Premature activation of the paramyxovirus fusion protein before target cell attachment with corruption of the viral fusion machinery.

Paramyxoviruses, including the childhood pathogen human parainfluenza virus type 3, enter host cells by fusion of the viral and target cell membranes. This fusion results from the concerted action of its two envelope glycoproteins, the hemagglutinin-neuraminidase (HN) and the fusion protein (F). The receptor-bound HN triggers F to undergo conformational changes that render it competent to mediate fusion of the viral and cellular membranes. We proposed that, if the fusion process could be activated prematurely before the virion reaches the target host cell, infection could be prevented. We identified a small molecule that inhibits paramyxovirus entry into target cells and prevents infection. We show here that this compound works by an interaction with HN that results in F-activation prior to receptor binding. The fusion process is thereby prematurely activated, preventing fusion of the viral membrane with target cells and precluding viral entry. This first evidence that activation of a paramyxovirus F can be specifically induced before the virus contacts its target cell suggests a new strategy with broad implications for the design of antiviral agents.

Web application for studying the free energy of binding and protonation states of protein-ligand complexes based on HINT.

A public web server performing computational titration at the active site in a protein-ligand complex has been implemented. This calculation is based on the Hydropathic interaction noncovalent force field. From 3D coordinate data for the protein, ligand and bridging waters (if available), the server predicts the best combination of protonation states for each ionizable residue and/or ligand functional group as well as the Gibbs free energy of binding for the ionization-optimized protein-ligand complex. The 3D structure for the modified molecules is available as output. In addition, a graph depicting how this energy changes with acidity, i.e., as a function of added protons, can be obtained. This data may prove to be of use in preparing models for virtual screening and molecular docking. A few illustrative examples are presented. In beta secretase (2va7) computational titration flipped the amide groups of Gln12 and Asn37 and protonated a ligand amine yielding an improvement of 6.37 kcal mol(-1) in the protein-ligand binding score. Protonation of Glu139 in mutant HIV-1 reverse transcriptase (2opq) allows a water bridge between the protein and inhibitor that increases the protein-ligand interaction score by 0.16 kcal mol(-1). In human sialidase NEU2 complexed with an isobutyl ether mimetic inhibitor (2f11) computational titration suggested that protonating Glu218, deprotonating Arg237, flipping the amide bond on Tyr334, and optimizing the positions of several other polar protons would increase the protein-ligand interaction score by 0.71 kcal mol(-1).

Docking and hydropathic scoring of polysubstituted pyrrole compounds with antitubulin activity.

Compounds that bind at the colchicine site of tubulin have drawn considerable attention with studies indicating that these agents suppress microtubule dynamics and inhibit tubulin polymerization. Data for 18 polysubstituted pyrrole compounds are reported, including antiproliferative activity against human MDA-MB-435 cells and calculated free energies of binding following docking the compounds into models of alphabeta-tubulin. These docking calculations coupled with HINT interaction analyses are able to represent the complex structures and the binding modes of inhibitors such that calculated and measured free energies of binding correlate with an r(2) of 0.76. Structural analysis of the binding pocket identifies important intermolecular contacts that mediate binding. As seen experimentally, the complex with JG-03-14 (3,5-dibromo-4-(3,4-dimethoxyphenyl)-1H-pyrrole-2-carboxylic acid ethyl ester) is the most stable. These results illuminate the binding process and should be valuable in the design of new pyrrole-based colchicine site inhibitors as these compounds have very accessible syntheses.

Complexity in modeling and understanding protonation states: computational titration of HIV-1-protease-inhibitor complexes.

The computational-titration (CT) algorithm based on the 'natural' Hydropathic INTeractions (HINT) force field is described. The HINT software model is an empirical, non-Newtonian force field derived from experimentally measured partition coefficients for solvent transfer between octanol and H(2)O (log P(o/w)). The CT algorithm allows the identification, modeling, and optimization of multiple protonation states of residues and ligand functional groups at the protein-ligand active site. The importance of taking into account pH and ionization states of residues, which strongly affect the process of ligand binding, for correctly predicting binding free energies is discussed. The application of the CT protocol to a set of six cyclic inhibitors in their complexes with HIV-1 protease is presented, and the advance of HINT as a virtual-screening tool is outlined.

Identification of novel allosteric regulators of human-erythrocyte pyruvate kinase.

