Exploring the Activity Profile of TbrPDEB1 and hPDE4 Inhibitors Using Free Energy Perturbation.
Ontology highlight
ABSTRACT:
SUBMITTER: Zara L
PROVIDER: S-EPMC9190044 | biostudies-literature | 2022 Jun
REPOSITORIES: biostudies-literature
Publications
Exploring the Activity Profile of TbrPDEB1 and hPDE4 Inhibitors Using Free Energy Perturbation.
Zara Lorena L Moraca Francesca F Van Muijlwijk-Koezen Jacqueline E JE Zarzycka Barbara B Abel Robert R de Esch Iwan J P IJP
ACS medicinal chemistry letters 20220523 6
Human African trypanosomiasis (HAT) is a neglected tropical disease caused by the parasite <i>Trypanosoma brucei</i> (<i>T.b</i>.). A validated target for the treatment of HAT is the parasitic <i>T.b.</i> cyclic nucleotide phosphodiesterase B1 (TbrPDEB1). Although nanomolar TbrPDEB1 inhibitors have been obtained, their activity against the off-target human PDE4 (hPDE4) is likely to lead to undesirable clinical side effects, such as nausea, emesis, and immune suppression. Thus, new and more selec ...[more]
Similar Datasets
Project description:Free energy perturbation (FEP) theory coupled to molecular dynamics (MD) or Monte Carlo (MC) statistical mechanics offers a theoretically precise method for determining the free energy differences of related biological inhibitors. Traditionally requiring extensive computational resources and expertise, it is only recently that its impact is being felt in drug discovery. A review of computer-aided anti-HIV efforts employing FEP calculations is provided here that describes early and recent successes in the design of human immunodeficiency virus type 1 (HIV-1) protease and non-nucleoside reverse transcriptase inhibitors. In addition, our ongoing work developing and optimizing leads for small molecule inhibitors of cyclophilin A (CypA) is highlighted as an update on the current capabilities of the field. CypA has been shown to aid HIV-1 replication by catalyzing the cis/trans isomerization of a conserved Gly-Pro motif in the Nterminal domain of HIV-1 capsid (CA) protein. In the absence of a functional CypA, e.g., by the addition of an inhibitor such as cyclosporine A (CsA), HIV-1 has reduced infectivity. Our simulations of acylurea-based and 1-indanylketone-based CypA inhibitors have determined that their nanomolar and micromolar binding affinities, respectively, are tied to their ability to stabilize Arg55 and Asn102. A structurally novel 1-(2,6-dichlorobenzamido) indole core was proposed to maximize these interactions. FEP-guided optimization, experimental synthesis, and biological testing of lead compounds for toxicity and inhibition of wild-type HIV-1 and CA mutants have demonstrated a dose-dependent inhibition of HIV-1 infection in two cell lines. While the inhibition is modest compared to CsA, the results are encouraging.
| S-EPMC3605731 | biostudies-literature
Project description:The main protease (Mpro) of the novel coronavirus SARS-CoV-2, which has caused the COVID-19 pandemic, is responsible for the maturation of its key proteins. Thus, inhibiting SARS-CoV-2 Mpro could prevent SARS-CoV-2 from multiplying. Because new inhibitors require thorough validation, repurposing current drugs could help reduce the validation process. Many recent studies used molecular docking to screen large databases for potential inhibitors of SARS-CoV-2 Mpro. However, molecular docking does not consider molecular dynamics and thus can be prone to error. In this work, we developed a protocol using free energy perturbation (FEP) to assess the potential inhibitors of SARS-CoV-2 Mpro. First, we validated both molecular docking and FEP on a set of 11 inhibitors of SARS-CoV-2 Mpro with experimentally determined inhibitory data. The experimentally deduced binding free energy exhibits significantly stronger correlation with that predicted by FEP (R = 0.94 ± 0.04) than with that predicted by molecular docking (R = 0.82 ± 0.08). This result clearly shows that FEP is the most accurate method available to predict the binding affinity of SARS-CoV-2 Mpro + ligand complexes. We subsequently used FEP to validate the top 33 compounds screened with molecular docking from the ZINC15 database. Thirteen of these compounds were predicted to bind strongly to SARS-CoV-2 Mpro, most of which are currently used as drugs for various diseases in humans. Notably, delamanid, an anti-tuberculosis drug, was predicted to inhibit SARS-CoV-2 Mpro in the nanomolar range. Because both COVID-19 and tuberculosis are lung diseases, delamanid has higher probability to be suitable for treating COVID-19 than other predicted compounds. Analysis of the complexes of SARS-CoV-2 Mpro and the top inhibitors revealed the key residues involved in the binding, including the catalytic dyad His14 and Cys145, which is consistent with the structural studies reported recently.
