Combining structural and coevolution information to unveil allosteric sites.

Understanding allosteric regulation in biomolecules is of great interest to pharmaceutical research and computational methods emerged during the last decades to characterize allosteric coupling. However, the prediction of allosteric sites in a protein structure remains a challenging task. Here, we integrate local binding site information, coevolutionary information, and information on dynamic allostery into a structure-based three-parameter model to identify potentially hidden allosteric sites in ensembles of protein structures with orthosteric ligands. When tested on five allosteric proteins (LFA-1, p38-α, GR, MAT2A, and BCKDK), the model successfully ranked all known allosteric pockets in the top three positions. Finally, we identified a novel druggable site in MAT2A confirmed by X-ray crystallography and SPR and a hitherto unknown druggable allosteric site in BCKDK validated by biochemical and X-ray crystallography analyses. Our model can be applied in drug discovery to identify allosteric pockets.

pIChemiSt ─ Free Tool for the Calculation of Isoelectric Points of Modified Peptides.

The isoelectric point (pI) is a fundamental physicochemical property of peptides and proteins. It is widely used to steer design away from low solubility and aggregation and guide peptide separation and purification. Experimental measurements of pI can be replaced by calculations knowing the ionizable groups of peptides and their corresponding pKa values. Different pKa sets are published in the literature for natural amino acids, however, they are insufficient to describe synthetically modified peptides, complex peptides of natural origin, and peptides conjugated with structures of other modalities. Noncanonical modifications (nCAAs) are ignored in the conventional sequence-based pI calculations, therefore producing large errors in their pI predictions. In this work, we describe a pI calculation method that uses the chemical structure as an input, automatically identifies ionizable groups of nCAAs and other fragments, and performs pKa predictions for them. The method is validated on a curated set of experimental measures on 29 modified and 119093 natural peptides, providing an improvement of R2 from 0.74 to 0.95 and 0.96 against the conventional sequence-based approach for modified peptides for the two studied pKa prediction tools, ACDlabs and pKaMatcher, correspondingly. The method is available in the form of an open source Python library at https://github.com/AstraZeneca/peptide-tools, which can be integrated into other proprietary and free software packages. We anticipate that the pI calculation tool may facilitate optimization and purification activities across various application domains of peptides, including the development of biopharmaceuticals.

Unraveling the Allosteric Cross-Talk between the Coactivator Peptide and the Ligand-Binding Site in the Glucocorticoid Receptor.

The glucocorticoid receptor (GR) is a nuclear receptor that controls critical biological processes by regulating the transcription of specific genes. There is a known allosteric cross-talk between the ligand and coregulator binding sites within the GR ligand-binding domain that is crucial for the control of the functional response. However, the molecular mechanisms underlying such an allosteric control remain elusive. Here, molecular dynamics (MD) simulations, bioinformatic analysis, and biophysical measurements are integrated to capture the structural and dynamic features of the allosteric cross-talk within the GR. We identified a network of evolutionarily conserved residues that enables the allosteric signal transduction, in agreement with experimental data. MD simulations clarify how such a network is dynamically interconnected and offer a mechanistic explanation of how different peptides affect the intensity of the allosteric signal. This study provides useful insights to elucidate the GR allosteric regulation, ultimately providing a foundation for designing novel drugs.

Adapting free energy perturbation simulations for large macrocyclic ligands: how to dissect contributions from direct binding and free ligand flexibility.

Large and flexible ligands gain increasing interest in the development of bioactive agents. They challenge the applicability of computational ligand optimization strategies originally developed for small molecules. Free energy perturbation (FEP) is often used for predicting binding affinities of small molecule ligands, however, its use for more complex ligands remains limited. Herein, we report the structure-based design of peptide macrocycles targeting the protein binding site of human adaptor protein 14-3-3. We observe a surprisingly strong dependency of binding affinities on relatively small variations in substituent size. FEP was performed to rationalize observed trends. To account for insufficient convergence of FEP, restrained calculations were performed and complemented with extensive REST MD simulations of the free ligands. These calculations revealed that changes in affinity originate both from altered direct interactions and conformational changes of the free ligand. In addition, MD simulations provided the basis to rationalize unexpected trends in ligand lipophilicity. We also verified the anticipated interaction site and binding mode for one of the high affinity ligands by X-ray crystallography. The introduced fully-atomistic simulation protocol can be used to rationalize the development of structurally complex ligands which will support future ligand maturation efforts.

Therapeutic Potential of Foldamers: From Chemical Biology Tools To Drug Candidates?

Over the past decade, foldamers have progressively emerged as useful architectures to mimic secondary structures of proteins. Peptidic foldamers, consisting of various amino acid based backbones, have been the most studied from a therapeutic perspective, while polyaromatic foldamers have barely evolved from their nascency and remain perplexing for medicinal chemists due to their poor drug-like nature. Despite these limitations, this compound class may still offer opportunities to study challenging targets or provide chemical biology tools. The potential of foldamer drug candidates reaching the clinic is still a stretch. Nevertheless, advances in the field have demonstrated their potential for the discovery of next generation therapeutics. In this perspective, the current knowledge of foldamers is reviewed in a drug discovery context. Recent advances in the early phases of drug discovery including hit finding, target validation, and optimization and molecular modeling are discussed. In addition, challenges and focus areas are debated and gaps highlighted.

