Rapid protein-ligand docking using soft modes from molecular dynamics simulations to account for protein deformability: binding of FK506 to FKBP

Most current docking methods to identify possible ligands and putative binding sites on a receptor molecule assume a rigid receptor structure to allow virtual screening of large ligand databases. However, binding of a ligand can lead to changes in the receptor protein conformation that are sterically necessary to accommodate a bound ligand. An approach is presented that allows relaxation of the protein conformation in precalculated soft flexible degrees of freedom during ligand-receptor docking. For the immunosuppressant FK506-binding protein FKBP, the soft flexible modes are extracted as principal components of motion from a molecular dynamics simulation. A simple penalty function for deformations in the soft flexible mode is used to limit receptor protein deformations during docking that avoids a costly recalculation of the receptor energy by summing over all receptor atom pairs at each step. Rigid docking of the FK506 ligand binding to an unbound FKBP conformation failed to identify a geometry close to experiment as favorable binding site. In contrast, inclusion of the flexible soft modes during systematic docking runs selected a binding geometry close to experiment as lowest energy conformation. This has been achieved at a modest increase of computational cost compared to rigid docking. The approach could provide a computationally efficient way to approximately account for receptor flexibility during docking of large numbers of putative ligands and putative docking geometries.     

FLIPDock: docking flexible ligands into flexible receptors

Conformational changes of biological macromolecules when binding with ligands have long been observed and remain a challenge for automated docking methods. Here we present a novel protein-ligand docking software called FLIPDock (Flexible LIgand-Protein Docking) allowing the automated docking of flexible ligand molecules into active sites of flexible receptor molecules. In FLIPDock, conformational spaces of molecules are encoded using a data structure that we have developed recently called the Flexibility Tree (FT). While the FT can represent fully flexible ligands, it was initially designed as a hierarchical and multiresolution data structure for the selective encoding of conformational subspaces of large biological macromolecules. These conformational subspaces can be built to span a range of conformations important for the biological activity of a protein. A variety of motions can be combined, ranging from domains moving as rigid bodies or backbone atoms undergoing normal mode-based deformations, to side chains assuming rotameric conformations. In addition, these conformational subspaces are parameterized by a small number of variables which can be searched during the docking process, thus effectively modeling the conformational changes in a flexible receptor. FLIPDock searches the variables using genetic algorithm-based search techniques and evaluates putative docking complexes with a scoring function based on the AutoDock3.05 force-field. In this paper, we describe the concepts behind FLIPDock and the overall architecture of the program. We demonstrate FLIPDock's ability to solve docking problems in which the assumption of a rigid receptor previously prevented the successful docking of known ligands. In particular, we repeat an earlier cross docking experiment and demonstrate an increased success rate of 93.5%, compared to original 72% success rate achieved by AutoDock over the 400 cross-docking calculations. We also demonstrate FLIPDock's ability to handle conformational changes involving backbone motion by docking balanol to an adenosine-binding pocket of protein kinase A.     

Similarity-driven flexible ligand docking

A similarity-driven approach to flexible ligand docking is presented. Given a reference ligand or a pharmacophore positioned in the protein active site, the method allows inclusion of a similarity term during docking. Two different algorithms have been implemented, namely, a similarity-penalized docking (SP-DOCK) and a similarity-guided docking (SG-DOCK). The basic idea is to maximally exploit the structural information about the ligand binding mode present in cases where ligand-bound protein structures are available, information that is usually ignored in standard docking procedures. SP-DOCK and SG-DOCK have been derived as modified versions of the program DOCK 4.0, where the similarity program MIMIC acts as a module for the calculation of similarity indices that correct docking energy scores at certain steps of the calculation. SP-DOCK applies similarity corrections to the set of ligand orientations at the end of the ligand incremental construction process, penalizing the docking energy and, thus, having only an effect on the relative ordering of the final solutions. SG-DOCK applies similarity corrections throughout the entire ligand incremental construction process, thus affecting not only the relative ordering of solutions but also actively guiding the ligand docking. The performance of SP-DOCK and SG-DOCK for binding mode assessment and molecular database screening is discussed. When applied to a set of 32 thrombin ligands for which crystal structures are available, SG-DOCK improves the average RMSD by ca. 1 A when compared with DOCK. When those 32 thrombin ligands are included into a set of 1,000 diverse molecules from the ACD, DIV, and WDI databases, SP-DOCK significantly improves the retrieval of thrombin ligands within the first 10% of each of the three databases with respect to DOCK, with minimal additional computational cost. In all cases, comparison of SP-DOCK and SG-DOCK results with those obtained by DOCK and MIMIC is performed.     