| S-EPMC9119318 | biostudies-literature
Project description:The rapid growth of structural information for G-protein-coupled receptors (GPCRs) has led to a greater understanding of their structure, function, selectivity, and ligand binding. Although novel ligands have been identified using methods such as virtual screening, computationally driven lead optimization has been possible only in isolated cases because of challenges associated with predicting binding free energies for related compounds. Here, we provide a systematic characterization of the performance of free-energy perturbation (FEP) calculations to predict relative binding free energies of congeneric ligands binding to GPCR targets using a consistent protocol and no adjustable parameters. Using the FEP+ package, first we validated the protocol, which includes a full lipid bilayer and explicit solvent, by predicting the binding affinity for a total of 45 different ligands across four different GPCRs (adenosine A2AAR, β1 adrenergic, CXCR4 chemokine, and δ opioid receptors). Comparison with experimental binding affinity measurements revealed a highly predictive ranking correlation (average spearman ρ = 0.55) and low root-mean-square error (0.80 kcal/mol). Next, we applied FEP+ in a prospective project, where we predicted the affinity of novel, potent adenosine A2A receptor (A2AR) antagonists. Four novel compounds were synthesized and tested in a radioligand displacement assay, yielding affinity values in the nanomolar range. The affinity of two out of the four novel ligands (plus three previously reported compounds) was correctly predicted (within 1 kcal/mol), including one compound with approximately a tenfold increase in affinity compared to the starting compound. Detailed analyses of the simulations underlying the predictions provided insights into the structural basis for the two cases where the affinity was overpredicted. Taken together, these results establish a protocol for systematically applying FEP+ to GPCRs and provide guidelines for identifying potent molecules in drug discovery lead optimization projects.
| S-EPMC6044636 | biostudies-literature
Project description:Scaffold hopping refers to computer-aided screening for active compounds with different structures against the same receptor to enrich privileged scaffolds, which is a topic of high interest in organic and medicinal chemistry. However, most approaches cannot efficiently predict the potency level of candidates after scaffold hopping. Herein, we identified potent PDE5 inhibitors with a novel scaffold via a free energy perturbation (FEP)-guided scaffold-hopping strategy, and FEP shows great advantages to precisely predict the theoretical binding potencies ΔG FEP between ligands and their target, which were more consistent with the experimental binding potencies ΔG EXP (the mean absolute deviations |ΔGFEP-ΔGEXP| < 2 kcal/mol) than those ΔG MM-PBSA or ΔG MM-GBSA predicted by the MM-PBSA or MM-GBSA method. Lead L12 had an IC50 of 8.7 nmol/L and exhibited a different binding pattern in its crystal structure with PDE5 from the famous starting drug tadalafil. Our work provides the first report via the FEP-guided scaffold hopping strategy for potent inhibitor discovery with a novel scaffold, implying that it will have a variety of future applications in rational molecular design and drug discovery.
