Testing a flexible-receptor docking algorithm in a model binding site

Sampling receptor flexibility is challenging for database docking. We consider a method that treats multiple flexible regions of the binding site independently, recombining them to generate different discrete conformations. This algorithm scales linearly rather than exponentially with the receptor's degrees of freedom. The method was first evaluated for its ability to identify known ligands of a hydrophobic cavity mutant of T4 lysozyme (L99A). Some 200000 molecules of the Available Chemical Directory (ACD) were docked against an ensemble of cavity conformations. Surprisingly, the enrichment of known ligands from among a much larger number of decoys in the ACD was worse than simply docking to the apo conformation alone. Large decoys, accommodated in the larger cavity conformations sampled in the ensemble, were ranked better than known small ligands. The calculation was redone with an energy correction term that considered the cost of forming the larger cavity conformations. Enrichment improved, as did the balance between high-ranking large and small ligands. In a second retrospective test, the ACD was docked against a conformational ensemble of thymidylate synthase. Compared to docking against individual enzyme conformations, the flexible receptor docking approach improved enrichment of known ligands. Including a receptor conformational energy weighting term improved enrichment further. To test the method prospectively, the ACD database was docked against another cavity mutant of lysozyme (L99A/M102Q). A total of 18 new compounds predicted to bind this polar cavity and to change its conformation were tested experimentally; 14 were found to bind. The bound structures for seven ligands were determined by X-ray crystallography. The predicted geometries of these ligands all corresponded to the observed geometries to within 0.7A RMSD or better. Significant conformational changes of the cavity were observed in all seven complexes. In five structures, part of the observed accommodations were correctly predicted; in two structures, the receptor conformational changes were unanticipated and thus never sampled. These results suggest that although sampling receptor flexibility can lead to novel ligands that would have been missed when docking a rigid structure, it is also important to consider receptor conformational energy.     

Probing molecular docking in a charged model binding site

A model binding site was used to investigate charge-charge interactions in molecular docking. This simple site, a small (180A(3)) engineered cavity in cyctochrome c peroxidase (CCP), is negatively charged and completely buried from solvent, allowing us to explore the balance between electrostatic energy and ligand desolvation energy in a system where many of the common approximations in docking do not apply. A database with about 5300 molecules was docked into this cavity. Retrospective testing with known ligands and decoys showed that overall the balance between electrostatic interaction and desolvation energy was captured. More interesting were prospective docking scre"ens that looked for novel ligands, especially those that might reveal problems with the docking and energy methods. Based on screens of the 5300 compound database, both high-scoring and low-scoring molecules were acquired and tested for binding. Out of 16 new, high-scoring compounds tested, 15 were observed to bind. All of these were small heterocyclic cations. Binding constants were measured for a few of these, they ranged between 20microM and 60microM. Crystal structures were determined for ten of these ligands in complex with the protein. The observed ligand geometry corresponded closely to that predicted by docking. Several low-scoring alkyl amino cations were also tested and found to bind. The low docking score of these molecules owed to the relatively high charge density of the charged amino group and the corresponding high desolvation penalty. When the complex structures of those ligands were determined, a bound water molecule was observed interacting with the amino group and a backbone carbonyl group of the cavity. This water molecule mitigates the desolvation penalty and improves the interaction energy relative to that of the "naked" site used in the docking screen. Finally, six low-scoring neutral molecules were also tested, with a view to looking for false negative predictions. Whereas most of these did not bind, two did (phenol and 3-fluorocatechol). Crystal structures for these two ligands in complex with the cavity site suggest reasons for their binding. That these neutral molecules do, in fact bind, contradicts previous results in this site and, along with the alkyl amines, provides instructive false negatives that help identify weaknesses in our scoring functions. Several improvements of these are considered.     

Biological interaction of thiamine with lysozyme using binding capacity concept and molecular docking

The binding of thiamine (vitamin B₁) on lysozyme has been examined at various ionic strengths of phosphate buffer (pH 6.9), various pH values, and various protein concentrations at 25°C using thiamine selective membrane electrode. This method is faster and more precise than equilibrium dialysis technique which can obtain sufficient and accurate data for binding analysis. The values of Hill equation parameters were estimated for each set using binding capacity concept and used for calculation of intrinsic binding affinity. The results represent two binding sets for thiamine on lysozyme at various experimental conditions.     

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.     

