Assessment of solvation effects on calculated binding affinity differences: trypsin inhibition by flavonoids as a model system for congeneric series

gmx_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS

Molecular mechanics/Poisson-Boltzmann (Generalized-Born) surface area is one of the most popular methods to estimate binding free energies. This method has been proven to balance accuracy and computational efficiency, especially when dealing with large systems. As a result of its popularity, several programs have been developed for performing MM/PB(GB)SA calculations within the GROMACS community. These programs, however, present several limitations. Here we present gmx_MMPBSA, a new tool to perform end-state free energy calculations from GROMACS molecular dynamics trajectories. gmx_MMPBSA provides the user with several options, including binding free energy calculations with different solvation models (PB, GB, or 3D-RISM), stability calculations, computational alanine scanning, entropy corrections, and binding free energy decomposition. Noteworthy, several promising methodologies to calculate relative binding free energies such as alanine scanning with variable dielectric constant and interaction entropy have also been implemented in gmx_MMPBSA. Two additional tools-gmx_MMPBSA_test and gmx_MMPBSA_ana-have been integrated within gmx_MMPBSA to improve its usability. Multiple illustrating examples can be accessed through gmx_MMPBSA_test, while gmx_MMPBSA_ana provides fast, easy, and efficient access to different graphics plotted from gmx_MMPBSA output files. The latest version (v1.4.3, 26/05/2021) is available free of charge (documentation, test files, and tutorials included) at https://github.com/Valdes-Tresanco-MS/gmx_MMPBSA.

Dual effects of quercetin on protein digestion and absorption in the digestive tract

Quercetin is a well-studied natural product with multiple pharmacological properties. In this study, we demonstrated that quercetin suppressed protein digestion in the intestinal fluid by inhibiting trypsin, a key digestive enzyme. However, we also observed a previously unknown property of quercetin: promoting the intestinal absorption of proteins. In addition, the promoted protein absorption was mediated by internalization of digested oligopeptides in the intestinal epithelia rather than increasing the intestinal paracellular permeability. Notably, four other flavonoids also achieved such enhanced intestinal absorption, suggesting that this effect was associated with the aglycone flavonol backbone, but not related to their inhibitory potencies against trypsin. This study demonstrates that quercetin exhibits dual effects on protein digestion and absorption: 1) suppressing protein digestion by inhibiting trypsin in the intestinal fluid; 2) promoting the intestinal absorption of oligopeptides in the intestinal villi cells.

Comparative studies on the interaction of nine flavonoids with trypsin

In this study, the interaction between nine classic flavonoids (including baicalin, quercetin, myricetin, rutin, puerarin, daidzein, liquiritin and isoliquiritin) and trypsin was investigated by fluorescence spectroscopy and molecular modeling methods. The results reveal that all flavonoids can interact with trypsin to form flavonoid-trypsin complexes. The binding parameters obtained from the data at different temperatures indicate that all flavonoids can spontaneously bind with trypsin with one binding site. The binding constants of trypsin with nine classic flavonoids are in the following order as: baicalin > myricetin > rutin > isoliquiritin > hesperidin > puerarin > quercetin > daidzein > liquiritin. The interaction forces between flavonoids and trypsin may be electrostatic forces (except for rutin/puerarin/daidzein), hydrophobic interactions as well as van der Waals forces. Synchronous fluorescence spectroscopy shows that the interaction between flavonoids and trypsin changes the hydrophobicity of the microenvironment of tryptophan (Trp) residues. All flavonoids close to tyrosine (Tyr) residues but have no effect on the microenvironment around Tyr residues except for hesperidin and liquiritin. Molecular modeling displays that all flavonoids bind directly into trypsin cavity site and lead to a decrease in enzyme activity.

The effect of multiple simulation parameters on MM/PBSA performance for binding affinity prediction of CB1 cannabinoid receptor agonists and antagonists

Both the inactive- and active-state CB1 receptor crystal structures have now been solved, allowing their application in various structure-based drug design methods. One potential method utilizing these crystal structures is the Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) method of predicting relative binding free.

Advances in the Treatment of Explicit Water Molecules in Docking and Binding Free Energy Calculations

Background:

The inclusion of direct effects mediated by water during the ligandreceptor recognition is a hot-topic of modern computational chemistry applied to drug discovery and development. Docking or virtual screening with explicit hydration is still debatable, despite the successful cases that have been presented in the last years. Indeed, how to select the water molecules that will be included in the docking process or how the included waters should be treated remain open questions.