| S-EPMC9072250 | biostudies-literature
Project description:NMR spectroscopic analysis of the C-terminal Kunitz domain fragment (alpha3(VI)) from the human alpha3-chain of type VI collagen has revealed that the side chain of Trp21 exists in two unequally populated conformations. The major conformation (M) is identical to the conformation observed in the X-ray crystallographic structure, while the minor conformation (m) cannot structurally be resolved in detail by NMR due to insufficient NOE data. In the present study, we have applied: (1) rigid and adiabatic mapping, (2) free energy simulations, and (3) molecular dynamic simulations to elucidate the structure of the m conformer and to provide a possible pathway of the Trp21 side chain between the two conformers. Adiabatic energy mapping of conformations of the Trp21 side chain obtained by energy minimization identified two energy minima: One corresponding to the conformation of Trp21 observed in the X-ray crystallographic structure and solution structure of alpha3(VI) (the M conformation) and the second corresponding to the m conformation predicted by NMR spectroscopy. A transition pathway between the M and m conformation is suggested. The free-energy difference between the two conformers obtained by the thermodynamic integration method is calculated to 1.77+/-0.7 kcal/mol in favor of the M form, which is in good agreement with NMR results. Structural and dynamic properties of the major and minor conformers of the alpha3(VI) molecule were investigated by molecular dynamic. Essential dynamics analysis of the two resulting 800 ps trajectories reveals that when going from the M to the m conformation only small, localized changes in the protein structure are induced. However, notable differences are observed in the mobility of the binding loop (residues Thr13-Ile18), which is more flexible in the m conformation than in the M conformation. This suggests that the reorientation of Trp2 might influence the inhibitory activity against trypsin, despite the relative large distance between the binding loop and Trp21.
| S-EPMC2144117 | biostudies-other
Project description:Free-energy calculations play an important role in the application of computational chemistry to a range of fields, including protein biochemistry, rational drug design, or materials science. Importantly, the free-energy difference is directly related to experimentally measurable quantities such as partition and adsorption coefficients, water activity, and binding affinities. Among several techniques aimed at predicting free-energy differences, perturbation approaches, involving the alchemical transformation of one molecule into another through intermediate states, stand out as rigorous methods based on statistical mechanics. However, despite the importance of free-energy calculations, the applicability of the perturbation approaches is still largely impeded by a number of challenges, including the definition of the perturbation path, i.e., alchemical changes leading to the transformation of one molecule to the other. To address this, an automatic perturbation topology builder based on a graph-matching algorithm is developed, which can identify the maximum common substructure (MCS) of two or multiple molecules and provide the perturbation topologies suitable for free-energy calculations using the GROMOS and the GROMACS simulation packages. Various MCS search options are presented leading to alternative definitions of the perturbation pathway. Moreover, perturbation topologies generated using the default multistate MCS search are used to calculate the changes in free energy between lysine and its two post-translational modifications, 3-methyllysine and acetyllysine. The pairwise free-energy calculations performed on this test system led to a cycle closure of 0.5 ± 0.3 and 0.2 ± 0.2 kJ mol-1, with GROMOS and GROMACS simulation packages, respectively. The same relative free energies between the three states are obtained by employing the enveloping distribution sampling (EDS) approach when compared to the pairwise perturbations. Importantly, this toolkit is made available online as an open-source Python package (https://github.com/drazen-petrov/SMArt).
| S-EPMC8479811 | biostudies-literature
Project description:Harnessing the power of graphics processing units (GPUs) to accelerate molecular dynamics (MD) simulations in the context of free-energy calculations has been a longstanding effort toward the development of versatile, high-performance MD engines. We report a new GPU-based implementation in NAMD of free-energy perturbation (FEP), one of the oldest, most popular importance-sampling approaches for the determination of free-energy differences that underlie alchemical transformations. Compared to the CPU implementation available since 2001 in NAMD, our benchmarks indicate that the new implementation of FEP in traditional GPU code is about four times faster, without any noticeable loss of accuracy, thereby paving the way toward more affordable free-energy calculations on large biological objects. Moreover, we have extended this new FEP implementation to a code path highly optimized for a single-GPU node, which proves to be up to nearly 30 times faster than the CPU implementation. Through optimized GPU performance, the present developments provide the community with a cost-effective solution for conducting FEP calculations. The new FEP-enabled code has been released with NAMD 3.0.
