Nadine Homeyer

MMPBSA.py: An Efficient Program for End-State Free Energy Calculations

MM-PBSA is a post-processing end-state method to calculate free energies of molecules in solution. MMPBSA.py is a program written in Python for streamlining end-state free energy calculations using ensembles derived from molecular dynamics (MD) or Monte Carlo (MC) simulations. Several implicit solvation models are available with MMPBSA.py, including the Poisson-Boltzmann Model, the Generalized Born Model, and the Reference Interaction Site Model. Vibrational frequencies may be calculated using normal mode or quasi-harmonic analysis to approximate the solute entropy. Specific interactions can also be dissected using free energy decomposition or alanine scanning. A parallel implementation significantly speeds up the calculation by dividing frames evenly across available processors. MMPBSA.py is an efficient, user-friendly program with the flexibility to accommodate the needs of users performing end-state free energy calculations. The source code can be downloaded at http://ambermd.org/ with AmberTools, released under the GNU General Public License.

Free Energy Calculations by the Molecular Mechanics Poisson−Boltzmann Surface Area Method

Detailed knowledge of how molecules recognize interaction partners and of the conformational preferences of biomacromolecules is pivotal for understanding biochemical processes. Such knowledge also provides the foundation for the design of novel molecules, as undertaken in pharmaceutical research. Computer‐based free energy calculations enable a detailed investigation of the energetic factors that are responsible for molecular stability or binding affinity. The Molecular Mechanics Poisson–Boltzmann Surface Area (MM‐PBSA) approach is an efficient method for the calculation of free energies of diverse molecular systems. Here we describe the concepts of this approach and outline the practical proceeding. Furthermore we give an overview of the wide spectrum of problems that have been addressed with this method and of successful analyses carried out, thereby focussing on ambitious and recent studies. Limits of the approach in terms of accuracy and applicability are discussed. Despite these limitations MM‐PBSA is a method with great potential that allows comparative free energy analyses for various molecular systems at low computational cost.

FEW: A workflow tool for free energy calculations of ligand binding

In the later stages of drug design projects, accurately predicting relative binding affinities of chemically similar compounds to a biomolecular target is of utmost importance for making decisions based on the ranking of such compounds. So far, the extensive application of binding free energy approaches has been hampered by the complex and time‐consuming setup of such calculations. We introduce the free energy workflow (FEW) tool that facilitates setup and execution of binding free energy calculations with the AMBER suite for multiple ligands. FEW allows performing free energy calculations according to the implicit solvent molecular mechanics (MM‐PB(GB)SA), the linear interaction energy, and the thermodynamic integration approaches. We describe the tools architecture and functionality and demonstrate in a show case study on Factor Xa inhibitors that the time needed for the preparation and analysis of free energy calculations is considerably reduced with FEW compared to a fully manual procedure.

Binding Free Energy Calculations for Lead Optimization: Assessment of Their Accuracy in an Industrial Drug Design Context

Correctly ranking compounds according to their computed relative binding affinities will be of great value for decision making in the lead optimization phase of industrial drug discovery. However, the performance of existing computationally demanding binding free energy calculation methods in this context is largely unknown. We analyzed the performance of the molecular mechanics continuum solvent, the linear interaction energy (LIE), and the thermodynamic integration (TI) approach for three sets of compounds from industrial lead optimization projects. The data sets pose challenges typical for this early stage of drug discovery. None of the methods was sufficiently predictive when applied out of the box without considering these challenges. Detailed investigations of failures revealed critical points that are essential for good binding free energy predictions. When data set-specific features were considered accordingly, predictions valuable for lead optimization could be obtained for all approaches but LIE. Our findings lead to clear recommendations for when to use which of the above approaches. Our findings also stress the important role of expert knowledge in this process, not least for estimating the accuracy of prediction results by TI, using indicators such as the size and chemical structure of exchanged groups and the statistical error in the predictions. Such knowledge will be invaluable when it comes to the question which of the TI results can be trusted for decision making.

Extension of the free energy workflow FEW towards implicit solvent/implicit membrane MM-PBSA calculations.