Erythrocyte pyruvate kinase (PK) is an important glycolytic enzyme, and manipulation of its regulatory behavior by allosteric modifiers is of interest for medicinal purposes. Human-erythrocyte PK was expressed in Rosetta cells and purified on an Ni-NTA column. A search of the small-molecules database of the National Cancer Institute (NCI), using the UNITY software, led to the identification of several compounds with similar pharmacophores as fructose-1,6-bisphosphate (FBP), the natural allosteric activator of the human kinases. The compounds were subsequently docked into the FBP binding site using the programs FlexX and GOLD, and their interactions with the protein were analyzed with the energy-scoring function of HINT. Seven promising candidates, compounds 1-7, were obtained from the NCI, and subjected to kinetics analysis, which revealed both activators and inhibitors of the R-isozyme of PK (R-PK). The allosteric effectors discovered in this study could prove to be lead compounds for developing medications for the treatment of hemolytic anemia, sickle-cell anemia, hypoxia-related diseases, and other disorders arising from erythrocyte PK malfunction.

The consequences of scoring docked ligand conformations using free energy correlations.

Ligands from a set of 19 protein-ligand complexes were re-docked with AutoDock, GOLD and FlexX using the scoring algorithms native to these programs supplemented by analysis using the HINT free energy force field. A HINT scoring function was calibrated for this data set using a simple linear regression of total HINT score for crystal-structure complexes vs. measured free energy of binding. This function had an r(2) of 0.84 and a standard error of +/-0.42 kcal mol(-1). The free energies of binding were calculated for the best poses using the AutoDock, GOLD and FlexX scoring functions. The AutoDock and GoldScore algorithms estimated more than half of the binding free energies within the reported calibration standard errors for these functions, while that of FlexX did not. In contrast, the calibrated HINT scoring function identified optimized poses with standard errors near +/-0.5 kcal mol(-1). When the metric of success is minimum RMSD (vs. crystallographic coordinates) the three docking programs were more successful, with mean RMSDs for the top-ranking poses in the 19 complexes of 3.38, 2.52 and 2.62 A for AutoDock, GOLD and FlexX, respectively. Two key observations in this study have general relevance for computational medicinal chemistry: first, while optimizing RMSD with docking score functions is clearly of value, these functions may be less well optimized for free energy of binding, which has broader applicability in virtual screening and drug discovery than RMSD; second, scoring functions uniquely calibrated for the data set or sets under study should nearly always be preferable to universal scoring functions. Due to these advantages, the poses selected by the HINT score also required less post-docking structure optimization to produce usable molecular models. Most of these features may be achievable with other scoring functions.

A second receptor binding site on human parainfluenza virus type 3 hemagglutinin-neuraminidase contributes to activation of the fusion mechanism.

The hemagglutinin-neuraminidase (HN) protein of paramyxoviruses carries out three discrete activities that each affect the ability of HN to promote viral fusion and entry: receptor binding, receptor cleaving (neuraminidase), and triggering of the fusion protein. The interrelationship between the receptor binding and fusion-triggering functions of HN has not been clear. For human parainfluenza type 3 (HPIV3), one bifunctional site on HN can carry out both receptor binding and neuraminidase activities, and this site's receptor binding can be inhibited by the small receptor analog zanamivir. We now report experimental evidence, complemented by computational data, for a second receptor binding site near the HPIV3 HN dimer interface. This second binding site can mediate receptor binding even in the presence of zanamivir, and it differs from the second receptor binding site of the paramyxovirus Newcastle disease virus in its function and its relationship to the primary binding site. This second binding site of HPIV3 HN is involved in triggering F. We suggest that the two receptor binding sites on HPIV3 HN each contribute in distinct ways to virus-cell interaction; one is the multifunctional site that contains both binding and neuraminidase activities, and the other contains binding activity and also is involved in fusion promotion.

The pH-dependent unfolding mechanism of P2 myelin protein: an experimental and computational study.

The P2 protein is a small, extrinsic protein of the myelin membrane in the peripheral nervous system that structurally belongs to the fatty acid binding proteins (FABPs) family, sharing with them a 10 strands beta-barrel structure. FABPs appear to be involved in cellular fatty acid transport, but very little is known about the role of P2 in the metabolism of peripheral myelin lipids. Study of protein conformation at different pHs is a useful tool for the characterization of the unfolding mechanisms and the intrinsic conformational properties of the protein, and may give insight into factors that guide protein folding pathways. In particular, low pH conditions have been shown to induce partially folded states in several proteins. In this paper, the acidic unfolding of purified P2 protein was studied with both spectroscopic techniques and molecular dynamics simulation. Both experimental and computational results indicate the presence of a partly folded state at low pH, which shows structural changes mainly involving the lid that is formed by the helix-turn-helix domain. The opening of the lid, together with a barrel relaxation, could regulate the ligand exchanges near the cell membrane, supporting the hypothesis that the P2 protein may transport fatty acids between Schwann cells and peripheral myelin.

Paramyxovirus receptor-binding molecules: engagement of one site on the hemagglutinin-neuraminidase protein modulates activity at the second site.

