Estimating protein-ligand binding free energy: atomic solvation parameters for partition coefficient and solvation free energy calculation

Solvation energy calculation is one of the main difficulties for the estimation of protein-ligand binding free energy and the correct scoring in docking studies. We have developed a new solvation energy estimation method for protein-ligand binding based on atomic solvation parameter (ASP), which has been shown to improve the power of protein-ligand binding free energy predictions. The ASP set, designed to handle both proteins and organic compounds and derived from experimental n-octanol/water partition coefficient (log P) data, contains 100 atom types (united model that treats hydrogen atoms implicitly) or 119 atom types (all-atom model that treats hydrogen atoms explicitly). By using this unified ASP set, an algorithm was developed for solvation energy calculation and was further integrated into a score function for predicting protein-ligand binding affinity. The score function reproduced the absolute binding free energies of a test set of 50 protein-ligand complexes with a standard error of 8.31 kJ/mol. As a byproduct, a conformation-dependent log P calculation algorithm named ASPLOGP was also implemented. The predictive results of ASPLOGP for a test set of 138 compounds were r = 0.968, s = 0.344 for the all-atom model and r = 0.962, s = 0.367 for the united model, which were better than previous conformation-dependent approaches and comparable to fragmental and atom-based methods. ASPLOGP also gave good predictive results for small peptides. The score function based on the ASP model can be applied widely in protein-ligand interaction studies and structure-based drug design.     

Quantum and molecular dynamics study for binding of macrocyclic inhibitors to human alpha-thrombin

Molecular dynamics simulations followed by quantum mechanical calculation and Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) analysis have been carried out to study binding of proline- and pyrazinone-based macrocyclic inhibitors (L86 and T76) to human alpha-thrombin. Detailed binding interaction energies between these inhibitors and individual protein fragments are calculated using DFT method based on a new quantum mechanical approach for computing protein-ligand interaction energy. The analysis of detailed interaction energies provides insight on the protein-ligand binding mechanism. Study shows that T76 and L86 bind to thrombin in a very similar "inhibition mode" except that T76 has relatively weaker binding interaction with Glu(217). The analysis from quantum calculation of binding interaction is consistent with the MM-PBSA calculation of binding free energy, and the calculated free energies for L86/T76-thrombin binding agree well with the experimental data.     

Wide-Angle X-Ray Solution Scattering for Protein-Ligand Binding: Multivariate Curve Resolution with Bayesian Confidence Intervals

A new way to use wide-angle x-ray solution scattering to study protein-ligand binding is presented. First, scattering patterns are measured at different protein and ligand concentrations. Multivariate curve resolution based on singular value decomposition and global analysis is applied to estimate the binding affinities and reference patterns (i.e., the scattering patterns of individual components). As validated by simulation, Bayesian confidence intervals provide accurate uncertainty estimates for the binding free energies and reference patterns. Experimental results from several protein-ligand systems demonstrate the feasibility of the approach, which promises to expand the role of wide-angle x-ray scattering as a quantitative biophysical tool.     

An efficient computational method for calculating ligand binding affinities

Virtual compound screening using molecular docking is widely used in the discovery of new lead compounds for drug design. However, the docking scores are not sufficiently precise to represent the protein-ligand binding affinity. Here, we developed an efficient computational method for calculating protein-ligand binding affinity, which is based on molecular mechanics generalized Born/surface area (MM-GBSA) calculations and Jarzynski identity. Jarzynski identity is an exact relation between free energy differences and the work done through non-equilibrium process, and MM-GBSA is a semimacroscopic approach to calculate the potential energy. To calculate the work distribution when a ligand is pulled out of its binding site, multiple protein-ligand conformations are randomly generated as an alternative to performing an explicit single-molecule pulling simulation. We assessed the new method, multiple random conformation/MM-GBSA (MRC-MMGBSA), by evaluating ligand-binding affinities (scores) for four target proteins, and comparing these scores with experimental data. The calculated scores were qualitatively in good agreement with the experimental binding affinities, and the optimal docking structure could be determined by ranking the scores of the multiple docking poses obtained by the molecular docking process. Furthermore, the scores showed a strong linear response to experimental binding free energies, so that the free energy difference of the ligand binding (ΔΔG) could be calculated by linear scaling of the scores. The error of calculated ΔΔG was within ≈±1.5 kcal•mol⁻¹ of the experimental values. Particularly, in the case of flexible target proteins, the MRC-MMGBSA scores were more effective in ranking ligands than those generated by the MM-GBSA method using a single protein-ligand conformation. The results suggest that, owing to its lower computational costs and greater accuracy, the MRC-MMGBSA offers efficient means to rank the ligands, in the post-docking process, according to their binding affinities, and to compare these directly with the experimental values.     

