Flexible docking using tabu search and an empirical estimate of binding affinity

This article describes the implementation of a new docking approach. The method uses a Tabu search methodology to dock flexibly ligand molecules into rigid receptor structures. It uses an empirical objective function with a small number of physically based terms derived from fitting experimental binding affinities for crystallographic complexes. This means that docking energies produced by the searching algorithm provide direct estimates of the binding affinities of the ligands. The method has been tested on 50 ligand-receptor complexes for which the experimental binding affinity and binding geometry are known. All water molecules are removed from the structures and ligand molecules are minimized in vacuo before docking. The lowest energy geometry produced by the docking protocol is within 1.5 Å root-mean square of the experimental binding mode for 86% of the complexes. The lowest energies produced by the docking are in fair agreement with the known free energies of binding for the ligands. Proteins 33:367–382, 1998. © 1998 Wiley-Liss, Inc.     

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.     

Glycogen phosphorylase inhibitors: a free energy perturbation analysis of glucopyranose spirohydantoin analogues

GP catalyzes the phosphorylation of glycogen to Glc-1-P. Because of its fundamental role in the metabolism of glycogen, GP has been the target for a systematic structure-assisted design of inhibitory compounds, which could be of value in the therapeutic treatment of type 2 diabetes mellitus. The most potent catalytic-site inhibitor of GP identified to date is spirohydantoin of glucopyranose (hydan). In this work, we employ MD free energy simulations to calculate the relative binding affinities for GP of hydan and two spirohydantoin analogues, methyl-hydan and n-hydan, in which a hydrogen atom is replaced by a methyl- or amino group, respectively. The results are compared with the experimental relative affinities of these ligands, estimated by kinetic measurements of the ligand inhibition constants. The calculated binding affinity for methyl-hydan (relative to hydan) is 3.75 +/- 1.4 kcal/mol, in excellent agreement with the experimental value (3.6 +/- 0.2 kcal/mol). For n-hydan, the calculated value is 1.0 +/- 1.1 kcal/mol, somewhat smaller than the experimental result (2.3 +/- 0.1 kcal/mol). A free energy decomposition analysis shows that hydan makes optimum interactions with protein residues and specific water molecules in the catalytic site. In the other two ligands, structural perturbations of the active site by the additional methyl- or amino group reduce the corresponding binding affinities. The computed binding free energies are sensitive to the preference of a specific water molecule for two well-defined positions in the catalytic site. The behavior of this water is analyzed in detail, and the free energy profile for the translocation of the water between the two positions is evaluated. The results provide insights into the role of water molecules in modulating ligand binding affinities. A comparison of the interactions between a set of ligands and their surrounding groups in X-ray structures is often used in the interpretation of binding free energy differences and in guiding the design of new ligands. For the systems in this work, such an approach fails to estimate the order of relative binding strengths, in contrast to the rigorous free energy treatment.     

Histone H3 Inhibits Ubiquitin-Ubiquitin Intermolecular Interactions to Enhance Binding to DNA Methyl Transferase 1

DNA methyltransferase 1 (Dnmt1) is crucial for cell maintenance and preferentially methylates hemimethylated DNA. Recently, a study revealed that Dnmt1 is timely and site-specifically activated by several types of two-mono-ubiquitinated histone H3 tails (H3Ts). However, the molecular mechanism of Dnmt1 activation has not yet been determined, in addition to the role of H3T. Based on experimental data, two-mono-ubiquitinated H3Ts activate Dnmt1 by binding, with different binding affinities. In contrast, ubiquitin molecules unlinked with H3T do not bind to Dnmt1. Despite the existence of experimental data, it is unclear why the binding affinities for Dnmt1 are different. To obtain new insights into the activation mechanism of Dnmt1, we performed all-atom molecular dynamics (MD) simulations on three systems: (1) K14/K18, (2) K14/K23 mono-ubiquitinated H3Ts, and (3) two ubiquitin molecules unlinked with H3T. As an analysis of our MD trajectories, these ubiquitylation patterns modulated ubiquitin-ubiquitin intermolecular interactions. More specifically, the intermolecular contacts between a pair of ubiquitin molecules linked with H3T became weak in the presence of H3T, indicating that H3T makes a cleft between them to inhibit their intermolecular interactions. For these three systems, the intermolecular interactions between the ubiquitin molecules calculated by our MD simulations are in good agreement with the binding affinities for Dnmt1 experimentally measured in a previous study. Therefore, we conclude that H3T acts as a spacer to inhibit ubiquitin-ubiquitin intermolecular interactions, enhancing binding to Dnmt1.     

