Predicting protein ligand binding motions with the conformation explorer
© Flores and Gerstein; licensee BioMed Central Ltd. 2011
Received: 31 May 2011
Accepted: 27 October 2011
Published: 27 October 2011
Knowledge of the structure of proteins bound to known or potential ligands is crucial for biological understanding and drug design. Often the 3D structure of the protein is available in some conformation, but binding the ligand of interest may involve a large scale conformational change which is difficult to predict with existing methods.
We describe how to generate ligand binding conformations of proteins that move by hinge bending, the largest class of motions. First, we predict the location of the hinge between domains. Second, we apply an Euler rotation to one of the domains about the hinge point. Third, we compute a short-time dynamical trajectory using Molecular Dynamics to equilibrate the protein and ligand and correct unnatural atomic positions. Fourth, we score the generated structures using a novel fitness function which favors closed or holo structures. By iterating the second through fourth steps we systematically minimize the fitness function, thus predicting the conformational change required for small ligand binding for five well studied proteins.
We demonstrate that the method in most cases successfully predicts the holo conformation given only an apo structure.
Conformational changes in proteins can take place in a wide variety of ways, not all of which have been formally classified. One important class of motions is shear, in which stacked side chains of the protein can slide without losing contact. In this work we focus on the largest class, domain hinge bending, in which one structural domain of the protein moves relative to another domain about a hinge which connects the two [1, 2]. Such motions typically involve the slowest degrees of freedom of that protein and so are difficult to predict by existing methods.
The prediction of ligand binding motions of the protein receptor has considerable potential applications in protein-protein and protein-ligand docking. Many methods can predict the side chain rearrangements required for docking [3, 4] but these assume that the large scale conformation is already nearly correct. Thus there is a need for a method that will put the receptor in the correct large scale conformation which can be a productive starting point .
Much work has been done in this area. Molecular Dynamics (MD) [6–9] explicitly computes the dynamical trajectory of molecules modeled as point masses connected by linear and nonlinear springs and can be used to predict conformational change, but usually only small- or moderate-scale domain motions can be reproduced  with many biologically relevant motions remaining out of reach . Accordingly several methods used MD to account for the fast fluctuations of proteins in drug docking by first computing the protein trajectory using MD [4, 12, 13]. One limitation of such techniques is that they may not escape the vicinity of an initial conformation, even in a time span experimentally known to be sufficient for conformational change . Althaus et al created a combinatorial tree of side-chain rotamers which they explored using a branch-and-cut algorithm,  without varying the backbone conformation. Sandak et al. created a flexible-receptor docking code which articulates the protein at a hinge point, but leaves the two resulting domains rigid . This method suffered from the opposite problem: it could generate large scale protein motions, but had no way of dealing with even small side chain rearrangements, a weakness leading to failure . The described methods are good at either treating the side-chain flexibility, or the large scale conformational changes, but not both simultaneously. Conformation Explorer uses Sandak et al.'s idea of moving domains about a hinge point to generate large scale conformational change, but also includes equilibration steps which permit relaxation and adjustment of all atoms.
Normal modes have also been used by many authors to predict the conformational changes of proteins . Comparison of the atomic coordinates of homologous pairs of proteins shows that the lowest order modes are most involved in conformational change, [18, 19] but also that multiple modes are needed to accurately represent the motion . It is possible to determine the correct combination of normal modes that will reproduce a desired motion, but this requires knowledge of at least a few interatomic distance constraints for the final structure .
In a different approach, a docked protein-ligand complex was displaced along the lowest-frequency normal mode directions to minimize non-bonded energy terms in an MD force field [22–24]. However a normal mode expansion assumes a quadratic potential and so is accurate only for small fluctuations about an equilibrium structure; therefore the method cannot be used to predict larger scale conformational changes such as we treat in this work. The method of Lindahl et al. gains improvements of 0.3 to 3.2Å for several proteins;  our method recapitulates much larger conformational changes as we will show.
Maiorov and Abagyan  rigidified all protein bonds except those in the interdomain linker and interface using Internal Coordinate Modeling, and then used the Biased Probability Monte Carlo protocol to generate potential alternate conformations of the protein. The method succeeded in generating a large number of alternate conformations, and some of these were somewhat similar to alternate conformations known crystallographically. However without referring to the known alternate conformations, it was impossible to determine which of the many predicted structures was thermodynamically plausible. Further, many energy evaluations and minimizations were expended in evaluating generated conformers which were later discarded. Lastly, it was not easy to know how long a thorough exploration of conformation space would take, and no clear way to restrict the search to a given region of interest. Our method is similar in several ways to Maiorov et al.'s, but also addresses these limitations.
In more recent work, de Groot et al.  showed they could find the holo conformations of several ligand-binding proteins. The method relies on tCONCOORD,  which determines flexible regions by analyzing hydrogen bonding networks. Once these are known, an ensemble of plausible structures is generated. An interative process involving docking, MD refinement, and filtering by radius of gyration then generates holo structures. However the radius of gyration must be provided by experiments, because their fitness function does not take into account the receptor conformation. Our work resembles de Groot's in the use of docking and MD refinement, but differs in that the hinge location is determined by the user (allowing the use of high-accuracy hinge detectors ), and in that it requires no experimental information about the holo complex. Our method is also computationally cheaper. Key to our method is the conformational sampling and the use of a fitness function f which includes terms to discriminate the holo conformation from the generated ensemble.