| S-EPMC7686227 | biostudies-literature
Project description:Machine learning offers great promise for fast and accurate binding affinity predictions. However, current models lack robust evaluation and fail on tasks encountered in (hit-to-) lead optimisation, such as ranking the binding affinity of a congeneric series of ligands, thereby limiting their application in drug discovery. Here, we address these issues by first introducing a novel attention-based graph neural network model called AEV-PLIG (atomic environment vector-protein ligand interaction graph). Second, we introduce a new and more realistic out-of-distribution test set called the OOD Test. We benchmark our model on this set, CASF-2016, and a test set used for free energy perturbation (FEP) calculations, that not only highlights the competitive performance of AEV-PLIG, but provides a realistic assessment of machine learning models with rigorous physics-based approaches. Moreover, we demonstrate how leveraging augmented data (generated using template-based modelling or molecular docking) can significantly improve binding affinity prediction correlation and ranking on the FEP benchmark (weighted mean PCC and Kendall's τ increases from 0.41 and 0.26 to 0.59 and 0.42). These strategies together are closing the performance gap with FEP calculations (FEP+ achieves weighted mean PCC and Kendall's τ of 0.68 and 0.49 on the FEP benchmark) while being ~400,000 times faster.
| S-EPMC11807228 | biostudies-literature
Project description:Partial molar volume is an important thermodynamic property that gives insights into molecular size and intermolecular interactions in solution. Theoretical frameworks for determining the partial molar volume (V°) of a solvated molecule generally apply Scaled Particle Theory or Kirkwood-Buff theory. With the current abilities to perform long molecular dynamics and Monte Carlo simulations, more direct methods are gaining popularity, such as computing V° directly as the difference in computed volume from two simulations, one with a solute present and another without. Thermodynamically, V° can also be determined as the pressure derivative of the free energy of solvation in the limit of infinite dilution. Both approaches are considered herein with the use of free energy perturbation (FEP) calculations to compute the necessary free energies of solvation at elevated pressures. Absolute and relative partial molar volumes are computed for benzene and benzene derivatives using the OPLS-AA force field. The mean unsigned error for all molecules is 2.8 cm(3) mol(-1). The present methodology should find use in many contexts such as the development and testing of force fields for use in computer simulations of organic and biomolecular systems, as a complement to related experimental studies, and to develop a deeper understanding of solute-solvent interactions.
| S-EPMC4872387 | biostudies-literature
Project description:Accurate methods to estimate free energies play an important role for studying diverse condensed-phase problems in chemistry and biochemistry. The most common methods used in conjunction with molecular dynamics (MD) and Monte Carlo statistical mechanics (MC) simulations are free energy perturbation (FEP) and thermodynamic integration (TI). For common applications featuring the conversion of one molecule to another, simulations are run in stages or multiple "λ-windows" to promote convergence of the results. For computation of absolute free energies of solvation or binding, calculations are needed in which the solute is typically annihilated in the solvent and in the complex. The present work addresses identification of optimal protocols for such calculations, specifically, the creation/annihilation of organic molecules in aqueous solution. As is common practice, decoupling of the perturbations for electrostatic and Lennard-Jones interactions was performed. Consistent with earlier reports, FEP calculations for molecular creations are much more efficient, while annihilations require many more windows and may converge to incorrect values. Strikingly, we find that as few as four windows may be adequate for creation calculations for solutes ranging from argon to ethylbenzene. For a larger druglike molecule, MIF180, which contains 22 non-hydrogen atoms and three rotatable bonds, 10 creation windows are found to be adequate to yield the correct free energy of hydration. Convergence is impeded with procedures that use any sampling in the annihilation direction, and there is no need for postprocessing methods such as the Bennett acceptance ratio (BAR).
| S-EPMC6615964 | biostudies-literature