RE-EDS Using GAFF Topologies: Application to Relative Hydration Free-Energy Calculations for Large Sets of Molecules

Free-energy differences between pairs of end-states can be estimated based on molecular dynamics (MD) simulations using standard pathway-dependent methods such as thermodynamic integration (TI), free-energy perturbation, or Bennett's acceptance ratio. Replica-exchange enveloping distribution sampling (RE-EDS), on the other hand, allows for the sampling of multiple end-states in a single simulation without the specification of any pathways. In this work, we use the RE-EDS method as implemented in GROMOS together with generalized AMBER force field (GAFF) topologies, converted to a GROMOS-compatible format with a newly developed GROMOS++ program amber2gromos, to compute relative hydration free energies for a series of benzene derivatives. The results obtained with RE-EDS are compared to the experimental data as well as calculated values from the literature. In addition, the estimated free-energy differences in water and in vacuum are compared to values from TI calculations carried out with GROMACS. The hydration free energies obtained using RE-EDS for multiple molecules are found to be in good agreement with both the experimental data and the results calculated using other free-energy methods. While all considered free-energy methods delivered accurate results, the RE-EDS calculations required the least amount of total simulation time. This work serves as a validation for the use of GAFF topologies with the GROMOS simulation package and the RE-EDS approach. Furthermore, the performance of RE-EDS for a large set of 28 end-states is assessed with promising results

eTOX ALLIES:an automated pipeLine for linear interaction energy-based simulations

Abstract Background Computational methods to predict binding affinities of small ligands toward relevant biological (off-)targets are helpful in prioritizing the screening and synthesis of new drug candidates, thereby speeding up the drug discovery process. However, use of ligand-based approaches can lead to erroneous predictions when structural and dynamic features of the target substantially affect ligand binding. Free energy methods for affinity computation can include steric and electrostatic protein–ligand interactions, solvent effects, and thermal fluctuations, but often they are computationally demanding and require a high level of supervision. As a result their application is typically limited to the screening of small sets of compounds by experts in molecular modeling. Results We have developed eTOX ALLIES, an open source framework that allows the automated prediction of ligand-binding free energies requiring the ligand structure as only input. eTOX ALLIES is based on the linear interaction energy approach, an efficient end-point free energy method derived from Free Energy Perturbation theory. Upon submission of a ligand or dataset of compounds, the tool performs the multiple steps required for binding free-energy prediction (docking, ligand topology creation, molecular dynamics simulations, data analysis), making use of external open source software where necessary. Moreover, functionalities are also available to enable and assist the creation and calibration of new models. In addition, a web graphical user interface has been developed to allow use of free-energy based models to users that are not an expert in molecular modeling. Conclusions Because of the user-friendliness, efficiency and free-software licensing, eTOX ALLIES represents a novel extension of the toolbox for computational chemists, pharmaceutical scientists and toxicologists, who are interested in fast affinity predictions of small molecules toward biological (off-)targets for which protein flexibility, solvent and binding site interactions directly affect the strength of ligand-protein binding

Effects of gabergic phenols on the dynamic and structure of lipid bilayers: A molecular dynamic simulation approach

γ-Aminobutyric acid (GABA) is the major inhibitory neurotransmitter in the vertebrate and invertebrate nervous system. GABAA receptors are activated by GABA and their agonists, and modulated by a wide variety of recognized drugs, including barbiturates, anesthetics, and benzodiazepines. The phenols propofol, thymol, chlorothymol, carvacrol and eugenol act as positive allosteric modulators on GABAA-R receptor. These GABAergic phenols interact with the lipid membrane, therefore, their anesthetic activity could be the combined result of their specific activity (with receptor proteins) as well as nonspecific interactions (with surrounding lipid molecules) modulating the supramolecular organization of the receptor environment. Therefore, we aimed to contribute to a description of the molecular events that occur at the membrane level as part of the mechanism of general anesthesia, using a molecular dynamic simulation approach. Equilibrium molecular dynamics simulations indicate that the presence of GABAergic phenols in a DPPC bilayer orders lipid acyl chains for carbons near the interface and their effect is not significant at the bilayer center. Phenols interacts with the polar interface of phospholipid bilayer, particularly forming hydrogen bonds with the glycerol and phosphate group. Also, potential of mean force calculations using umbrella sampling show that propofol partition is mainly enthalpic driven at the polar region and entropic driven at the hydrocarbon chains. Finally, potential of mean force indicates that propofol partition into a gel DPPC phase is not favorable. Our in silico results were positively contrasted with previous experimental data.Fil: Miguel, Virginia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Biológicas y Tecnológicas. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Instituto de Investigaciones Biológicas y Tecnológicas; ArgentinaFil: Villarreal, Marcos Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Físico-química de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Investigaciones en Físico-química de Córdoba; ArgentinaFil: Garcia, Daniel Asmed. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Biológicas y Tecnológicas. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Instituto de Investigaciones Biológicas y Tecnológicas; Argentin