Binding free energy calculations of galectin-3-ligand interactions

Galectins show remarkable binding specificity towards beta-galactosides. A recently developed method for calculating binding free energies between a protein and its substrates has been used to evaluate the binding specificity of galectin-3. Five disaccharides and a tetrasaccharide were used as the substrates. The calculated binding free energies agree quite well with the experimental data and the ranking of binding affinities is well reproduced. For all the six protein-ligand complexes it was observed that electrostatic interactions oppose binding whereas the non-polar contributions drive complex formation. The observed binding specificity of galectin-3 for galactosides rather than glucosides is discussed in light of our results.     

Prediction of ligand-binding sites of proteins by molecular docking calculation for a random ligand library

A new approach to predicting the ligand-binding sites of proteins was developed, using protein-ligand docking computation. In this method, many compounds in a random library are docked onto the whole protein surface. We assumed that the true ligand-binding site would exhibit stronger affinity to the compounds in the random library than the other sites, even if the random library did not include the ligand corresponding to the true binding site. We also assumed that the affinity of the true ligand-binding site would be correlated to the docking scores of the compounds in the random library, if the ligand-binding site was correctly predicted. We call this method the molecular-docking binding-site finding (MolSite) method. The MolSite method was applied to 89 known protein-ligand complex structures extracted from the Protein Data Bank, and it predicted the correct binding sites with about 80-99% accuracy, when only the single top-ranked site was adopted. In addition, the average docking score was weakly correlated to the experimental protein-ligand binding free energy, with a correlation coefficient of 0.44.     

An all atom energy based computational protocol for predicting binding affinities of protein-ligand complexes

We report here a computationally fast protocol for predicting binding affinities of non-metallo protein-ligand complexes. The protocol builds in an all atom energy based empirical scoring function comprising electrostatics, van der Waals, hydrophobicity and loss of conformational entropy of protein side chains upon ligand binding. The method is designed to ensure transferability across diverse systems and has been validated on a heterogenous dataset of 161 complexes consisting of 55 unique protein targets. The scoring function trained on a dataset of 61 complexes yielded a correlation of r=0.92 for the predicted binding free energies against the experimental binding affinities. Model validation and parameter analysis studies ensure the predictive ability of the scoring function. When tested on the remaining 100 protein-ligand complexes a correlation of r=0.92 was recovered. The high correlation obtained underscores the potential applicability of the methodology in drug design endeavors. The scoring function has been web enabled at as binding affinity prediction of protein-ligand (BAPPL) server.     

Energetic contributions of amino acid residues and its cross-talk to delineate ligand-binding mechanism

Receptor-based QSAR approaches can enumerate the energetic contributions of amino acid residues toward ligand binding only when experimental binding affinity is associated. The structural data of protein-ligand complexes are witnessing a tremendous growth in the Protein Data Bank deposited with a few entries on binding affinity. We present here a new approach to compute the E nergetic CONT ributions of A mino acid residues and its possible C ross-T alk (ECONTACT) to study ligand binding using per-residue energy decomposition, molecular dynamics simulations and rescoring method without the need for experimental binding affinity. This approach recognizes potential cross-talks among amino acid residues imparting a nonadditive effect to the binding affinity with evidence of correlative motions in the dynamics simulations. The protein-ligand interaction energies deduced from multiple structures are decomposed into per-residue energy terms, which are employed as variables to principal component analysis and generated cross-terms. Out of 16 cross-talks derived from eight datasets of protein-ligand systems, the ECONTACT approach is able to associate 10 potential cross-talks with site-directed mutagenesis, free energy, and dynamics simulations data strongly. We modeled these key determinants of ligand binding using joint probability density function (jPDF) to identify cross-talks in protein structures. The top two cross-talks identified by ECONTACT approach corroborated with the experimental findings. Furthermore, virtual screening exercise using ECONTACT models better discriminated known inhibitors from decoy molecules. This approach proposes the jPDF metric to estimate the probability of observing cross-talks in any protein-ligand complex. The source code and related resources to perform ECONTACT modeling is available freely at https://www.gujaratuniversity.ac.in/econtact /.     