The mentioned f deserves motivation. There are numerous potentials which pick out the correct ligand-binding pose in a given receptor [28–30]. However these typically have no terms to discriminate favorable from unfavorable receptor conformations. Other force fields have been trained and tested against ensembles of crystallographically obtained protein structures, but it is not clear how they would perform when the holo structure is unavailable . To overcome these limitations f includes terms (radius of gyration RG, stability EF, and docked energy GD) which help discriminate predicted holo complexes from a generated ensemble.
In this work we show that the Conformation Explorer can predictably, controllably, and economically generate multiple alternate conformations of proteins without experimental constraints  on the holo complex. The conformations can be equilibrated to ensure they are free of steric strain (close contact between atoms, or unnatural bond lengths and angles) .
We demonstrate that as an application of the method, we can predict the ligand bound, or holo, conformation of a protein given only the ligand and the coordinates of the protein in the apo state. The exploration of phase space is performed in such a way as to minimize f, which includes terms measuring the radius of gyration RG, stability EF, and docked energy GD. We will formally define all of these terms in the Methods section.
As mentioned, the method involves the identification of the hinge and rotations about that hinge, followed by equilibrations and docking runs. The direction of the rotations and the choice of intermediate structures to which the rotations are applied, are subject to optimization. We describe how to perform these steps.
Determining and recording the hinge location
The hinge can be detected in several ways:
Hinge predictors such as FlexOracle,  TLSMD,  HingeMaster,  and others [35–37] can be used to predict the hinge location if at least one structure is known. The HingeMaster web server makes the use of the first three easy  and was used in this work.
If the protein has been crystallized in two different conformations, these can be inspected visually to determine the hinge location. This is usually the best and most accurate method .
By other experimental means
Proteolysis,  hydrogen/deuterium exchange,  optical trapping,  Fluorescent Resonance Energy Transfer (FRET),  Nuclear Magnetic Resonance,  and many other techniques can be used to determine the hinge location.
Assignment of domains and calculation of centers of mass
Preparation of protein and ligand
The coordinates of the ligand are required in PDB format, as is the GROMACS .itp (include topology) and the AutoDock .pdbq (structure + charges + bond mobility) files. The latter two can be generated from the first using the PRODRG program .
Definition of Rotations
The three Euler rotations mentioned earlier are the space-centered rotation of the M domain about the z-axis, Rz(θz), about the x-axis, Rx(θx), and about the y-axis, Ry(θy), applied in that order. (Figure 2). The θz, θx, and θy are the angles of the corresponding rotations. The ligand is not rotated, i.e. remains stationary with respect to S.
The preceding rotation step invariably results in unphysical bond lengths and angles in the boundary between L and M, and often in steric clashes between M and the rest of the protein and/or ligand. A Molecular Dynamics equilibration is performed using the GROMACS mdrun program for 3000 time steps (6 ps of simulation time). We solvated using a water box with a neutralizing atmosphere of Cl- and Na+ ions. We found that 1000 time steps (2ps) were sufficient to relieve the most significant steric strains, and 10000 time steps were sufficient to allow an exponential decrease in enthalpy to level off (data not shown), indicating a better equilibrated structure. It was not sufficient time to allow substantial domain motions. Equilibration significantly increased the accuracy of ligand-binding prediction, compared to what we got with no equilibration.
We calculate the angular position of M of a generated structure in θyθxθz space by determining the Ry(θy)Rx(θx)Rz(θz), rotation that would have to be applied to M of the starting structure in standard orientation, to obtain a structure similar to the generated one. This is done by first structurally aligning the generated structure with the starting structure by minimizing the root-mean-square deviation (RMSD) between the S domains of the two structures. The θy, θx, and θz angles are then computed. This calculation is performed immediately following each equilibration step above. Note that in the course of the equilibration the angular position of M will change. We refer to this as drift.
At the end of each equilibration the ligand is removed from the structure file and then docked again to the protein using AutoDock 3 . The docking code reports multiple poses (docked ligand atomic positions) with corresponding free energies of binding; we record the lowest of the latter as the GD, as we discuss below. The corresponding ligand coordinates are then appended to the protein structure file.
Terms of f: GD, RG, EF
The fitness function f we devised to discriminate the holo structure from the many conformations generated includes four quantities which separately have strengths and weaknesses, but which together form a predictor. In this section we describe the terms, which are combined in a weighted sum to create f.
GD, as mentioned, was computed using AutoDock at the end of each equilibration. This quantity was found early on to have significant ability to discriminate between near-holo structures and decoys. However in some cases an unnatural cleft was generated by the mentioned rotations, to which the ligand docked with high affinity. Such unnatural conformations often have large radii of gyration and can be filtered out on that basis. We also found that this measure did not vary smoothly with sRMSD (the RMSD between M domains of two proteins, after they have been optimally superimposed on their S domains) , thus no gradient was available along which to minimize. For these reasons we added terms to f as follows.