PyBindE: Development of a Simple Python MM-PBSA Implementation for Estimating Protein-Protein and Protein-Ligand Binding Energies

Tese de mestrado, Bioquímica (Bioquímica), Universidade de Lisboa, Faculdade de Ciências, 2022Given the importance of proteins, the study of their interactions and binding affinities has been one of the most broadly populated fields of research for many years. Many approaches exist to calculate protein-protein and protein-ligand binding free energies, with single-trajectory MM-PBSA being a pop ular choice due to its more rigorous theoretical framework, when compared with methods, such as molec ular docking, while still possessing reasonable speed. MM-PBSA is particularly useful when the relative energy differences between system configurations are concerned, being able to provide insights about the forces involved in the binding process and their energetic contribution. In the present work, we describe a newly developed, DelPhi-based, single-trajectory MM-PBSA im plementation (PyBindE) written in Python, designed to be compatible with GROMOS force fields. A validation of this method was performed using a set of 37 HIV-1 protease-inhibitor complexes with experimentally-determined inhibition constants. These systems were also used as a validation set for g_mmpbsa, one widely used MM-PBSA implementation, originally validated using AMBER, thus com parisons with this method can be drawn. Molecular dynamics (MD) simulations of 150 ns were run in triplicate for every system, and MM-PBSA calculations were performed on the full trajectories, in 1500 snapshots per replicate. For 9 of the systems used for validation, the ligands of these systems con tained amine groups with pKa values ( 9) above physiological pH, and as such, different protonation scenarios for the ligands and the catalytic aspartate residues (Asp-25) were also explored. Furthermore, the impact of different values of the solute dielectric constant, on the correlation with experimental data, was studied for all different protonation cases. A practical application of PyBindE is also presented for the case of β-2 Microglobulin (β2M) D76N mutants, the causing-agents of a fatal form of amyloidosis. MM-PBSA was used to study the binding of 212 dimers derived from a Monte-Carlo Ensemble Docking protocol, determining the forces responsible for their binding and aggregation, and ranking the most stable binding modes. MM-PBSA calculations were run on 100 ns of MD trajectory for each dimer. Results of the comparison with g_mmpbsa are also analysed. Our validation results show an adequate correlation, 0.56, with experimental data when the correct ligand and catalytic aspartate residue protonations are employed, with a dielectric constant of 8. We found that underestimating the polar solvation contribution to the binding free energy resulted in an improvement of the correlations with our method, suggesting the need to optimize our parameterization and/or polar solvation calculation procedures. Regardless, our correlation results are higher than those reported for many standard MM-PBSA methods, with minimal parameter tweaking. The usefulness of PybindE was also highlighted in the calculation of binding free energies for β2M dimers. This method allowed the distinction of several binding modes from which different oligomerization patterns were then predicted. Overall, the results using PyBindE for the study of protein-protein binding affinities revealed a higher accuracy than g_mmpbsa, that often predicted positive binding energies suggesting unbinding events, which were not observed in the MD simulations

