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Nicolas Froloff Andreas Windemuth Barry Honig 《Protein science : a publication of the Protein Society》1997,6(6):1293-1301
This paper describes a methodology to calculate the binding free energy (ΔG) of a protein-ligand complex using a continuum model of the solvent. A formal thermodynamic cycle is used to decompose the binding free energy into electrostatic and non-electrostatic contributions. In this cycle, the reactants are discharged in water, associated as purely nonpolar entities, and the final complex is then recharged. The total electrostatic free energies of the protein, the ligand, and the complex in water are calculated with the finite difference Poisson-Boltzmann (FDPB) method. The nonpolar (hydrophobic) binding free energy is calculated using a free energy-surface area relationship, with a single alkane/water surface tension coefficient (γaw). The loss in backbone and side-chain configurational entropy upon binding is estimated and added to the electrostatic and the nonpolar components of ΔG. The methodology is applied to the binding of the murine MHC class I protein H-2Kb with three distinct peptides, and to the human MHC class I protein HLA-A2 in complex with five different peptides. Despite significant differences in the amino acid sequences of the different peptides, the experimental binding free energy differences (ΔΔGexp) are quite small (<0.3 and <2.7 kcal/mol for the H-2Kb and HLA-A2 complexes, respectively). For each protein, the calculations are successful in reproducing a fairly small range of values for ΔΔGcalc (<4.4 and <5.2 kcal/mol, respectively) although the relative peptide binding affinities of H-2Kb and HLA-A2 are not reproduced. For all protein-peptide complexes that were treated, it was found that electrostatic interactions oppose binding whereas nonpolar interactions drive complex formation. The two types of interactions appear to be correlated in that larger nonpolar contributions to binding are generally opposed by increased electrostatic contributions favoring dissociation. The factors that drive the binding of peptides to MHC proteins are discussed in light of our results. 相似文献
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药物与靶标的结合是启动药理作用的本源,共价键药物是以共享电子的方式来实现与靶标的结合,其中大多为抗感染、抗肿瘤以及心脑血管、神经系统和代谢类药物。简介共价键药物与非共价键药物的区别以及既往的重磅级共价键药物与靶标的结合特点,分类综述靶向共价键药物的理性设计及与靶标的结合反应。 相似文献
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Xiufeng Yang Haotian Li Yangyu Huang Shiyong Liu 《Protein science : a publication of the Protein Society》2013,22(12):1808-1811
We have developed a non‐redundant protein–RNA binding benchmark dataset derived from the available protein–RNA structures in the Protein Database Bank. It consists of 73 complexes with measured binding affinity. The experimental conditions (pH and temperature) for binding affinity measurements are also listed in our dataset. This binding affinity dataset can be used to compare and develop protein–RNA scoring functions. The predicted binding free energy of the 73 complexes from three available scoring functions for protein–RNA docking has a low correlation with the binding Gibbs free energy calculated from Kd. © 2013 The Protein Society 相似文献
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Virtual drug screening using protein-ligand docking techniques is a time-consuming process, which requires high computational power for binding affinity calculation. There are millions of chemical compounds available for docking. Eliminating compounds that are unlikely to exhibit high binding affinity from the screening set should speed-up the virtual drug screening procedure. We performed docking of 6353 ligands against twenty-one protein X-ray crystal structures. The docked ligands were ranked according to their calculated binding affinities, from which the top five hundred and the bottom five hundred were selected. We found that the volume and number of rotatable bonds of the top five hundred docked ligands are similar to those found in the crystal structures and corresponded with the volume of the binding sites. In contrast, the bottom five hundred set contains ligands that are either too large to enter the binding site, or too small to bind with high specificity and affinity to the binding site. A pre-docking filter that takes into account shapes and volumes of the binding sites as well as ligand volumes and flexibilities can filter out low binding affinity ligands from the screening sets. Thus, the virtual drug screening procedure speed is increased. 相似文献
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Marshall SA Vizcarra CL Mayo SL 《Protein science : a publication of the Protein Society》2005,14(5):1293-1304
Successfully modeling electrostatic interactions is one of the key factors required for the computational design of proteins with desired physical, chemical, and biological properties. In this paper, we present formulations of the finite difference Poisson-Boltzmann (FDPB) model that are pairwise decomposable by side chain. These methods use reduced representations of the protein structure based on the backbone and one or two side chains in order to approximate the dielectric environment in and around the protein. For the desolvation of polar side chains, the two-body model has a 0.64 kcal/mol RMSD compared to FDPB calculations performed using the full representation of the protein structure. Screened Coulombic interaction energies between side chains are approximated with an RMSD of 0.13 kcal/mol. The methods presented here are compatible with the computational demands of protein design calculations and produce energies that are very similar to the results of traditional FDPB calculations. 相似文献
