Many proteins of widely differing functionality and structure are capable of binding heparin and heparan sulfate. Since crystallizing protein-heparin complexes for structure determination is generally difficult, computational docking can be a useful approach for understanding specific interactions. Previous studies used programs originally developed for docking small molecules to well-defined pockets, rather than for docking polysaccharides to highly charged shallow crevices that usually bind heparin. We have extended the program PIPER and the automated protein-protein docking server ClusPro to heparin docking. Using a molecular mechanics energy function for scoring and the fast Fourier transform correlation approach, the method generates and evaluates close to a billion poses of a heparin tetrasaccharide probe. The docked structures are clustered using pairwise root mean square deviations as the distance measure. It was shown that clustering of heparin molecules close to each other but having different orientations and selecting the clusters with the highest protein-ligand contacts reliably predicts the heparin binding site. In addition, the centers of the five most populated clusters include structures close to the native orientation of the heparin. These structures can provide starting points for further refinement by methods that account for flexibility such as molecular dynamics. The heparin docking method is available as an advanced option of the ClusPro server at http://cluspro.bu.edu/.
proteins of widely differing functionality and structure are
capable of binding heparin and heparan sulfate. Since crystallizing
protein–heparin complexes for structure determination is generally
difficult, computational docking can be a useful approach for understanding
specific interactions. Previous studies used programs originally developed
for docking small molecules to well-defined pockets, rather than for
docking polysaccharides to highly charged shallow crevices that usually
bind heparin. We have extended the program PIPER and the automated
protein–protein docking server ClusPro to heparin docking.
Using a molecular mechanics energy function for scoring and the fast
Fourier transform correlation approach, the method generates and evaluates
close to a billion poses of a heparin tetrasaccharide probe. The docked
structures are clustered using pairwise root-mean-square deviations
as the distance measure. It was shown that clustering of heparin molecules
close to each other but having different orientations and selecting
the clusters with the highest protein–ligand contacts reliably
predicts the heparin binding site. In addition, the centers of the
five most populated clusters include structures close to the native
orientation of the heparin. These structures can provide starting
points for further refinement by methods that account for flexibility
such as molecular dynamics. The heparin docking method is available
as an advanced option of the ClusPro server at http://cluspro.bu.edu/.
The potential utility of synthetic macrocycles as drugs, particularly against low
druggability targets such as protein-protein interactions, has been widely discussed.
There is little information, however, to guide the design of macrocycles for good target
protein-binding activity or bioavailability. To address this knowledge gap we analyze the
binding modes of a representative set of macrocycle-protein complexes. The results,
combined with consideration of the physicochemical properties of approved macrocyclic
drugs, allow us to propose specific guidelines for the design of synthetic macrocycles
libraries possessing structural and physicochemical features likely to favor strong
binding to protein targets and also good bioavailability. We additionally provide evidence
that large, natural product derived macrocycles can bind to targets that are not druggable
by conventional, drug-like compounds, supporting the notion that natural product inspired
synthetic macrocycles can expand the number of proteins that are druggable by synthetic
druglikeness; druggability; ligand efficiency; binding mode; macrocyclic drugs
In screening a library of natural and synthetic products for eukaryotic translation modulators, we identified two natural products, isohymenialdisine and hymenialdisine, that exhibit stimulatory effects on translation. The characterization of these compounds lead to the insight that mRNA used to program the translation extracts during high throughput assay set-up was leading to phosphorylation of eIF2α, a potent negative regulatory event that is mediated by one of four kinases. We identified double-stranded RNA-dependent protein kinase (PKR) as the eIF2α kinase that was being activated by exogenously added mRNA template. Characterization of the mode of action of isohymenialdisine revealed that it directly acts on PKR by inhibiting autophosphorylation, perturbs the PKR-eIF2α phosphorylation axis, and can be modeled into the PKR ATP binding site. Our results identify a source of false positives for high throughput screening (HTS) campaigns using translation extracts, raising a cautionary note for this type of screen.
