Docking analysis of transient complexes: interaction of ferredoxin-NADP+ reductase with ferredoxin and flavodoxin

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.     

Molecular dynamics simulations on interaction between bacterial proteins: Implication on pathogenic activities

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.     

Great interactions: How binding incorrect partners can teach us about protein recognition and function

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.     

Prion protein region 23–32 interacts with tubulin and inhibits microtubule assembly

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.     

Conformational frustration in calmodulin–target recognition

Calmodulin (CaM) is a primary calcium (Ca²⁺)‐signaling protein that specifically recognizes and activates highly diverse target proteins. We explored the molecular basis of target recognition of CaM with peptides representing the CaM‐binding domains from two Ca²⁺‐CaM‐dependent kinases, CaMKI and CaMKII, by employing experimentally constrained molecular simulations. Detailed binding route analysis revealed that the two CaM target peptides, although similar in length and net charge, follow distinct routes that lead to a higher binding frustration in the CaM–CaMKII complex than in the CaM–CaMKI complex. We discovered that the molecular origin of the binding frustration is caused by intermolecular contacts formed with the C‐domain of CaM that need to be broken before the formation of intermolecular contacts with the N‐domain of CaM. We argue that the binding frustration is important for determining the kinetics of the recognition process of proteins involving large structural fluctuations. Copyright © 2015 John Wiley & Sons, Ltd.     

Laser-flash absorption spectroscopy study of the competition between ferredoxin and flavodoxin photoreduction by Photosystem I in Synechococcus sp. PCC 7002: Evidence for a strong preference for ferredoxin

The competition between ferredoxin and flavodoxin for electrons from Photosystem I was analyzed by flash absorption spectroscopy of the photoreduction processes that take place in the presence of both acceptor proteins in vitro. Steady state photoreduction assays indicate a strong inhibition of the apparent flavodoxin photoreduction activities of Photosystem I in the presence of ferredoxin. Flash-absorption experiments carried out at 626 nm, a wavelength where the reduction of ferredoxin shows no spectral contribution, show that the photoreduction of oxidized flavodoxin and flavodoxin semiquinone are inhibited by ferredoxin in a quantitatively similar way. The experimental data can be satisfactorily described by a reaction model that assumes that both redox states of flavodoxin do not compete with ferredoxin for binding on PS I and that the binding equilibrium between ferredoxin and PS I is not changed in their presence. In contrast, a model which assumes that ferredoxin and flavodoxin actually compete for binding to PS I gives poor results. Similarly, experimental data observed in the presence of both redox states of flavodoxin can also be quantitatively described under the assumption that the binding equilibrium between flavodoxin semiquinone and PS I is not disturbed by oxidized flavodoxin. Taken together, this analysis shows that PS I favors ferredoxin over flavodoxin and flavodoxin semiquinone over oxidized flavodoxin. This behavior is in accordance with the values of the dissociation constants for complexes between PS I and its acceptors. However, in case of ferredoxin the observed preference is stronger than expected from these values, indicating that ferredoxin is almost absolutely preferred by PS I over flavodoxin and is always reduced first.     

Discovery of binding proteins for a protein target using protein–protein docking‐based virtual screening

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.     

Intuitive,but not simple: Including explicit water molecules in protein–protein docking simulations improves model quality

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.     

Ionic strength and pH dependence of the reaction between plastocyanin and Photosystem 1. Evidence of a rate-limiting conformational change

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.     

DockRank: Ranking docked conformations using partner‐specific sequence homology‐based protein interface prediction

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.     

Predicting protein complex geometries with a neural network

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.     

iATTRACT: Simultaneous global and local interface optimization for protein–protein docking refinement

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.     

Crystal structure of a ferredoxin reductase for the CYP199A2 system from Rhodopseudomonas palustris