The hemagglutinin-neuraminidase (HN) protein of paramyxoviruses carries out three different activities: receptor binding, receptor cleaving (neuraminidase), and triggering of the fusion protein. These three discrete properties each affect the ability of HN to promote viral fusion and entry. For human parainfluenza type 3, one bifunctional site on HN can carry out both binding and neuraminidase, and the receptor mimic, zanamivir, impairs viral entry by blocking receptor binding. We report here that for Newcastle disease virus, the HN receptor avidity is increased by zanamivir, due to activation of a second site that has higher receptor avidity. Only certain receptor mimics effectively activate the second site (site II) via occupation of site I; yet without activation of this second site, binding is mediated entirely by site I. Computational modeling designed to complement the experimental approaches suggests that the potential for small molecule receptor mimics to activate site II, upon binding to site I, directly correlates with their predicted strengths of interaction with site I. Taken together, the experimental and computational data show that the molecules with the strongest interactions with site I-zanamivir and BCX 2798-lead to the activation of site II. The finding that site II, once activated, shows higher avidity for receptor than site I, suggests paradigms for further elucidating the regulation of HN's multiple functions in the viral life cycle.

Tools for building a comprehensive modeling system for virtual screening under real biological conditions: The Computational Titration algorithm.

Computational tools utilizing a unique empirical modeling system based on the hydrophobic effect and the measurement of logP(o/w) (the partition coefficient for solvent transfer between 1-octanol and water) are described. The associated force field, Hydropathic INTeractions (HINT), contains much rich information about non-covalent interactions in the biological environment because of its basis in an experiment that measures interactions in solution. HINT is shown to be the core of an evolving virtual screening system that is capable of taking into account a number of factors often ignored such as entropy, effects of solvent molecules at the active site, and the ionization states of acidic and basic residues and ligand functional groups. The outline of a comprehensive modeling system for virtual screening that incorporates these features is described. In addition, a detailed description of the Computational Titration algorithm is provided. As an example, three complexes of dihydrofolate reductase (DHFR) are analyzed with our system and these results are compared with the experimental free energies of binding.

Free energy of ligand binding to protein: evaluation of the contribution of water molecules by computational methods.

One of the more challenging issues in medicinal chemistry is the computation of the free energy of ligand binding to macromolecular targets. This allows for the screening of libraries of chemicals for fast and inexpensive identification of lead compounds. Many attempts have been made and several algorithms have been developed for this purpose. Whereas enthalpic contributions are evaluated using methods and equations for which there is a reasonable consensus among researchers, the entropic contribution is evaluated using very different, and, in some cases, very approximate methods, or it is entirely ignored. Entropic contributions are of primary importance in the formation of many ligand-protein complexes, as well as in protein folding. The hydrophobic interaction, associated with the release of water molecules from the protein active site and the ligand, plays a significant role in complex formation, predominantly contributing to the total entropy change and, in some cases, to the total free energy of binding. There are distinct approaches for the evaluation of the contribution of water molecules to the free energy of binding based on Newtonian mechanics force fields, multi-parameter empirical scoring functions and experimental force fields. This review describes these methods -- discussing both their advantages and limitations. Particular emphasis will be placed on HINT (Hydropatic INTeractions), a "natural" force field that takes into account in a unified way enthalpic and entropic contributions of all interacting atoms in protein-ligand complexes, including released and structured water molecules. As a case-study, the contribution of water molecules to the binding free energy of HIV-1 protease inhibitors is evaluated.

Computational titration analysis of a multiprotic HIV-1 protease-ligand complex.

A new computational method for analyzing the protonation states of protein-ligand complexes with multiple ionizable groups is applied to the structurally characterized complex between the peptide Glu-Asp-Leu and HIV-1 protease. This complex has eight ionizable groups at the active site: four from the ligand and four Asp residues on the protein. Correlation, with an error of ca. 0.6 kcal mol-1, is made between the calculated titration curve and the experimental titration curve. The analysis suggests that between four and five of the eight ionizable groups are protonated at the pH of crystallization.

Simple, intuitive calculations of free energy of binding for protein-ligand complexes. 3. The free energy contribution of structural water molecules in HIV-1 protease complexes.

Structural water molecules within protein active sites are relevant for ligand-protein recognition because they modify the active site geometry and contribute to binding affinity. In this work an analysis of the interactions between 23 ligands and dimeric HIV-1 protease is reported. The X-ray structures of these complexes show the presence of four types of structural water molecules: water 301 (on the symmetry axis), water 313, water 313bis, and peripheral waters. Except for water 301, these are generally complemented with a symmetry-related set. The GRID program was used both for checking water locations and for placing water molecules that appear to be missing from the complexes due to crystallographic uncertainty. Hydropathic analysis of the energetic contributions using HINT indicates a significant improvement of the correlation between HINT scores and the experimentally determined binding constants when the appropriate bridging water molecules are taken into account. In the absence of water r2 = 0.30 with a standard error of +/- 1.30 kcal mol(-1) and when the energetic contributions of the constrained waters are included r2 = 0.61 with a standard error of +/- 0.98 kcal mol(-1). HINT was shown to be able to map quantitatively the contribution of individual structural waters to binding energy. The order of relevance for the various types of water is water 301 > water 313 > water 313bis > peripheral waters. Thus, to obtain the most reliable free energy predictions, the contributions of structural water molecules should be included. However, care must be taken to include the effects of water molecules that add information value and not just noise.