Accurate Calculation of Solvation Free Energies in Supercritical Fluids by Fully Atomistic Simulations: Probing the Theory of Solutions in Energy Representation.

Accurate calculation of solvation free energies (SFEs) is a fundamental problem of theoretical chemistry. In this work we perform a careful validation of the theory of solutions in energy representation (ER method) developed by Matubayasi et al. [J. Chem. Phys. 2000, 113, 6070-6081] for SFE calculations in supercritical solvents. This method can be seen as a bridge between the molecular simulations and the classical (not quantum) density functional theory (DFT) formulated in energy representation. We performed extensive calculations of SFEs of organic molecules of different chemical natures in pure supercritical CO2 (sc-CO2) and in sc-CO2 with addition of 6 mol % of ethanol, acetone, and n-hexane as cosolvents. We show that the ER method reproduces SFE data calculated by a method free of theoretical approximations (the Bennett's acceptance ratio) with the mean absolute error of only 0.05 kcal/mol. However, the ER method requires by an order less computational resources. Also, we show that the quality of ER calculations should be carefully monitored since the lack of sampling can result into a considerable bias in predictions. The present calculations reproduce the trends in the cosolvent-induced solubility enhancement factors observed in experimental data. Thus, we think that molecular simulations coupled with the ER method can be used for quick calculations of the effect of variation of temperature, pressure, and cosolvent concentration on SFE and hence solubility of bioactive compounds in supercritical fluids. This should dramatically reduce the burden of experimental work on optimizing solvency of supercritical solvents.

Prediction of cosolvent effect on solvation free energies and solubilities of organic compounds in supercritical carbon dioxide based on fully atomistic molecular simulations.

The solubility of organic compounds in supercritical fluids can be dramatically affected by addition of a suitable cosolvent (entrainer) at small concentrations. This makes the screening of the best-suited cosolvent an important task for the supercritical technology. The present study aims to improve our fundamental understanding of solvation in supercritical CO2 with cosolvents. We address the following questions: (1) How does the solvation free energy depend on the chemical class of an organic solute and the chemical nature of co-solvents? (2) Which intermolecular interactions determine the effect of a cosolvent on the solubility of organic compounds? We performed extensive calculations of solvation free energies of monofunctional organic molecules at infinite dilution in supercritical media by the Bennett's acceptance ratio method based on fully atomistic molecular dynamics sampling. Sixteen monofunctional organic molecules were solvated in pure sc-CO2 and sc-CO2 with addition of 6 molar % of cosolvents of different chemical nature: ethanol, acetone, and n-hexane. Cosolvent-induced solubility enhancement (CISE) factors were also calculated. It was found that formation of significant number of hydrogen bonds between a solute and cosolvent molecules leads to a profound solubility enhancement. The cosolvent effect is proportional to the number of hydrogen bonds. When polar cosolvents do not form hydrogen bonds with solutes, the CISE correlates with the dipole moment of solute molecules. However, the electrostatic interactions have a small impact on the solubility enhancement compared to hydrogen bonding. Addition of a nonpolar cosolvent, n-hexane, has a very little effect on the solvation Gibbs free energy of studied small organic molecules. The observed trends were discussed in line with available experimental data.

The study of correlations between hydrogen bonding characteristics in liquid, sub- and supercritical methanol. Molecular dynamics simulations and Raman spectroscopy analysis.

Molecular dynamics (MD) studies of hydrogen bonding (H-bonding) in liquid, sub- and supercritical methanol have been performed in a wide range of thermodynamic parameters of state, using various potential models and two H-bond criteria. It was shown that there is the universal correlation between the average number of H-bonds per molecule (n(HB)) and the mole fraction of H-bonded molecules (X(HB)) for the studied thermodynamic parameters of state. The same feature was observed for the correlations between fractions of molecules forming one (f1), two (f2), three (f3) H-bonds and X(HB). These correlations served to fit experimental Raman spectra of methanol recorded under sub- and supercritical conditions. The advantage of the approach used here is that f1, f2, f3 values have a clear physical meaning and are dependent on the values of state parameters.

Molecular-scale insights into the mechanisms of ionic liquids interactions with carbon nanotubes.