Exploring the interaction of some N-benzyloxycarbonyl-L-phenyl alanyl-L-alanine ketones and bovine spleen cathepsin B by molecular modeling and binding free energy calculation

A semi-empirical method for estimation of binding free energy, recently proposed by Aqvist and coworkers, has been effectively tested in several protein-ligand binding cases. We have applied this linear interaction energy method to predict the binding of some N-benzyloxycarbonyl-L-phenyl alanyl-L-alanine ketones with bovine cathepsin B and computed the respective absolute binding constants from averages of molecular dynamics simulations. It is found that the computer simulation results agree well with available experimental data and make it possible to understand better the origin of tight binding and inhibitor specificity of cathepsin B.     

Open-ITC: an alternate computational approach to analyze the isothermal titration calorimetry data of complex binding mechanisms

Isothermal titration calorimetry (ITC) is an important technique used in quantitatively analyzing the global mechanism of protein-protein or protein-ligand interactions through thermodynamic measurements. Among different binding mechanisms, the parallel and ligand induced protein oligomerization mechanisms are technically difficult to analyze compared with a sequential binding mechanism. Here, we present a methodology implemented as a program "Open-ITC" that eliminates the need for exact analytical expressions for free ligand concentrations [L] and mole fractions of bound ligand θ that are required for the thermogram analysis. Adopting a genetic algorithm-based optimization, the thermodynamic parameters are determined, and its standard error is evaluated at the global minimum by calculating the Jacobian matrix. This approach yielded a statistically consistent result for a single-site and a two-site binding protein-ligand system. Further, a comparative simulation of a two-step sequential, a parallel, and a ligand induced oligomerization model revealed that their mechanistic differences are discernable in ITC thermograms, only if the first binding step is weaker compared with the second binding step (K(1) 相似文献   

Development, validation, and application of adapted PEOE charges to estimate pKa values of functional groups in protein-ligand complexes

For routine pK(a) calculations of protein-ligand complexes in drug design, the PEOE method to compute partial charges was modified. The new method is applicable to a large scope of proteins and ligands. The adapted charges were parameterized using experimental free energies of solvation of amino acids and small organic ligands. For a data set of 80 small organic molecules, a correlation coefficient of r(2) = 0.78 between calculated and experimental solvation free energies was obtained. Continuum electrostatics pK(a) calculations based on the Poisson-Boltzmann equation were carried out on a validation set of nine proteins for which 132 experimental pK(a) values are known. In total, an overall RMSD of 0.88 log units between calculated and experimentally determined data is achieved. In particular, the predictions of significantly shifted pK(a) values are satisfactory, and reasonable estimates of protonation states in the active sites of lysozyme and xylanase could be obtained. Application of the charge-assignment and pK(a)-calculation procedure to protein-ligand complexes provides clear structural interpretations of experimentally observed changes of protonation states of functional groups upon complex formation. This information is essential for the interpretation of thermodynamic data of protein-ligand complex formation and provides the basis for the reliable factorization of the free energy of binding in enthalpic and entropic contributions. The modified charge-assignment procedure forms the basis for future automated pK(a) calculations of protein-ligand complexes.     