Getting it right: modeling of pH, solvent and "nearly" everything else in virtual screening of biological targets.

"Getting it right" refers to the careful modeling of all elements in the living system, i.e. biological macromolecules, ligands and water molecules. In addition, careful attention should be paid to the protonation state of ionizable functional groups on the ligands and residues at the active site. Computational technology based on the empirical HINT program is described to: (1) calculate free energy scores for ligand binding; (2) include the implicit and explicit effects of water in and around the ligand binding site; and (3) incorporate the effects of global and local pH in molecular models. This last point argues for the simultaneous consideration of a number of molecular models, each with different protonation profiles. Data from recent studies of protein-ligand systems (trypsin, thrombin, neuraminidase, HIV-1 protease and others) are used to illustrate the concepts in the paper. Also discussed are experimental factors related to accurate free energy predictions with this and other computational technologies.

Simple, intuitive calculations of free energy of binding for protein-ligand complexes. 2. Computational titration and pH effects in molecular models of neuraminidase-inhibitor complexes.

One factor that can strongly influence predicted free energy of binding is the ionization state of functional groups on the ligands and at the binding site at which calculations are performed. This analysis is seldom performed except in very detailed computational simulations. In this work, we address the issues of (i) modeling the complexity resulting from the different ionization states of ligand and protein residues involved in binding, (ii) if, and how, computational methods can evaluate the pH dependence of ligand inhibition constants, and (iii) how to score the protonation-dependent models. We developed a new and fairly rapid protocol called "computational titration" that enables parallel modeling of multiple ionization ensembles for each distinct protonation level. Models for possible protonation combinations for site/ligand ionizable groups are built, and the free energy of interaction for each of them is quantified by the HINT (Hydropathic INTeractions) software. We applied this procedure to the evaluation of the binding affinity of nine inhibitors (six derived from 2,3-didehydro-2-deoxy-N-acetylneuraminic acid, DANA) of influenza virus neuraminidase (NA), a surface glycoprotein essential for virus replication and thus a pharmaceutically relevant target for the design of anti-influenza drugs. The three-dimensional structures of the NA enzyme-inhibitor complexes indicate considerable complexity as the ligand-protein recognition site contains several ionizable moieties. Each computational titration experiment reveals a peak HINT score as a function of added protons. This maximum HINT score indicates the optimum pH (or the optimum protonation state of each inhibitor-protein binding site) for binding. The pH at which inhibition is measured and/or crystals were grown and analyzed can vary from this optimum. A protonation model is proposed for each ligand that reconciles the experimental complex structure with measured inhibition and the free energy of binding. Computational titration methods allow us to analyze the effect of pH in silico and may be helpful in improving ligand binding free energy prediction when protonation or deprotonation of the residues or ligand functional groups at the binding site might be significant.

Simple, intuitive calculations of free energy of binding for protein-ligand complexes. 1. Models without explicit constrained water.

The prediction of the binding affinity between a protein and ligands is one of the most challenging issues for computational biochemistry and drug discovery. While the enthalpic contribution to binding is routinely available with molecular mechanics methods, the entropic contribution is more difficult to estimate. We describe and apply a relatively simple and intuitive calculation procedure for estimating the free energy of binding for 53 protein-ligand complexes formed by 17 proteins of known three-dimensional structure and characterized by different active site polarity. HINT, a software model based on experimental LogP(o/w) values for small organic molecules, was used to evaluate and score all atom-atom hydropathic interactions between the protein and the ligands. These total scores (H(TOTAL)), which have been previously shown to correlate with DeltaG(interaction) for protein-protein interactions, correlate with DeltaG(binding) for protein-ligand complexes in the present study with a standard error of +/-2.6 kcal mol(-1) from the equation DeltaG(binding) = -0.001 95 H(TOTAL) - 5.543. A more sophisticated model, utilizing categorized (by interaction class) HINT scores, produces a superior standard error of +/-1.8 kcal mol(-1). It is shown that within families of ligands for the same protein binding site, better models can be obtained with standard errors approaching +/-1.0 kcal mol(-1). Standardized methods for preparing crystallographic models for hydropathic analysis are also described. Particular attention is paid to the relationship between the ionization state of the ligands and the pH conditions under which the binding measurements are made. Sources and potential remedies of experimental and modeling errors affecting prediction of DeltaG(binding) are discussed.