Where i counts over all atoms, r i is the distance (in Angstroms) from the atom to the center of mass, and M is the total mass. It also varies smoothly with sRMSD, and so helps deal with the noise issues mentioned above and leads to improved convergence. However RG alone is not a good predictive measure because it is trivially possible to minimize it with an unstable protein structure consisting of distorted interpenetrating domains. This problem led to the introduction of the next quantity into f.
Energy of folding, or EF, can be used to discriminate energetically feasible conformers from those with excessive interpenetration or unnatural orientation of domains. This quantity is estimated using FoldX. The latter is a force field empirically fitted by Guerois et al. using a database of mutationally induced changes in protein stability . However since a wide variety of conformers, including both the holo and the apo, are stable, this measure alone cannot find the holo structure in an ensemble. The point of computing this quantity is again to exclude unphysical structures.
Figure of merit: sRMSD
Once the alternate conformers have been generated by rotation, equilibration, and re-docking, we are faced with the problem of determining how distant these are from the known target conformer, in our case the protein from the co-crystallized complex. For this purpose Maiorov et al. used a "static" root-mean-square deviation (sRMSD), defined as the RMSD of the Cα atoms in M, given that the Cα atoms of S are optimally superimposed. We use the same measure in the current work.
Five-fold leave-one-out cross-validation of f
We computed GD, EF, and RG2 for every structure in each of the five generated ensembles (corresponding to the five proteins). We then divided the proteins into a single test protein and a training set consisting of the remaining four proteins. We did this five times, choosing a different test protein each time. Each time, f was fitted to the training set as follows.
Where x is a matrix with one row for each structure in the training set and one column for each of the quantities GD, EF, and RG2. The element in a given row and column is the physical quantity corresponding to that column, computed on the structure corresponding to that row. λ is a three-element column vector of weights to be fitted as follows.
Where y is a column vector with each row containing the quantity sRMSD, computed for the structure corresponding to that row. Rows in y correspond to rows in x.
Five different λ's were computed in five-fold leave-one-out bootstrapping,  with one λ used for each protein, except where noted below.
Holo, predicted, apo, and infeasible structures
Minimization of f by line search algorithm
We minimized f using the line search algorithm, which successively varies each of the angles (θy, θx, θz) over its full range. After each angle is varied, the conformer that minimized f over the full range of that angle is used as a starting point for the next minimization. The sampling of θy, θx, θz characteristic of this method can be appreciated graphically in Figure 3 (lines are not straight due to drift as discussed). The line search minimization can be considered converged when no Ry(θy), Rx(θx), or Rz(θz) rotation from the lowest-scoring conformer can further reduce the value of f. The algorithm is summarized in Additional File 1. In the following sections we describe the proteins we used for training, testing, and applying f.
Glutamine binding protein (GlnBP)
The motion of GlnBP  as it binds glutamine involves large-displacement domain hinge bending, experimentally determined to take ~5ns  6 ns Molecular Dynamics simulations (requiring 42 days on a dual-processor machine) of the apo structure failed to result in domain closure, possibly in part because the dynamics of the apo structure were computed with no ligand present . The apo or starting structure used,  and the holo structure  both originate in E. coli.
For GlnBP FlexOracle  found a hinge at residues 86-89 and 182-185. We selected residues 88,89,181, and 182 as the hinge location for the purpose of generating the rotations, but since the flexure occurs at the boundary between L and M, this adjustment makes little difference.
Acetyl-CoA carboxylase (ACC), found in all animals, plants, and bacteria, catalyzes the carboxylation of acetyl-CoA to malonyl-CoA, the first committed step of fatty acid synthesis. The first half-reaction is the formation of carboxybiotin which is catalyzed by the Biotin carboxylase (BC) subunit .
Pyruvate carboxylase (PC) is found in many eukaryotes and some prokaryotes. It plays a role in gluconeogenesis, mediating the carboxylation of pyruvate to oxaloacetate. It has three functional domains, of which biotin carboxylase (BC) is one. The half-reaction catalyzed by BC is common to ACC and PC, although the second half-reaction catalyzed by a different subunit differs from enzyme to enzyme depending on the substrate . Upon binding ATP, M rotates approximately 45° with respect to S. The large scale of this change and the existence of structural domains makes BC a good test case for our method.
We used the BC subunit of Aquifex aeolicus PC  as the starting or apo structure. The ligand-free BC subunit of E. coli ACC has been crystallized (PDB ID: 1DV1), but many residues are disordered and so the structure of the M domain is not entirely clear . No ATP-bound structure of A. aeolicus BC is available, and so we used ATP-bound BC from E. coli ACC  as our holo structure. The sequence differences between the starting and holo structures had an impact on the results as we will discuss. FlexOracle  predicts a hinge at residues 86-89 and 182-185; this can be used to choose the location of L.