Grid-based state space exploration for molecular binding

Binding processes are difficult to sample with molecular-dynamics (MD) simulations. In particular, the state space exploration is often incomplete. Evaluating the molecular interaction energy on a grid circumvents this problem but is heavily limited by state space dimensionality. Here, we make the first steps towards a low-dimensional grid-based model of molecular binding. We discretise the state space of relative positions and orientations of the two molecules under the rigid body assumption.The corresponding program is published as the Python package molgri. For the rotational component of the grids, we test algorithms based on Euler angles, polyhedra and quaternions, of which the polyhedra-based are the most uniform. The program outputs a sequence of molecular structures that can be easily processed by standard MD programs to calculate grid point energies. We demonstrate the grid-based approach on two molecular systems: a water dimer and a coiled-coil protein interacting with a chloride anion. For the second system we relax the rigid-body assumption and improve the accuracy of the grid point energies by an energy minimisation. In both cases, oriented bonding patterns and energies confirm expectations from chemical intuition and MD simulations. We also demonstrate how analysis of energy contributions on a grid can be performed and demonstrate that electrostatically-driven association is sufficiently resolved by point-energy calculations. Overall, grid-based models of molecular binding are potentially a powerful complement to molecular sampling approaches, and we see the potential to expand the method to quantum chemistry and flexible docking applications.Comment: 13 pages, 7 figure

Computational Algorithms for Predicting Membrane Protein Assembly From Angstrom to Micron Scale

Biological barriers in the human body are one of the most crucial interfaces perfected through evolution for diverse and unique functions. Of the wide range of barriers, the paracellular protein interfaces of epithelial and endothelial cells called tight junctions with high molecular specificities are vital for homeostasis and to maintain proper health. While the breakdown of these barriers is associated with serious pathological consequences, their intact presence also poses a challenge to effective delivery of therapeutic drugs. Complimenting a rigorous combination of in vitro and in vivo approaches to establishing the fundamental biological construct, in addition to elucidating pathological implications and pharmaceutical interests, a systematic in silico approach is undertaken in this work in order to complete the molecular puzzle of the tight junctions. This work presents a bottom-up approach involving a careful consideration of protein interactions with Angstrom-level details integrated systematically, based on the principles of statistical thermodynamics and probabilities and designed using well-structured computational algorithms, up to micron-level molecular architecture of tight junctions, forming a robust prediction with molecular details packed for up to four orders of magnitude in length scale. This work is intended to bridge the gap between the computational nano-scale studies and the experimental micron-scale observations and provide a molecular explanation for cellular behaviors in the maintenance, and the adverse consequences of breakdown of these barriers. Furthermore, a comprehensive understanding of tight junctions shall enable development of safe strategies for enhanced delivery of therapeutics

Carbohydrate-derived amphiphilic macromolecules: a biophysical structural characterization and analysis of binding behaviors to model membranes.

The design and synthesis of enhanced membrane-intercalating biomaterials for drug delivery or vascular membrane targeting is currently challenged by the lack of screening and prediction tools. The present work demonstrates the generation of a Quantitative Structural Activity Relationship model (QSAR) to make a priori predictions. Amphiphilic macromolecules (AMs) "stealth lipids" built on aldaric and uronic acids frameworks attached to poly(ethylene glycol) (PEG) polymer tails were developed to form self-assembling micelles. In the present study, a defined set of novel AM structures were investigated in terms of their binding to lipid membrane bilayers using Quartz Crystal Microbalance with Dissipation (QCM-D) experiments coupled with computational coarse-grained molecular dynamics (CG MD) and all-atom MD (AA MD) simulations. The CG MD simulations capture the insertion dynamics of the AM lipophilic backbones into the lipid bilayer with the PEGylated tail directed into bulk water. QCM-D measurements with Voigt viscoelastic model analysis enabled the quantitation of the mass gain and rate of interaction between the AM and the lipid bilayer surface. Thus, this study yielded insights about variations in the functional activity of AM materials with minute compositional or stereochemical differences based on membrane binding, which has translational potential for transplanting these materials in vivo. More broadly, it demonstrates an integrated computational-experimental approach, which can offer a promising strategy for the in silico design and screening of therapeutic candidate materials