear magnetic resonance (NMR) techniques and deposited in the Protein Data Bank, rather than on ensembles specifically generated for this study. The measures used for structure comparison are based on detecting binding hot spots, i.e., protein regions that are major contributors to the binding free energy. The main tool of the analysis is computational solvent mapping, which explores the surface of proteins by docking a large number of small “probe” molecules. Although we consider conformational ensembles obtained by NMR techniques, the analysis is independent of the method used for generating the structures. Finding the energetically most important regions, mapping can identify binding site residues using ligand-free models based on NMR data. In addition, the method selects conformations that are similar to some peptide-bound or ligand-bound structure in terms of the properties of the binding site. This agrees with the conformational selection model of molecular recognition, which assumes such pre-existing conformations. The analysis also shows the maximum level of similarity between unbound and bound states that is achieved without any influence from a ligand. Further shift toward the bound structure assumes protein-peptide or protein-ligand interactions, either selecting higher energy conformations that are not part of the NMR ensemble, or leading to induced fit. Thus, forming the sites in protein-protein interfaces that bind peptides and can be targeted by small ligands always includes conformational selection, although other recognition mechanisms may also be involved.     

Probabilistic Prediction of Contacts in Protein-Ligand Complexes

We introduce a statistical method for evaluating atomic level 3D interaction patterns of protein-ligand contacts. Such patterns can be used for fast separation of likely ligand and ligand binding site combinations out of all those that are geometrically possible. The practical purpose of this probabilistic method is for molecular docking and scoring, as an essential part of a scoring function. Probabilities of interaction patterns are calculated conditional on structural x-ray data and predefined chemical classification of molecular fragment types. Spatial coordinates of atoms are modeled using a Bayesian statistical framework with parametric 3D probability densities. The parameters are given distributions a priori, which provides the possibility to update the densities of model parameters with new structural data and use the parameter estimates to create a contact hierarchy. The contact preferences can be defined for any spatial area around a specified type of fragment. We compared calculated contact point hierarchies with the number of contact atoms found near the contact point in a reference set of x-ray data, and found that these were in general in a close agreement. Additionally, using substrate binding site in cathechol-O-methyltransferase and 27 small potential binder molecules, it was demonstrated that these probabilities together with auxiliary parameters separate well ligands from decoys (true positive rate 0.75, false positive rate 0). A particularly useful feature of the proposed Bayesian framework is that it also characterizes predictive uncertainty in terms of probabilities, which have an intuitive interpretation from the applied perspective.     

Geometric Similarities of Protein-Protein Interfaces at Atomic Resolution Are Only Observed within Homologous Families: An Exhaustive Structural Classification Study

To elucidate the structural basis of the diversity and universality in protein-protein interactions, an exhaustive all-against-all structural comparison of all known protein interfaces in the Protein Data Bank was performed at atomic resolution. After similar interfaces were clustered, approximately 20,000 structural motifs with at least two members were identified, out of which 3678 motifs consisted of at least 10 interfaces. Except for some trivial interfaces involving single α helices, almost all motifs were found to be confined within single protein families. Furthermore, the interaction partners of each motif were found to be very limited, and, accordingly, the interaction networks of the motifs tend to be small and are much more restricted than the binding sites for small ligand molecules. These findings suggest that, at the level of atomic structures, protein-protein interactions are precisely designed; hence, protein interfaces with multiple interacting partners should involve incompletely overlapping multiple interfaces and/or accommodate structural changes upon binding to their targets.     

