共查询到20条相似文献，搜索用时 15 毫秒
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Katarzyna M. Osiecka Hanna Nieznanska Krzysztof J. Skowronek Justyna Karolczak Gabriela Schneider Krzysztof Nieznanski 《Proteins》2009,77(2):279-296
In previous studies we have demonstrated that prion protein (PrP) binds directly to tubulin and this interaction leads to the inhibition of microtubule formation by inducement of tubulin oligomerization. This report is aimed at mapping the regions of PrP and tubulin involved in the interaction and identification of PrP domains responsible for tubulin oligomerization. Preliminary studies focused our attention to the N‐terminal flexible part of PrP encompassing residues 23–110. Using a panel of deletion mutants of PrP, we identified two microtubule‐binding motifs at both ends of this part of the molecule. We found that residues 23–32 constitute a major site of interaction, whereas residues 101–110 represent a weak binding site. The crucial role of the 23–32 sequence in the interaction with tubulin was confirmed employing chymotryptic fragments of PrP. Surprisingly, the octarepeat region linking the above motifs plays only a supporting role in the interaction. The binding of Cu2+ to PrP did not affect the interaction. We also demonstrate that PrP deletion mutants lacking residues 23–32 exhibit very low efficiency in the inducement of tubulin oligomerization. Moreover, a synthetic peptide corresponding to this sequence, but not that identical with fragment 101–110, mimics the effects of the full‐length protein on tubulin oligomerization and microtubule assembly. At the cellular level, peptide composed of the PrP motive 23–30 and signal sequence (1–22) disrupted the microtubular cytoskeleton. Using tryptic and chymotryptic fragments of α‐ and β‐tubulin, we mapped the docking sites for PrP within the C‐terminal domains constituting the outer surface of microtubule. Proteins 2009. © 2009 Wiley‐Liss, Inc. 相似文献
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Target structure‐based virtual screening, which employs protein‐small molecule docking to identify potential ligands, has been widely used in small‐molecule drug discovery. In the present study, we used a protein–protein docking program to identify proteins that bind to a specific target protein. In the testing phase, an all‐to‐all protein–protein docking run on a large dataset was performed. The three‐dimensional rigid docking program SDOCK was used to examine protein–protein docking on all protein pairs in the dataset. Both the binding affinity and features of the binding energy landscape were considered in the scoring function in order to distinguish positive binding pairs from negative binding pairs. Thus, the lowest docking score, the average Z‐score, and convergency of the low‐score solutions were incorporated in the analysis. The hybrid scoring function was optimized in the all‐to‐all docking test. The docking method and the hybrid scoring function were then used to screen for proteins that bind to tumor necrosis factor‐α (TNFα), which is a well‐known therapeutic target for rheumatoid arthritis and other autoimmune diseases. A protein library containing 677 proteins was used for the screen. Proteins with scores among the top 20% were further examined. Sixteen proteins from the top‐ranking 67 proteins were selected for experimental study. Two of these proteins showed significant binding to TNFα in an in vitro binding study. The results of the present study demonstrate the power and potential application of protein–protein docking for the discovery of novel binding proteins for specific protein targets. Proteins 2014; 82:2472–2482. © 2014 Wiley Periodicals, Inc. 相似文献
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A major challenge of the protein docking problem is to define scoring functions that can distinguish near‐native protein complex geometries from a large number of non‐native geometries (decoys) generated with noncomplexed protein structures (unbound docking). In this study, we have constructed a neural network that employs the information from atom‐pair distance distributions of a large number of decoys to predict protein complex geometries. We found that docking prediction can be significantly improved using two different types of polar hydrogen atoms. To train the neural network, 2000 near‐native decoys of even distance distribution were used for each of the 185 considered protein complexes. The neural network normalizes the information from different protein complexes using an additional protein complex identity input neuron for each complex. The parameters of the neural network were determined such that they mimic a scoring funnel in the neighborhood of the native complex structure. The neural network approach avoids the reference state problem, which occurs in deriving knowledge‐based energy functions for scoring. We show that a distance‐dependent atom pair potential performs much better than a simple atom‐pair contact potential. We have compared the performance of our scoring function with other empirical and knowledge‐based scoring functions