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Solvation effect is an important factor for protein–ligand binding in aqueous water. Previous scoring function of protein–ligand interactions rarely incorporates the solvation model into the quantification of protein–ligand interactions, mainly due to the immense computational cost, especially in the structure‐based virtual screening, and nontransferable application of independently optimized atomic solvation parameters. In order to overcome these barriers, we effectively combine knowledge‐based atom–pair potentials and the atomic solvation energy of charge‐independent implicit solvent model in the optimization of binding affinity and specificity. The resulting scoring functions with optimized atomic solvation parameters is named as specificity and affinity with solvation effect (SPA‐SE). The performance of SPA‐SE is evaluated and compared to 20 other scoring functions, as well as SPA. The comparative results show that SPA‐SE outperforms all other scoring functions in binding affinity prediction and “native” pose identification. Our optimization validates that solvation effect is an important regulator to the stability and specificity of protein–ligand binding. The development strategy of SPA‐SE sets an example for other scoring function to account for the solvation effect in biomolecular recognitions. Proteins 2015; 83:1632–1642. © 2015 Wiley Periodicals, Inc. 相似文献
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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. 相似文献
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Zinc endopeptidase thermolysin can be inhibited by a series of phosphorus-containing peptide analogues, Cbz-Gly-psi (PO2)-X-Leu-Y-R (ZGp(X)L(y)R), where X = NH, O, or CH2; Y = NH or O; R = Leu, Ala, Gly, Phe, H, or CH3. The affinity correlation as well as an X-ray crystallography study suggest that these inhibitors bind to thermolysin in an identical mode. In this work, we calculate the electrostatic binding free energies for a series of 13 phosphorus-containing inhibitors with modifications at X, Y, and R moieties using finite difference solution to the Poisson-Boltzmann equation. A method has been developed to include the solvation entropy changes due to binding different ligands to a macromolecule. We demonstrate that the electrostatic energy and empirically derived solvation entropy can account for most of the binding energy differences in this series. By analyzing the binding contribution from individual residues, we show that the energy of a hydrogen bond is not confined to the donor and acceptor. In particular, the positive charges on Zn and Arg 203, which are not the acceptors, contribute significantly to the hydrogen bonds between two amides of ZGpLL and the thermolysin. 相似文献
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In recent years, protein-ligand docking has become a powerful tool for drug development. Although several approaches suitable for high throughput screening are available, there is a need for methods able to identify binding modes with high accuracy. This accuracy is essential to reliably compute the binding free energy of the ligand. Such methods are needed when the binding mode of lead compounds is not determined experimentally but is needed for structure-based lead optimization. We present here a new docking software, called EADock, that aims at this goal. It uses an hybrid evolutionary algorithm with two fitness functions, in combination with a sophisticated management of the diversity. EADock is interfaced with the CHARMM package for energy calculations and coordinate handling. A validation was carried out on 37 crystallized protein-ligand complexes featuring 11 different proteins. The search space was defined as a sphere of 15 A around the center of mass of the ligand position in the crystal structure, and on the contrary to other benchmarks, our algorithm was fed with optimized ligand positions up to 10 A root mean square deviation (RMSD) from the crystal structure, excluding the latter. This validation illustrates the efficiency of our sampling strategy, as correct binding modes, defined by a RMSD to the crystal structure lower than 2 A, were identified and ranked first for 68% of the complexes. The success rate increases to 78% when considering the five best ranked clusters, and 92% when all clusters present in the last generation are taken into account. Most failures could be explained by the presence of crystal contacts in the experimental structure. Finally, the ability of EADock to accurately predict binding modes on a real application was illustrated by the successful docking of the RGD cyclic pentapeptide on the alphaVbeta3 integrin, starting far away from the binding pocket. 相似文献
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The adaptation of forcefield-based scoring function to specific receptors remains an important challenge for in-silico drug discovery. Here we compare binding energies of forcefield-based scoring functions with models that are reparameterized on the basis of large-scale quantum calculations of the receptor. We compute binding energies of eleven ligands to the human estrogen receptor subtype alpha (ERalpha) and four ligands to the human retinoic acid receptor of isotype gamma (RARgamma). Using the FlexScreen all-atom receptor-ligand docking approach, we compare docking simulations parameterized by quantum-mechanical calculation of a large protein fragment with purely forcefield-based models. The use of receptor flexibility in the FlexScreen permits the treatment of all ligands in the same receptor model. We find a high correlation between the classical binding energy obtained in the docking simulation and quantum mechanical binding energies and a good correlation with experimental affinities R=0.81 for ERalpha and R=0.95 for RARgamma using the quantum derived scoring functions. A significant part of this improvement is retained, when only the receptor is treated with quantum-based parameters, while the ligands are parameterized with a purely classical model. 相似文献
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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. 相似文献