BACKGROUND The number of high-resolution structures of pharmacologically relevant membrane proteins has been strongly increasing. This makes computing relative affinities of chemically similar compounds binding to a membrane protein possible in order to guide decision making in drug design. However, the preparation step of such calculations is time-consuming and complex. METHODS We extended the free energy workflow tool FEW, available in AMBER, towards facilitating the setup of molecular dynamics simulations with explicit membrane, and the setup and execution of effective binding energy calculations according to a 1-trajectory implicit solvent/implicit membrane MM-PBSA approach for multiple ligands binding to the same membrane protein. RESULTS We validated the implemented protocol initially on two model systems, a sodium ion in the presence of an implicit membrane slab and a proton traversing the M2 proton-channel of the influenza A virus. For the latter, we found a good agreement for several important events along the proton pathway with those obtained in a recent computational study. Finally, we performed a case study on effective binding energy calculations for a set of inhibitors binding to the M2 proton-channel. CONCLUSIONS From the case study, we estimate a considerable speed up in the setup and analysis times for implicit solvent/implicit membrane MM-PBSA calculations by the extended version of FEW compared to a manual preparation. GENERAL SIGNIFICANCE Together with the overall runtime and the analysis results, this suggests that such type of calculations can be valuable in later stages of drug design projects on membrane proteins. This article is part of a Special Issue entitled Recent developments of molecular dynamics.

Molecular Mechanisms of Glutamine Synthetase Mutations that Lead to Clinically Relevant Pathologies

Glutamine synthetase (GS) catalyzes ATP-dependent ligation of ammonia and glutamate to glutamine. Two mutations of human GS (R324C and R341C) were connected to congenital glutamine deficiency with severe brain malformations resulting in neonatal death. Another GS mutation (R324S) was identified in a neurologically compromised patient. However, the molecular mechanisms underlying the impairment of GS activity by these mutations have remained elusive. Molecular dynamics simulations, free energy calculations, and rigidity analyses suggest that all three mutations influence the first step of GS catalytic cycle. The R324S and R324C mutations deteriorate GS catalytic activity due to loss of direct interactions with ATP. As to R324S, indirect, water-mediated interactions reduce this effect, which may explain the suggested higher GS residual activity. The R341C mutation weakens ATP binding by destabilizing the interacting residue R340 in the apo state of GS. Additionally, the mutation is predicted to result in a significant destabilization of helix H8, which should negatively affect glutamate binding. This prediction was tested in HEK293 cells overexpressing GS by dot-blot analysis: Structural stability of H8 was impaired through mutation of amino acids interacting with R341, as indicated by a loss of masking of an epitope in the glutamate binding pocket for a monoclonal anti-GS antibody by L-methionine-S-sulfoximine; in contrast, cells transfected with wild type GS showed the masking. Our analyses reveal complex molecular effects underlying impaired GS catalytic activity in three clinically relevant mutants. Our findings could stimulate the development of ATP binding-enhancing molecules by which the R324S mutant can be repaired extrinsically.

Determinants of the species selectivity of oxazolidinone antibiotics targeting the large ribosomal subunit

Abstract Oxazolidinone antibiotics bind to the highly conserved peptidyl transferase center in the ribosome. For developing selective antibiotics, a profound understanding of the selectivity determinants is required. We have performed for the first time technically challenging molecular dynamics simulations in combination with molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) free energy calculations of the oxazolidinones linezolid and radezolid bound to the large ribosomal subunits of the eubacterium Deinococcus radiodurans and the archaeon Haloarcula marismortui. A remarkably good agreement of the computed relative binding free energy with selectivity data available from experiment for linezolid is found. On an atomic level, the analyses reveal an intricate interplay of structural, energetic, and dynamic determinants of the species selectivity of oxazolidinone antibiotics: A structural decomposition of free energy components identifies influences that originate from first and second shell nucleotides of the binding sites and lead to (opposing) contributions from interaction energies, solvation, and entropic factors. These findings add another layer of complexity to the current knowledge on structure-activity relationships of oxazolidinones binding to the ribosome and suggest that selectivity analyses solely based on structural information and qualitative arguments on interactions may not reach far enough. The computational analyses presented here should be of sufficient accuracy to fill this gap.

