Protein–protein interactions of huntingtin in the hippocampus

Molecular Biology - Huntingtin (HTT) occurs in the neuronal cytoplasm and can interact with structural elements of synapses. Huntington’s disease (HD) results from pathological expansion of a...     

Mass spectrometry-based approaches to protein–ligand interactions

One of the greatest current challenges in proteomics is to develop an understanding of cellular communication and regulation processes, most of which involve noncovalent interactions of proteins with various binding partners. Mass spectrometry plays an important role in all aspects of these research efforts. This article provides a survey of mass spectrometry-based approaches for exploring protein–ligand interactions. A wide array of techniques is available, and the choice of method depends on the specific problem at hand. For example, the high-throughput screening of compound libraries for binding to a specific receptor requires different approaches than structural studies on multiprotein complexes. This review is directed to readers wishing to obtain a concise yet comprehensive overview of existing experimental techniques. Specific emphasis is placed on emerging methods that have been developed within the last few years.     

Variation of protein binding cavity volume and ligand volume in protein–ligand complexes

We have systematically analyzed the variation of protein binding cavity volume of 200 protein–ligand complexes belonging to eight protein families. Wide variation in protein binding cavity volume for the same protein is observed on binding different ligands. Analysis of individual protein families shows high correlation between atom–atom interactions in binding site and ligand volume. This study implies the significance of protein flexibility in docking small molecule inhibitors on the basis of protein binding cavity volume with respect to ligand volume.     

NLDB: a database for 3D protein–ligand interactions in enzymatic reactions

NLDB (Natural Ligand DataBase; URL: http://nldb.hgc.jp) is a database of automatically collected and predicted 3D protein–ligand interactions for the enzymatic reactions of metabolic pathways registered in KEGG. Structural information about these reactions is important for studying the molecular functions of enzymes, however a large number of the 3D interactions are still unknown. Therefore, in order to complement such missing information, we predicted protein–ligand complex structures, and constructed a database of the 3D interactions in reactions. NLDB provides three different types of data resources; the natural complexes are experimentally determined protein–ligand complex structures in PDB, the analog complexes are predicted based on known protein structures in a complex with a similar ligand, and the ab initio complexes are predicted by docking simulations. In addition, NLDB shows the known polymorphisms found in human genome on protein structures. The database has a flexible search function based on various types of keywords, and an enrichment analysis function based on a set of KEGG compound IDs. NLDB will be a valuable resource for experimental biologists studying protein–ligand interactions in specific reactions, and for theoretical researchers wishing to undertake more precise simulations of interactions.     

Identification of individual protein–ligand NOEs in the limit of intermediate exchange

Interactions of proteins with small molecules or other macromolecules play key roles in many biological processes and in drug action, and NMR is an excellent tool for their structural characterization. Frequently, however, line broadening due to intermediate exchange completely eliminates the signals needed for measuring specific intermolecular NOEs. This limits the use of NMR for detailed structural studies in such kinetic situations. Here we show that an optimally chosen excess of ligand over protein can reduce the extent of line broadening for both the ligand and the protein. This makes observation of ligand resonances possible but reduces the size of the measurable NOEs due to the residual line broadening and the non-stoichiometric concentrations. Because the solubility of small molecule drug leads are often limited to high micromolar concentrations, protein concentrations are restricted to even lower values in the low micromolar range. At these non-stoichiometric concentrations and in the presence of significant residual line broadening, conventional NOESY experiments very often are not sensitive enough to observe intermolecular NOEs since the signals inverted by the NOESY preparation pulse sequence relax prior to significant NOE build up. Thus, we employ methods related to driven NOE spectroscopy to investigate protein–ligand interactions in the intermediate exchange regime. In this approach, individual protein resonances are selectively irradiated for up to five seconds to build up measurable NOEs at the ligand resonances. To enable saturation of individual protein resonances we prepare deuterated protein samples selectively protonated at a few sites so that the 1D ¹H spectrum of the protein is resolved well enough to permit irradiation of individual protein signals, which do not overlap with the ligand spectrum. This approach is suitable for measuring a sufficiently large number of protein–ligand NOEs that allow calculation of initial complex structures, suitable for structure-based optimization of primary drug leads obtained from high-throughput screening. The method was applied to measure individual intermolecular NOEs between the anti-apoptotic protein Bcl-xL at 25 μM and a “first generation” small-molecule ligand, for which the spectrum is entirely broadened at stoichiometric concentrations. This approach is general and can also be used to characterize protein–protein or protein–nucleic-acid complexes.     