such as ZDOCK 3.0, ZRANK, ITScore‐PP, EMPIRE, and RosettaDock. In spite of the simplicity of the method and its functional form, our neural network‐based scoring function achieves a reasonable performance in rigid‐body unbound docking of proteins. Proteins 2010. © 2009 Wiley‐Liss, 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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Cells are interactive living systems where proteins movements, interactions and regulation are substantially free from centralized management. How protein physico‐chemical and geometrical properties determine who interact with whom remains far from fully understood. We show that characterizing how a protein behaves with many potential interactors in a complete cross‐docking study leads to a sharp identification of its cellular/true/native partner(s). We define a sociability index, or S‐index, reflecting whether a protein likes or not to pair with other proteins. Formally, we propose a suitable normalization function that accounts for protein sociability and we combine it with a simple interface‐based (ranking) score to discriminate partners from non‐interactors. We show that sociability is an important factor and that the normalization permits to reach a much higher discriminative power than shape complementarity docking scores. The social effect is also observed with more sophisticated docking algorithms. Docking conformations are evaluated using experimental binding sites. These latter approximate in the best possible way binding sites predictions, which have reached high accuracy in recent years. This makes our analysis helpful for a global understanding of partner identification and for suggesting discriminating strategies. These results contradict previous findings claiming the partner identification problem being solvable solely with geometrical docking. Proteins 2016; 85:137–154. © 2016 Wiley Periodicals, Inc. 相似文献
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Li C. Xue Rafael A. Jordan EL‐Manzalawy Yasser Drena Dobbs Vasant Honavar 《Proteins》2014,82(2):250-267
Selecting near‐native conformations from the immense number of conformations generated by docking programs remains a major challenge in molecular docking. We introduce DockRank, a novel approach to scoring docked conformations based on the degree to which the interface residues of the docked conformation match a set of predicted interface residues. DockRank uses interface residues predicted by partner‐specific sequence homology‐based protein–protein interface predictor (PS‐HomPPI), which predicts the interface residues of a query protein with a specific interaction partner. We compared the performance of DockRank with several state‐of‐the‐art docking scoring functions using Success Rate (the percentage of cases that have at least one near‐native conformation among the top m conformations) and Hit Rate (the percentage of near‐native conformations that are included among the top m conformations). In cases where it is possible to obtain partner‐specific (PS) interface predictions from PS‐HomPPI, DockRank consistently outperforms both (i) ZRank and IRAD, two state‐of‐the‐art energy‐based scoring functions (improving Success Rate by up to 4‐fold); and (ii) Variants of DockRank that use predicted interface residues obtained from several protein interface predictors that do not take into account the binding partner in making interface predictions (improving success rate by up to 39‐fold). The latter result underscores the importance of using partner‐specific interface residues in scoring docked conformations. We show that DockRank, when used to re‐rank the conformations returned by ClusPro, improves upon the original ClusPro rankings in terms of both Success Rate and Hit Rate. DockRank is available as a server at http://einstein.cs.iastate.edu/DockRank/ . Proteins 2014; 82:250–267. © 2013 Wiley Periodicals, Inc. 相似文献
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Protein‐protein interactions are abundant in the cell but to date structural data for a large number of complexes is lacking. Computational docking methods can complement experiments by providing structural models of complexes based on structures of the individual partners. A major caveat for docking success is accounting for protein flexibility. Especially, interface residues undergo significant conformational changes upon binding. This limits the performance of docking methods that keep partner structures rigid or allow limited flexibility. A new docking refinement approach, iATTRACT, has been developed which combines simultaneous full interface flexibility and rigid body optimizations during docking energy minimization. It employs an atomistic molecular mechanics force field for intermolecular interface interactions and a structure‐based force field for intramolecular contributions. The approach was systematically evaluated on a large protein‐protein docking benchmark, starting from an enriched decoy set of rigidly docked protein–protein complexes deviating by up to 15 Å from the native structure at the interface. Large improvements in sampling and slight but significant improvements in scoring/discrimination of near native docking solutions were observed. Complexes with initial deviations at the interface of up to 5.5 Å were refined to significantly better agreement with the native structure. Improvements in the fraction of native contacts were especially favorable, yielding increases of up to 70%. Proteins 2015; 83:248–258. © 2014 Wiley Periodicals, Inc. 相似文献