Exploring the Interaction of Some N- Benzyloxycarbonyl-L-Phenyl Alanyl-L-Alanine Ketones and Bovine Spleen Cathepsin B by Molecular Modeling and Binding Free Energy Calculation

Abstract

A semi-empirical method for estimation of binding free energy, recently proposed by Aqvist and coworkers, has been effectively tested in several protein-ligand binding cases. We have applied this linear interaction energy method to predict the binding of some N-benzy- loxycarbonyl-L-phenyl alanyl-L-alanine ketones with bovine cathepsin B and computed the respective absolute binding constants from averages of molecular dynamics simulations. It is found that the computer simulation results agree well with available experimental data and make it possible to understand better the origin of tight binding and inhibitor specificity of cathepsin B.     

SCORE: A New Empirical Method for Estimating the Binding Affinity of a Protein-Ligand Complex

A new method is presented to estimate the binding affinity of a protein-ligand complex with known three-dimensional structure. The method, SCORE, uses an empirical scoring function to describe the binding free energy, which includes terms to account for van der Waals contact, metal-ligand bonding, hydrogen bonding, desolvation effect, and deformation penalty upon the binding process. The coefficients of each term are obtained by multivariate regressional analysis of a diverse training set of 170 protein-ligand complexes. The final scoring function reproduces the binding free energies of the whole training set with a cross-validated deviation of 6.3 kJ/mol. The predictive ability of the function is further tested by a set of 11 endothiapepsin complexes and the internal consistency of the function is demonstrated in a stepwise procedure named Evolutionary Test. A major innovation of this method is the introduction of an atomic binding score which allows the researcher to inspect and optimize the lead compound rationally in a structure-based drug design scheme.     

Tetracycline-Tet Repressor Binding Specificity: Insights from Experiments and Simulations

Tetracycline (Tc) antibiotics have been put to new uses in the construction of artificial gene regulation systems, where they bind to the Tet repressor protein (TetR) and modulate its affinity for DNA. Many Tc variants have been produced, both to overcome bacterial resistance and to achieve a broad range of binding strengths. To better understand TetR-Tc binding, we investigate a library of 16 tetracyclines, using fluorescence experiments and molecular dynamics free energy simulations (MDFE). The relative TetR binding free energies are computed by reversibly transforming one Tc variant into another during the simulation, with no adjustable parameters. The chemical variations involve polar and nonpolar substitutions along one entire edge of the elongated Tc structure, which provides many of the protein-ligand contacts. The binding constants span five orders of magnitude. The simulations reproduce the experimental binding free energies, when available, within the uncertainty of either method (±0.5 kcal/mol), and reveal many additional details. Contributions of individual Tc substituents are evaluated, along with their additivity and transferability among different positions on the Tc scaffold; differences between D- and B-class repressors are quantified. With increasing computer power, the MDFE approach provides an attractive complement to experiment and should play an increasing role in the understanding and engineering of protein-ligand recognition.     

Analytical expressions for the homotropic binding of ligand to protein dimers and trimers

Cooperative binding of a ligand to multiple subsites on a protein is a common theme among enzymes and receptors. The analysis of cooperative binding data (either positive or negative) often relies on the assumption that free ligand concentration, L, can be approximated by the total ligand concentration, L(T). When this approximation does not hold, such analyses result in inaccurate estimates of dissociation constants. Presented here are exact analytical expressions for equilibrium concentrations of all enzyme and ligand species (in terms of K(d) values and total concentrations of protein and ligand) for homotropic dimeric and trimeric protein-ligand systems. These equations circumvent the need to approximate L and are provided in Excel worksheets suitable for simulation and least-squares fitting. The equations and worksheets are expanded to treat cases where binding signals vary with distinct site occupancy.     

Analysis of knowledge-based protein-ligand potentials using a self-consistent method

We propose a self-consistent approach to analyze knowledge-based atom-atom potentials used to calculate protein-ligand binding energies. Ligands complexed to actual protein structures were first built using the SMoG growth procedure (DeWitte & Shakhnovich, 1996) with a chosen input potential. These model protein-ligand complexes were used to construct databases from which knowledge-based protein-ligand potentials were derived. We then tested several different modifications to such potentials and evaluated their performance on their ability to reconstruct the input potential using the statistical information available from a database composed of model complexes. Our data indicate that the most significant improvement resulted from properly accounting for the following key issues when estimating the reference state: (1) the presence of significant nonenergetic effects that influence the contact frequencies and (2) the presence of correlations in contact patterns due to chemical structure. The most successful procedure was applied to derive an atom-atom potential for real protein-ligand complexes. Despite the simplicity of the model (pairwise contact potential with a single interaction distance), the derived binding free energies showed a statistically significant correlation (approximately 0.65) with experimental binding scores for a diverse set of complexes.     