Ribose Binding Protein (RBP)
RBP is a member of a large family of bacterial periplasmic binding proteins,  and displays clear domain hinge bending motion . It has a triple stranded hinge, with a small fragment of the N-terminus crossing over onto the M-domain. It binds ribose, a small, oxygen-rich ligand which we will discuss in more depth later.
Adenylate Kinase (ADK)
Results of five-fold leave-one-out cross-validation.
lowest in ensemble
The motion of ADK involves not two domains but three. Here we predicted the motion of the LID domain (residues 121-158) with respect to the CORE domain (residues 1-24, 81-118, 161-214). Since the AMPbd domain (residues 27-78) moves separately, for computing sRMSD we aligned based only on the CORE domain.
MurA, which has been cyrstallized bound to the antibiotic T6361 . The peculiarity of this ligand is that it binds to the open conformation of MurA, rather than the closed, and thus interferes with the mechanism of motion . In this case if the predicted bound conformation is correct, it should not differ significantly from the open structure by the measure of sRMSD. The starting structure used was taken from the complex of MurA with an analogue of its natural ligand .
ABC transporter periplasmic ligand-binding protein (ABC-PBP)
ABC-PBP is a very sparsely characterized protein. Its natural ligand is unknown, and few other details are available about its function . It has been crystallized in the apo form (PDB ID: 3n0w). These characteristics make the protein a typical case in which a motion predictor like CE would be useful. In this work we make a blind prediction of its holo conformation.
Natural vs. non-natural ligands
That the docking is not effective for any small and oxygen-rich ligand.
That the precise choice of ligand is unimportant for the objective of predicting closed structures.
Results of running CE on GlnBP with various ligands.
Glutamine binding protein (GlnBP)
We ran CE on this protein to demonstrate the online results browser, and to rationalize the three terms of the fitness function.
Figure 3.a shows each of the conformers (represented as spheres), positioned according to the angular orientation of Domain M, and colored by sRMSD. The conformer with lowest sRMSD is shown with a larger sphere than the others. The starting structure is indicated with the blue arrow, and displayed in inset 3.b. As mentioned sRMSD is our figure of merit for scoring our predictions. The generated structure with lowest sRMSD is shown in inset 3.c. The reader can verify that this structure is qualitatively similar to the experimentally known holo structure, shown in inset 3.d.
The structure with lowest GD is shown in inset 3.e, and is also qualitatively similar to the holo.
The structure with lowest gyration radius is shown in inset 3.f. The holo tends to have a smaller RG than the apo structure. RG cannot be used alone in f since it is trivially possible to minimize this quantity with a compact structure which is unstable, has significantly distorted domains, and does not resemble the holo. The gyration radius appears squared for the physical reasons mentioned earlier.
The structure with most favorable EF, inset 3.g, is quite close to the starting structure. This should not be surprising, since such structures have gone through a minimum of structural manipulation and so have low steric strain.
The significant sequence and structural differences between the starting and holo structures, which come from different organisms, made comparison on the basis of sRMSD somewhat difficult. The structure that minimizes f (Figure 4) bears clear similarities to the holo in relative domain orientation.
Ribose Binding Protein (RBP)
In Figure 4, the structure that minimizes f with ATP as a ligand shows good agreement with the holo. The natural ligand led to poorer performance as mentioned.
Adenylate Kinase (ADK)
The starting structure had 17Å, while the predicted structure (with ATP docked) had 5.3Å sRMSD (Table 1) - almost 10 Å lower. The qualitative agreement was excellent, as can be appreciated in Figure 4. On the other hand docking SO4, a ligand it has been crystallized with, led to poor results.
We have now shown that the Conformation Explorer can predict the bound conformation for four cases in which a large scale closing motion is required. But what if the ligand binds to the open conformation? One such case is that of MurA. The predicted structure is more closed than the holo, indicating that f is biased towards closed structures (Figure 4).
Computational cost ranged from about 6 hours (for GlnBP) to 12 hours (for RBP) on a single processor. For comparison, the method of Seeliger et al.  requires about four days on a 50-node cluster.
The user's job is queued upon submission. Each time an angle is varied over its full range by the Line Search minimizer, the user is emailed a progress notice. This process can be tracked and inspected using a viewer similar to that described in Figure 3. A few iterations later the job will converge and the user will receive a link to a viewer that animates the ligand binding motion. Links to structural coordinate files of the trajectory and the predicted holo structure are also provided.
Prediction of protein conformational change
For RBP, ADK, GlnBP, and BC, large scale motions of the M domain are required for binding. For three of those, we successfully predicted the closed state within 6Å sRMSD. For BC, the predicted sRMSD was higher, but still about 4Å lower than for the starting structure, and with qualitative similarities to the holo (Figure 4).
Ligands binding to the open conformation
MurA is a special case in which the ligand binds the open form. Our predicted structure was closed, with sRMSD higher than for its starting structure, by about 4Å. This is mostly because f, as a result of being trained on RBP, ADK, GlnBP, and BC for this protein, is explicitly biased towards closed structures through its gyration_radius term. If the docked_energy term were used alone in place of f, sRMSD would be much better, though still 0.6Å higher than for the starting structure (Table 1).