Cytochrome P450‐199A2 from Rhodopseudomonas palustris oxidizes para‐substituted benzoic acids and may play a role in lignin and aromatic acid degradation pathways in the bacterium. CYP199A2 has an associated [2Fe‐2S] ferredoxin, palustrisredoxin (Pux) but not a ferredoxin reductase. A genome search identified the palustrisredoxin reductase (PuR) gene. PuR was produced in Escherichia coli and shown to be a flavin‐dependent protein that supports efficient electron transfer from NADH to Pux, thus reconstituting CYP199A2 monooxygenase activity (k_cat = 37.9 s^–1 with 4‐methoxybenzoic acid). The reduction of Pux by PuR shows K_m = 4.2 μM and k_cat = 262 s^–1 in 50 mM Tris, pH 7.4. K_m is increased to 154 μM in the presence of 200 mM KCl, indicating the importance of ionic interactions in PuR/Pux binding. The crystal structure of PuR has been determined at 2.2 Å resolution and found to be closely related to that of other oxygenase‐coupled NADH‐dependent ferredoxin reductases. Residues on the surface that had been proposed to be involved in ferredoxin reductase‐ferredoxin binding are conserved in PuR. However, Lys328 in PuR lies over the FAD isoalloxazine ring and, together with His11 and Gln41, render the electrostatic potential of the surface more positive and may account for the greater involvement of electrostatic interactions in ferredoxin binding by PuR. Consistent with these observations the K328G mutation weakened Pux binding and virtually eliminated the dependence of PuR/Pux binding on salt concentration, thus confirming that the FAD si side surface in the vicinity of Lys328 is the ferredoxin binding site. Proteins 2009. © 2009 Wiley‐Liss, Inc.     

Molecular docking of superantigens with class II major histocompatibility complex proteins

The molecular recognition of two superantigens with class II major histocompatibility complex molecules was simulated by using protein– protein docking. Superantigens studied were staphylococcal enterotoxin B (SEB) and toxic shock syndrome toxin-1 (TSST-1) in their crystallographic assemblies with HLA-DR1. Rigid-body docking was performed sampling configurational space of the interfacial surfaces by employing a strategy of partitioning the contact regions on HLA-DR1 into separate molecular recognition units. Scoring of docked conformations was based on an electrostatic continuum model evaluated with the finite-difference Poisson– Boltzmann method. Estimates of nonpolar contributions were derived from the buried molecular surface areas. We found for both superantigens that docking the HLA-DR1 surface complementary with the SEB and TSST-1 contact regions containing a homologous hydrophobic surface loop provided sufficient recognition for the reconstitution of native-like conformers exhibiting the highest-scoring free energies. For the SEB complex, the calculations were successful in reproducing the total association free energy. A comparison of the free-energy determinants of the conserved hydrophobic contact residue indicates functional similarity between the two proteins for this interface. Though both superantigens share a common global association mode, differences in binding topology distinguish the conformational specificities underlying recognition. © 1998 John Wiley & Sons, Ltd.     

Molecular architecture of LSM14 interactions involved in the assembly of mRNA silencing complexes

The LSM domain‐containing protein LSM14/Rap55 plays a role in mRNA decapping, translational repression, and RNA granule (P‐body) assembly. How LSM14 interacts with the mRNA silencing machinery, including the eIF4E‐binding protein 4E‐T and the DEAD‐box helicase DDX6, is poorly understood. Here we report the crystal structure of the LSM domain of LSM14 bound to a highly conserved C‐terminal fragment of 4E‐T. The 4E‐T C‐terminus forms a bi‐partite motif that wraps around the N‐terminal LSM domain of LSM14. We also determined the crystal structure of LSM14 bound to the C‐terminal RecA‐like domain of DDX6. LSM14 binds DDX6 via a unique non‐contiguous motif with distinct directionality as compared to other DDX6‐interacting proteins. Together with mutational and proteomic studies, the LSM14‐DDX6 structure reveals that LSM14 has adopted a divergent mode of binding DDX6 in order to support the formation of mRNA silencing complexes and P‐body assembly.     

Protein social behavior makes a stronger signal for partner identification than surface geometry

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.     

Model of a complex between the tetrahemic cytochrome c3 and the ferredoxin I from Desulfovibrio desulfuricans (Norway strain)

A three-dimensional model of an electron-transfer complex between the tetrahemic cytochrome c3 and the ferredoxin I from the sulfate-reducing bacterium Desulfovibrio desulfuricans (Norway strain) has been generated through computer graphics methods. The model is based on the known X-ray structure of the cytochrome and on a model of the ferredoxin that has been derived through computer graphics modeling and energy minimization methods, from the X-ray structure of the homologous ferredoxin from Peptococcus aerogenes. Four possible models of interaction between the two molecules were examined by bringing in close proximity each of the four hemes and the redox center (4Fe-4S) of the ferredoxin and by optimizing the ion pairs interactions. One of these models shows by far the "best" structure in terms of charges, interactions, and complementarity of the topology of the contact surfaces. In this complex, the distance between the iron atoms of the ferredoxin redox center and the hemic iron atom is 11.8 A, which compares well with those found between redox centers in other complexes. The contact surface area between the two molecules is 170 A2.