Complex-type-dependent scoring functions in protein-protein docking

A major challenge in the field of protein-protein docking is to discriminate between the many wrong and few near-native conformations, i.e. scoring. Here, we introduce combinatorial complex-type-dependent scoring functions for different types of protein-protein complexes, protease/inhibitor, antibody/antigen, enzyme/inhibitor and others. The scoring functions incorporate both physical and knowledge-based potentials, i.e. atomic contact energy (ACE), the residue pair potential (RP), electrostatic and van der Waals' interactions. For different type complexes, the weights of the scoring functions were optimized by the multiple linear regression method, in which only top 300 structures with ligand root mean square deviation (L_RMSD) less than 20 A from the bound (co-crystallized) docking of 57 complexes were used to construct a training set. We employed the bound docking studies to examine the quality of the scoring function, and also extend to the unbound (separately crystallized) docking studies and extra 8 protein-protein complexes. In bound docking of the 57 cases, the first hits of protease/inhibitor cases are all ranked in the top 5. For the cases of antibody/antigen, enzyme/inhibitor and others, there are 17/19, 5/6 and 13/15 cases with the first hits ranked in the top 10, respectively. In unbound docking studies, the first hits of 9/17 protease/inhibitor, 6/19 antibody/antigen, 1/6 enzyme/inhibitor and 6/15 others' complexes are ranked in the top 10. Additionally, for the extra 8 cases, the first hits of the two protease/inhibitor cases are ranked in the top for the bound and unbound test. For the two enzyme/inhibitor cases, the first hits are ranked 1st for bound test, and the 119th and 17th for the unbound test. For the others, the ranks of the first hits are the 1st for the bound test and the 12th for the 1WQ1 unbound test. To some extent, the results validated our divide-and-conquer strategy in the docking study, which might hopefully shed light on the prediction of protein-protein interactions.     

Ligand solvation in molecular docking

Solvation plays an important role in ligand‐protein association and has a strong impact on comparisons of binding energies for dissimilar molecules. When databases of such molecules are screened for complementarity to receptors of known structure, as often occurs in structure‐based inhibitor discovery, failure to consider ligand solvation often leads to putative ligands that are too highly charged or too large. To correct for the different charge states and sizes of the ligands, we calculated electrostatic and non‐polar solvation free energies for molecules in a widely used molecular database, the Available Chemicals Directory (ACD). A modified Born equation treatment was used to calculate the electrostatic component of ligand solvation. The non‐polar component of ligand solvation was calculated based on the surface area of the ligand and parameters derived from the hydration energies of apolar ligands. These solvation energies were subtracted from the ligand‐receptor interaction energies. We tested the usefulness of these corrections by screening the ACD for molecules that complemented three proteins of known structure, using a molecular docking program. Correcting for ligand solvation improved the rankings of known ligands and discriminated against molecules with inappropriate charge states and sizes. Proteins 1999;34:4–16. © 1999 Wiley‐Liss, Inc.     

Incorporating replacement free energy of binding‐site waters in molecular docking

Binding‐site water molecules play a crucial role in protein‐ligand recognition, either being displaced upon ligand binding or forming water bridges to stabilize the complex. However, rigorously treating explicit binding‐site waters is challenging in molecular docking, which requires to fully sample ensembles of waters and to consider the free energy cost of replacing waters. Here, we describe a method to incorporate structural and energetic properties of binding‐site waters into molecular docking. We first developed a solvent property analysis (SPA) program to compute the replacement free energies of binding‐site water molecules by post‐processing molecular dynamics trajectories obtained from ligand‐free protein structure simulation in explicit water. Next, we implemented a distance‐dependent scoring term into DOCK scoring function to take account of the water replacement free energy cost upon ligand binding. We assessed this approach in protein targets containing important binding‐site waters, and we demonstrated that our approach is reliable in reproducing the crystal binding geometries of protein‐ligand‐water complexes, as well as moderately improving the ligand docking enrichment performance. In addition, SPA program (free available to academic users upon request) may be applied in identifying hot‐spot binding‐site residues and structure‐based lead optimization. Proteins 2014; 82:1765–1776. © 2014 Wiley Periodicals, Inc.     

