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1.
Maristella De Cicco Lech‐G. Milroy Sonja A. Dames 《Protein science : a publication of the Protein Society》2018,27(2):546-560
Increased efforts have been undertaken to better understand the formation of signaling complexes at cellular membranes. Since the preparation of proteins containing a transmembrane domain or a prenylation motif is generally challenging an alternative membrane anchoring unit that is easy to attach, water‐soluble and binds to different membrane mimetics would find broad application. The 33‐residue long FATC domain of yeast TOR1 (y1fatc) fulfills these criteria and binds to neutral and negatively charged micelles, bicelles, and liposomes. As a case study, we fused it to the FKBP506‐binding region of the protein FKBP38 (FKBP38‐BD) and used 1H–15N NMR spectroscopy to characterize localization of the chimeric protein to micelles, bicelles, and liposomes. Based on these and published data for y1fatc, its use as a C‐terminally attachable membrane anchor for other proteins is compatible with a wide range of buffer conditions (pH circa 6–8.5, NaCl 0 to >150 mM, presence of reducing agents, different salts such as MgCl2 and CaCl2). The high water‐solubility of y1fatc enables its use for titration experiments against a membrane‐localized interaction partner of the fused target protein. Results from studies with peptides corresponding to the C‐terminal 17–11 residues of the 33‐residue long domain by 1D 1H NMR and CD spectroscopy indicate that they still can interact with membrane mimetics. Thus, they may be used as membrane anchors if the full y1fatc sequence is disturbing or if a chemically synthesized y1fatc peptide shall be attached by native chemical ligation, for example, unlabeled peptide to 15N‐labeled target protein for NMR studies. 相似文献
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We present an updated version of the protein–RNA docking benchmark, which we first published four years back. The non‐redundant protein–RNA docking benchmark version 2.0 consists of 126 test cases, a threefold increase in number compared to its previous version. The present version consists of 21 unbound–unbound cases, of which, in 12 cases, the unbound RNAs are taken from another complex. It also consists of 95 unbound–bound cases where only the protein is available in the unbound state. Besides, we introduce 10 new bound–unbound cases where only the RNA is found in the unbound state. Based on the degree of conformational change of the interface residues upon complex formation the benchmark is classified into 72 rigid‐body cases, 25 semiflexible cases and 19 full flexible cases. It also covers a wide range of conformational flexibility including small side chain movement to large domain swapping in protein structures as well as flipping and restacking in RNA bases. This benchmark should provide the docking community with more test cases for evaluating rigid‐body as well as flexible docking algorithms. Besides, it will also facilitate the development of new algorithms that require large number of training set. The protein–RNA docking benchmark version 2.0 can be freely downloaded from http://www.csb.iitkgp.ernet.in/applications/PRDBv2 . Proteins 2017; 85:256–267. © 2016 Wiley Periodicals, Inc. 相似文献
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Stuti Sharma Stephan Wilkens 《Protein science : a publication of the Protein Society》2017,26(5):1070-1079
Vacuolar H+‐ATPase (V‐ATPase) is a large, multisubunit membrane protein complex responsible for the acidification of subcellular compartments and the extracellular space. V‐ATPase activity is regulated by reversible disassembly, resulting in cytosolic V1‐ATPase and membrane‐integral V0 proton channel sectors. Reversible disassembly is accompanied by transient interaction with cellular factors and assembly chaperones. Quantifying protein‐protein interactions involving membrane proteins, however, is challenging. Here we present a novel method to determine kinetic constants of membrane protein–protein interactions using biolayer interferometry (BLI). Yeast vacuoles are solubilized, vacuolar proteins are reconstituted into lipid nanodiscs with native vacuolar lipids and biotinylated membrane scaffold protein (MSP) followed by affinity purification of nanodisc‐reconstituted V‐ATPase (V1V0ND). We show that V1V0ND can be immobilized on streptavidin‐coated BLI sensors to quantitate binding of a pathogen derived inhibitor and to measure the kinetics of nucleotide dependent enzyme dissociation. 相似文献
4.