Objective:

In this review, we will discuss some of the most recent methods that can be used in computational drug discovery and drug development when the effect of a single water, or of a small network of interacting waters, needs to be explicitly considered.

Results:

Here, we analyse the software to aid the selection, or to predict the position, of water molecules that are going to be explicitly considered in later docking studies. We also present software and protocols able to efficiently treat flexible water molecules during docking, including examples of applications. Finally, we discuss methods based on molecular dynamics simulations that can be used to integrate docking studies or to reliably and efficiently compute binding energies of ligands in presence of interfacial or bridging water molecules.

Conclusions:

Software applications aiding the design of new drugs that exploit water molecules, either as displaceable residues or as bridges to the receptor, are constantly being developed. Although further validation is needed, workflows that explicitly consider water will probably become a standard for computational drug discovery soon.

OptMAVEn-2.0: De novo Design of Variable Antibody Regions against Targeted Antigen Epitopes

Monoclonal antibodies are becoming increasingly important therapeutic agents for the treatment of cancers, infectious diseases, and autoimmune disorders. However, laboratory-based methods of developing therapeutic monoclonal antibodies (e.g., immunized mice, hybridomas, and phage display) are time-consuming and are often unable to target a specific antigen epitope or reach (sub)nanomolar levels of affinity. To this end, we developed Optimal Method for Antibody Variable region Engineering (OptMAVEn) for de novo design of humanized monoclonal antibody variable regions targeting a specific antigen epitope. In this work, we introduce OptMAVEn-2.0, which improves upon OptMAVEn by (1) reducing computational resource requirements without compromising design quality; (2) clustering the designs to better identify high-affinity antibodies; and (3) eliminating intra-antibody steric clashes using an updated set of clashing parts from the Modular Antibody Parts (MAPs) database. Benchmarking on a set of 10 antigens revealed that OptMAVEn-2.0 uses an average of 74% less CPU time and 84% less disk storage relative to OptMAVEn. Testing on 54 additional antigens revealed that computational resource requirements of OptMAVEn-2.0 scale only sub-linearly with respect to antigen size. OptMAVEn-2.0 was used to design and rank variable antibody fragments targeting five epitopes of Zika envelope protein and three of hen egg white lysozyme. Among the top five ranked designs for each epitope, recovery of native residue identities is typically 45–65%. MD simulations of two designs targeting Zika suggest that at least one would bind with high affinity. OptMAVEn-2.0 can be downloaded from our GitHub repository and webpage as (links in Summary and Discussion section).

An Efficient Implementation of the Nwat-MMGBSA Method to Rescore Docking Results in Medium-Throughput Virtual Screenings

Nwat-MMGBSA is a variant of MM-PB/GBSA based on the inclusion of a number of explicit water molecules that are the closest to the ligand in each frame of a molecular dynamics trajectory. This method demonstrated improved correlations between calculated and experimental binding energies in both protein-protein interactions and ligand-receptor complexes, in comparison to the standard MM-GBSA. A protocol optimization, aimed to maximize efficacy and efficiency, is discussed here considering penicillopepsin, HIV1-protease, and BCL-XL as test cases. Calculations were performed in triplicates on both classic HPC environments and on standard workstations equipped by a GPU card, evidencing no statistical differences in the results. No relevant differences in correlation to experiments were also observed when performing Nwat-MMGBSA calculations on 4 or 1 ns long trajectories. A fully automatic workflow for structure-based virtual screening, performing from library set-up to docking and Nwat-MMGBSA rescoring, has then been developed. The protocol has been tested against no rescoring or standard MM-GBSA rescoring within a retrospective virtual screening of inhibitors of AmpC β-lactamase and of the Rac1-Tiam1 protein-protein interaction. In both cases, Nwat-MMGBSA rescoring provided a statistically significant increase in the ROC AUCs of between 20 and 30%, compared to docking scoring or to standard MM-GBSA rescoring.