A new implicit solvent model for protein-ligand docking

Peroxisomes are small subcellular compartments responsible for a range of essential metabolic processes. Efforts in predicting peroxisomal protein import are challenged by species variation and sparse sequence data sets with experimentally confirmed localization. We present a predictor of peroxisomal import based on the presence of the dominant peroxisomal targeting signal one (PTS1), a seemingly wellconserved but highly unspecific motif. The signal appears to rely on subtle dependencies with the preceding residues. We evaluate prediction accuracies against two alternative predictor services, PEROXIP and the PTS1 PREDICTOR. We test the integrity of prediction on a range of prokaryotic and eukaryotic proteomes lacking peroxisomes. Similarly we test the accuracy on peroxisomal proteins known to not overlap with training data. The model identified a number of proteins within the RIKEN IPS7 mouse protein dataset as potentially novel peroxisomal proteins. Three were confirmed in vitro using immunofluorescent detection of myc-epitope-tagged proteins in transiently transfected BHK-21 cells (Dhrs2, Serhl, and Ehhadh). The final model has a superior specificity to both alternatives, and an accuracy better than PEROXIP and on par with PTS1 PREDICTOR. Thus, the model we present should prove invaluable for labeling PTS1 targeted proteins with high confidence. We use the predictor to screen several additional eukaryotic genomes to revise previously estimated numbers of peroxisomal proteins. Available at http://pprowler.itee.uq.edu.au.     

Domain motions in bacteriophage T4 lysozyme: A comparison between molecular dynamics and crystallographic data

A comparison of a series of extended molecular dynamics (MD) simulations of bacteriophage T4 lysozyme in solvent with X-ray data is presented. Essential dynamics analyses were used to derive collective fluctuations from both the simulated trajectories and a distribution of crystallographic conformations. In both cases the main collective fluctuations describe domain motions. The protein consists of an N- and C-terminal domain connected by a long helix. The analysis of the distribution of crystallographic conformations reveals that the N-terminal helix rotates together with either of these two domains. The main domain fluctuation describes a closure mode of the two domains in which the N-terminal helix rotates concertedly with the C-terminal domain, while the domain fluctuation with second largest amplitude corresponds to a twisting mode of the two domains, with the N-terminal helix rotating concertedly with the N-terminal domain. For the closure mode, the difference in hinge-bending angle between the most open and most closed X-ray structure along this mode is 49 degrees. In the MD simulation that shows the largest fluctuation along this mode, a rotation of 45 degrees was observed. Although the twisting mode has much less freedom than the closure mode in the distribution of crystallographic conformations, experimental results suggest that it might be functionally important. Interestingly, the twisting mode is sampled more extensively in all MD simulations than it is in the distribution of X-ray conformations. Proteins 31:116–127, 1998. © 1998 Wiley-Liss, Inc.     

Understanding the conformational flexibility and electrostatic properties of curcumin in the active site of rhAChE via molecular docking,molecular dynamics,and charge density analysis

Acetylcholinesterase (AChE) is an important enzyme responsible for Alzheimer’s disease, as per report, keto-enol form of curcumin inhibits this enzyme. The present study aims to understand the binding mechanism of keto-enol curcumin with the recombinant human Acetylcholinesterase (rhAChE) from its conformational flexibility, intermolecular interactions, charge density distribution, and the electrostatic properties at the active site of rhAChE. To accomplish this, a molecular docking analysis of curcumin with the rhAChE was performed, which gives the structure and conformation of curcumin in the active site of rhAChE. Further, the charge density distribution and the electrostatic properties of curcumin molecule (lifted from the active site of rhAChE) were determined from the high level density functional theory (DFT) calculations coupled with the charge density analysis. On the other hand, the curcumin molecule was optimized (gas phase) using DFT method and further, the structure and charge density analysis were also carried out. On comparing the conformation, charge density distribution and the electrostatic potential of the active site form of curcumin with the corresponding gas phase form reveals that the above said properties are significantly altered when curcumin is present in the active site of rhAChE. The conformational stability and the interaction of curcumin in the active site are also studied using molecular dynamics simulation, which shows a large variation in the conformational geometry of curcumin as well as the intermolecular interactions.     

Integrating atom-based and residue-based scoring functions for protein-protein docking

Most scoring functions for protein-protein docking algorithms are either atom-based or residue-based, with the former being able to produce higher quality structures and latter more tolerant to conformational changes upon binding. Earlier, we developed the ZRANK algorithm for reranking docking predictions, with a scoring function that contained only atom-based terms. Here we combine ZRANK's atom-based potentials with five residue-based potentials published by other labs, as well as an atom-based potential IFACE that we published after ZRANK. We simultaneously optimized the weights for selected combinations of terms in the scoring function, using decoys generated with the protein-protein docking algorithm ZDOCK. We performed rigorous cross validation of the combinations using 96 test cases from a docking benchmark. Judged by the integrative success rate of making 1000 predictions per complex, addition of IFACE and the best residue-based pair potential reduced the number of cases without a correct prediction by 38 and 27% relative to ZDOCK and ZRANK, respectively. Thus combination of residue-based and atom-based potentials into a scoring function can improve performance for protein-protein docking. The resulting scoring function is called IRAD (integration of residue- and atom-based potentials for docking) and is available at http://zlab.umassmed.edu.     