细胞蛋白质相互作用的结构基础

随着人类基因组计划的进行 ,大量基因被发现和定位 ,基因的功能问题将成为今后研究的热点。大多数基因的最终产物是相应的蛋白质 ,因此要认识基因的功能 ,必然要研究基因所表达的蛋白质。蛋白质的功能往往体现在与其他蛋白质及 /或核酸的相互作用之中。细胞各种重要的生理过程 ,包括信号的转导 ,细胞对外界环境及内环境变化的反应等 ,都是以蛋白质间相互作用为纽带 ,并形成网络。所以 ,近年来 ,蛋白质间相互作用的研究逐渐得到重视。蛋白质分子的结构域有很多种 ,但是现在明确作为为介导蛋白质 蛋白质间相互作用的结构域并不多 ,这里取已明…     

蛋白质相互作用研究方法及其应用

过去10年来,蛋白质组学得到迅速发展,蛋白质间的相互作用作为蛋白质组学的重要内容,更是成为国内外竞相研究的重点,研究方法的快速发展为蛋白质间相互作用的研究奠定了坚实基础。着重就经典的噬菌体展示、酵母双杂交以及新近发展起来的串联亲和纯化、荧光共振能量转移技术和表面等离子共振等蛋白质相互作用研究方法的原理及应用作一综述并展望其发展前景。     

Real-Time Insights into Biological Events: In-Cell Processes and Protein-Ligand Interactions

FlowNMR has the aim of continuously monitoring processes that occur in conditions that are not compatible with being carried out within a closed tube. However, it is sample intensive and not suitable for samples, such as proteins or living cells, that are often available in limited volumes and possibly low concentrations. We here propose a dialysis-based modification of a commercial flowNMR setup that allows for recycling the medium while confining the sample (proteins and cells) within the active volume of the tube. This approach is demonstrated in the specific cases of in-cell NMR and protein-based ligand studies.     

Geometric De-noising of Protein-Protein Interaction Networks

Understanding complex networks of protein-protein interactions (PPIs) is one of the foremost challenges of the post-genomic era. Due to the recent advances in experimental bio-technology, including yeast-2-hybrid (Y2H), tandem affinity purification (TAP) and other high-throughput methods for protein-protein interaction (PPI) detection, huge amounts of PPI network data are becoming available. Of major concern, however, are the levels of noise and incompleteness. For example, for Y2H screens, it is thought that the false positive rate could be as high as 64%, and the false negative rate may range from 43% to 71%. TAP experiments are believed to have comparable levels of noise.We present a novel technique to assess the confidence levels of interactions in PPI networks obtained from experimental studies. We use it for predicting new interactions and thus for guiding future biological experiments. This technique is the first to utilize currently the best fitting network model for PPI networks, geometric graphs. Our approach achieves specificity of 85% and sensitivity of 90%. We use it to assign confidence scores to physical protein-protein interactions in the human PPI network downloaded from BioGRID. Using our approach, we predict 251 interactions in the human PPI network, a statistically significant fraction of which correspond to protein pairs sharing common GO terms. Moreover, we validate a statistically significant portion of our predicted interactions in the HPRD database and the newer release of BioGRID. The data and Matlab code implementing the methods are freely available from the web site: http://www.kuchaev.com/Denoising.     

Conformational Dynamics and Cooperativity Drive the Specificity of a Protein-Ligand Interaction

Molecular recognition is critical for the fidelity of signal transduction in biology. Conversely, the disruption of protein-protein interactions can lead to disease. Thus, comprehension of the molecular determinants of specificity is essential for understanding normal biological signaling processes and for the development of precise therapeutics. Although high-resolution structures have provided atomic details of molecular interactions, much less is known about the influence of cooperativity and conformational dynamics. Here, we used the Tiam2 PSD-95/Dlg/ZO-1 (PDZ) domain and a quadruple mutant (QM), engineered by swapping the identity of four residues important for specificity in the Tiam1 PDZ into the Tiam2 PDZ domain, as a model system to investigate the role of cooperativity and dynamics in PDZ ligand specificity. Surprisingly, equilibrium binding experiments found that the ligand specificity of the Tiam2 QM was switched to that of the Tiam1 PDZ. NMR-based studies indicated that Tiam2 QM PDZ, but not other mutants, had extensive microsecond to millisecond motions distributed throughout the entire domain suggesting structural cooperativity between the mutated residues. Thermodynamic analyses revealed energetic cooperativity between residues in distinct specificity subpockets that was dependent upon the identity of the ligand, indicating a context-dependent binding mechanism. Finally, isothermal titration calorimetry experiments showed distinct entropic signatures along the mutational trajectory from the Tiam2 wild-type to the QM PDZ domain. Collectively, our studies provide unique insights into how structure, conformational dynamics, and thermodynamics combine to modulate ligand-binding specificity and have implications for the evolution, regulation, and design of protein-ligand interactions.     