High Throughput Screens; Translation; PKR; eIF2α; Isohymenialdisine; Hymenialdisine
The protein docking server ClusPro has been participating in CAPRI since its introduction in 2004. This paper evaluates the performance of ClusPro 2.0 for targets 46–58 in rounds 22–27 of CAPRI. The analysis leads to a number of important observations. First, ClusPro reliably yields acceptable or medium accuracy models for targets of moderate difficulty that have also been successfully predicted by other groups, and fails only for targets that have few acceptable models submitted. Second, the quality of automated docking by ClusPro is very close to that of the best human predictor groups, including our own submissions. This is very important, because servers have to submit results within 48 hours and the predictions should be reproducible, whereas human predictors have several weeks and can use any type of information. Third, while we refined the ClusPro results for manual submission by running computationally costly Monte Carlo minimization simulations, we observed significant improvement in accuracy only for two of the six complexes correctly predicted by ClusPro. Fourth, new developments, not seen in previous rounds of CAPRI, are that the top ranked model provided by ClusPro was acceptable or better quality for all these six targets, and that the top ranked model was also the highest quality for five of the six, confirming that ranking models based on cluster size can reliably identify the best near-native conformations.
protein-protein docking; structure refinement; method development; CAPRI docking experiment; web based server; user community
Peptide-mediated interactions, in which a short linear motif binds to a globular domain, play major roles in cellular regulation. An accurate structural model of this type of interaction is an excellent starting point for the characterization of the binding specificity of a given peptide-binding domain. A number of different protocols have recently been proposed for the accurate modeling of peptide-protein complex structures, given the structure of the protein receptor and the binding site on its surface. When no information about the peptide binding site(s) is a priori available, there is a need for new approaches to locate peptide-binding sites on the protein surface. While several approaches have been proposed for the general identification of ligand binding sites, peptides show very specific binding characteristics, and therefore, there is a need for robust and accurate approaches that are optimized for the prediction of peptide-binding sites.
Here we present PeptiMap, a protocol for the accurate mapping of peptide binding sites on protein structures. Our method is based on experimental evidence that peptide-binding sites also bind small organic molecules of various shapes and polarity. Using an adaptation of ab initio ligand binding site prediction based on fragment mapping (FTmap), we optimize a protocol that specifically takes into account peptide binding site characteristics. In a high-quality curated set of peptide-protein complex structures PeptiMap identifies for most the accurate site of peptide binding among the top ranked predictions. We anticipate that this protocol will significantly increase the number of accurate structural models of peptide-mediated interactions.
protein peptide interactions; FFT sampling; binding site detection; mapping; PeptiDB
Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side chain sampling and backbone relaxation, and evaluated packing, electrostatic and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of methodological improvement.
CAPRI; hemagglutinin; binding; deep mutational scanning; yeast display
Background: The use of alternative flame retardants has increased since the phase out of pentabromodiphenyl ethers (pentaBDEs). One alternative, Firemaster® 550 (FM550), induces obesity in rats. Triphenyl phosphate (TPP), a component of FM550, has a structure similar to that of organotins, which are obesogenic in rodents.
Objectives: We tested the hypothesis that components of FM550 are biologically active peroxisome proliferator-activated receptor γ (PPARγ) ligands and estimated indoor exposure to TPP.
Methods: FM550 and its components were assessed for ligand binding to and activation of human PPARγ. Solvent mapping was used to model TPP in the PPARγ binding site. Adipocyte and osteoblast differentiation were assessed in bone marrow multipotent mesenchymal stromal cell models. We estimated exposure of children to TPP using a screening-level indoor exposure model and house dust concentrations determined previously.
Results: FM550 bound human PPARγ, and binding appeared to be driven primarily by TPP. Solvent mapping revealed that TPP interacted with binding hot spots within the PPARγ ligand binding domain. FM550 and its organophosphate components increased human PPARγ1 transcriptional activity in a Cos7 reporter assay and induced lipid accumulation and perilipin protein expression in BMS2 cells. FM550 and TPP diverted osteogenic differentiation toward adipogenesis in primary mouse bone marrow cultures. Our estimates suggest that dust ingestion is the major route of exposure of children to TPP.