Automatic identification and representation of protein binding sites for molecular docking.

Molecular docking is a popular way to screen for novel drug compounds. The method involves aligning small molecules to a protein structure and estimating their binding affinity. To do this rapidly for tens of thousands of molecules requires an effective representation of the binding region of the target protein. This paper presents an algorithm for representing a protein's binding site in a way that is specifically suited to molecular docking applications. Initially the protein's surface is coated with a collection of molecular fragments that could potentially interact with the protein. Each fragment, or probe, serves as a potential alignment point for atoms in a ligand, and is scored to represent that probe's affinity for the protein. Probes are then clustered by accumulating their affinities, where high affinity clusters are identified as being the "stickiest" portions of the protein surface. The stickiest cluster is used as a computational binding "pocket" for docking. This method of site identification was tested on a number of ligand-protein complexes; in each case the pocket constructed by the algorithm coincided with the known ligand binding site. Successful docking experiments demonstrated the effectiveness of the probe representation.     

Calculation of ligand-nucleic acid binding free energies with the generalized-born model in DOCK

The calculation of ligand-nucleic acid binding free energies is investigated by including solvation effects computed with the generalized-Born model. Modifications of the solvation module in DOCK, including introduction of all-atom parameters and revision of coefficients in front of different terms, are shown to improve calculations involving nucleic acids. This computing scheme is capable of calculating binding energies, with reasonable accuracy, for a wide variety of DNA-ligand complexes, RNA-ligand complexes, and even for the formation of double-stranded DNA. This implementation of GB/SA is also shown to be capable of discriminating strong ligands from poor ligands for a series of RNA aptamers without sacrificing the high efficiency of the previous implementation. These results validate this approach to screening large databases against nucleic acid targets.     

Structure and dynamics of the active site gorge of acetylcholinesterase: synergistic use of molecular dynamics simulation and X-ray crystallography.

The active site of acetylcholinesterase (AChE) from Torpedo californica is located 20 A from the enzyme surface at the bottom of a narrow gorge. To understand the role of this gorge in the function of AChE, we have studied simulations of its molecular dynamics. When simulations were conducted with pure water filling the gorge, residues in the vicinity of the active site deviated quickly and markedly from the crystal structure. Further study of the original crystallographic data suggests that a bis-quaternary decamethonium (DECA) ion, acquired during enzyme purification, residues in the gorge. There is additional electron density within the gorge that may represent small bound cations. When DECA and 2 cations are placed within the gorge, the simulation and the crystal structure are dramatically reconciled. The small cations, more so than DECA, appear to stabilize part of the gorge wall through electrostatic interactions. This part of the gorge wall is relatively thin and may regulate substrate, product, and water movement through the active site.     

Positions of His-64 and a bound water in human carbonic anhydrase II upon binding three structurally related inhibitors.

The 3-dimensional structure of human carbonic anhydrase II (HCAII; EC 4.2.1.1) complexed with 3 structurally related inhibitors, 1a, 1b, and 1c, has been determined by X-ray crystallographic methods. The 3 inhibitors (1a = C8H12N2O4S3) vary only in the length of the substituent on the 4-amino group: 1a, proton; 1b, methyl; and 1c, ethyl. The binding constants (Ki's) for 1a, 1b, and 1c to HCAII are 1.52, 1.88, and 0.37 nM, respectively. These structures were solved to learn if any structural cause could be found for the difference in binding. In the complex with inhibitors 1a and 1b, electron density can be observed for His-64 and a bound water molecule in the native positions. When inhibitor 1c is bound, the side chain attached to the 4-amino group is positioned so that His-64 can only occupy the alternate position and the bound water is absent. While a variety of factors contribute to the observed binding constants, the major reason 1c binds tighter to HCAII than does 1a or 1b appears to be entropy: the increase in entropy when the bound water molecule is released contributes to the increase in binding and overcomes the small penalty for putting the His-64 side chain in a higher energy state.     