Small, oxygen-rich ligands
The ligands SO4 and ribose docked deep inside one of the two domains of ADK and RBP (respectively), leading to poor sRMSD. SO4, ribose, and oxalic acid behaved similarly with GlnBP, leading to the highest sRMSD compared with other ligands (glutamine, leucine T6361, cAMP, ATP). This may be due to an insufficient desolvation penalty in the docking force field, or to the lack of explicit ions in the docking, at least for this class of ligands.
The above issue goes to the root of the utility of docking in predicting closed structures. We observed that the docked ligands in the predicted structures had relatively few receptor contacts in common with those in experimentally observed holo structures. And yet each of our five-fold leave-one-out studies verified the utility of the docked_energy term in f, for the purpose of detecting closed structures. It may be that the docking force field is biased to favor closed structures, in which the ligand can maximize the number of contacts in a non-specific manner. The cleft may also have non-specific ligand-binding properties . This would explain the success of using various non-natural ligands to predict the closed form of GlnBP (table 2). It would also explain why CE performed well on RBP when ATP was used instead of its natural ligand (table 1). CE's performance on GlnBP, BC, and MurA was somewhat worse (~1.5Å on average) when ATP was substituted for the natural ligand, indicating at least some predicted interactions are specific.
We conclude that for many hinge bending proteins it is possible to generate conformers similar to the closed structure given the apo. We note that due to shortcomings in the docking method, any small, oxygen-rich ligands should be replaced, and ATP is a good alternative. The hinge location was predicted using an existing hinge prediction server, and iterative rotations, equilibrations, and docking runs resulted in prediction of closed structures similar to those known crystallographically, in four of the five cases. The computational cost was moderate, permitting practical implementation on a single processor.
We greatly appreciate help from Robert Bjornson in running an early version of Conformation Explorer on Yale's Bulldog cluster. William Grenawitzke wrote a script to dock ligands to generated protein conformers using AutoDock. Qingping Xu and Johan Åqvist provided useful advice. Trilok Shahi provided editorial help. SF gratefully acknowledges support from the Swedish Research Council through the eSSENCE project grant (essenceofscience.se).
- Gerstein M, Krebs W: A database of macromolecular motions. Nucleic acids research 1998, 26(18):4280–4290. 10.1093/nar/26.18.4280PubMed CentralView ArticlePubMedGoogle Scholar
- Gerstein MRJ, Johnson T, Tsai J, Krebs W: Studying Macromolecular Motions in a Database Framework: from Structure to Sequence. Rigidity Theory and Applications 1999, 401–442.Google Scholar
- Dominguez C, Boelens R, Bonvin AM: HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. J Am Chem Soc 2003, 125(7):1731–1737. 10.1021/ja026939xView ArticlePubMedGoogle Scholar
- Carlson HAMK, McCammon JA: Method for Including the Dynamic Fluctuations of a Protein in Computer-Aided Drug Design. J Phys Chem A 1999, 103: 10213–10219. 10.1021/jp991997zView ArticleGoogle Scholar
- Seeliger D, Haas J, de Groot BL: Geometry-based sampling of conformational transitions in proteins. Structure 2007, 15(11):1482–1492. 10.1016/j.str.2007.09.017View ArticlePubMedGoogle Scholar
- LE van der Spoel, LE D, Hess B, Groenhof G, Mark AE, Berendsen HJ: GROMACS: Fast, flexible, and free. Journal of Computational Chemistry 2005, 16(26):1701–1718.View ArticleGoogle Scholar
- Karplus M, McCammon JA: Molecular dynamics simulations of biomolecules. Nat Struct Mol Biol 2002, 9(9):646–652. 10.1038/nsb0902-646View ArticleGoogle Scholar
- Adcock SA, McCammon JA: Molecular Dynamics: A Survey of Methods for Simulating the Activity of Proteins. Chemical Reviews 2006, 106(5):1589–1615. 10.1021/cr040426mPubMed CentralView ArticlePubMedGoogle Scholar
- McCammon JA, Gelin BR, Karplus M: Dynamics of folded proteins. Nature 1977, 267(5612):585–590. 10.1038/267585a0View ArticlePubMedGoogle Scholar
- Frembgen-Kesner T, Elcock AH: Computational sampling of a cryptic drug binding site in a protein receptor: explicit solvent molecular dynamics and inhibitor docking to p38 MAP kinase. Journal of molecular biology 2006, 359(1):202–214. 10.1016/j.jmb.2006.03.021View ArticlePubMedGoogle Scholar
- Bowers KJ, Chow E, Xu H, Dror RO, Eastwood MP, Gregersen BA, Klepeis JL, Kolossvary I, Moraes MA, Sacerdoti FD, et al.: Scalable algorithms for molecular dynamics simulations on commodity clusters. In Proceedings of the 2006 ACM/IEEE conference on Supercomputing. Tampa, Florida: ACM; 2006:84.View ArticleGoogle Scholar