Prediction of ligand binding using an approach designed to accommodate diversity in protein-ligand interactions

Computational determination of protein-ligand interaction potential is important for many biological applications including virtual screening for therapeutic drugs. The novel internal consensus scoring strategy is an empirical approach with an extended set of 9 binding terms combined with a neural network capable of analysis of diverse complexes. Like conventional consensus methods, internal consensus is capable of maintaining multiple distinct representations of protein-ligand interactions. In a typical use the method was trained using ligand classification data (binding/no binding) for a single receptor. The internal consensus analyses successfully distinguished protein-ligand complexes from decoys (r2, 0.895 for a series of typical proteins). Results are superior to other tested empirical methods. In virtual screening experiments, internal consensus analyses provide consistent enrichment as determined by ROC-AUC and pROC metrics.     

The role of backbone motions in ligand binding to the c-Src SH3 domain.

The Src homology 3 (SH3) domain of pp60(c-src) (Src) plays dual roles in signal transduction, through stabilizing the repressed form of the Src kinase and through mediating the formation of activated signaling complexes. Transition of the Src SH3 domain between a variety of binding partners during progression through the cell cycle requires adjustment of a delicate free energy balance. Although numerous structural and functional studies of SH3 have provided an in-depth understanding of structural determinants for binding, the origins of binding energy in SH3-ligand interactions are not fully understood. Considering only the protein-ligand interface, the observed favorable change in standard enthalpy (DeltaH=-9.1 kcal/mol) and unfavorable change in standard entropy (TDeltaS=-2.7 kcal/mol) upon binding the proline-rich ligand RLP2 (RALPPLPRY) are inconsistent with the predominantly hydrophobic interaction surface. To investigate possible origins of ligand binding energy, backbone dynamics of free and RLP2-bound SH3 were performed via (15)N NMR relaxation and hydrogen-deuterium (H/(2)H) exchange measurements. On the ps-ns time scale, assuming uncorrelated motions, ligand binding results in a significant reduction in backbone entropy (-1.5(+/-0.6) kcal/mol). Binding also suppresses motions on the micros-ms time scale, which may additionally contribute to an unfavorable change in entropy. A large increase in protection from H/(2)H exchange is observed upon ligand binding, providing evidence for entropy loss due to motions on longer time scales, and supporting the notion that stabilization of pre-existing conformations within a native state ensemble is a fundamental paradigm for ligand binding. Observed changes in motion on all three time scales occur at locations both near and remote from the protein-ligand interface. The propagation of ligand binding interactions across the SH3 domain has potential consequences in target selection through altering both free energy and geometry in intact Src, and suggests that looking beyond the protein-ligand interface is essential in understanding ligand binding energetics.     

Explaining cyclodextrin-mycotoxin interactions using a 'natural' force field

Docking techniques and the HINT (Hydropathic Interaction) program were used to explain interactions of aflatoxin B(1) and ochratoxin A with beta- and gamma-cyclodextrins. The work was aimed at designing a chemosensor to identify very low concentrations of these mycotoxins by exploiting the affinity of the cyclodextrin cavity for many small organic molecules. Actually, the inclusion of the fluorescent portion of these toxins into the cavity may lower the quenching effect of the solvent, thus enhancing the luminescence. HINT is a 'natural' force field, based on experimentally determined LogP(octanol/water) values, that is able to consider both enthalpic and entropic contributions to the binding free energy with an unified approach. HINT is normally applied to predict the DeltaG degrees of binding for protein-ligand, protein-protein, and protein-DNA interactions. The leading forces in biomolecular processes are the same as those involved in organic host-guest inclusion phenomena, therefore we applied this methodology for the first time to cyclodextrin complexes. The results allowed us to explain spectroscopic data in absence of available crystallographic or NMR structural data.