Binding of small-molecule ligands to proteins: "what you see" is not always "what you get"

New estimators for calculating solvation entropy and enthalpy and comparative assessments of their accuracy and precision

We present two new methods for estimating the entropy and enthalpy decomposition of free energy calculations. These methods are based on temperature derivatives of the Bennett Acceptance Ratio and the Multistate Bennett Acceptance Ratio estimators, respectively. We test the accuracy of these new estimators using a simple one-dimensional model. A detailed assessment of their performance is reported by studying the solvation of N-methylacetamide. Finally, we quantify the free energies of solvation for 11 model compounds using the OPLS-AA force field and a variation of this force field. Thermodynamic decompositions of these calculated free energies are obtained to highlight the utility of these quantities for refining force field parameters by comparing computed free energies and their decompositions to their experimental counterparts.

A displaced-solvent functional analysis of model hydrophobic enclosures

Calculation of protein-ligand binding affinities continues to be a hotbed of research. Although many techniques for computing protein-ligand binding affinities have been introduced--ranging from computationally very expensive approaches, such as free energy perturbation (FEP) theory; to more approximate techniques, such as empirically derived scoring functions, which, although computationally efficient, lack a clear theoretical basis--there remains pressing need for more robust approaches. A recently introduced technique, the displaced-solvent functional (DSF) method, was developed to bridge the gap between the high accuracy of FEP and the computational efficiency of empirically derived scoring functions. In order to develop a set of reference data to test the DSF theory for calculating absolute protein-ligand binding affinities, we have pursued FEP theory calculations of the binding free energies of a methane ligand with 13 different model hydrophobic enclosures of varying hydrophobicity. The binding free energies of the methane ligand with the various hydrophobic enclosures were then recomputed by DSF theory and compared with the FEP reference data. We find that the DSF theory, which relies on no empirically tuned parameters, shows excellent quantitative agreement with the FEP. We also explored the ability of buried solvent accessible surface area and buried molecular surface area models to describe the relevant physics, and find the buried molecular surface area model to offer superior performance over this dataset.

Optimized hydrophobic interactions and hydrogen bonding at the target-ligand interface leads the pathways of drug-designing

BACKGROUND

Weak intermolecular interactions such as hydrogen bonding and hydrophobic interactions are key players in stabilizing energetically-favored ligands, in an open conformational environment of protein structures. However, it is still poorly understood how the binding parameters associated with these interactions facilitate a drug-lead to recognize a specific target and improve drugs efficacy. To understand this, comprehensive analysis of hydrophobic interactions, hydrogen bonding and binding affinity have been analyzed at the interface of c-Src and c-Abl kinases and 4-amino substituted 1H-pyrazolo [3, 4-d] pyrimidine compounds.

METHODOLOGY

In-silico docking studies were performed, using Discovery Studio software modules LigandFit, CDOCKER and ZDOCK, to investigate the role of ligand binding affinity at the hydrophobic pocket of c-Src and c-Abl kinase. Hydrophobic and hydrogen bonding interactions of docked molecules were compared using LigPlot program. Furthermore, 3D-QSAR and MFA calculations were scrutinized to quantify the role of weak interactions in binding affinity and drug efficacy.

CONCLUSIONS

The in-silico method has enabled us to reveal that a multi-targeted small molecule binds with low affinity to its respective targets. But its binding affinity can be altered by integrating the conformationally favored functional groups at the active site of the ligand-target interface. Docking studies of 4-amino-substituted molecules at the bioactive cascade of the c-Src and c-Abl have concluded that 3D structural folding at the protein-ligand groove is also a hallmark for molecular recognition of multi-targeted compounds and for predicting their biological activity. The results presented here demonstrate that hydrogen bonding and optimized hydrophobic interactions both stabilize the ligands at the target site, and help alter binding affinity and drug efficacy.

The Binding Energy Distribution Analysis Method (BEDAM) for the Estimation of Protein-Ligand Binding Affinities

The Binding Energy Distribution Analysis Method (BEDAM) for the computation of receptor-ligand standard binding free energies with implicit solvation is presented. The method is based on a well established statistical mechanics theory of molecular association. It is shown that, in the context of implicit solvation, the theory is homologous to the test particle method of solvation thermodynamics with the solute-solvent potential represented by the effective binding energy of the protein-ligand complex. Accordingly, in BEDAM the binding constant is computed by means of a weighted integral of the probability distribution of the binding energy obtained in the canonical ensemble in which the ligand is positioned in the binding site but the receptor and the ligand interact only with the solvent continuum. It is shown that the binding energy distribution encodes all of the physical effects of binding. The balance between binding enthalpy and entropy is seen in our formalism as a balance between favorable and unfavorable binding modes which are coupled through the normalization of the binding energy distribution function. An efficient computational protocol for the binding energy distribution based on the AGBNP2 implicit solvent model, parallel Hamiltonian replica exchange sampling and histogram reweighting is developed. Applications of the method to a set of known binders and non-binders of the L99A and L99A/M102Q mutants of T4 lysozyme receptor are illustrated. The method is able to discriminate without error binders from non-binders, and the computed standard binding free energies of the binders are found to be in good agreement with experimental measurements. Analysis of the results reveals that the binding affinities of these systems reflect the contributions from multiple conformations spanning a wide range of binding energies.