Force spectroscopy studies on protein–ligand interactions: A single protein mechanics perspective

Protein–ligand interactions are ubiquitous and play important roles in almost every biological process. The direct elucidation of the thermodynamic, structural and functional consequences of protein–ligand interactions is thus of critical importance to decipher the mechanism underlying these biological processes. A toolbox containing a variety of powerful techniques has been developed to quantitatively study protein–ligand interactions in vitro as well as in living systems. The development of atomic force microscopy-based single molecule force spectroscopy techniques has expanded this toolbox and made it possible to directly probe the mechanical consequence of ligand binding on proteins. Many recent experiments have revealed how ligand binding affects the mechanical stability and mechanical unfolding dynamics of proteins, and provided mechanistic understanding on these effects. The enhancement effect of mechanical stability by ligand binding has been used to help tune the mechanical stability of proteins in a rational manner and develop novel functional binding assays for protein–ligand interactions. Single molecule force spectroscopy studies have started to shed new lights on the structural and functional consequence of ligand binding on proteins that bear force under their biological settings.     

Time efficient detection of protein–ligand interactions with the polarization optimized PO-WaterLOGSY NMR experiment

The identification of compounds that bind to a protein of interest is of central importance in contemporary drug research. For screening of compound libraries, NMR techniques are widely used, in particular the Water-Ligand Observed via Gradient SpectroscopY (WaterLOGSY) experiment. Here we present an optimized experiment, the polarization optimized WaterLOGSY (PO-WaterLOGSY). Based on a water flip-back strategy in conjunction with model calculations and numerical simulations, the PO-WaterLOGSY is optimized for water polarization recovery. Compared to a standard setup with the conventional WaterLOGSY, time consuming relaxation delays have been considerably shortened and can even be omitted through this approach. Furthermore, the robustness of the pulse sequence in an industrial setup was increased by the use of hard pulse trains for selective water excitation and water suppression. The PO-WaterLOGSY thus yields increased time efficiency by factor of 3–5 when compared with previously published schemes. These time savings have a substantial impact in drug discovery, since significantly larger compound libraries can be tested in screening campaigns. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

SH3 domains: modules of protein–protein interactions

Src homology 3 (SH3) domains are involved in the regulation of important cellular pathways, such as cell proliferation, migration and cytoskeletal modifications. Recognition of polyproline and a number of noncanonical sequences by SH3 domains has been extensively studied by crystallography, nuclear magnetic resonance and other methods. High-affinity peptides that bind SH3 domains are used in drug development as candidates for anticancer treatment. This review summarizes the latest achievements in deciphering structural determinants of SH3 function.     

Importance of phospholipid bilayer integrity in the analysis of protein–lipid interactions

The integrity of supported phospholipid bilayer membranes is of crucial importance for the investigation of lipid–protein interactions. Therefore we recorded the formation of supported membranes on SiO₂ and mica by quartz crystal microbalance and controlled the integrity by atomic force microscopy. This study aims to analyze how membrane defects affect protein–lipid interactions. The experiments focused on a lipid mixture of POPC/DOPC/Chol/POPS/PI(4,5)P₂ (37:20:20:20:3) and the binding of the peripheral membrane associated protein annexin A2. We found that formation of a continuous undisturbed bilayer is an indispensable precondition for a reliable determination and quantification of lipid–protein-interactions. If membrane defects were present, protein adsorption causes membrane disruption and lipid detachment on a support thus leading to false determination of binding constants. Our results obtained for PI(4,5)P₂ and cholesterol containing supported membranes yield new knowledge to construct functional surfaces that may cover nanoporous substrates, form free standing membranes or may be used for lab-on-a-chip applications.     