Conclusions: Our findings suggest that FM550 components bind and activate PPARγ. In addition, in vitro exposure initiated adipocyte differentiation and antagonized osteogenesis. TPP likely is a major contributor to these biological actions. Given that TPP is ubiquitous in house dust, further studies are warranted to investigate the health effects of FM550.
Citation: Pillai HK, Fang M, Beglov D, Kozakov D, Vajda S, Stapleton HM, Webster TF, Schlezinger JJ. 2014. Ligand binding and activation of PPARγ by Firemaster® 550: effects on adipogenesis and osteogenesis in vitro. Environ Health Perspect 122:1225–1232; http://dx.doi.org/10.1289/ehp.1408111
Our work is motivated by energy minimization of biological macromolecules, an essential step in computational docking. By allowing some ligand flexibility, we generalize a recently introduced novel representation of rigid body minimization as an optimization on the SO(3)×R3 manifold, rather than on the commonly used Special Euclidean group SE(3). We show that the resulting flexible docking can also be formulated as an optimization on a Lie group that is the direct product of simpler Lie groups for which geodesics and exponential maps can be easily obtained. Our computational results for a local optimization algorithm developed based on this formulation show that it is about an order of magnitude faster than the state-of-the-art local minimization algorithms for computational protein-small molecule docking.
Computational solvent mapping finds binding hot spots, determines their druggability and provides information for drug design. While mapping of a ligand-bound structure yields more accurate results, usually the apo structure serves as the starting point in design. The FTFlex algorithm, implemented as a server, can modify an apo structure to yield mapping results that are similar to those of the respective bound structure. Thus, FTFlex is an extension of our FTMap server, which only considers rigid structures. FTFlex identifies flexible residues within the binding site and determines alternative conformations using a rotamer library. In cases where the mapping results of the apo structure were in poor agreement with those of the bound structure, FTFlex was able to yield a modified apo structure, which lead to improved FTMap results. In cases where the mapping results of the apo and bound structures were in good agreement, no new structure was predicted.
Availability: FTFlex is freely available as a web-based server at http://ftflex.bu.edu/.
email@example.com or firstname.lastname@example.org
Supplementary data are available at Bioinformatics online.
We report a comprehensive analysis of binding energy hot spots at the protein-protein interaction (PPI) interface between NF-κB Essential Modulator (NEMO) and IκB kinase subunit β (IKKβ), an interaction that is critical for NF-κB pathway signaling, using experimental alanine scanning mutagenesis and also the FTMap method for computational fragment screening. The experimental results confirm that the previously identified NBD region of IKKβ contains the highest concentration of hot spot residues, the strongest of which are W739, W741 and L742 (ΔΔG = 4.3, 3.5 and 3.2 kcal/mol, respectively). The region occupied by these residues defines a potentially druggable binding site on NEMO that extends for ~16 Å to additionally include the regions that bind IKKβ L737 and F734. NBD residues D738 and S740 are also important for binding but do not make direct contact with NEMO, instead likely acting to stabilize the active conformation of surrounding residues. We additionally found two previously unknown hot spot regions centered on IKKβ residues L708/V709 and L719/I723. The computational approach successfully identified all three hot spot regions on IKKβ. Moreover, the method was able to accurately quantify the energetic importance of all hot spots residues involving direct contact with NEMO. Our results provide new information to guide the discovery of small molecule inhibitors that target the NEMO/IKKβ interaction. They additionally clarify the structural and energetic complementarity between “pocket-forming” and “pocket occupying” hot spot residues, and further validate computational fragment mapping as a method for identifying hot spots at PPI interfaces.