The RCK2 domain uses a coordination site present in Kir channels to confer sodium sensitivity to Slo2.2 channels

Slo2 Na(+)-activated potassium channels are widely expressed in neurons and other cells, such as kidney, heart, and skeletal muscle. Although their important physiological roles continue to be appreciated, molecular determinants responsible for sensing intracellular Na(+) remain unknown. Here we report identification of an Na(+) regulatory site, similar to an Na(+) coordination motif described in Kir channels, localized in the RCK2 domain of Slo2.2 channels. Molecular simulations of the homology-modeled Slo2.2 RCK2 domain provided structural insights into the organization of this Na(+) coordination site. Furthermore, free energy calculations reproduced the experimentally derived monovalent cation selectivity. Our results suggest that Slo2.2 and Kir channels share a similar mechanism to coordinate Na(+). The localization of an Na(+) sensor within the RCK2 domain of Slo2.2 further supports the role of RCK (regulators of conductance of K(+)) domains of Slo channels in coupling ion sensing to channel gating.

Prediction of protein-ligand binding affinity by free energy simulations: assumptions, pitfalls and expectations

Many limitations of current computer-aided drug design arise from the difficulty of reliably predicting the binding affinity of a small molecule to a biological target. There is thus a strong interest in novel computational methodologies that claim predictions of greater accuracy than current scoring functions, and at a throughput compatible with the rapid pace of drug discovery in the pharmaceutical industry. Notably, computational methodologies firmly rooted in statistical thermodynamics have received particular attention in recent years. Yet free energy calculations can be daunting to learn for a novice user because of numerous technical issues and various approaches advocated by experts in the field. The purpose of this article is to provide an overview of the current capabilities of free energy calculations and to discuss the applicability of this technology to drug discovery.

Accounting for ligand conformational restriction in calculations of protein-ligand binding affinities

The conformation adopted by a ligand on binding to a receptor may differ from its lowest-energy conformation in solution. In addition, the bound ligand is more conformationally restricted, which is associated with a configurational entropy loss. The free energy change due to these effects is often neglected or treated crudely in current models for predicting binding affinity. We present a method for estimating this contribution, based on perturbation theory using the quasi-harmonic model of Karplus and Kushick as a reference system. The consistency of the method is checked for small model systems. Subsequently we use the method, along with an estimate for the enthalpic contribution due to ligand-receptor interactions, to calculate relative binding affinities. The AMBER force field and generalized Born implicit solvent model is used. Binding affinities were estimated for a test set of 233 protein-ligand complexes for which crystal structures and measured binding affinities are available. In most cases, the ligand conformation in the bound state was significantly different from the most favorable conformation in solution. In general, the correlation between measured and calculated ligand binding affinities including the free energy change due to ligand conformational change is comparable to or slightly better than that obtained by using an empirically-trained docking score. Both entropic and enthalpic contributions to this free energy change are significant.

Binary image representation of a ligand binding site: its application to efficient sampling of a conformational ensemble

BACKGROUND

Modelling the ligand binding site of a protein is an important component of understanding protein-ligand interactions and is being actively studied. Even if the side chains are restricted to rotamers, a set of commonly-observed low-energy conformations, the exhaustive combinatorial search of ligand binding site conformers is known as NP-hard. Here we propose a new method, ROTAIMAGE, for modelling the plausible conformers for the ligand binding site given a fixed backbone structure.

RESULTS

ROTAIMAGE includes a procedure of selecting ligand binding site residues, exhaustively searching rotameric conformers, clustering them by dissimilarities in pocket shape, and suggesting a representative conformer per cluster. Prior to the clustering, the list of conformers generated by exhaustive search can be reduced by pruning the conformers that have near identical pocket shapes, which is done using simple bit operations. We tested our approach by modelling the active-site inhibitor binding pockets of matrix metalloproteinase-1 and -13. For both cases, analyzing the conformers based on their pocket shapes substantially reduced the 'computational complexity' (10 to 190 fold). The subsequent clustering revealed that the pocket shapes of both proteins could be grouped into approximately 10 distinct clusters. At this level of clustering, the conformational space spanned by the known crystal structures was well covered. Heatmap analysis identified a few bit blocks that combinatorially dictated the clustering pattern. Using this analytical approach, we demonstrated that each of the bit blocks was associated with a specific pocket residue. Identification of residues that influenced the shape of the pocket is an interesting feature unique to the ROTAIMAGE algorithm.