pyDock: electrostatics and desolvation for effective scoring of rigid-body protein-protein docking

The accurate scoring of rigid-body docking orientations represents one of the major difficulties in protein-protein docking prediction. Other challenges are the development of faster and more efficient sampling methods and the introduction of receptor and ligand flexibility during simulations. Overall, good discrimination of near-native docking poses from the very early stages of rigid-body protein docking is essential step before applying more costly interface refinement to the correct docking solutions. Here we explore a simple approach to scoring of rigid-body docking poses, which has been implemented in a program called pyDock. The scheme is based on Coulombic electrostatics with distance dependent dielectric constant, and implicit desolvation energy with atomic solvation parameters previously adjusted for rigid-body protein-protein docking. This scoring function is not highly dependent on specific geometry of the docking poses and therefore can be used in rigid-body docking sets generated by a variety of methods. We have tested the procedure in a large benchmark set of 80 unbound docking cases. The method is able to detect a near-native solution from 12,000 docking poses and place it within the 100 lowest-energy docking solutions in 56% of the cases, in a completely unrestricted manner and without any other additional information. More specifically, a near-native solution will lie within the top 20 solutions in 37% of the cases. The simplicity of the approach allows for a better understanding of the physical principles behind protein-protein association, and provides a fast tool for the evaluation of large sets of rigid-body docking poses in search of the near-native orientation.     

Molecular docking of balanol to dynamics snapshots of protein kinase A

Even if the structure of a receptor has been determined experimentally, it may not be a conformation to which a ligand would bind when induced fit effects are significant. Molecular docking using such a receptor structure may thus fail to recognize a ligand to which the receptor can bind with reasonable affinity. Here, we examine one way to alleviate this problem by using an ensemble of receptor conformations generated from a molecular dynamics simulation for molecular docking. Two molecular dynamics simulations were conducted to generate snapshots for protein kinase A: one with the ligand bound, the other without. The ligand, balanol, was then docked to conformations of the receptors presented by these trajectories. The Lamarckian genetic algorithm in Autodock [Goodsell et al. J Mol Recognit 1996;9(1):1-5; Morris et al. J Comput Chem 1998;19(14):1639-1662] was used in the docking. Three ligand models were used: rigid, flexible, and flexible with torsional potentials. When the snapshots were taken from the molecular dynamics simulation of the protein-ligand complex, the correct docking structure could be recovered easily by the docking algorithm in all cases. This was an easier case for challenging the docking algorithm because, by using the structure of the protein in a protein-ligand complex, one essentially assumed that the protein already had a pocket to which the ligand can fit well. However, when the snapshots were taken from the ligand-free protein simulation, which is more useful for a practical application when the structure of the protein-ligand complex is not known, several clusters of structures were found. Of the 10 docking runs for each snapshot, at least one structure was close to the correctly docked structure when the flexible-ligand models were used. We found that a useful way to identify the correctly docked structure was to locate the structure that appeared most frequently as the lowest energy structure in the docking experiments to different snapshots.     

Selection and flexible optimization of binding modes from conformation ensembles

This paper describes a new semi-flexible docking approach named Fado (flexible alignment and docking), which incorporates flexibility by using an ensemble of precomputed ligand conformers. A primary ligand is defined as a linear combination over all input conformers. An optimization with regard to the linear coefficients makes the ligand flexible. Initially, a point matching problem utilizing the Merck Molecular Force Field (MMFF) is modeled in order to compute the correct orientation of the ligand with respect to the target. The problem is then solved through a local optimization approach (RPROP). This is done for 20 randomized ligand orientations, yielding 20 binding modes per complex. Evaluating these modes illustrates that our method is able to reproduce the binding modes of molecules within a few minutes of CPU time. A representative dataset of diverse protein-ligand complexes could be reproduced with 78% accuracy below 2A RMSD distance to the reference crystal structure. Fado is available upon request to the authors (see also http://www.zib.eu/Numerik/projects/docking/projectlong.en.html).     