IKKγ; alanine scanning mutagenesis; protein-protein interactions; IKKγ; fluorescence polarization; fluorescence anisotropy
Our work is motivated by energy minimization in the space of rigid affine transformations of macromolecules, an essential step in computational protein-protein docking. We introduce a novel representation of rigid body motion that leads to a natural formulation of the energy minimization problem as an optimization on the SO(3)×R3 manifold, rather than the commonly used SE(3). The new representation avoids the complications associated with optimization on the SE(3) manifold and provides additional flexibilities for optimization not available in that formulation. The approach is applicable to general rigid body minimization problems. Our computational results for a local optimization algorithm developed based on the new approach show that it is about an order of magnitude faster than a state of art local minimization algorithms for computational protein-protein docking.
An outstanding challenge has been to understand the mechanism whereby proteins associate. We report here the results of exhaustively sampling the conformational space in protein–protein association using a physics-based energy function. The agreement between experimental intermolecular paramagnetic relaxation enhancement (PRE) data and the PRE profiles calculated from the docked structures shows that the method captures both specific and non-specific encounter complexes. To explore the energy landscape in the vicinity of the native structure, the nonlinear manifold describing the relative orientation of two solid bodies is projected onto a Euclidean space in which the shape of low energy regions is studied by principal component analysis. Results show that the energy surface is canyon-like, with a smooth funnel within a two dimensional subspace capturing over 75% of the total motion. Thus, proteins tend to associate along preferred pathways, similar to sliding of a protein along DNA in the process of protein-DNA recognition.
Proteins rarely act alone. Instead, they tend to bind to other proteins to form structures known as complexes. When two proteins come together to form a complex, they twist and turn through a series of intermediate states before they form the actual complex. These intermediate states are difficult to study because they don’t last for very long, which means that our knowledge of how complexes are formed remains incomplete.
One promising approach for studying the formation of complexes is called paramagnetic relaxation enhancement. In this technique certain areas in one of the proteins are labelled with magnetic particles, which produce signals when the two proteins are close to each other. Repeating the measurement several times with the magnetic particles in different positions provides information about the overall structure of the complex. Computational modelling can then be used to work out the fine details of the structure, including the shapes of the intermediate structures made by the proteins as they interact.
A computer method called docking can be used to predict the most favourable positions that the proteins can take, relative to one another, in a complex. This involves calculating the energy contained in the system, with the correct structure having the lowest energy. Docking methods also predict protein models with slightly higher energies, but with structures that are radically different. Modellers usually ignore these structures, but comparing the docking results to paramagnetic relaxation enhancement data, Kozakov et al. found that these structures actually represent the intermediate states.
Analysing the structure of the intermediate states revealed that the movement of the two proteins relative to one another is severely restricted as they form the final complex. Kozakov et al. found that proteins associate along preferred pathways, similar to the way a protein slides along DNA in the process of protein-DNA recognition. Knowing that the movement of the proteins is restricted in this way will enable researchers to improve the efficiency of docking calculations.
encounter landscapes; FFT sampling; protein–protein interactions; none
Initiation of transcription in human mitochondria involves two factors, TFAM and TFB2M, in addition to the mitochondrial RNA polymerase, POLRMT. We have investigated the organization of the human mitochondrial transcription initiation complex on the light-strand promoter (LSP) through solution X-ray scattering, electron microscopy (EM) and biochemical studies. Our EM results demonstrate a compact organization of the initiation complex, suggesting that protein–protein interactions might help mediate initiation. We demonstrate that, in the absence of DNA, only POLRMT and TFAM form a stable interaction, albeit one with low affinity. This is consistent with the expected transient nature of the interactions necessary for initiation and implies that the promoter DNA acts as a scaffold that enables formation of the full initiation complex. Docking of known crystal structures into our EM maps results in a model for transcriptional initiation that strongly correlates with new and existing biochemical observations. Our results reveal the organization of TFAM, POLRMT and TFB2M around the LSP and represent the first structural characterization of the entire mitochondrial transcriptional initiation complex.