CONCLUSIONS

ROTAIMAGE is a novel algorithm that was efficient in exploring the conformational space of the ligand binding site. Its ability to identify 'key' pocket residues also provides further insight into conformational flexibility with specific implications for protein-ligand interactions.

The role of conformational entropy in molecular recognition by calmodulin

The physical basis for high affinity interactions involving proteins is complex and potentially involves a range of energetic contributions. Among these are changes in protein conformational entropy, which cannot yet be reliably computed from molecular structures. We have recently employed changes in conformational dynamics as a proxy for changes in conformational entropy of calmodulin upon association with domains from regulated proteins. The apparent change in conformational entropy was linearly related to the overall binding entropy. This view warrants a more quantitative foundation. Here we calibrate an “entropy meter” employing an experimental dynamical proxy based on NMR relaxation and show that changes in the conformational entropy of calmodulin are a significant component of the energetics of binding. Furthermore, the distribution of motion at the interface between the target domain and calmodulin are surprisingly non-complementary. These observations promote modification of our understanding of the energetics of protein-ligand interactions.

Naturally occurring variability in the envelope glycoprotein of HIV-1 and development of cell entry inhibitors

Naturally occurring genetic variability across HIV-1 subtypes causes amino acid polymorphisms in encoded HIV-1 proteins including the envelope glycoproteins associated with viral entry. The effects of amino acid polymorphisms on the mechanism of HIV-1 entry into cells, a process initiated by the binding of the viral envelope glycoprotein gp120 to the cellular CD4 receptor, are largely unknown. In this study, we demonstrate that amino acid polymorphisms affect the structural stability and domain cooperativity of gp120 and that those differences are reflected in the binding mechanism of the viral envelope glycoprotein to the cell surface receptor and coreceptor. Moreover, subtype differences also affect the binding behavior of experimental HIV cell entry inhibitors. While gp120-A has a slightly lower denaturation temperature than gp120-B, the most notable stability difference is that for gp120-B the van't Hoff to calorimetric enthalpy ratio (DeltaH(vH)/DeltaH) is 0.95 whereas for gp120-A is 0.6, indicative of more cooperative domain/domain interactions in gp120-B, as this protein more closely approaches a two-state transition. Isothermal titration calorimetry demonstrates that CD4 and 17b (a surrogate antibody for the chemokine coreceptor) exhibit 7- and 3-fold weaker binding affinities for gp120-A. The binding of these proteins as well as that of the experimental entry inhibitor NBD-556 induces smaller conformational changes in gp120-A as evidenced by significantly smaller binding enthalpies and binding entropies. Together, these results describe the effects of gp120 polymorphisms on binding to host cell receptors and emphasize that guidelines for developing future entry inhibitors must recognize and deal with genomic differences between HIV strains.

Docking ligands into flexible and solvated macromolecules. 5. Force-field-based prediction of binding affinities of ligands to proteins

We report herein our efforts in the development of three empirical scoring functions with application in protein-ligand docking. A first scoring function was developed from 209 crystal structures of protein-ligand complexes and a second one from 946 cross-docked complexes. Tuning of the coefficients for the different terms making up these functions was performed by an iterative approach to optimize the correlations between observed activities and calculated scores. A third scoring function was developed from libraries of known actives and decoys docked to six different protein conformational ensembles. In the latter case, the tuning of the coefficients was performed so as to optimize the area under the curve of a receiver operating characteristic (ROC) for the discrimination of actives and inactives. The newly developed scoring functions were next assessed on independent sets of protein-ligand complexes for their ability to predict binding affinities and to discriminate actives from inactives. In the first validation the first function, which was trained on active compounds only, performed as well as other commonly used ones. On a high-throughput virtual screening validation on five protein conformational ensembles, the third scoring function that included data from inactive compounds performed significantly better. This validation showed that the inclusion of data from inactive compounds is critical for performance in virtual high-throughput screening applications.