The Binding Database: data management and interface design

MOTIVATION: The large and growing body of experimental data on biomolecular binding is of enormous value in developing a deeper understanding of molecular biology, in developing new therapeutics, and in various molecular design applications. However, most of these data are found only in the published literature and are therefore difficult to access and use. No existing public database has focused on measured binding affinities and has provided query capabilities that include chemical structure and sequence homology searches. METHODS & RESULTS: We have created Binding DataBase (BindingDB), a public, web-accessible database of measured binding affinities. BindingDB is based upon a relational data specification for describing binding measurements via Isothermal Titration Calorimetry (ITC) and enzyme inhibition. A corresponding XML Document Type Definition (DTD) is used to create and parse intermediate files during the on-line deposition process and will also be used for data interchange, including collection of data from other sources. The on-line query interface, which is constructed with Java Servlet technology, supports standard SQL queries as well as searches for molecules by chemical structure and sequence homology. The on-line deposition interface uses Java Server Pages and JavaBean objects to generate dynamic HTML and to store intermediate results. The resulting data resource provides a range of functionality with brisk response-times, and lends itself well to continued development and enhancement.     

Blind Prediction of Charged Ligand Binding Affinities in a Model Binding Site

Predicting absolute protein–ligand binding affinities remains a frontier challenge in ligand discovery and design. This becomes more difficult when ionic interactions are involved because of the large opposing solvation and electrostatic attraction energies. In a blind test, we examined whether alchemical free-energy calculations could predict binding affinities of 14 charged and 5 neutral compounds previously untested as ligands for a cavity binding site in cytochrome c peroxidase. In this simplified site, polar and cationic ligands compete with solvent to interact with a buried aspartate. Predictions were tested by calorimetry, spectroscopy, and crystallography. Of the 15 compounds predicted to bind, 13 were experimentally confirmed, while 4 compounds were false negative predictions. Predictions had a root-mean-square error of 1.95 kcal/mol to the experimental affinities, and predicted poses had an average RMSD of 1.7 Å to the crystallographic poses. This test serves as a benchmark for these thermodynamically rigorous calculations at predicting binding affinities for charged compounds and gives insights into the existing sources of error, which are primarily electrostatic interactions inside proteins. Our experiments also provide a useful set of ionic binding affinities in a simplified system for testing new affinity prediction methods.     

Determination of equilibrium binding affinity of distamycin and netropsin to the synthetic deoxyoligonucleotide sequence d(GGTATACC)2 by quantitative DNase I footprinting

A new method for determining the equilibrium binding constant of antitumor drugs to specific DNA sequences by quantitative DNase I footprinting is presented. The use of a short synthetic DNA oligomer to define a homogeneous population of DNA binding sites enables the calculation of the free drug concentration and the fraction of DNA sites complexed with drug in solution and is described for the first time. Since a 1:1 stoichiometry is observed for each drug-oligomer DNA complex, it becomes possible to calculate equilibrium binding constants in solution. By use of this technique, the binding affinities of the nonintercalating drugs netropsin and distamycin to the synthetic oligonucleotide d(GGTATACC)2 are determined to be Ka (25 degrees C) = 1.0 X 10(5) and 2.0 X 10(5) M-1, respectively. Quantitation of the temperature dependence associated with complex formation results in a determination of standard enthalpies of -3.75 and -8.48 kcal mol-1 for the binding of netropsin and distamycin, respectively. Calculation of other thermodynamic parameters are found to be in agreement with previous studies and indicate that the DNA binding process for these compounds is predominantly enthalpy driven. This method of quantitative DNase I footprinting is demonstrated to be a useful technique for the measurement of drug affinities to specific binding sites on DNA oligomers which are designed and synthesized expressly for this purpose. Applications of the technique to the determination of drug binding affinities at specific sites within native DNA sequences are discussed.     