Model of three-dimensional structure of vitamin D receptor and its binding mechanism with 1alpha,25-dihydroxyvitamin D(3).

Comparative modeling of the vitamin D receptor three-dimensional structure and computational docking of 1alpha,25-dihydroxyvitamin D(3) into the putative binding pocket of the two deletion mutant receptors: (207-423) and (120-422, Delta [164-207]) are reported and evaluated in the context of extensive mutagenic analysis and crystal structure of holo hVDR deletion protein published recently. The obtained molecular model agrees well with the experimentally determined structure. Six different conformers of 1alpha,25-dihydroxyvitamin D(3) were used to study flexible docking to the receptor. On the basis of values of conformational energy of various complexes and their consistency with functional activity, it appears that 1alpha,25-dihydroxyvitamin D(3) binds the receptor in its 6-s-trans form. The two lowest energy complexes obtained from docking the hormone into the deletion protein (207-423) differ in conformation of ring A and orientation of the ligand molecule in the VDR pocket. 1alpha,25-Dihydroxyvitamin D(3) possessing the A-ring conformation with axially oriented 1alpha-hydroxy group binds receptor with its 25-hydroxy substituent oriented toward the center of the receptor cavity, whereas ligand possessing equatorial conformation of 1alpha-hydroxy enters the pocket with A ring directed inward. The latter conformation and orientation of the ligand is consistent with the crystal structure of hVDR deletion mutant (118-425, Delta [165-215]). The lattice model of rVDR (120-422, Delta [164-207]) shows excellent agreement with the crystal structure of the hVDR mutant. The complex obtained from docking the hormone into the receptor has lower energy than complexes for which homology modeling was used. Thus, a simple model of vitamin D receptor with the first two helices deleted can be potentially useful for designing a general structure of ligand, whereas the advanced lattice model is suitable for examining binding sites in the pocket.     

Effective handling of induced-fit motion in flexible docking

For structure-based drug design, where various ligand structures need to be docked to a target protein structure, a docking method that can handle conformational flexibility of not only the ligand, but also the protein, is indispensable. We have developed a simple and effective approach for dealing with the local induced-fit motion of the target protein, and implemented it in our docking tool, ADAM. Our approach efficiently combines the following two strategies: a vdW-offset grid in which the protein cavity is enlarged uniformly, and structure optimization allowing the motion of ligand and protein atoms. To examine the effectiveness of our approach, we performed docking validation studies, including redocking in 18 test cases and foreign-docking, in which various ligands from foreign crystal structures of complexes are docked into a target protein structure, in 22 cases (on five target proteins). With the original ADAM, the correct docking modes (RMSD < 2.0 A) were not present among the top 20 models in one case of redocking and four cases of foreign-docking. When the handling of induced-fit motion was implemented, the correct solutions were acquired in all 40 test cases. In foreign-docking on thymidine kinase, the correct docking modes were obtained as the top-ranked solutions for all 10 test ligands by our combinatorial approach, and this appears to be the best result ever reported with any docking tool. The results of docking validation have thus confirmed the effectiveness of our approach, which can provide reliable docking models even in the case of foreign-docking, where conformational change of the target protein cannot be ignored. We expect that this approach will contribute substantially to actual drug design, including virtual screening.     

A mining minima approach to exploring the docking pathways of p-nitrocatechol sulfate to YopH

Using the docking of p-nitrocatechol sulfate to Yersinia protein tyrosine phosphatase YopH as an example, we showed that an approach based on mining minima followed by cluster and similarity analysis could generate useful insights into docking pathways. Our simulation treated both the ligand and the protein as flexible molecules so that the coupling between their motion could be properly accounted for. Our simulation identified three docking poses; the one with the lowest energy agreed well with experimental structure. The model also predicted the side-chain conformations of the amino acids lying in the binding pocket correctly with the exception of three residues that appeared to be stabilized by two structural water molecules in the crystal structure. The implicit solvent model employed in the simulation could not capture such effects well. We also found four major pathways leading to these docking poses after the ligand entered the mouth of the binding pocket. In addition, the sulfate group of p-nitrocatechol sulfate was found to be important both in binding the ligand to the pocket and in guiding the ligand to dock into the pocket. The coupling of the motion between the protein and the ligand also played an important role in facilitating ligand loading and unloading.     