- Lin J-H, Perryman AL, Schames JR, McCammon JA: Computational Drug Design Accommodating Receptor Flexibility: The Relaxed Complex Scheme. Journal of the American Chemical Society 2002, 124(20):5632–5633. 10.1021/ja0260162View ArticlePubMedGoogle Scholar
- Lin J-H, Perryman AL, Schames JR, McCammon JA: The relaxed complex method: Accommodating receptor flexibility for drug design with an improved scoring scheme. Biopolymers 2003, 68(1):47–62. 10.1002/bip.10218View ArticlePubMedGoogle Scholar
- Pang A, Arinaminpathy Y, Sansom MS, Biggin PC: Interdomain dynamics and ligand binding: molecular dynamics simulations of glutamine binding protein. FEBS Lett 2003, 550(1–3):168–174. 10.1016/S0014-5793(03)00866-4View ArticlePubMedGoogle Scholar
- Althaus EOK, Lenhof HP, Muller P: A Combinatorial Approach to Protein Docking with Flexible Side-Chains. Journal of Computational Biology 2000, 9(4):597–612.View ArticleGoogle Scholar
- Sandak B, Wolfson HJ, Nussinov R: Flexible docking allowing induced fit in proteins: insights from an open to closed conformational isomers. Proteins 1998, 32(2):159–174. 10.1002/(SICI)1097-0134(19980801)32:2<159::AID-PROT3>3.0.CO;2-GView ArticlePubMedGoogle Scholar
- Cavasotto CN, Kovacs JA, Abagyan RA: Representing receptor flexibility in ligand docking through relevant normal modes. J Am Chem Soc 2005, 127(26):9632–9640. 10.1021/ja042260cView ArticlePubMedGoogle Scholar
- Krebs WG, Alexandrov V, Wilson CA, Echols N, Yu H, Gerstein M: Normal mode analysis of macromolecular motions in a database framework: developing mode concentration as a useful classifying statistic. Proteins 2002, 48(4):682–695. 10.1002/prot.10168View ArticlePubMedGoogle Scholar
- Alexandrov V, Lehnert U, Echols N, Milburn D, Engelman D, Gerstein M: Normal modes for predicting protein motions: a comprehensive database assessment and associated Web tool. Protein Sci 2005, 14(3):633–643. 10.1110/ps.04882105PubMed CentralView ArticlePubMedGoogle Scholar
- Petrone P, Pande VS: Can conformational change be described by only a few normal modes? Biophysical journal 2006, 90(5):1583–1593. 10.1529/biophysj.105.070045PubMed CentralView ArticlePubMedGoogle Scholar
- Zheng W, Brooks BR: Normal-Modes-Based Prediction of Protein Conformational Changes Guided by Distance Constraints. Biophysical journal 2005, 88(5):3109–3117. 10.1529/biophysj.104.058453PubMed CentralView ArticlePubMedGoogle Scholar
- Lindahl E, Delarue M: Refinement of docked protein-ligand and protein-DNA structures using low frequency normal mode amplitude optimization. Nucleic acids research 2005, 33(14):4496–4506. 10.1093/nar/gki730PubMed CentralView ArticlePubMedGoogle Scholar
- Keseru GM, Kolossvary I: Fully Flexible Low-Mode Docking: Application to Induced Fit in HIV Integrase. Journal of the American Chemical Society 2001, 123(50):12708–12709. 10.1021/ja0160086View ArticlePubMedGoogle Scholar
- Zacharias M, Sklenar H: Harmonic modes as variables to approximately account for receptor flexibility in ligand-receptor docking simulations: Application to DNA minor groove ligand complex. Journal of Computational Chemistry 1999, 20(3):287–300. 10.1002/(SICI)1096-987X(199902)20:3<287::AID-JCC1>3.0.CO;2-HView ArticleGoogle Scholar
- Maiorov V, Abagyan R: A new method for modeling large-scale rearrangements of protein domains. Proteins 1997, 27(3):410–424. 10.1002/(SICI)1097-0134(199703)27:3<410::AID-PROT9>3.0.CO;2-GView ArticlePubMedGoogle Scholar
- Seeliger D, de Groot BL: Conformational transitions upon ligand binding: holo-structure prediction from apo conformations. PLoS Comput Biol 2010, 6(1):e1000634. 10.1371/journal.pcbi.1000634PubMed CentralView ArticlePubMedGoogle Scholar
- Flores SC, Keating KS, Painter J, Morcos F, Nguyen K, Merritt EA, Kuhn LA, Gerstein MB: HingeMaster: Normal mode hinge prediction approach and integration of complementary predictors. Proteins 2008.Google Scholar
- Gohlke H, Hendlich M, Klebe G: Knowledge-based scoring function to predict protein-ligand interactions. Journal of molecular biology 2000, 295(2):337–356. 10.1006/jmbi.1999.3371View ArticlePubMedGoogle Scholar
- Muegge I, Martin YC: A general and fast scoring function for protein-ligand interactions: a simplified potential approach. J Med Chem 1999, 42(5):791–804. 10.1021/jm980536jView ArticlePubMedGoogle Scholar
- Korb O, Stutzle T, Exner TE: Empirical scoring functions for advanced protein-ligand docking with PLANTS. J Chem Inf Model 2009, 49(1):84–96. 10.1021/ci800298zView ArticlePubMedGoogle Scholar
- Englebienne P, Moitessier N: Docking ligands into flexible and solvated macromolecules. 4. Are popular scoring functions accurate for this class of proteins? J Chem Inf Model 2009, 49(6):1568–1580. 10.1021/ci8004308View ArticlePubMedGoogle Scholar
- Maiorov V AR: Energy strain in three-dimensional protein structures. Fold Des 1998, 3(4):259–269. 10.1016/S1359-0278(98)00037-6View ArticlePubMedGoogle Scholar