Leveraging Data Fusion Strategies in Multireceptor Lead Optimization MM/GBSA End-Point Methods

Accurate and efficient affinity calculations are critical to enhancing the contribution of in silico modeling during the lead optimization phase of a drug discovery campaign. Here, we present a large-scale study of the efficacy of data fusion strategies to leverage results from end-point MM/GBSA calculations in multiple receptors to identify potent inhibitors among an ensemble of congeneric ligands. The retrospective analysis of 13 congeneric ligand series curated from publicly available data across seven biological targets demonstrates that in 90% of the individual receptor structures MM/GBSA scores successfully identify subsets of inhibitors that are more potent than a random selection, and data fusion strategies that combine MM/GBSA scores from each of the receptors significantly increase the robustness of the predictions. Among nine different data fusion metrics based on consensus scores or receptor rankings, the SumZScore (i.e., converting MM/GBSA scores into standardized Z-Scores within a receptor and computing the sum of the Z-Scores for a given ligand across the ensemble of receptors) is found to be a robust and physically meaningful metric for combining results across multiple receptors. Perhaps most surprisingly, even with relatively low to modest overall correlations between SumZScore and experimental binding affinities, SumZScore tends to reliably prioritize subsets of inhibitors that are at least as potent as those that are prioritized from a "best" single receptor identified from known compounds within the congeneric series.

An insight into the potentially old-wonder molecule-quercetin: the perspectives in foresee

Use of phyto-medicine and digitalization of phyto-compounds has been fallen enthralling field of science in recent years. Quercetin, a flavonoid with brilliant citron yellow pigment, is typically found in fruits and leafy vegetables in reasonable amount. Quercetin's potentials as an antioxidant, immune-modulator, antiinflammatory, anti-cancer, and others have been the subject of interest in this review. Although, profiling the insights in to the molecular characterization of quercetin with various targets provided the loop-holes in understanding the knowledge for the aforementioned mechanisms, still necessitates research globally to unearth it completely. Thus, the available science on the synthesis and significant role played by the old molecule - quercetin which does wonders even now have been vividly explained in the present review to benefit the scientific community.

The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities

INTRODUCTION

The molecular mechanics energies combined with the Poisson-Boltzmann or generalized Born and surface area continuum solvation (MM/PBSA and MM/GBSA) methods are popular approaches to estimate the free energy of the binding of small ligands to biological macromolecules. They are typically based on molecular dynamics simulations of the receptor-ligand complex and are therefore intermediate in both accuracy and computational effort between empirical scoring and strict alchemical perturbation methods. They have been applied to a large number of systems with varying success.

AREAS COVERED

The authors review the use of MM/PBSA and MM/GBSA methods to calculate ligand-binding affinities, with an emphasis on calibration, testing and validation, as well as attempts to improve the methods, rather than on specific applications.

EXPERT OPINION

MM/PBSA and MM/GBSA are attractive approaches owing to their modular nature and that they do not require calculations on a training set. They have been used successfully to reproduce and rationalize experimental findings and to improve the results of virtual screening and docking. However, they contain several crude and questionable approximations, for example, the lack of conformational entropy and information about the number and free energy of water molecules in the binding site. Moreover, there are many variants of the method and their performance varies strongly with the tested system. Likewise, most attempts to ameliorate the methods with more accurate approaches, for example, quantum-mechanical calculations, polarizable force fields or improved solvation have deteriorated the results.

Drug screening boosted by hyperpolarized long-lived states in NMR

Transverse and longitudinal relaxation times (T_1ρ and T₁) have been widely exploited in NMR to probe the binding of ligands and putative drugs to target proteins. We have shown recently that long-lived states (LLS) can be more sensitive to ligand binding. LLS can be excited if the ligand comprises at least two coupled spins. Herein we broaden the scope of ligand screening by LLS to arbitrary ligands by covalent attachment of a functional group, which comprises a pair of coupled protons that are isolated from neighboring magnetic nuclei. The resulting functionalized ligands have longitudinal relaxation times T₁(¹H) that are sufficiently long to allow the powerful combination of LLS with dissolution dynamic nuclear polarization (D-DNP). Hyperpolarized weak “spy ligands” can be displaced by high-affinity competitors. Hyperpolarized LLS allow one to decrease both protein and ligand concentrations to micromolar levels and to significantly increase sample throughput.