Complexing of Basic Pancreatic Proteinase Inhibitor with Soybean Phospholipid Multilamellar Vesicles
Tiourina OP Sharf TV Selishcheva AA Sorokoumova GM Shvets VI Larionova NI 《Biochemistry. Biokhimii?a》2001,66(3):340-344
The formation of complexes of basic pancreatic proteinase inhibitor (BPTI) with multilamellar vesicles (MLV) from six preparations of soybean phospholipids of various composition was studied. When BPTI, a non-membrane protein, interacts with MLV, the vesicles aggregate, forming a precipitate of protein–lipid complexes. The BPTI content in the protein–lipid complexes increases with decreasing pH of the medium and on addition of negatively charged components into the lipid mixture. The protein-induced aggregation of the phospholipid vesicles is suggested to be mainly determined by electrostatic forces. The antiproteinase activity of BPTI in the complexes was rather low but increased up to 70% of the initial activity on addition of an ionic detergent (sodium deoxycholate). 相似文献
5.
Yusuke Yamada Michio Inoue Tomoo Shiba Masato Kawasaki Ryuichi Kato Kazuhisa Nakayama Soichi Wakatsuki 《Acta Crystallographica. Section D, Structural Biology》2005,61(6):731-736
Crystallization of protein–protein complexes is an important step in the studies of biological functions of proteins. However, weak and transient, even though specific, interactions often present difficulties in crystallization of protein complexes due to the heterogeneity of the sample mixture. For example, the γ1‐ear domain of the AP‐1 complex and the GAE domain of GGA1, responsible for the interaction with accessory proteins involved in vesicular transport, are known to interact with target proteins with affinities of the order of 1–100 µM. Such low affinities have hampered crystallization trials of the complexes. To overcome this problem, the γ1‐ear and GAE domains were first co‐crystallized with excess amounts of the peptides. Co‐crystals of both domains were obtained and the complex structures were determined at 2.5–2.9 Å resolution. Based on the crystal packing of γ1‐ear and the cognate peptide, γ1‐ear fused with a peptide tag at the N‐terminus was prepared. The peptide‐tagged γ1‐ear readily crystallized and the crystal diffracted far better, 1.9–2.2 Å resolution, compared with the co‐crystallized complex, giving significantly more details without affecting the overall complex structure. 相似文献
6.
Lipids are one of the major components of biological membranes including the plasma membrane, which is the interface between the cell and the environment. It has become clear that membrane lipids also serve as substrates for the generation of numerous signalling lipids such as phosphatidic acid, phosphoinositides, sphingolipids, lysophospholipids, oxylipins, N‐acylethanolamines, free fatty acids and others. The enzymatic production and metabolism of these signalling molecules are tightly regulated and can rapidly be activated upon abiotic stress signals. Abiotic stress like water deficit and temperature stress triggers lipid‐dependent signalling cascades, which control the expression of gene clusters and activate plant adaptation processes. Signalling lipids are able to recruit protein targets transiently to the membrane and thus affect conformation and activity of intracellular proteins and metabolites. In plants, knowledge is still scarce of lipid signalling targets and their physiological consequences. This review focuses on the generation of signalling lipids and their involvement in response to abiotic stress. We describe lipid‐binding proteins in the context of changing environmental conditions and compare different approaches to determine lipid–protein interactions, crucial for deciphering the signalling cascades. 相似文献
7.
Joël Janin 《Proteins》1995,21(1):30-39
The affinity of two molecules for each other and its temperature dependence are determined by the change in enthalpy, free enthalpy, entropy, and heat capacity upon dissociation. As we know the forces that stabilize-protein–protein or protein–DNA association and the three-dimensional structures of the complex, we can in principle derive values for each one of these parameters. The calculation is done first in gas phase by molecular mechanics, then in solution with the help of hydration parameters calibrated on small molecules. However, estimates of enthalpy and entropy changes in gas phase have excessively large error bars even under the approximation that the components of the complex associate as rigid bodies. No reliable result can be expected at the end. The fit to experimental values derived from binding and calorimetric measurements is poor, except for the dissociation heat capacity. This parameter can be attributed mostly to the hydration step and it correlates with the size of the interface. Many protein–protein complexes have interface areas in the range 1200–2000 Å2 and only small conformation changes, so the rigid body approximation applies. It is less generally valid in protein–DNA complexes, which have interfaces covering 2200–3100 Å2, large dissociation heat capacities, and affect both the conformation and the dynamics of their components. © 1995 Wiley-Liss, Inc. 相似文献
8.