LigandRNA: computational predictor of RNA–ligand interactions

RNA molecules have recently become attractive as potential drug targets due to the increased awareness of their importance in key biological processes. The increase of the number of experimentally determined RNA 3D structures enabled structure-based searches for small molecules that can specifically bind to defined sites in RNA molecules, thereby blocking or otherwise modulating their function. However, as of yet, computational methods for structure-based docking of small molecule ligands to RNA molecules are not as well established as analogous methods for protein-ligand docking. This motivated us to create LigandRNA, a scoring function for the prediction of RNA–small molecule interactions. Our method employs a grid-based algorithm and a knowledge-based potential derived from ligand-binding sites in the experimentally solved RNA–ligand complexes. As an input, LigandRNA takes an RNA receptor file and a file with ligand poses. As an output, it returns a ranking of the poses according to their score. The predictive power of LigandRNA favorably compares to five other publicly available methods. We found that the combination of LigandRNA and Dock6 into a “meta-predictor” leads to further improvement in the identification of near-native ligand poses. The LigandRNA program is available free of charge as a web server at http://ligandrna.genesilico.pl.     

Systems for the detection and analysis of protein–protein interactions

The analysis of protein–protein interactions is important for developing a better understanding of the functional annotations of proteins that are involved in various biochemical reactions in vivo. The discovery that a protein with an unknown function binds to a protein with a known function could provide a significant clue to the cellular pathway concerning the unknown protein. Therefore, information on protein–protein interactions obtained by the comprehensive analysis of all gene products is available for the construction of interactive networks consisting of individual protein–protein interactions, which, in turn, permit elaborate biological phenomena to be understood. Systems for detecting protein–protein interactions in vitro and in vivo have been developed, and have been modified to compensate for limitations. Using these novel approaches, comprehensive and reliable information on protein–protein interactions can be determined. Systems that permit this to be achieved are described in this review.K. Kuroda, M. Kato and J. Mima contributed equally to this work.     

Detection of protein–ligand interactions by NMR using reductive methylation of lysine residues

We show that reductive methylation of proteins can be used for highly sensitive NMR identification of conformational changes induced by metal- and small molecule binding, as well as protein-protein interactions. Reductive methylation of proteins introduces two (13)C-methyl groups on each lysine in the protein of interest. This method works well even when the lysines are not actively involved in the interaction, due to changes in the microenvironments of lysine residues. Most lysine residues are located on the protein exterior, and the exposed (13)C-methyl groups may exhibit rapid localized motions. These motions could be faster than the tumbling rate of the molecule as a whole. Thus, this technique has great potential in the study of large molecular weight systems which are currently beyond the scope of conventional NMR methods.     

Recent advances in predicting protein–protein interactions with the aid of artificial intelligence algorithms

Protein–protein interactions (PPIs) are essential in the regulation of biological functions and cell events, therefore understanding PPIs have become a key issue to understanding the molecular mechanism and investigating the design of drugs. Here we highlight the major developments in computational methods developed for predicting PPIs by using types of artificial intelligence algorithms. The first part introduces the source of experimental PPI data. The second part is devoted to the PPI prediction methods based on sequential information. The third part covers representative methods using structural information as the input feature. The last part is methods designed by combining different types of features. For each part, the state-of-the-art computational PPI prediction methods are reviewed in an inclusive view. Finally, we discuss the flaws existing in this area and future directions of next-generation algorithms.     

Features of protein−protein interactions in the cyanobacterial photoprotection mechanism

Photoprotective mechanisms of cyanobacteria are characterized by several features associated with the structure of their water-soluble antenna complexes–the phycobilisomes (PBs). During energy transfer from PBs to chlorophyll of photosystem reaction centers, the “energy funnel” principle is realized, which regulates energy flux due to the specialized interaction of the PBs core with a quenching molecule capable of effectively dissipating electron excitation energy into heat. The role of the quencher is performed by ketocarotenoid within the photoactive orange carotenoid protein (OCP), which is also a sensor for light flux. At a high level of insolation, OCP is reversibly photoactivated, and this is accompanied by a sig- nificant change in its structure and spectral characteristics. Such conformational changes open the possibility for pro- tein–protein interactions between OCP and the PBs core (i.e., activation of photoprotection mechanisms) or the fluores- cence recovery protein. Even though OCP was discovered in 1981, little was known about the conformation of its active form until recently, as well as about the properties of homologs of its N and C domains. Studies carried out during recent years have made a breakthrough in understanding of the structural-functional organization of OCP and have enabled discovery of new aspects of the regulation of photoprotection processes in cyanobacteria. This review focuses on aspects of protein–pro- tein interactions between the main participants of photoprotection reactions and on certain properties of representatives of newly discovered families of OCP homologs.