Explicit Ligand Hydration Shells Improve the Correlation between MM-PB/GBSA Binding Energies and Experimental Activities

Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) and Molecular Mechanics Generalized Born Surface Area (MM-GBSA) methods are widely used for drug design/discovery purposes. However, it is not clear if the correlation between predicted and experimental binding affinities can be improved by explicitly considering selected water molecules in the calculation of binding energies, since different and sometimes diverging opinions are found in the literature. In this work, we evaluated how variably populated hydration shells explicitly considered around the ligands may affect the correlation between MM-PB/GBSA computed binding energy and biological activities (IC50 and ΔGbind, depending on the available experimental data). Four different systems-namely, the DNA-topoisomerase complex, α-thrombin, penicillopepsin, and avidin-were considered and ligand hydration shells populated by 10-70 water molecules were systematically evaluated. We found that the consideration of a hydration shell populated by a number of water residues (Nwat) between 30 and 70 provided, in all of the considered examples, a positive effect on correlation between MM-PB/GBSA calculated binding affinities and experimental activities, with a negligible increment of computational cost.

Natural occurring polyphenols as template for drug design. Focus on serine proteases

Several major physio-pathological processes, including cancer, inflammatory states and thrombosis, are all strongly dependent upon the fine regulation of proteolytic enzyme activities, and dramatic are the consequences of unbalanced equilibria between enzymes and their cognate inhibitors. In this perspective, the discovery of small-molecule ligands able to modulate catalytic activities has a massive therapeutic potential and is a stimulating goal. Numerous recent experimental evidences revealed that proteolytic enzymes can be opportunely targeted, reporting on small ligands capable of binding to these biological macromolecules with drug-like potencies, and primarily with comparable (or even higher) efficiency with respect to their endogenous binding partner. In particular, natural occurring polyphenols and their derivatives recently disclosed these intriguing abilities, making them promising templates for drug design and development. In this review, we compared the inhibitory capacities of a set of monomeric polyphenols toward serine proteases activity, and finally summarized the data with an emphasis on the derivation of a pharmacophore model.

Cleavage of four carbon-carbon bonds during biosynthesis of the griseorhodin a spiroketal pharmacophore

The rubromycins, such as gamma-rubromycin, heliquinomycin, and griseorhodin A, are a family of extensively modified aromatic polyketides that inhibit HIV reverse transcriptase and human telomerase. Telomerase inhibition crucially depends on the presence of a spiroketal moiety that is unique among aromatic polyketides. Biosynthetic incorporation of this pharmacophore into the rubromycins results in a dramatic distortion of the overall polyketide structure, but how this process is achieved by the cell has been obscure. To identify the enzymes involved in spiroketal construction, we generated 14 gene-deletion variants of the griseorhodin A biosynthetic gene cluster isolated from the tunicate-associated bacterium Streptomyces sp. JP95. Heterologous expression and metabolic analysis allowed for an assignment of most genes to various stages of griseorhodin tailoring and pharmacophore generation. The isolation of the novel advanced intermediate lenticulone, which exhibits cytotoxic, antibacterial, and elastase-inhibiting activity, provided direct evidence that the spiroketal is formed by cleavage of four carbon-carbon bonds in a pentangular polyketide precursor. This remarkable transformation is followed by an epoxidation catalyzed by an unusual cytochrome P450/NADPH:ubiquinone oxidoreductase pair that utilizes a saturated substrate. In addition, the absolute configuration of griseorhodin A was determined by quantum-chemical circular dichroism (CD) calculations in combination with experimental CD measurements.

Overcoming the inadequacies or limitations of experimental structures as drug targets by using computational modeling tools and molecular dynamics simulations

X-ray crystallography, NMR spectroscopy, and cryoelectron microscopy stand out as powerful tools that enable us to obtain atomic detail about biomolecules that can be potentially targeted by drugs. This knowledge is essential if virtual screening or structure-based ligand-design methods are going to be used in drug discovery. However, the macromolecule of interest is not always amenable to these types of experiment or, as is often the case, the conformation found experimentally cannot be used directly for docking studies because of significant changes between apo and bound forms. Furthermore, sometimes the desired insight into the binding mechanism cannot be gained because the structure of the ligand-receptor complex, not having been time-resolved, represents the endpoint of the binding process and therefore retains little or no information about the intermediate stages that led to its creation. Molecular dynamics (MD) simulations are routinely applied these days to the study of biomolecular systems with the aims of sampling configuration space more efficiently and getting a better understanding of the factors that determine structural stability and relevant biophysical and biochemical processes such as protein folding, ligand binding, and enzymatic reactions. This field has matured significantly in recent years, and strategies have been devised (for example activated, steered, or targeted MD) that allow the calculated trajectories to be biased in attempts to properly shape a ligand binding pocket or simulate large-scale motions involving one or more protein domains. On the other hand, low-frequency motions can be simulated quite inexpensively by calculation of normal modes which allow the investigation of alternative receptor conformations. Selected examples in which these methods have been applied to several medicinal chemistry and in silico pharmacology endeavors are presented.