Efficient DNA repair mechanisms frequently limit the effectiveness of chemotherapeutic agents that act through DNA damaging mechanisms. Consequently, proteins involved in DNA repair have increasingly become attractive targets of high‐throughput screening initiatives to identify modulators of these pathways. Disruption of the XRCC4‐Ligase IV interaction provides a novel means to efficiently halt repair of mammalian DNA double strand break repair; however; the extreme affinity of these proteins presents a major obstacle for drug discovery. A better understanding of the interaction surfaces is needed to provide a more specific target for inhibitor studies. To clearly define key interface(s) of Ligase IV necessary for interaction with XRCC4, we developed a competitive displacement assay using ESI‐MS/MS and determined the minimal inhibitory fragment of the XRCC4‐interacting region (XIR) capable of disrupting a complex of XRCC4/XIR. Disruption of a single helix (helix 2) within the helix‐loop‐helix clamp of Ligase IV was sufficient to displace XIR from a preformed complex. Dose‐dependent response curves for the disruption of the complex by either helix 2 or helix‐loop‐helix fragments revealed that potency of inhibition was greater for the larger helix‐loop‐helix peptide. Our results suggest a susceptibility to inhibition at the interface of helix 2 and future studies would benefit from targeting this surface of Ligase IV to identify modulators that disrupt its interaction with XRCC4. Furthermore, helix 1 and loop regions of the helix‐loop‐helix clamp provide secondary target surfaces to identify adjuvant compounds that could be used in combination to more efficiently inhibit XRCC4/Ligase IV complex formation and DNA repair. Proteins 2014; 82:187–194. © 2013 Wiley Periodicals, Inc. 相似文献
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Mark Nicholas Wass Carles Pons Florencio Pazos Alfonso Valencia 《Molecular systems biology》2011,7(1)
Deciphering the whole network of protein interactions for a given proteome (‘interactome’) is the goal of many experimental and computational efforts in Systems Biology. Separately the prediction of the structure of protein complexes by docking methods is a well‐established scientific area. To date, docking programs have not been used to predict interaction partners. We provide a proof of principle for such an approach. Using a set of protein complexes representing known interactors in their unbound form, we show that a standard docking program can distinguish the true interactors from a background of 922 non‐redundant potential interactors. We additionally show that true interactions can be distinguished from non‐likely interacting proteins within the same structural family. Our approach may be put in the context of the proposed ‘funnel‐energy model’; the docking algorithm may not find the native complex, but it distinguishes binding partners because of the higher probability of favourable models compared with a collection of non‐binders. The potential exists to develop this proof of principle into new approaches for predicting interaction partners and reconstructing biological networks. 相似文献
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Young‐Joo Yun Jeong‐Yong Suh 《Protein science : a publication of the Protein Society》2012,21(11):1726-1733
Enzyme I initiates a series of phosphotransfer reactions during sugar uptake in the bacterial phosphotransferase system. Here, we have isolated a stable recombinant C-terminal domain of Enzyme I (EIC) of Escherichia coli and characterized its interaction with the N-terminal domain of Enzyme I (EIN) and also with various ligands. EIC can phosphorylate EIN, but their binding is transient regardless of the presence of phosphoenolpyruvate (PEP). Circular dichroism and NMR indicate that ligand binding to EIC induces changes near aromatic groups but not in the secondary structure of EIC. Binding of PEP to EIC is an endothermic reaction with the equilibrium dissociation constant (KD) of 0.28 mM, whereas binding of the inhibitor oxalate is an exothermic reaction with KD of 0.66 mM from calorimetry. The binding thermodynamics of EIC and PEP compared to that of Enzyme I (EI) and PEP reveals that domain–domain motion in EI can contribute as large as ∼−3.2 kcal/mol toward PEP binding. 相似文献
13.