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Yaqin Hou Haihua Quan Weiwei Xu Yongli Bao Yuxin Li Yuan Fu Shuxue Zou 《Protein science : a publication of the Protein Society》2013,22(8):1060-1070
A plethora of both experimental and computational methods have been proposed in the past 20 years for the identification of hot spots at a protein–protein interface. The experimental determination of a protein–protein complex followed by alanine scanning mutagenesis, though able to determine hot spots with much precision, is expensive and has no guarantee of success while the accuracy of the current computational methods for hot‐spot identification remains low. Here, we present a novel structure‐based computational approach that accurately determines hot spots through docking into a set of proteins homologous to only one of the two interacting partners of a compound capable of disrupting the protein–protein interaction (PPI). This approach has been applied to identify the hot spots of human activin receptor type II (ActRII) critical for its binding toward Cripto‐I. The subsequent experimental confirmation of the computationally identified hot spots portends a potentially accurate method for hot‐spot determination in silico given a compound capable of disrupting the PPI in question. The hot spots of human ActRII first reported here may well become the focal points for the design of small molecule drugs that target the PPI. The determination of their interface may have significant biological implications in that it suggests that Cripto‐I plays an important role in both activin and nodal signal pathways. 相似文献
13.
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
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The electron-transfer reaction between spinach wild-type plastocyanin (Pc(WT)) two site-directed mutants, Pc(Thr79His) and Pc(Lys81His), and spinach Photosystem 1 particles, has been studied as a function of protein concentration, ionic strength and pH by using laser-flash absorption spectroscopy. The kinetic data are interpreted using the simplest possible three-step model, involving a rate-limiting conformational change preceding intracomplex electron transfer. The three proteins show similar concentration, pH and ionic strength dependencies. The effects of ionic strength and pH on the reaction indicate a strong influence of complementary charges on complex formation and stabilization. Studies with apoprotein support the opinion that the hydrophobic patch is critical for an productive interaction with the reaction center of Photosystem 1. Together with earlier site-directed mutagenesis studies, the absence of a detectable Photosystem 1 reaction in the presence of reduced azurin, stellacyanin, cytochrome c and cytochrome c551, demonstrates the existence of a high level of specificity in the protein-protein interface in the formation of an efficient electron-transfer complex. 相似文献
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Characterizing the nature of interaction between proteins that have not been experimentally cocrystallized requires a computational docking approach that can successfully predict the spatial conformation adopted in the complex. In this work, the Hydropathic INTeractions (HINT) force field model was used for scoring docked models in a data set of 30 high‐resolution crystallographically characterized “dry” protein–protein complexes and was shown to reliably identify native‐like models. However, most current protein–protein docking algorithms fail to explicitly account for water molecules involved in bridging interactions that mediate and stabilize the association of the protein partners, so we used HINT to illuminate the physical and chemical properties of bridging waters and account for their energetic stabilizing contributions. The HINT water Relevance metric identified the “truly” bridging waters at the 30 protein–protein interfaces and we utilized them in “solvated” docking by manually inserting them into the input files for the rigid body ZDOCK program. By accounting for these interfacial waters, a statistically significant improvement of ～24% in the average hit‐count within the top‐10 predictions the protein–protein dataset was seen, compared to standard “dry” docking. The results also show scoring improvement, with medium and high accuracy models ranking much better than incorrect ones. These improvements can be attributed to the physical presence of water molecules that alter surface properties and better represent native shape and hydropathic complementarity between interacting partners, with concomitantly more accurate native‐like structure predictions. Proteins 2014; 82:916–932. © 2013 Wiley Periodicals, Inc. 