By means of fully atomistic molecular simulations we study basic mechanisms of carbon nanotube interactions with several different room temperature ionic liquids (RTILs) in their mixtures with acetonitrile. To understand the effects of the cation molecular geometry on the properties of the interface structure in the RTIL systems, we investigate a set of three RTILs with the same TFSI (bis (trifluoromethylsulfonyl)imide) anion but with different cations, namely, EMIm (1-ethyl-3-methylimidazolium), BMIm (1-butyl-3-methylimidazolium) and OMIm (1-octyl-3-methylimidazolium) ions. The cations have identical charged methylimidazolium 'heads' but different nonpolar alkyl 'tails' where the length of the tail increases from EMIm to OMIm. The analysis of the simulation data results in the following conclusions: There is an enrichment of all molecular components of ionic liquids under study at the CNT surface with formation of several distinct layers even at the non-charged CNT surface. Mixing RTIL with acetonitrile decreases ion-counterion correlations in the electric double layer. Increase of the length of the non-polar 'tail' of cations increases the propensity of imidazolium-based cations to lay parallel to the CNT surface. At the CNT cathode TFSI anions and molecular cations are preferentially oriented parallel to the surface. At the CNT anode the TFSI anions are oriented parallel to the surface, however the preferred orientations of cations depend on the length of non-polar tail: EMIm cations are oriented perpendicular to the surface, BMIm cations can be in both parallel as well as perpendicular orientations, OMIm cations are oriented parallel to the surface. As a result, by applying an electric potential on the CNT electrode and/or varying the structure of molecular ions it is possible to change molecular ion orientations at the surface and, consequently, the structure of the electrical double layer at the CNT-RTIL interface.

Salting out in organic solvents: a new route to carbon nanotube bundle engineering.

In this study we investigate salt effects on bundle formation of carbon nanotubes (CNTs) dispersed in an organic solvent, N-methyl-2-pyrrolidone (NMP). Addition of NaI salt leads to self-assembly of CNTs into well-recognizable bundles. It is possible to control the size of the CNT bundles by varying the salt concentration.

Toward a universal model to calculate the solvation thermodynamics of druglike molecules: the importance of new experimental databases.

We demonstrate that a new free energy functional in the integral equation theory of molecular liquids gives accurate calculations of hydration thermodynamics for druglike molecules. The functional provides an improved description of excluded volume effects by incorporating two free coefficients. When the values of these coefficients are obtained from experimental data for simple organic molecules, the hydration free energies of an external test set of druglike molecules can be calculated with an accuracy of about 1 kcal/mol. The 3D RISM/UC method proposed here is easily implemented using existing computational software and allows in silico screening of the solvation thermodynamics of potential pharmaceutical molecules at significantly lower computational expense than explicit solvent simulations.

Hydration thermodynamics using the reference interaction site model: speed or accuracy?

We report a method to dramatically improve the accuracy of hydration free energies (HFE) calculated by the 1D and 3D reference interaction site models (RISM) of molecular integral equation theory. It is shown that the errors in HFEs calculated by RISM approaches using the Gaussian fluctuations (GF) free energy functional are not random, but can be decomposed into linear combination of contributions from different structural elements of molecules (number of double bonds, number of OH groups, etc.). Therefore, by combining RISM/GF with cheminformatics, one can develop an accurate method for HFE prediction. We call this approach the structural description correction model (SDC) ( Ratkova et al. J. Phys. Chem. B 2010 , 114 , 12068 ). In this work, we investigated the prediction quality of the SDC model combined with 1D and 3D RISM approaches. In parallel, we analyzed the computational performance of these two methods. The SDC model parameters were obtained by fitting against a training set of 53 simple organic molecules. To demonstrate that the values of these parameters were transferable between different classes of molecules, the models were tested against 98 more complex molecules (including 38 polyfragment compounds). The results show that the 3D RISM/SDC model predicts the HFEs with very good accuracy (RMSE of 0.47 kcal/mol), while the 1D RISM approach provides only moderate accuracy (RMSE of 1.96 kcal/mol). However, a single 1D RISM/SDC calculation takes only a few seconds on a PC, whereas a single 3D RISM/SDC HFE calculation is approximately 100 times more computationally expensive. Therefore, we suggest that one should use the 1D RISM/SDC model for large-scale high-throughput screening of molecular hydration properties, while further refinement of these properties for selected compounds should be carried out with the more computationally expensive but more accurate 3D RISM/SDC model.

Ion interactions with the carbon nanotube surface in aqueous solutions: understanding the molecular mechanisms.

We study the molecular mechanisms of alkali halide ion interactions with the single-wall carbon nanotube surface in water by means of fully atomistic molecular dynamics simulations. We focus on the basic physical-chemical principles of ion-nanotube interactions in aqueous solutions and discuss them in light of recent experimental findings on selective ion effects on carbon nanotubes.

Towards a universal method for calculating hydration free energies: a 3D reference interaction site model with partial molar volume correction.

We report a simple universal method to systematically improve the accuracy of hydration free energies calculated using an integral equation theory of molecular liquids, the 3D reference interaction site model. A strong linear correlation is observed between the difference of the experimental and (uncorrected) calculated hydration free energies and the calculated partial molar volume for a data set of 185 neutral organic molecules from different chemical classes. By using the partial molar volume as a linear empirical correction to the calculated hydration free energy, we obtain predictions of hydration free energies in excellent agreement with experiment (R = 0.94, σ = 0.99 kcal mol (- 1) for a test set of 120 organic molecules).