A new implicit solvent model for protein-ligand docking

A new implicit solvent model for computing the electrostatics binding free energy in protein-ligand docking is proposed. The new method is based on an adaptation of the screening coulombic potentials proposed originally by Hassan et al. (J Phys Chem B 2000;104:6490-6498). In essence, it relies on two basic assumptions; (i) solvent screening can be accounted for by means of radially dependent sigmoidal dielectric functions and; (ii) the effective atom Born radii can be expressed only as a function of the exposed atom surface. Parameters of the model other than radii and charges are generic. These were optimized for a dataset of 826 protein-ligand complexes, comprising both X-ray complexes for 23 receptors as well as decoys generated by docking computations. We show that the new model provides satisfactory results when benchmarked against reference values based on the numerical solution of the Poisson equation, with a root mean square error of 4.2 kcal/mol over a range of approximately 40 kcal/mol in electrostatics binding free energies, a cross-validated r2 of 0.81, a slope of 0.97, and an intercept of 1.06 kcal/mol. We show that the model is appropriate for ligands of different sizes, polarities, overall charge, and chemical composition. Furthermore, not only the total value of the electrostatic contribution to the binding free energy, but also its components (coulombic term, receptor desolvation, and ligand desolvation) are reasonably well reproduced. Computation times of approximately 0.030 s per pose are obtained on a single processor desktop workstation.

S1 subsite in snake venom thrombin-like enzymes: can S1 subsite lipophilicity be used to sort binding affinities of trypsin-like enzymes to small-molecule inhibitors?

Thrombin-like enzymes isolated from snake venoms comprise a group of serine proteinases responsible for many important coagulation disorders in the envenomed victims. Besides, these proteinases have great biotechnological interest as antithrombotic agents and as diagnostic tools. However, in spite of the recent overflow of snake venom thrombin-like enzymes (SVTLEs) on protein sequence databases, there is a lack of three-dimensional (3D) structural information on this family. Without such 3D structures available many aspects of the biological function and biochemical properties of these enzymes still remain obscure. Therefore, we have gone through a series of computational techniques, which enabled us to identify the set of residues involved in molecular recognition of inhibitors bound to the S1 subsite of snake venom thrombin-like enzymes (SVTLEs) and ultimately conclude that nonpolar (van der Waals) intermolecular interactions and ligand's hydrophobicity are the most important factors affecting binding affinities to the S1 subsite of a SVTLE isolated from the venom of Lachesis muta muta (Lmm-TLE). Consequently, we have proposed that S1 subsite lipophilicity may be used to sort binding affinities of trypsin-like enzymes to small molecules by showing that the inhibitory potency of several S1-directed compounds follows subsite lipophilicity among Lmm-TLE and other three homologous proteases. Noteworthy, in the course of our analyses we determined that thrombin's S1 subsite should, in fact, be considered less lipophilic than that of trypsin if we account for the presence of the sodium-controlled water channel communicating with the S1 subsite in the coagulant enzyme.

Modulation of Binding Strength in Several Classes of Active Site Inhibitors of Acetylcholinesterase Studied by Comparative Binding Energy Analysis

The comparative binding energy (COMBINE) methodology has been used to identify the key residues that modulate the inhibitory potencies of three structurally different classes of acetylcholinesterase inhibitors (tacrines, huprines, and dihydroquinazolines) targeting the catalytic active site of this enzyme. The extended set of energy descriptors and the partial least-squares methodology used by COMBINE analysis on a unique training set containing all the compounds yielded an interpretable model that was able to fit and predict the activities of the whole series of inhibitors reasonably well (r2 = 0.91 and q2 = 0.76, 4 principal components). A more robust model (q2 = 0.81 and SDEP = 0.25, 3 principal components) was obtained when the same chemometric analysis was applied to the huprines set alone, but the method was unable to provide predictive models for the other two families when they were treated separately from the rest. This finding appears to indicate that the enrichment in chemical information brought about by the inclusion of different classes of compounds into a single training set can be beneficial when an internally consistent set of pharmacological data can be derived. The COMBINE model was externally validated when it was shown to predict the activity of an additional set of compounds that were not employed in model construction. Remarkably, the differences in inhibitory potency within the whole series were found to be finely tuned by the electrostatic contribution to the desolvation of the binding site and a network of secondary interactions established between the inhibitor and several protein residues that are distinct from those directly involved in the anchoring of the ligand. This information can now be used to advantage in the design of more potent inhibitors.