Prediction of the binding affinities of peptides to class II MHC using a regularized thermodynamic model

Background  

The binding of peptide fragments of antigens to class II MHC is a crucial step in initiating a helper T cell immune response. The identification of such peptide epitopes has potential applications in vaccine design and in better understanding autoimmune diseases and allergies. However, comprehensive experimental determination of peptide-MHC binding affinities is infeasible due to MHC diversity and the large number of possible peptide sequences. Computational methods trained on the limited experimental binding data can address this challenge. We present the MultiRTA method, an extension of our previous single-type RTA prediction method, which allows the prediction of peptide binding affinities for multiple MHC allotypes not used to train the model. Thus predictions can be made for many MHC allotypes for which experimental binding data is unavailable.     

Solution structure of a cyanovirin-N:Man alpha 1-2Man alpha complex: structural basis for high-affinity carbohydrate-mediated binding to gp120

BACKGROUND: Cyanovirin-N (CVN) is a novel, 11 kDa cyanobacterial protein that potently inhibits viral entry by diverse strains of HIV through high-affinity carbohydrate-mediated interactions with the viral envelope glycoprotein gp120. CVN contains two symmetry-related carbohydrate binding sites of differing affinities that selectively bind to Man(8) D1D3 and Man(9) with nanomolar affinities, the carbohydrates that also mediate CVN:gp120 binding. High-resolution structural studies of CVN in complex with a representative oligosaccharide are desirable for understanding the structural basis for this unprecedented specificity. RESULTS: We have determined by multidimensional heteronuclear NMR spectroscopy the three-dimensional solution structure of CVN in complex with two equivalents of the disaccharide Manalpha1-2Manalpha, a high-affinity ligand which represents the terminal-accessible disaccharide present in Man(8) D1D3 and Man(9). The structure reveals that the bound disaccharide adopts the stacked conformation, thereby explaining the selectivity for Man(8) D1D3 and Man(9) over other oligomannose structures, and presents two novel carbohydrate binding sites that account for the differing affinities of the two sites. The high-affinity site comprises a deep pocket that nearly envelops the disaccharide, while the lower-affinity site comprises a semicircular cleft that partially surrounds the disaccharide. The approximately 40 A spacing of the two binding sites provides a simple model for CVN:gp120 binding. CONCLUSIONS: The CVN:Manalpha1-2Manalpha complex provides the first high-resolution structure of a mannose-specific protein-carbohydrate complex with nanomolar affinity and presents a new carbohydrate binding motif, as well as a new class of carbohydrate binding protein, that facilitates divalent binding via a monomeric protein.     

Affinity and specificity of protein U1A-RNA complex formation based on an additive component free energy model

An MM-GBSA computational protocol was used to investigate wild-type U1A-RNA and F56 U1A mutant experimental binding free energies. The trend in mutant binding free energies compared to wild-type is well-reproduced. Following application of a linear-response-like equation to scale the various energy components, the binding free energies agree quantitatively with observed experimental values. Conformational adaptation contributes to the binding free energy for both the protein and the RNA in these systems. Small differences in DeltaGs are the result of different and sometimes quite large relative contributions from various energetic components. Residual free energy decomposition indicates differences not only at the site of mutation, but throughout the entire protein. MM-GBSA and ab initio calculations performed on model systems suggest that stacking interactions may nearly, but not completely, account for observed differences in mutant binding affinities. This study indicates that there may be different underlying causes of ostensibly similar experimentally observed binding affinities of different mutants, and thus recommends caution in the interpretation of binding affinities and specificities purely by inspection.     

How Structure Defines Affinity in Protein-Protein Interactions

Protein-protein interactions (PPI) in nature are conveyed by a multitude of binding modes involving various surfaces, secondary structure elements and intermolecular interactions. This diversity results in PPI binding affinities that span more than nine orders of magnitude. Several early studies attempted to correlate PPI binding affinities to various structure-derived features with limited success. The growing number of high-resolution structures, the appearance of more precise methods for measuring binding affinities and the development of new computational algorithms enable more thorough investigations in this direction. Here, we use a large dataset of PPI structures with the documented binding affinities to calculate a number of structure-based features that could potentially define binding energetics. We explore how well each calculated biophysical feature alone correlates with binding affinity and determine the features that could be used to distinguish between high-, medium- and low- affinity PPIs. Furthermore, we test how various combinations of features could be applied to predict binding affinity and observe a slow improvement in correlation as more features are incorporated into the equation. In addition, we observe a considerable improvement in predictions if we exclude from our analysis low-resolution and NMR structures, revealing the importance of capturing exact intermolecular interactions in our calculations. Our analysis should facilitate prediction of new interactions on the genome scale, better characterization of signaling networks and design of novel binding partners for various target proteins.     