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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Ferredoxin (Fd) interacts with ferredoxin-NADP(+) reductase (FNR) to transfer two electrons to the latter, one by one, which will finally be used to reduce NADP(+) to NADPH. The formation of a transient complex between Fd and FNR is required for the electron transfer (ET), and extensive mutational and crystallographic studies have been reported to characterize such protein-protein interaction. However, some aspects of the association mechanism still remain unclear. Moreover, in spite of their structural differences, flavodoxin (Fld) can replace Fd in its function and interact with FNR to transfer electrons with only slightly lower efficiency. Although crystallographic structures for the FNR:Fd association have been reported, experimental structural data for the FNR:Fld interaction are highly elusive. We have modeled here the interactions between FNR and both of its protein partners, Fd and Fld, using surface energy analysis, computational rigid-body docking simulations, and interface side-chain refinement. The results, consistent with previous experimental data, suggest the existence of alternative binding modes in these ET proteins. 相似文献
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We perform molecular dynamics simulation studies on interaction between bacterial proteins: an outer‐membrane protein STY3179 and a yfdX protein STY3178 of Salmonella Typhi. STY3179 has been found to be involved in bacterial adhesion and invasion. STY3178 is recently biophysically characterized. It is a soluble protein having antibiotic binding and chaperon activity capabilities. These two proteins co‐occur and are from neighboring gene in Salmonella Typhi‐occurrence of homologs of both STY3178 and STY3179 are identified in many Gram‐negative bacteria. We show using homology modeling, docking followed by molecular dynamics simulation that they can form a stable complex. STY3178 belongs to aqueous phase, while the beta barrel portion of STY3179 remains buried in DPPC bilayer with extra‐cellular loops exposed to water. To understand the molecular basis of interaction between STY3178 and STY3179, we compute the conformational thermodynamics which indicate that these two proteins interact through polar and acidic residues belonging to their interfacial region. Conformational thermodynamics results further reveal instability of certain residues in extra‐cellular loops of STY3179 upon complexation with STY3178 which is an indication for binding with host cell protein laminin. 相似文献
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Michael A. Hough John F. Hall Lalji D. Kanbi S. Samar Hasnain 《Acta Crystallographica. Section D, Structural Biology》2001,57(3):355-360
The small blue copper protein rusticyanin from Thiobacillus ferrooxidans contains a type 1 Cu centre with a single axial ligand, Met148, which together with the His‐Cys‐His trigonal planar ligands produces a distorted trigonal pyramidal coordination geometry to copper. Type 1 Cu sites are found in cupredoxins and several multicopper proteins, including oxidases and nitrite reductases. The role of the axial ligand has been extensively debated in terms of its function in the fine tuning of the redox potential and spectroscopic properties of type 1 Cu sites. Numerous mutations of the Met ligand in azurins have been studied, but interpretation of the results has been complicated by the presence of the additional carbonyl oxygen ligand from Gly45, a neighbouring residue to the coordinating His46. The importance of the axial ligand has been further emphasized by the finding that the type 1 centre in Rhus vernicifera stellacyanin, with the lowest redox potential in a type 1 Cu site of 184 mV, has Gln as the axial ligand, whilst fungal laccase and ceruloplasmin, which have redox potentials of 550–800 mV, have a Leu in this position. Here, the crystal structure of the M148Q mutant of rusticyanin at 1.5 Å resolution is presented. This is a significantly higher resolution than that of the structures of native rusticyanin. In addition, the M148Q structure is that of the oxidized protein while the native structures to date are of the reduced protein. The mutant protein crystallizes with two molecules per asymmetric unit, in contrast to the one present in the native crystal form. This mutant's redox potential (550 mV at pH 3.2) is lowered compared with that of the native protein (∼670 mV at pH 3.2) by about 120 mV. The type 1 Cu site of M148Q closely mimics the structural characteristics of the equivalent site in non‐glycosylated cucumber stellacyanin (redox potential ∼260 mV) and, owing to the absence in rusticyanin of the fifth, carbonyl ligand present in azurin, may provide a better model for the R. vernicifera stellacyanin (redox potential ∼184 mV) type 1 Cu site, which also lacks the fifth ligand. Furthermore, the presence of two molecules in the asymmetric unit cell indicates a potential binding region of the redox partners. 相似文献