Evaluation of Docking Performance: Comparative Data on Docking Algorithms

Docking molecules into their respective 3D macromolecular targets is a widely used method for lead optimization. However, the best known docking algorithms often fail to position the ligand in an orientation close to the experimental binding mode. It was reported recently that consensus scoring enhances the hit rates in a virtual screening experiment. This methodology focused on the top-ranked pose, with the underlying assumption that the orientation/conformation of the docked compound is the most accurate. In an effort to eliminate the scoring function bias, and assess the ability of the docking algorithms to provide solutions similar to the crystallographic modes, we investigated the most known docking programs and evaluated all of the resultant poses. We present the results of an extensive computational study in which five docking programs (FlexX, DOCK, GOLD, LigandFit, Glide) were investigated against 14 protein families (69 targets). Our findings show that some algorithms perform consistently better than others, and a correspondence between the nature of the active site and the best docking algorithm can be found.

Guanidinium receptors as enantioselective amino acid membrane carriers

A number of artificial carriers for the transport of zwitterionic aromatic amino acids across bulk model membranes (U-tube type) have been prepared and evaluated. 1,2-Dichloroethane and dichloromethane were employed in the organic phase. All compounds are based on a bicyclic chiral guanidinium scaffold that ideally complements the carboxylate function. The guanidinium central moiety was attached to crown ethers or lasalocid A as specific subunits for ammonium recognition as well as to aromatic or hydrophobic residues to evaluate their potential interaction with the side chains of the guest amino acids. The subunits were linked to the guanidinium through ester or amide connectors. Amides were found to be better carriers than esters, though less enantioselective. On the other hand, crown ethers were superior to lasalocid derivatives. As expected, transport rates were dependent on the carrier concentration in the liquid membrane. Reciprocally, enantioselectivities were much higher at lower carrier concentrations. The results show that our previously proposed three-point binding model (J. Am. Chem. Soc. 1992, 114, 1511-1512), involving the participation of the aromatic or hydrophobic residue to interact with the side chains of the amino acid guest, is unnecessary to explain the high enantioselectivities observed. Molecular dynamics fully support a two-point model involving only the guanidinium and crown ether moieties. These molecules constitute the first examples of chiral selectors for underivatized amino acids acting as carriers under neutral conditions.

The biochemistry and medical significance of the flavonoids

Flavonoids are plant pigments that are synthesised from phenylalanine, generally display marvelous colors known from flower petals, mostly emit brilliant fluorescence when they are excited by UV light, and are ubiquitous to green plant cells. The flavonoids are used by botanists for taxonomical classification. They regulate plant growth by inhibition of the exocytosis of the auxin indolyl acetic acid, as well as by induction of gene expression, and they influence other biological cells in numerous ways. Flavonoids inhibit or kill many bacterial strains, inhibit important viral enzymes, such as reverse transcriptase and protease, and destroy some pathogenic protozoans. Yet, their toxicity to animal cells is low. Flavonoids are major functional components of many herbal and insect preparations for medical use, e.g., propolis (bee's glue) and honey, which have been used since ancient times. The daily intake of flavonoids with normal food, especially fruit and vegetables, is 1-2 g. Modern authorised physicians are increasing their use of pure flavonoids to treat many important common diseases, due to their proven ability to inhibit specific enzymes, to simulate some hormones and neurotransmitters, and to scavenge free radicals.

Automated docking and molecular dynamics simulations of nimesulide in the cyclooxygenase active site of human prostaglandin-endoperoxide synthase-2 (COX-2)

Molecular models of the complex between the selective COX-2 inhibitor nimesulide and the cyclooxygenase active site of human prostaglandin-endoperoxide synthase-2 have been built using a combination of homology modelling, conformational searching and automated docking techniques. The stability of the resulting complexes has been assessed by molecular dynamics simulations and interaction energy decomposition. It is found that nimesulide exploits the extra space made available by the replacement at position 523 of an isoleucine residue in COX-1 by a valine in COX-2 and establishes electrostatic interactions with both Arg-106 and Arg-499 (Arg-120 and Arg-513 in PGHS-1 numbering). Two alternate binding modes are proposed which are compatible with the pharmacological profile of this agent as a COX-2 selective inhibitor.