Barley xyloglucan xyloglucosyl transferases bind xyloglucan-derived oligosaccharides in their acceptor-binding regions in multiple conformational states

Three barley xyloglucan endotransglycosylases (HvXETs), known as xyloglucan xyloglucosyl transferases (EC 2.4.1.207), were subjected to kinetic and computational docking studies. The k(cat) x K(m)(-1) values with the reduced [3H]-labelled XXXG, XXLG/XLXG and XLLG acceptor substrates were 0.02 x 10(-2), 0.1 x 10(-2) and 3.2 x 10(-2) s(-1) microM(-1), while the K(m) constants were 10.6, 8.6 and 5.3 mM, obtained for HvXET3, HvXET4 and HvXET6, respectively. Docking of XLLG in acceptor-binding regions revealed that at least two conformational states were likely to participate in all isoforms. The assessments of kinetic and computational data indicated that the disposition of aromatic residues at the entrance to the active sites and the flexibility of proximal COOH-terminal loops could orient acceptors more or less favourably during binding, thus leading to tighter or weaker K(m) constants. The data suggested that binding of acceptors in HvXETs is guided by contributions from the conserved residues in the active sites and by the of neighbouring loops.

Combining molecular dynamics with bayesian analysis to predict and evaluate ligand-binding mutations in influenza hemagglutinin

Influenza virus attaches to and infects target cells via binding of cell-surface glycans by the viral hemagglutinin. This binding specificity is considered a major reason why avian influenza is typically poorly transmitted between humans, while swine influenza is better transmitted due to glycan similarity between the human and swine upper respiratory tract. Predicting mutations that control glycan binding is thus important to continued surveillance against new pandemic influenza strains. We have designed a molecular-dynamics approach for scoring potential mutants with predictive power for both receptor-binding-domain and allosteric mutations similar to those identified from clinical isolates of avian influenza. We have performed thousands of simulations of 17 different hemagglutinin mutants totaling >1 ms in length and employ a bayesian model to rank mutations that disrupt the stability of the hemagglutinin-ligand complex. Based on our simulations, we predict a significantly increased k(off) for seven of these mutants. This means of using molecular dynamics analysis to make experimentally verifiable predictions offers a potentially general method to identify ligand-binding mutants, particularly allosteric ones. Our analysis of ligand dissociation provides a means to evaluate mutants prior to experimental mutagenesis and testing and constitutes an important step toward understanding the determinants of ligand binding by H5N1 influenza.

Macromolecular micromovements: how RNA polymerase translocates

Multi-subunit DNA-dependent RNA polymerases synthesize RNA molecules thousands of nucleotides long. The reiterative reaction of nucleotide condensation occurs at rates of tens of nucleotides per second, invariably linked to the translocation of the enzyme along the DNA template, or threading of the DNA and the nascent RNA molecule through the enzyme. Reiteration of the nucleotide addition/translocation cycle without dissociation from the DNA and RNA requires both isomorphic and metamorphic conformational flexibility of a magnitude substantial enough to accommodate the requisite molecular motions. Here we review some of the more recently acquired insights into the structural flexibility and morphic fluctuations of RNA polymerases and their mechanistic implications.

In silico analysis of antibody-carbohydrate interactions and its application to xenoreactive antibodies

Antibody-carbohydrate interactions play central roles in stimulating adverse immune reactions. The most familiar example of such a process is the reaction observed in ABO-incompatible blood transfusion and organ transplantation. The ABO blood groups are defined by the presence of specific carbohydrates expressed on the surface of red blood cells. Preformed antibodies in the incompatible recipient (i.e., different blood groups) recognize cells exhibiting host-incompatible ABO system antigens and proceed to initiate lysis of the incompatible cells. Pig-to-human xenotransplantation presents a similar immunological barrier. Antibodies present in humans recognize carbohydrate antigens on the surface of pig organs as foreign and proceed to initiate hyperacute xenograft rejection. The major carbohydrate xenoantigens all bear terminal Gal alpha(1,3)Gal epitopes (or alphaGal). In this study, we have developed and validated a site mapping technique to investigate protein-ligand recognition and applied it to antibody-carbohydrate systems. This site mapping technique involves the use of molecular docking to generate a series of antibody-carbohydrate complexes, followed by analysis of the hydrogen bonding and van der Waals interactions occurring in each complex. The technique was validated by application to a series of antibody-carbohydrate crystal structures. In each case, the majority of interactions made in the crystal structure complex were able to be reproduced. The technique was then applied to investigate xenoantigen recognition by a panel of monoclonal anti-alphaGal antibodies. The results indicate that there is a significant overlap of the antibody regions engaging the xenoantigens across the panel. Likewise, similar regions of the xenoantigens interact with the antibodies.

Trapping moving targets with small molecules

Structure-based drug design traditionally uses static protein models as inspirations for focusing on "active" site targets. Allosteric regulation of biological macromolecules, however, is affected by both conformational and dynamic properties of the protein or protein complex and can potentially lead to more avenues for therapeutic development. We discuss the advantages of searching for molecules that conformationally trap a macromolecule in its inactive state. Although multiple methodologies exist to probe protein dynamics and ligand binding, our current discussion highlights the use of nuclear magnetic resonance spectroscopy in the drug discovery and design process.