Virtually all docking methods include some local continuous minimization of an energy/scoring function in order to remove steric clashes and obtain more reliable energy values. In this paper, we describe an efficient rigid-body optimization algorithm that, compared to the most widely used algorithms, converges approximately an order of magnitude faster to conformations with equal or slightly lower energy. The space of rigid body transformations is a nonlinear manifold, namely, a space which locally resembles a Euclidean space. We use a canonical parametrization of the manifold, called the exponential parametrization, to map the Euclidean tangent space of the manifold onto the manifold itself. Thus, we locally transform the rigid body optimization to an optimization over a Euclidean space where basic optimization algorithms are applicable. Compared to commonly used methods, this formulation substantially reduces the dimension of the search space. As a result, it requires far fewer costly function and gradient evaluations and leads to a more efficient algorithm. We have selected the LBFGS quasi-Newton method for local optimization since it uses only gradient information to obtain second order information about the energy function and avoids the far more costly direct Hessian evaluations. Two applications, one in protein-protein docking, and the other in protein-small molecular interactions, as part of macromolecular docking protocols are presented. The code is available to the community under open source license, and with minimal effort can be incorporated into any molecular modeling package.
Motivation: An effective docking algorithm for antibody–protein antigen complex prediction is an important first step toward design of biologics and vaccines. We have recently developed a new class of knowledge-based interaction potentials called Decoys as the Reference State (DARS) and incorporated DARS into the docking program PIPER based on the fast Fourier transform correlation approach. Although PIPER was the best performer in the latest rounds of the CAPRI protein docking experiment, it is much less accurate for docking antibody–protein antigen pairs than other types of complexes, in spite of incorporating sequence-based information on the location of the paratope. Analysis of antibody–protein antigen complexes has revealed an inherent asymmetry within these interfaces. Specifically, phenylalanine, tryptophan and tyrosine residues highly populate the paratope of the antibody but not the epitope of the antigen.
Results: Since this asymmetry cannot be adequately modeled using a symmetric pairwise potential, we have removed the usual assumption of symmetry. Interaction statistics were extracted from antibody–protein complexes under the assumption that a particular atom on the antibody is different from the same atom on the antigen protein. The use of the new potential significantly improves the performance of docking for antibody–protein antigen complexes, even without any sequence information on the location of the paratope. We note that the asymmetric potential captures the effects of the multi-body interactions inherent to the complex environment in the antibody–protein antigen interface.
Availability: The method is implemented in the ClusPro protein docking server, available at http://cluspro.bu.edu.
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Supplementary data are available at Bioinformatics online.
The goal of this paper is to reduce the complexity of the side chain search within docking problems. We apply six methods of generating side chain conformers to unbound protein structures, and determine their ability of obtaining the bound conformation in small ensembles of conformers. Methods are evaluated in terms of the positions of side chain end groups. Results for 68 protein complexes yield two important observations. First, the end group positions change less than 1 Å upon association for over 60% of interface side chains. Thus, the unbound protein structure carries substantial information about the side chains in the bound state, and the inclusion of the unbound conformation into the ensemble of conformers is very beneficial. Second, considering each surface side chain separately in its protein environment, small ensembles of low energy states include the bound conformation for a large fraction of side chains. In particular, the ensemble consisting of the unbound conformation and the two highest probability predicted conformers includes the bound conformer with an accuracy of 1 Å for 78% of interface side chains. Since more than 60% of the interface side chains have only one conformer and many others only a few, these ensembles of low energy states substantially reduce the complexity of side chain search in docking problems. This approach was already used for finding pockets in protein-protein interfaces that can bind small molecules to potentially disrupt protein-protein interactions. Side chain search with the reduced search space will also be incorporated into protein docking algorithms.