- Flores SC, Gerstein MB: FlexOracle: predicting flexible hinges by identification of stable domains. BMC Bioinformatics 2007, 8: 215. 10.1186/1471-2105-8-215PubMed CentralView ArticlePubMedGoogle Scholar
- Painter J, Merritt EA: Optimal description of a protein structure in terms of multiple groups undergoing TLS motion. Acta Crystallogr D Biol Crystallogr 2006, 62(Pt 4):439–450.View ArticlePubMedGoogle Scholar
- Chennubhotla C, Rader AJ, Yang LW, Bahar I: Elastic network models for understanding biomolecular machinery: from enzymes to supramolecular assemblies. Phys Biol 2005, 2(4):S173–180. 10.1088/1478-3975/2/4/S12View ArticlePubMedGoogle Scholar
- Siddiqui AS, Barton GJ: Continuous and discontinuous domains: an algorithm for the automatic generation of reliable protein domain definitions. Protein Sci 1995, 4(5):872–884.PubMed CentralView ArticlePubMedGoogle Scholar
- Holm L, Sander C: Parser for protein folding units. Proteins 1994, 19(3):256–268. 10.1002/prot.340190309View ArticlePubMedGoogle Scholar
- Flores S, Echols N, Milburn D, Hespenheide B, Keating K, Lu J, Wells S, Yu EZ, Thorpe M, Gerstein M: The Database of Macromolecular Motions: new features added at the decade mark. Nucleic acids research 2006, (34 Database):D296–301.PubMed CentralView ArticlePubMedGoogle Scholar
- EJ JK, Jaton JC: Selective reduction and proteolysis in the hinge region of liganded and unliganded antibodies: identical kinetics suggest lack of major conformational change in the hinge region. Eur J Immunol 1978, 8(5):309–314. 10.1002/eji.1830080505View ArticleGoogle Scholar
- Yu S, Fan F, Flores S, Mei F, Cheng X: Dissecting the Mechanism of Epac Activation via Hydrogen-Deuterium Exchange FT-IR and Structural Modeling. Biochemistry 2006, 45(51):15318–15326. 10.1021/bi061701xPubMed CentralView ArticlePubMedGoogle Scholar
- Eickel VDD, Carter N, Lockhart A, Jones JK, Cross R: Kinesin heads fused to hinge-free myosin tails drive efficient motility. FEBS Lett 2004, 569(1–3):54–58. 10.1016/j.febslet.2004.05.050View ArticlePubMedGoogle Scholar
- Margittai MWJ, Schweinberger E, Schroder GF, Felekyan S, Haustein E, Konig M, Fasshauer D, Grubmuller H, Jahn R, et al.: Single-molecule fluorescence resonance energy transfer reveals a dynamic equilibrium between closed and open conformations of syntaxin 1. Proceedings of the National Academy of Sciences of the United States of America 2003, 100(26):15516–15521. 10.1073/pnas.2331232100PubMed CentralView ArticlePubMedGoogle Scholar
- Wishart MBaDS: NMR: prediction of protein flexibility. Nature Protocols 2006, 1: 683–688. 10.1038/nprot.2006.108View ArticlePubMedGoogle Scholar
- Wetlaufer DB: Nucleation, rapid folding, and globular intrachain regions in proteins. Proceedings of the National Academy of Sciences of the United States of America 1973, 70(3):697–701. 10.1073/pnas.70.3.697PubMed CentralView ArticlePubMedGoogle Scholar
- Humphrey W, Dalke A, Schulten K: VMD: visual molecular dynamics. J Mol Graph 1996, 14(1):33–38. 27–38 27-38 10.1016/0263-7855(96)00018-5View ArticlePubMedGoogle Scholar
- Cavasotto CN, Orry AJW, Murgolo NJ, Czarniecki MF, Kocsi SA, Hawes BE, O'Neill KA, Hine H, Burton MS, Voigt JH, et al.: Discovery of Novel Chemotypes to a G-Protein-Coupled Receptor through Ligand-Steered Homology Modeling and Structure-Based Virtual Screening. Journal of Medicinal Chemistry 2008, 51(3):581–588. 10.1021/jm070759mView ArticlePubMedGoogle Scholar
- Phatak SS, Gatica EA, Cavasotto CN: Ligand-steered modeling and docking: A benchmarking study in class A G-protein-coupled receptors. J Chem Inf Model 2010, 50(12):2119–2128. 10.1021/ci100285fView ArticlePubMedGoogle Scholar
- van Aalten DMFBR, Findlay JBC, Hendlich M, Hooft RWW, Vriend G: PRODRG, a program for generating molecular topologies and unique molecular descriptors from coordinates of small molecules. J Comput Aided Mol Des 1996, 10: 255. 10.1007/BF00355047View ArticlePubMedGoogle Scholar
- Morris GMGD, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ: a Lamarckian Genetic Algorithm and Empirical Binding Free Energy Function. J Computational Chemistry 1998, 19: 1639–1662. 10.1002/(SICI)1096-987X(19981115)19:14<1639::AID-JCC10>3.0.CO;2-BView ArticleGoogle Scholar
- Newcomer MLB, Quiocho FA: The Radius of Gyration of L-Arabinose- binding Protein Decreases upon Binding of Ligand. J Biol Chem 1981, 254(24):13218.Google Scholar
- Abele RSD, Keinanen K, Koch M, Madden DR: A Molecular Envelope of the Ligand-Binding Domain of a Glutamate Receptor in the Presence and Absence of Agonist. Biochemistry 1999, 38: 10949. 10.1021/bi982928yView ArticlePubMedGoogle Scholar
- Svergun DIKM: Small-angle scattering studies of biological macromolecules in solution. Reports on Progress in Physics 2003, 66: 1735–1782. 10.1088/0034-4885/66/10/R05View ArticleGoogle Scholar
- Radius of Gyration Wikipedia 2011.