Evolution of Specificity in Protein-Protein Interactions

Hub proteins are proteins that maintain promiscuous molecular recognition. Because they are reported to play essential roles in cellular control, there has been a special interest in the study of their structural and functional properties, yet the mechanisms by which they evolve to maintain functional interactions are poorly understood. By combining biophysical simulations of coarse-grained proteins and analysis of proteins-complex crystallographic structures, we seek to elucidate those mechanisms. We focus on two types of hub proteins: Multi hubs, which interact with their partners through different interfaces, and Singlish hubs, which do so through a single interface. We show that loss of structural stability is required for the evolution of protein-protein-interaction (PPI) networks, and it is more profound in Singlish hub systems. In addition, different ratios of hydrophobic to electrostatic interfacial amino acids are shown to support distinct network topologies (i.e., Singlish and Multi systems), and therefore underlie a fundamental design principle of PPI in a crowded environment. We argue that the physical nature of hydrophobic and electrostatic interactions, in particular, their favoring of either same-type interactions (hydrophobic-hydrophobic), or opposite-type interactions (negatively-positively charged) plays a key role in maintaining the network topology while allowing the protein amino acid sequence to evolve.     

Evolution of Specificity in Protein-Protein Interactions

Hub proteins are proteins that maintain promiscuous molecular recognition. Because they are reported to play essential roles in cellular control, there has been a special interest in the study of their structural and functional properties, yet the mechanisms by which they evolve to maintain functional interactions are poorly understood. By combining biophysical simulations of coarse-grained proteins and analysis of proteins-complex crystallographic structures, we seek to elucidate those mechanisms. We focus on two types of hub proteins: Multi hubs, which interact with their partners through different interfaces, and Singlish hubs, which do so through a single interface. We show that loss of structural stability is required for the evolution of protein-protein-interaction (PPI) networks, and it is more profound in Singlish hub systems. In addition, different ratios of hydrophobic to electrostatic interfacial amino acids are shown to support distinct network topologies (i.e., Singlish and Multi systems), and therefore underlie a fundamental design principle of PPI in a crowded environment. We argue that the physical nature of hydrophobic and electrostatic interactions, in particular, their favoring of either same-type interactions (hydrophobic-hydrophobic), or opposite-type interactions (negatively-positively charged) plays a key role in maintaining the network topology while allowing the protein amino acid sequence to evolve.     

Rapid Quantification of Protein-Ligand Binding via 19F NMR Lineshape Analysis

Fluorine incorporation is ideally suited to many NMR techniques, and incorporation of fluorine into proteins and fragment libraries for drug discovery has become increasingly common. Here, we use one-dimensional ¹⁹F NMR lineshape analysis to quantify the kinetics and equilibrium thermodynamics for the binding of a fluorine-labeled Src homology 3 (SH3) protein domain to four proline-rich peptides. SH3 domains are one of the largest and most well-characterized families of protein recognition domains and have a multitude of functions in eukaryotic cell signaling. First, we showe that fluorine incorporation into SH3 causes only minor structural changes to both the free and bound states using amide proton temperature coefficients. We then compare the results from lineshape analysis of one-dimensional ¹⁹F spectra to those from two-dimensional ¹H-¹⁵N heteronuclear single quantum coherence spectra. Their agreement demonstrates that one-dimensional ¹⁹F lineshape analysis is a robust, low-cost, and fast alternative to traditional heteronuclear single quantum coherence-based experiments. The data show that binding is diffusion limited and indicate that the transition state is highly similar to the free state. We also measured binding as a function of temperature. At equilibrium, binding is enthalpically driven and arises from a highly positive activation enthalpy for association with small entropic contributions. Our results agree with those from studies using different techniques, providing additional evidence for the utility of ¹⁹F NMR lineshape analysis, and we anticipate that this analysis will be an effective tool for rapidly characterizing the energetics of protein interactions.