rotamer libraries; side chain flexibility; protein binding; structure prediction; preexisting ensemble of conformers
Formaldehyde has long been recognized as a hazardous environmental agent highly reactive with DNA. Recently, it has been realized that due to the activity of histone demethylation enzymes within the cell nucleus, formaldehyde is produced endogenously, in direct vicinity of genomic DNA. Should it lead to extensive DNA damage? We address this question with the aid of a computational mapping method, analogous to X-ray and nuclear magnetic resonance techniques for observing weakly specific interactions of small organic compounds with a macromolecule in order to establish important functional sites. We concentrate on the leading reaction of formaldehyde with free bases: hydroxymethylation of cytosine amino groups. Our results show that in B-DNA, cytosine amino groups are totally inaccessible for the formaldehyde attack. Then, we explore the effect of recently discovered transient flipping of Watson–Crick (WC) pairs into Hoogsteen (HG) pairs (HG breathing). Our results show that the HG base pair formation dramatically affects the accessibility for formaldehyde of cytosine amino nitrogens within WC base pairs adjacent to HG base pairs. The extensive literature on DNA interaction with formaldehyde is analyzed in light of the new findings. The obtained data emphasize the significance of DNA HG breathing.
Binding hot spots, protein sites with high-binding affinity, can be identified using X-ray crystallography or NMR by screening libraries of small organic molecules that tend to cluster at such regions. FTMAP, a direct computational analog of the experimental screening approaches, globally samples the surface of a target protein using small organic molecules as probes, finds favorable positions, clusters the conformations and ranks the clusters on the basis of the average energy. The regions that bind several probe clusters predict the binding hot spots, in good agreement with experimental results. Small molecules discovered by fragment-based approaches to drug design also bind at the hot spot regions. To identify such molecules and their most likely bound positions, we extend the functionality of FTMAP (http://ftmap.bu.edu/param) to accept any small molecule as an additional probe. In its updated form, FTMAP identifies the hot spots based on a standard set of probes, and for each additional probe shows representative structures of nearby low energy clusters. This approach helps to predict bound poses of the user-selected molecules, detects if a compound is not likely to bind in the hot spot region, and provides input for the design of larger ligands.
Our approach to protein-protein docking includes three main steps. First we run PIPER, a rigid body docking program based on the Fast Fourier Transform (FFT) correlation approach, extended to use pairwise interactions potentials. Next, the 1000 best energy conformations are clustered, and the 30 largest clusters are retained for refinement. Third, the stability of the clusters is analyzed by short Monte Carlo simulations, and the structures are refined by the medium-range optimization method SDU. The first two steps of this approach are implemented in the ClusPro 2.0 protein-protein docking server. Despite being fully automated, the last step is computationally too expensive to be included in the server. Comparing the models obtained in CAPRI rounds 13–19 by ClusPro, by the refinement of the ClusPro predictions, and by all predictor groups, we arrived at three conclusions. First, for the first time in the CAPRI history, our automated ClusPro server was able to compete with the best human predictor groups. Second, selecting the top ranked models, our current protocol reliably generates high quality structures of protein-protein complexes from the structures of separately crystallized proteins, even in the absence of biological information, provided that there is limited backbone conformational change. Third, despite occasional successes, homology modeling requires further improvement to achieve reliable docking results.
Structures of the influenza A virus M2 proton channel have been determined by X-ray crystallography in the open conformation, and by NMR in the closed state. Whereas the X-ray structure shows a single inhibitor molecule in the middle of the channel, four inhibitor molecules bind the channel’s outer surface in the NMR structure. Although in both structures the strongest hot spots (i.e., regions which substantially contribute to the free energy of binding any potential ligand) lie inside the pore, hot spots also are found at exterior locations. By considering all available models, we propose the primary drug binding site is inside the pore, but that exterior binding also occurs under appropriate conditions.