- Guerois R, Nielsen JE, Serrano L: Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. Journal of molecular biology 2002, 320(2):369–387. 10.1016/S0022-2836(02)00442-4View ArticlePubMedGoogle Scholar
- Hsiao CD, Sun YJ, Rose J, Wang BC: The crystal structure of glutamine-binding protein from Escherichia coli. Journal of molecular biology 1996, 262(2):225–242. 10.1006/jmbi.1996.0509View ArticlePubMedGoogle Scholar
- Sun YJ, Rose J, Wang BC, Hsiao CD: The structure of glutamine-binding protein complexed with glutamine at 1.94 A resolution: comparisons with other amino acid binding proteins. Journal of molecular biology 1998, 278(1):219–229. 10.1006/jmbi.1998.1675View ArticlePubMedGoogle Scholar
- Wallace AC, Laskowski RA, Thornton JM: LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng 1995, 8(2):127–134. 10.1093/protein/8.2.127View ArticlePubMedGoogle Scholar
- Kondo S, Nakajima Y, Sugio S, Yong-Biao J, Sueda S, Kondo H: Structure of the biotin carboxylase subunit of pyruvate carboxylase from Aquifex aeolicus at 2.2 A resolution. Acta Crystallogr D Biol Crystallogr 2004, 60(Pt 3):486–492.View ArticlePubMedGoogle Scholar
- Thoden JB, Blanchard CZ, Holden HM, Waldrop GL: Movement of the biotin carboxylase B-domain as a result of ATP binding. J Biol Chem 2000, 275(21):16183–16190. 10.1074/jbc.275.21.16183View ArticlePubMedGoogle Scholar
- Bjorkman AJ, Binnie RA, Zhang H, Cole LB, Hermodson MA, Mowbray SL: Probing protein-protein interactions. The ribose-binding protein in bacterial transport and chemotaxis. J Biol Chem 1994, 269(48):30206–30211.PubMedGoogle Scholar
- Reinstein J, Vetter IR, Schlichting I, Rosch P, Wittinghofer A, Goody RS: Fluorescence and NMR investigations on the ligand binding properties of adenylate kinases. Biochemistry 1990, 29(32):7440–7450. 10.1021/bi00484a013View ArticlePubMedGoogle Scholar
- Eschenburg S, Priestman MA, Abdul-Latif FA, Delachaume C, Fassy F, Schonbrunn E: A novel inhibitor that suspends the induced fit mechanism of UDP-N-acetylglucosamine enolpyruvyl transferase (MurA). J Biol Chem 2005, 280(14):14070–14075. 10.1074/jbc.M414412200View ArticlePubMedGoogle Scholar
- Skarzynski T, Kim DH, Lees WJ, Walsh CT, Duncan K: Stereochemical course of enzymatic enolpyruvyl transfer and catalytic conformation of the active site revealed by the crystal structure of the fluorinated analogue of the reaction tetrahedral intermediate bound to the active site of the C115A mutant of MurA. Biochemistry 1998, 37(8):2572–2577. 10.1021/bi9722608View ArticlePubMedGoogle Scholar
- Xu Q: Private conversation.
- SC LJ, Yang J, Carriero NJ, Gerstein MB: Hinge Atlas: relating protein sequence to sites of structural flexibility. BMC Bioinformatics 2007.Google Scholar
- Bhinge A, Chakrabarti P, Uthanumallian K, Bajaj K, Chakraborty K, Varadarajan R: Accurate detection of protein:ligand binding sites using molecular dynamics simulations. Structure 2004, 12(11):1989–1999. 10.1016/j.str.2004.09.005View ArticlePubMedGoogle Scholar
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