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A practical method to estimate binding free energy, ΔGbind, of a given ligand structure to the target receptor has been developed. The method assumes that ΔGbind is given by the summation of intermolecular interaction energy, ΔGinter, and partial desolvation energy, ΔGdesolv. ΔGdesolv is calculated from the buried surface area in the complex between the ligand and receptor, based on solvation energy, ΔGsolv, formulated by an equation which can be calibrated with observed values. Then, the method was applied to arabinose-binding protein (ABP) and dihydrofolate reductase (DHFR), after recalibrating the weights for ΔGinter and each term of ΔGdesolv using observed ΔGbind data for 29 known ligands to avidin (AV). The usefulness of our method was confirmed by the fact that correlation coefficients between the calculated and observed ΔGbind's in AV, ABP and DHFR were 0.92, 0.77, and 0.88, whereas the corresponding values obtained by simple force field calculation were 0.79, 0.30, and 0.79, respectively. Further investigations to improve the method and validate the parameters are in progress. Proteins 33:62–73, 1998. © 1998 Wiley-Liss, Inc. 相似文献
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Molecular dynamics simulations of triclinic hen egg white lysozyme in aqueous solution were performed to calculate the intrinsic pKas of 14 ionizable residues. An all-atom model was used for both solvent and solute, and a single 180 ps simulation in conjunction with a Gaussian fluctuation analysis method was used. An advantage of the Gaussian fluctuation method is that it only requires a single simulation of the system in a reference state to calculate all the pKas in the protein, in contrast to multiple simulations for the free energy perturbation method. pKint shifts with respect to reference titratable residues were evaluated and compared to results obtained using a finite difference Poisson-Boltzmann (FDPB) method with a continuum solvent model; overall agreement with the direction of the shifts was generally observed, though the magnitude of the shifts was typically larger with the explicit solvent model. The contribution of the first solvation shell to the total charging free energies of the titratable groups was explicitly evaluated and found to be significant. Dielectric shielding between pairs of titratable groups was examined and found to be smaller than expected. The effect of the approximations used to treat the long-range interactions on the pKint shifts is discussed. © 1994 Wiley-Liss, Inc. 相似文献
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Due to their involvement in many pathological conditions, matrix metalloproteinases (MMPs), are very attractive therapeutic targets. Our study focuses on one of them, MMP-2, which is involved in tumor progression and metastasis. Recently, the solution structure of the catalytic domain of MMP-2 complexed with a hydroxamic acid inhibitor (SC-74020) was published by Feng et al. Using the Hanessian group published binding affinity data and the structure published by Feng as a basis, we have built a binding affinity model by targeting the S(2)' pocket of the enzyme with a set of nine alpha-N-sulfonylamino hydroxamic acid derivatives. Two binding geometries of each ligand have been generated corresponding to two binding modes denoted A and B, respectively, of which the first one is targeting the S(2)' pocket and the second one the S(1) pocket. For the binding affinity model developed for mode A the computed activities show a rmsd of 0.583 kcal/mol as compared with the experimental data, and a correlation coefficient r(2) of 0.779, while in the case of the binding mode B a rmsd of 0.834 kcal/mol and correlation coefficient r(2) of 0.500, respectively, were obtained. In conclusion, our data suggest a higher probability for the Phe(76) gated S(2)' open form pocket to accommodate the substituent alpha versus the wide solvent exposed S(1) subsite, probability which some research groups could have overlooked due to extensive use in their calculations of non revealing S(2)' pocket open state crystallographic structures instead of NMR ones. 相似文献
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M. D. Cummings T. N. Hart R. J. Read 《Protein science : a publication of the Protein Society》1995,4(10):2087-2099
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. 相似文献
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Protein interacting with C kinase (PICK1) is well conserved throughout evolution and plays a critical role in synaptic plasticity by regulating the trafficking and posttranslational modification of its interacting proteins. PICK1 contains a single PSD95/DlgA/Zo-1 (PDZ) protein-protein interaction domain, which is promiscuous and shown to interact with over 60 proteins, most of which play roles in neuronal function. Several reports have suggested the role of PICK1 in disorders such as epilepsy, pain, brain trauma and stroke, drug abuse and dependence, schizophrenia and psychosis. Importantly, lead compounds that block PICK1 interactions are also now becoming available. Here, a new modeling approach was developed to investigate binding affinities of PDZ interactions. Using these methods, the binding affinities of all major PICK1 interacting proteins are reported and the effects of PICK1 mutations on these interactions are described. These modeling methods have important implications in defining the binding properties of proteins interacting with PICK1 as well as the general structural requirements of PDZ interactions. The study also provides modeling methods to support in the drug design of ligands for PDZ domains, which may further aid in development of the family of PDZ domains as a drug target. 相似文献
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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. 相似文献
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Pellecchia M Meininger D Dong Q Chang E Jack R Sem DS 《Journal of biomolecular NMR》2002,22(2):165-173