The steroid and xenobiotic-responsive human pregnane X receptor (PXR) binds a broad range of structurally diverse compounds. The structures of the apo and ligand-bound forms of PXR are very similar, in contrast to most promiscuous proteins that generally adapt their shape to different ligands. We investigated the structural origins of PXR's recognition promiscuity using computational solvent mapping, a technique developed for the identification and characterization of hot spots, i.e., regions of the protein surface that are major contributors to the binding free energy. Results reveal that the smooth and nearly spherical binding site of PXR has a well-defined hot spot structure, with four hot spots located on four different sides of the pocket and a fifth close to its center. Three of these hot spots are already present in the ligand-free protein. The most important hot spot is defined by three structurally and sequentially conserved residues, W299, F288, and Y306. This largely hydrophobic site is not very specific, and interacts with all known PXR ligands. Depending on their sizes and shapes, individual PXR ligands extend into 2, 3, or 4 more hot spot regions. The large number of potential arrangements within the binding site explains why PXR is able to accommodate a large variety of compounds. All five hot spots include at least one important residue, which is conserved in all mammalian PXRs, suggesting that the hot spot locations have remained largely invariant during mammalian evolution. The same side chains also show a high level of structural conservation across hPXR structures. However, each of the hPXR hot spots also includes residues with moveable side chains, further increasing the size variation in ligands that PXR can bind. Results also suggest a unique signal transduction mechanism between the PXR homodimerization interface and its co-activator binding site.
The interactions of beta2 glycoprotein I (B2GPI) with the receptors of the low-density lipoprotein receptor (LDLR) family are implicated in the clearance of negatively charged phospholipids and apoptotic cells and, in the presence of autoimmune anti-B2GPI antibodies, in cell activation, which might play a role in the pathology of antiphospholipid syndrome (APS). The ligand-binding domains of the lipoprotein receptors consist of multiple homologous LA modules connected by flexible linkers. In this study, we investigated at the atomic level the features of the LA modules required for binding to B2GPI. To compare the binding interface in B2GPI/LA complex to that observed in the high-resolution co-crystal structure of the receptor associated protein (RAP) with the LA modules 3 and 4 from the LDLR, we used the LA module 4 from the LDLR in our studies. Using solution NMR spectroscopy, we found that LA4 interacts with B2GPI and the binding site for B2GPI on the 15N-labeled LA4 is formed by the calcium coordinating residues of the LA module. We built a model for the complex between domain V of B2GPI (B2GPI-DV) and LA4 without introducing any experimentally derived constraints into the docking procedure. Our model, which is in the agreement with the NMR data, suggests that the binding interface of B2GPI for the lipoprotein receptors is centered at three lysine residues of B2GPI-DV, Lys 308, Lys 282 and Lys317.
LDLR; lipoprotein receptors; B2GPI; beta2-glycoprotein I; PIPER; molecular docking; antiphospholipid syndrome; APS
Motivation: The binding sites of proteins generally contain smaller regions that provide major contributions to the binding free energy and hence are the prime targets in drug design. Screening libraries of fragment-sized compounds by NMR or X-ray crystallography demonstrates that such ‘hot spot’ regions bind a large variety of small organic molecules, and that a relatively high ‘hit rate’ is predictive of target sites that are likely to bind drug-like ligands with high affinity. Our goal is to determine the ‘hot spots’ computationally rather than experimentally.
Results: We have developed the FTMAP algorithm that performs global search of the entire protein surface for regions that bind a number of small organic probe molecules. The search is based on the extremely efficient fast Fourier transform (FFT) correlation approach which can sample billions of probe positions on dense translational and rotational grids, but can use only sums of correlation functions for scoring and hence is generally restricted to very simple energy expressions. The novelty of FTMAP is that we were able to incorporate and represent on grids a detailed energy expression, resulting in a very accurate identification of low-energy probe clusters. Overlapping clusters of different probes are defined as consensus sites (CSs). We show that the largest CS is generally located at the most important subsite of the protein binding site, and the nearby smaller CSs identify other important subsites. Mapping results are presented for elastase whose structure has been solved in aqueous solutions of eight organic solvents, and we show that FTMAP provides very similar information. The second application is to renin, a long-standing pharmaceutical target for the treatment of hypertension, and we show that the major CSs trace out the shape of the first approved renin inhibitor, aliskiren.
Availability: FTMAP is available as a server at http://ftmap.bu.edu/.
Supplementary information: Supplementary Material is available at Bioinformatics online.