Alchemical Free Energy Calculations and Isothermal Titration Calorimetry Measurements of Aminoadamantanes Bound to the Closed State of Influenza A/M2TM

Adamantane derivatives, such as amantadine and rimantadine, have been reported to block the transmembrane domain (TM) of the M2 protein of influenza A virus (A/M2) but their clinical use has been discontinued due to evolved resistance in humans. Although experiments and simulations have provided adequate information about the binding interaction of amantadine or rimantadine to the M2 protein, methods for predicting binding affinities of whole series of M2 inhibitors have so far been scarcely applied. Such methods could assist in the development of novel potent inhibitors that overcome A/M2 resistance. Here we show that alchemical free energy calculations of ligand binding using the Bennett acceptance ratio (BAR) method are valuable for determining the relative binding potency of A/M2 inhibitors of the aminoadamantane type covering a binding affinity range of only ∼2 kcal mol(-1). Their binding affinities measured by isothermal titration calorimetry (ITC) against the A/M2TM tetramer from the Udorn strain in its closed form at pH 8 were used as experimental probes. The binding constants of rimantadine enantiomers against M2TMUdorn were measured for the first time and found to be equal. Two series of alchemical free energy calculations were performed using 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) and 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) lipids to mimic the membrane environment. A fair correlation was found for DPPC that was significantly improved using DMPC, which resembles more closely the DPC lipids used in the ITC experiments. This demonstrates that binding free energy calculations by the BAR approach can be used to predict relative binding affinities of aminoadamantane derivatives toward M2TM with good accuracy.

Complex long-distance effects of mutations that confer linezolid resistance in the large ribosomal subunit.

The emergence of multidrug-resistant pathogens will make current antibiotics ineffective. For linezolid, a member of the novel oxazolidinone class of antibiotics, 10 nucleotide mutations in the ribosome have been described conferring resistance. Hypotheses for how these mutations affect antibiotics binding have been derived based on comparative crystallographic studies. However, a detailed description at the atomistic level of how remote mutations exert long-distance effects has remained elusive. Here, we show that the G2032A-C2499A double mutation, located > 10 Å away from the antibiotic, confers linezolid resistance by a complex set of effects that percolate to the binding site. By molecular dynamics simulations and free energy calculations, we identify U2504 and C2452 as spearheads among binding site nucleotides that exert the most immediate effect on linezolid binding. Structural reorganizations within the ribosomal subunit due to the mutations are likely associated with mutually compensating changes in the effective energy. Furthermore, we suggest two main routes of information transfer from the mutation sites to U2504 and C2452. Between these, we observe cross-talk, which suggests that synergistic effects observed for the two mutations arise in an indirect manner. These results should be relevant for the development of oxazolidinone derivatives that are active against linezolid-resistant strains.

Glutamine synthetase mutations that cause glutamine deficiency: mechanistic insights from molecular dynamics simulations

Glutamine synthetase (GS) is a key enzyme in nitrogen storage and metabolism as it catalyzes the ligation of glutamate and ammonia to glutamine with the help of ATP [1]. The specific function of GS depends on its localization: In astrocytes in brain tissue, GS is part of the glutamate-glutamine cycling, that way detoxifying cytotoxic ammonia and neurotoxic glutamate by conversion to glutamine; several links between the loss of GS activity and neurological disorders such as Alzheimer’s disease and epilepsy have been described. In liver tissue, GS plays an important role in eliminating ammonia; a loss of GS activity there leads to hyperammonemia, the main trigger of hepatic encephalopathy. Two mutations in GS (R324C and R341C) have been linked to congenital glutamine deficiency with severe brain malformations resulting in neonatal death [2]. In a single case known to date, another GS mutation (R324S) was identified in a boy, now five years old, who is neurologically compromised [3]. So far, the molecular mechanisms of these mutations on GS deactivation have not been understood. We performed molecular dynamics (MD) simulations of human wild type GS (wtGS) and the GS mutants R324C, R324S, and R341C in order to reveal the molecular mechanisms of GS deactivation. For the wtGS and the three mutants, four different states each of the enzymatic process were simulated for a length of 100 ns, resulting in a total simulation time of 1.6 µs for systems of ~35,000 atoms. In mammals GS is a homodecamer formed by two pentameric rings, with the active sites located at the interfaces between two dimers (Figure 1A, B). To reduce computational costs, we performed the MD simulations on dimers of GS only. Initial tests on wtGS showed that this leads to similar structural and dynamics features in the active site as observed for the decamer. Figure 1 Structure of human GS (PDB entry: 2QC8 [1]) in cartoon representation; (A) top view, (B) side view. Each subunit is colored differently