Ligand entry pathways in the ligand binding domain of PPARγ receptor

To address the question of ligand entry process, we report targeted molecular dynamics simulations of the entry of the flexible ionic ligand GW0072 in the ligand binding domain of the nuclear receptor PPARγ. Starting with the ligand outside the receptor the simulations led to a ligand docked inside the binding pocket resulting in a structure very close to the holo-form of the complex. The results showed that entry process is guided by hydrophobic interactions and that entry pathways are very similar to exit pathways. We suggest that TMD method may help in discriminating between ligands generated by in silico docking.     

The lack of a solvent accessible hydroxide or water ligand to iron at the 3Fe center of Azotobacter vinelandii ferredoxin I

The X-ray crystal structure of Azotobacter vinelandii ferredoxin I (FdI) describes a planar 3Fe-3S center in which one of the iron atoms is ligated to a solvent accessible oxo ligand, presumably from water or hydroxide (Ghosh et al., (1982) J. Mol. Biol. 158, 73-109). Efforts to displace the proposed oxo ligand with cyanide were unsuccessful, even in 80% dimethylsulfoxide. In addition, comparison of the electron spin echo envelopes for H2O- and D2O-equilibrated samples of FdI showed only a slight deuterium modulation, far less than would be expected were water to be bound as an iron ligand. These results do not support the presence of a solvent accessible oxo ligand to the 3Fe center as described in the X-ray crystal structure.     

Insights from comprehensive multiple receptor docking to HDAC8

A systematic investigation of the available crystal structures of HDAC8 and of the influence of different receptor structures and docking protocols is presented. The study shows that the open conformation of HDAC8 may be preferred by ligands with flexible surface binding groups, as such a conformation allows the ligands to minimize their exposure to solvent upon binding. This observation allowed us to rationalize the excellent potency of pyrazole-based inhibitors compared to that of isoxazole-based inhibitors.     

Modeling correlated main-chain motions in proteins for flexible molecular recognition

We describe a new method for modeling protein and ligand main-chain flexibility, and show its ability to model flexible molecular recognition. The goal is to sample the full conformational space, including large-scale motions that typically cannot be reached in molecular dynamics simulations due to the computational intensity, as well as conformations that have not been observed yet by crystallography or NMR. A secondary goal is to assess the degree of flexibility consistent with protein-ligand recognition. Flexibility analysis of the target protein is performed using the graph-theoretic algorithm FIRST, which also identifies coupled networks of covalent and noncovalent bonds within the protein. The available conformations of the flexible regions are then explored with ROCK by random-walk sampling of the rotatable bonds. ROCK explores correlated motions by only sampling dihedral angles that preserve the coupled bond networks in the protein and generates conformers with good stereochemistry, without using a computationally expensive potential function. A representative set of the conformational ensemble generated this way can be used as targets for docking with SLIDE, which handles the flexibility of protein and ligand side-chains. The realism of this protein main-chain conformational sampling is assessed by comparison with time-resolved NMR studies of cyclophilin A motions. ROCK is also effective for modeling the flexibility of large cyclic and polycyclic ligands, as demonstrated for cyclosporin and zearalenol. The use of this combined approach to perform docking with main-chain flexibility is illustrated for the cyclophilin A-cyclosporin complex and the estrogen receptor in complex with zearalenol, while addressing the question of how much flexibility is allowed without hindering molecular recognition.     

Binding mode prediction for a flexible ligand in a flexible pocket using multi-conformation simulated annealing pseudo crystallographic refinement

We describe multi-conformation simulated annealing-pseudo-crystallographic refinement (MCSA-PCR), a technique developed for predicting the binding mode of a flexible ligand in a flexible binding pocket. To circumvent the local-minimum problem efficiently, this method performs multiple independent cycles of simulated annealing with explicit solvent, "growing" the ligand in the binding pocket each time. From the ensemble of structures, a pseudo-crystallographic electron density map is calculated, and then conventional crystallographic refinement methods are used to best fit a single, optimal structure into the density map. The advantage of the MCSA-PCR method is that it provides a direct means to evaluate the accuracy and uniqueness of the calculated solution, provides a measure of ligand and protein dynamics from the refined B-factors, and facilitates comparison with X-ray crystallographic data. Here, we show that our MCSA-PCR method succeeds in predicting the correct binding mode of the VSV8 peptide to the major histocompatibility complex (MHC) receptor. Importantly, there is a significant correlation between the experimentally determined crystallographic water molecules and water density observed in the pseudo map by MCSA-PCR. Furthermore, comparison of different approaches for extracting a single, most probable structure from the calculated ensemble reveals the power of the PCR method and provides insights into the nature of the energetic landscape.     