Modeling the binding modes of stilbene analogs to cyclooxygenase-2: a molecular docking study

Stilbene analogs are a new class of anti-inflammatory compounds that effectively inhibit COX-2, which is the major target in the treatment of inflammation and pain. In this study, docking simulations were conducted using AutoDock 4 software that focused on the binding of this class of compounds to COX-2 protein. Our aim was to better understand the structural and chemical features responsible for the recognition mechanism of these compounds, and to explore their binding modes of interaction at the active site by comparing them with COX-2 co-crystallized with SC-558. The docking results allowed us to provide a plausible explanation for the different binding affinities observed experimentally. These results show that important conserved residues, in particular Arg513, Phe518, Trp387, Leu352, Leu531 and Arg120, could be essential for the binding of the ligands to COX-2 protein. The quality of the docking model was estimated based on the binding energies of the studied compounds. A good correlation was obtained between experimental logAr values and the predicted binding energies of the studied compounds.     

ATLAS: A database linking binding affinities with structures for wild‐type and mutant TCR‐pMHC complexes

The ATLAS (Altered TCR Ligand Affinities and Structures) database ( https://zlab.umassmed.edu/atlas/web /) is a manually curated repository containing the binding affinities for wild‐type and mutant T cell receptors (TCRs) and their antigens, peptides presented by the major histocompatibility complex (pMHC). The database links experimentally measured binding affinities with the corresponding three dimensional (3D) structures for TCR‐pMHC complexes. The user can browse and search affinities, structures, and experimental details for TCRs, peptides, and MHCs of interest. We expect this database to facilitate the development of next‐generation protein design algorithms targeting TCR‐pMHC interactions. ATLAS can be easily parsed using modeling software that builds protein structures for training and testing. As an example, we provide structural models for all mutant TCRs in ATLAS, built using the Rosetta program. Utilizing these structures, we report a correlation of 0.63 between experimentally measured changes in binding energies and our predicted changes. Proteins 2017; 85:908–916. © 2016 Wiley Periodicals, Inc.     

Parameterization of an effective potential for protein–ligand binding from host–guest affinity data

Force field accuracy is still one of the “stalemates” in biomolecular modeling. Model systems with high quality experimental data are valuable instruments for the validation and improvement of effective potentials. With respect to protein–ligand binding, organic host–guest complexes have long served as models for both experimental and computational studies because of the abundance of binding affinity data available for such systems. Binding affinity data collected for cyclodextrin (CD) inclusion complexes, a popular model for molecular recognition, is potentially a more reliable resource for tuning energy parameters than hydration free energy measurements. Convergence of binding free energy calculations on CD host–guest systems can also be obtained rapidly, thus offering the opportunity to assess the robustness of these parameters. In this work, we demonstrate how implicit solvent parameters can be developed using binding affinity experimental data and the binding energy distribution analysis method (BEDAM) and validated using the Grid Inhomogeneous Solvation Theory analysis. These new solvation parameters were used to study protein–ligand binding in two drug targets against the HIV‐1 virus and improved the agreement between the calculated and the experimental binding affinities. This work illustrates how benchmark sets of high quality experimental binding affinity data and physics‐based binding free energy models can be used to evaluate and optimize force fields for protein–ligand systems. Copyright © 2015 John Wiley & Sons, Ltd.     

Functional equivalence of the nicotinic acetylcholine receptor transmitter binding sites in the open state

The subunits of the muscle-type nicotinic acetylcholine receptor (AChR) are not uniformly oriented in the resting closed conformation: the two α subunits are rotated relative to its non-α subunits. In contrast, all the subunits overlay well with one another when agonist is bound to the AChR, suggesting that they are uniformly oriented in the open receptor. This gating-dependent increase in orientational uniformity due to rotation of the α subunits might affect the relative affinities of the two transmitter binding sites, making the two affinities dissimilar (functionally non-equivalent) in the initial ligand-bound closed state but similar (functionally equivalent) in the open state. To test this hypothesis, we measured single-channel activity of the αG153S gain-of-function mutant receptor evoked by choline, and estimated the resting closed-state and open-state affinities of the two transmitter binding sites. Both model-independent analyses and maximum-likelihood estimation of microscopic rate constants indicate that channel opening makes the binding sites' affinities more similar to each other. These results support the hypothesis that open-state affinities to the transmitter binding sites are primarily determined by the α subunits.