Progress in molecular docking

Background: In recent years, since the molecular docking technique can greatly improve the efficiency and reduce the research cost, it has become a key tool in computer-assisted drug design to predict the binding affinity and analyze the interactive mode. Results: This study introduces the key principles, procedures and the widely-used applications for molecular docking. Also, it compares the commonly used docking applications and recommends which research areas are suitable for them. Lastly, it briefly reviews the latest progress in molecular docking such as the integrated method and deep learning. Conclusion: Limited to the incomplete molecular structure and the shortcomings of the scoring function, current docking applications are not accurate enough to predict the binding affinity. However, we could improve the current molecular docking technique by integrating the big biological data into scoring function.     

Atomic solvation parameters in the analysis of protein-protein docking results.

Several sets of amino acid surface areas and transfer free energies were used to derive a total of nine sets of atomic solvation parameters (ASPs). We tested the accuracy of each of these sets of parameters in predicting the experimentally determined transfer free energies of the amino acid derivatives from which the parameters were derived. In all cases, the calculated and experimental values correlated well. We then chose three parameter sets and examined the effect of adding an energetic correction for desolvation based on these three parameter sets to the simple potential function used in our multiple start Monte Carlo docking method. A variety of protein-protein interactions and docking results were examined. In the docking simulations studied, the desolvation correction was only applied during the final energy calculation of each simulation. For most of the docking results we analyzed, the use of an octanol-water-based ASP set marginally improved the energetic ranking of the low-energy dockings, whereas the other ASP sets we tested disturbed the ranking of the low-energy dockings in many of the same systems. We also examined the correlation between the experimental free energies of association and our calculated interaction energies for a series of proteinase-inhibitor complexes. Again, the octanol-water-based ASP set was compatible with our standard potential function, whereas ASP sets derived from other solvent systems were not.     

Comprehensive mapping of cystic fibrosis mutations to CFTR protein identifies mutation clusters and molecular docking predicts corrector binding site

Cystic Fibrosis (CF) is caused by mutations in the CFTR gene, of which over 2000 have been reported to date. Mutations have yet to be analyzed in aggregate to assess their distribution across the tertiary structure of the CFTR protein, an approach that could provide valuable insights into the structure‐function relationship of CFTR. In addition, the binding site of Class I correctors (VX‐809, VX‐661, and C18) is not well understood. In this study, exonic CFTR mutations and mutant allele frequencies described in 3 curated databases (ABCMdb, CFTR1, and CFTR2, comprising >130 000 data points) were mapped to 2 different structural models: a homology model of full‐length CFTR protein in the open‐channel state, and a cryo‐electron microscopy core‐structure of CFTR in the closed‐channel state. Accordingly, residue positions of 6 high‐frequency mutant CFTR alleles were found to spatially co‐localize in CFTR protein, and a significant cluster was identified at the NBD1:ICL4 interdomain interface. In addition, immunoblotting confirmed the approximate binding site of Class I correctors, demonstrating that these small molecules act via a similar mechanism in vitro, and in silico molecular docking generated binding poses for their complex with the cryo‐electron microscopy structure to suggest the putative corrector binding site is a multi‐domain pocket near residues F374‐L375. These results confirm the significance of interdomain interfaces as susceptible to disruptive mutation, and identify a putative corrector binding site. The structural pharmacogenomics approach of mapping mutation databases to protein models shows promise for facilitating drug discovery and personalized medicine for monogenetic diseases.     

Design,synthesis, crystal structure,biological evaluation and molecular docking studies of carbazole-arylpiperazine derivatives

Subtype-selective α₁-adrenoceptor (AR) antagonists display optimum therapeutic efficacies for the treatment of benign prostatic hyperplasia (BPH). In this study, we designed and synthesized novel carbazole-arylpiperazines derivatives (1 and 2) on the basis of the proposed pharmacophore model for α₁-AR antagonists. Structural properties were investigated using single-crystal X-ray diffraction analysis. Comparison of crystal structures with ligand-based pharmacophore models revealed that the two agents may possess antagonistic effects on α_1D subtype. Tissue functional assay in vitro showed that compound 2 exerted strong antagonistic activity on α_1B-AR (pA₂ 7.13) with a poor selectivity for α_1A and α_1D subtypes. Compound 1 exhibited enhanced antagonistic effect on α_1D subtype (pA₂ 7.06) and excellent selectivity for α_1D over α_1B (α_1D/α_1B ratio = 79.4). To illustrate the relationship between antagonistic activity and chemical structure, molecular docking studies were performed using the homology models of α₁ receptors. Binding mechanism indicated that small hydrophobic substituents attached to the arylpiperazine moiety were essential for rational design of α_1D-selective antagonists.