The effect of multiple binding modes on empirical modeling of ligand docking to proteins

A popular approach to the computational modeling of ligand/receptor interactions is to use an empirical free energy like model with adjustable parameters. Parameters are learned from one set of complexes, then used to predict another set. To improve these empirical methods requires an independent way to study their inherent errors. We introduce a toy model of ligand/receptor binding as a workbench for testing such errors. We study the errors incurred from the two state binding assumption--the assumption that a ligand is either bound in one orientation, or unbound. We find that the two state assumption can cause large errors in free energy predictions, but it does not affect rank order predictions significantly. We show that fitting parameters using data from high affinity ligands can reduce two state errors; so can using more physical models that do not use the two state assumption. We also find that when using two state models to predict free energies, errors are more severe on high affinity ligands than low affinity ligands. And we show that two state errors can be diagnosed by systematically adding new binding modes when predicting free energies: if predictions worsen as the modes are added, then the two state assumption in the fitting step may be at fault.

Comparative binding energy analysis of HIV-1 protease inhibitors: incorporation of solvent effects and validation as a powerful tool in receptor-based drug design

A comparative binding energy (COMBINE) analysis (Ortiz et al. J. Med. Chem. 1995, 38, 2681-2691) has been performed on a training set of 33 HIV-1 protease inhibitors, and the resulting regression models have been validated using an additional external set of 16 inhibitors. This data set was originally reported by Holloway et al. (J. Med. Chem. 1995, 38, 305-317), who showed the usefulness of molecular mechanics interaction energies for predicting the activity of novel HIV-1 protease inhibitors within the framework of the MM2X force field and linear regression techniques. We first used the AMBER force field on the same set of three-dimensional structures to check up on any possible force-field dependencies. In agreement with the previous findings, the calculated raw ligand-receptor interaction energies were highly correlated with the inhibitory activities (r2 = 0.81), and the linear regression model relating both magnitudes had an acceptable predictive ability both in internal validation tests (q2 = 0.79, SDEPcv = 0.61) and when applied to the external set of 16 different inhibitors (SDEPex = 1.08). When the interaction energies were further analyzed using the COMBINE formalism, the resulting PLS model showed improved fitting properties (r2 = 0.89) and provided better estimations for the activity of the compounds in the external data set (SDEPex = 0.83). Computation of the electrostatic part of the ligand-receptor interactions by numerically solving the Poisson-Boltzmann equation did not improve the quality of the linear regression model. On the contrary, incorporation of the solvent-screened residue-based electrostatic interactions and two additional descriptors representing the electrostatic energy contributions to the partial desolvation of both the ligands and the receptor resulted in a COMBINE model that achieved a remarkable predictive ability, as assessed by both internal (q2 = 0.73, SDEPcv = 0.69) and external validation tests (SDEPex = 0.59). Finally, when all the inhibitors studied were merged into a single expanded set, a new model was obtained that explained 91% of the variance in biological activity (r2 = 0.91), with very high predictive ability (q2 = 0.81, SDEPcv = 0.66). In addition, the COMBINE analysis provided valuable information about the relative importance of the contributions to the activity of individual residues that can be fruitfully used to design better inhibitors. All in all, COMBINE analysis is validated as a powerful methodology for predicting binding affinities and pharmacological activities of congeneric ligands that bind to a common receptor.

Stacking interactions and intercalative DNA binding

The DNA-binding properties of many ligands can be rationalized on the basis of their structural and electronic complementarity with the functional groups present in the minor and major grooves of particular DNA sequences. Specific hydrogen bonding patterns are particularly useful for the purpose of sequence recognition. Less obvious, however, is the influence of base composition on the conformational preferences of individual base steps and on the binding of intercalating moieties which become sandwiched between contiguous base pairs. Improved knowledge of stacking interactions may lead to a better understanding of the architecture and inherent flexibility of particular DNA sequences and may provide insight into the principles that dictate the structural changes and specificity patterns observed in the binding of some intercalating ligands to DNA.