Molecular dynamics simulation of the docking of substrates to proteins

A simple method is described to perform docking of subtrates to proteins or probes to receptor molecules by a modification of molecular dynamics simulations. The method consists of a separation of the center-of-mass motion of the substrate from its internal and rotational motions, and a separate coupling to different thermal baths for both types of motion of the substrate and for the motion of the receptor. Thus the temperatures and the time constants of coupling to the baths can be arbitrarily varied for these three types of motion, allowing either a frozen or a flexible receptor and allowing control of search rate without disturbance of internal structure. In addition, an extra repulsive term between substrate and protein was applied to smooth the interaction. The method was applied to a model substrate docking onto a model surface, and to the docking of phosphocholine onto immunoglobulin McPC603, in both cases with a frozen receptor. Using transrational temperatures of the substrate in the range of 1300–1700 K and room temperature for the internal degrees of freedom of the substrate, an efficient nontrapping exploratory search (“helicopter view”) is obtained, which visits the correct binding sites. Low energy conformations can then be further investigated by separate search or by dynamic simulated annealing. In both cases the correct minima were identified. The possibility to work with flexible receptors is discussed. © 1994 Wiley-Liss, Inc.     

Accounting for loop flexibility during protein-protein docking

Although reliable docking can now be achieved for systems that do not undergo important induced conformational change upon association, the presence of flexible surface loops, which must adapt to the steric and electrostatic properties of a partner, generally presents a major obstacle. We report here the first docking method that allows large loop movements during a systematic exploration of the possible arrangements of the two partners in terms of position and rotation. Our strategy consists in taking into account an ensemble of possible loop conformations by a multi-copy representation within a reduced protein model. The docking process starts from regularly distributed positions and orientations of the ligand around the whole receptor. Each starting configuration is submitted to energy minimization during which the best-fitting loop conformation is selected based on the mean-field theory. Trials were carried out on proteins with significant differences in the main-chain conformation of the binding loop between isolated form and complexed form, which were docked to their partner considered in their bound form. The method is able to predict complexes very close to the crystal complex both in terms of relative position of the two partners and of the geometry of the flexible loop. We also show that introducing loop flexibility on the isolated protein form during systematic docking largely improves the predictions of relative position of the partners in comparison with rigid-body docking.     

New general approach for determining the solution structure of a ligand bound weakly to a receptor: structure of a fibrinogen Aalpha-like peptide bound to thrombin (S195A) obtained using NOE distance constraints and an ECEPP/3 flexible docking program.

A new approach incorporating flexible docking simulations and NMR data is presented for calculating the bound conformation of a ligand that interacts weakly with an enzyme. This approach consists of sampling directly the conformation of a flexible ligand inside a receptor active site containing surrounding flexible loops. To make this sampling efficient, a ligand-growing procedure has been adopted. Optimization of the ECEPP/3-plus-NOE constraint function is carried out by using a collective variable Monte Carlo minimization technique. Numerous energy minimizations are made possible for such a large system by using a Bezier splines energy grid technique. This new flexible docking approach was applied to determine the structure of a fibrinogen Aalpha-like peptide (7DFLAEGGGVRGPRV20) bound to an active site mutant of thrombin [thrombin(S195A)]. Structure calculations of the bound ligand, using 2D-transferred NOESY distance constraints in the DIANA program, showed that the N-terminal portion of the peptide (D7-R16) involves a chain reversal, whereas the C-terminal portion (G17-V20) adopts a fold that exists in several different orientations. In addition, the ECEPP/3 flexible docking package was used to assess the conformational variability of the ligand and surrounding 60D-insertion loop of thrombin. Amino acid residues (17-20) of the peptide interact with a region of the enzyme that exhibits broad specificity, with a preferred direction between the 60D-insertion loop and Pro37 of thrombin.