Protein–protein interactions (PPIs) are involved in diverse functions in a cell. To optimize functional roles of interactions, proteins interact with a spectrum of binding affinities. Interactions are conventionally classified into permanent and transient, where the former denotes tight binding between proteins that result in strong complexes, whereas the latter compose of relatively weak interactions that can dissociate after binding to regulate functional activity at specific time point. Knowing the type of interactions has significant implications for understanding the nature and function of PPIs. In this study, we constructed amino acid substitution models that capture mutation patterns at permanent and transient type of protein interfaces, which were found to be different with statistical significance. Using the substitution models, we developed a novel computational method that predicts permanent and transient protein binding interfaces (PBIs) in protein surfaces. Without knowledge of the interacting partner, the method uses a single query protein structure and a multiple sequence alignment of the sequence family. Using a large dataset of permanent and transient proteins, we show that our method, BindML+, performs very well in protein interface classification. A very high area under the curve (AUC) value of 0.957 was observed when predicted protein binding sites were classified. Remarkably, near prefect accuracy was achieved with an AUC of 0.991 when actual binding sites were classified. The developed method will be also useful for protein design of permanent and transient PBIs. © Proteins 2013. © 2012 Wiley Periodicals, Inc. 相似文献
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Lydie Vamparys Benoist Laurent Alessandra Carbone Sophie Sacquin‐Mora 《Proteins》2016,84(10):1408-1421
Protein–protein interactions play a key part in most biological processes and understanding their mechanism is a fundamental problem leading to numerous practical applications. The prediction of protein binding sites in particular is of paramount importance since proteins now represent a major class of therapeutic targets. Amongst others methods, docking simulations between two proteins known to interact can be a useful tool for the prediction of likely binding patches on a protein surface. From the analysis of the protein interfaces generated by a massive cross‐docking experiment using the 168 proteins of the Docking Benchmark 2.0, where all possible protein pairs, and not only experimental ones, have been docked together, we show that it is also possible to predict a protein's binding residues without having any prior knowledge regarding its potential interaction partners. Evaluating the performance of cross‐docking predictions using the area under the specificity‐sensitivity ROC curve (AUC) leads to an AUC value of 0.77 for the complete benchmark (compared to the 0.5 AUC value obtained for random predictions). Furthermore, a new clustering analysis performed on the binding patches that are scattered on the protein surface show that their distribution and growth will depend on the protein's functional group. Finally, in several cases, the binding‐site predictions resulting from the cross‐docking simulations will lead to the identification of an alternate interface, which corresponds to the interaction with a biomolecular partner that is not included in the original benchmark. Proteins 2016; 84:1408–1421. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc. 相似文献
16.
Yuki Ohmuro‐Matsuyama Takahiro Yamashita Huan Lin Hideki Yamaji Hiroshi Ueda 《Luminescence》2018,33(1):125-130
Protein–protein interaction assays are important in various fields including molecular biology, diagnostics, and drug screening. We recently designed a novel protein–protein interaction assay, the firefly luminescent intermediate‐based protein interaction assay (FlimPIA), that exploited the unique reaction mechanism of firefly luciferase (Fluc). Using two mutant Flucs, each impaired with one of the two half‐reactions, namely adenylation and subsequent oxidative luminescent steps, FlimPIA detects the proximity of the two proteins tethered to the mutant Flucs. Here, we found that introducing a mutation into a residue in the hinge region (S440) of the mutant with lowered adenylation activity (‘Acceptor’ Fluc) further improved the response of FlimPIA by lowering the residual adenylation activity. Mutants with bulkier residues showed greater inhibition, probably due to increased steric hindrance at the adenylation conformation. As a result, the FlimPIA with S440 L acceptor showed the best signal/background ratio for the detection of rapamycin‐induced FKBP12–FRB interactions. 相似文献
17.
《Acta Crystallographica. Section D, Structural Biology》2018,74(1):10-20
Biomineralization is the process of mineral formation by organisms and involves the uptake of ions from the environment in order to produce minerals, with the process generally being mediated by proteins. Most proteins that are involved in mineral interactions are predicted to contain disordered regions containing large numbers of negatively charged amino acids. Magnetotactic bacteria, which are used as a model system for iron biomineralization, are Gram‐negative bacteria that can navigate through geomagnetic fields using a specific organelle, the magnetosome. Each organelle comprises a membrane‐enveloped magnetic nanoparticle, magnetite, the formation of which is controlled by a specific set of proteins. One of the most abundant of these proteins is MamC, a small magnetosome‐associated integral membrane protein that contains two transmembrane α‐helices connected by an ∼21‐amino‐acid peptide. In vitro studies of this MamC peptide showed that it forms a helical structure that can interact with the magnetite surface and affect the size and shape of the growing crystal. Our results show that a disordered structure of the MamC magnetite‐interacting component (MamC‐MIC) abolishes its interaction with magnetite particles. Moreover, the size and shape of magnetite crystals grown in in vitro magnetite‐precipitation experiments in the presence of this disordered peptide were different from the traits of crystals grown in the presence of other peptides or in the presence of the helical MIC. It is suggested that the helical structure of the MamC‐MIC is important for its function during magnetite formation. 相似文献
18.
Alberto Meseguer Lluis Dominguez Patricia M. Bota Joaquim Aguirre‐Plans Jaume Bonet Narcis Fernandez‐Fuentes Baldo Oliva 《Protein science : a publication of the Protein Society》2020,29(10):2112-2130
Protein–protein interactions (PPIs) in all the molecular aspects that take place both inside and outside cells. However, determining experimentally the structure and affinity of PPIs is expensive and time consuming. Therefore, the development of computational tools, as a complement to experimental methods, is fundamental. Here, we present a computational suite: MODPIN, to model and predict the changes of binding affinity of PPIs. In this approach we use homology modeling to derive the structures of PPIs and score them using state‐of‐the‐art scoring functions. We explore the conformational space of PPIs by generating not a single structural model but a collection of structural models with different conformations based on several templates. We apply the approach to predict the changes in free energy upon mutations and splicing variants of large datasets of PPIs to statistically quantify the quality and accuracy of the predictions. As an example, we use MODPIN to study the effect of mutations in the interaction between colicin endonuclease 9 and colicin endonuclease 2 immune protein from Escherichia coli. Finally, we have compared our results with other state‐of‐art methods. 相似文献
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Wataru Nemoto Yoshihiro Yamanishi Vachiranee Limviphuvadh Akira Saito Hiroyuki Toh 《Proteins》2016,84(9):1224-1233
G Protein‐Coupled Receptors (GPCRs) are important pharmaceutical targets. More than 30% of currently marketed pharmaceutical medicines target GPCRs. Numerous studies have reported that GPCRs function not only as monomers but also as homo‐ or hetero‐dimers or higher‐order molecular complexes. Many GPCRs exert a wide variety of molecular functions by forming specific combinations of GPCR subtypes. In addition, some GPCRs are reportedly associated with diseases. GPCR oligomerization is now recognized as an important event in various biological phenomena, and many researchers are investigating this subject. We have developed a support vector machine (SVM)‐based method to predict interacting pairs for GPCR oligomerization, by integrating the structure and sequence information of GPCRs. The performance of our method was evaluated by the Receiver Operating Characteristic (ROC) curve. The corresponding area under the curve was 0.938. As far as we know, this is the only prediction method for interacting pairs among GPCRs. Our method could accelerate the analyses of these interactions, and contribute to the elucidation of the global structures of the GPCR networks in membranes. Proteins 2016; 84:1224–1233. © 2016 Wiley Periodicals, Inc. 相似文献