Browse
Submit Data
Databases
API
Help

Dataset Information

26 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Predicting Binding Free Energies in a Large Combinatorial Chemical Space Using Multisite ? Dynamics.

ABSTRACT: In this study, we demonstrate the extensive scalability of the biasing potential replica exchange multisite ? dynamics (BP-REX MS?D) free energy method by calculating binding affinities for 512 inhibitors to HIV Reverse Transcriptase (HIV-RT). This is the largest exploration of chemical space using free energy methods known to date, requires only a few simulations, and identifies 55 new inhibitor designs against HIV-RT predicted to be at least as potent as a tight binding reference compound (i.e., as potent as 56 nM). We highlight that BP-REX MS?D requires an order of magnitude less computational resources than conventional free energy methods while maintaining a similar level of precision, overcomes the inherent poor scalability of conventional free energy methods, and enables the exploration of combinatorially large chemical spaces in the context of in silico drug discovery.

SUBMITTER: Vilseck JZ

PROVIDER: S-EPMC6091208 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Predicting Binding Free Energies in a Large Combinatorial Chemical Space Using Multisite λ Dynamics.

Vilseck Jonah Z JZ Armacost Kira A KA Hayes Ryan L RL Goh Garrett B GB Brooks Charles L CL

The journal of physical chemistry letters 20180606 12

In this study, we demonstrate the extensive scalability of the biasing potential replica exchange multisite λ dynamics (BP-REX MSλD) free energy method by calculating binding affinities for 512 inhibitors to HIV Reverse Transcriptase (HIV-RT). This is the largest exploration of chemical space using free energy methods known to date, requires only a few simulations, and identifies 55 new inhibitor designs against HIV-RT predicted to be at least as potent as a tight binding reference compound (i.e ...[more]

PMID: 29847134

Similar Datasets

Accurate Receptor-Ligand Binding Free Energies from Fast QM Conformational Chemical Space Sampling.

Project description:Small molecule receptor-binding is dominated by weak, non-covalent interactions such as van-der-Waals hydrogen bonding or electrostatics. Calculating these non-covalent ligand-receptor interactions is a challenge to computational means in terms of accuracy and efficacy since the ligand may bind in a number of thermally accessible conformations. The conformational rotamer ensemble sampling tool (CREST) uses an iterative scheme to efficiently sample the conformational space and calculates energies using the semi-empirical 'Geometry, Frequency, Noncovalent, eXtended Tight Binding' (GFN2-xTB) method. This combined approach is applied to blind predictions of the modes and free energies of binding for a set of 10 drug molecule ligands to the cucurbit[n]urils CB[8] receptor from the recent 'Statistical Assessment of the Modeling of Proteins and Ligands' (SAMPL) challenge including morphine, hydromorphine, cocaine, fentanyl, and ketamine. For each system, the conformational space was sufficiently sampled for the free ligand and the ligand-receptor complexes using the quantum chemical Hamiltonian. A multitude of structures makes up the final conformer-rotamer ensemble, for which then free energies of binding are calculated. For those large and complex molecules, the results are in good agreement with experimental values with a mean error of 3 kcal/mol. The GFN2-xTB energies of binding are validated by advanced density functional theory calculations and found to be in good agreement. The efficacy of the automated QM sampling workflow allows the extension towards other complex molecular interaction scenarios.

| S-EPMC8002627 | biostudies-literature

Predicting Binding Free Energies: Frontiers and Benchmarks.

Project description:Binding free energy calculations based on molecular simulations provide predicted affinities for biomolecular complexes. These calculations begin with a detailed description of a system, including its chemical composition and the interactions among its components. Simulations of the system are then used to compute thermodynamic information, such as binding affinities. Because of their promise for guiding molecular design, these calculations have recently begun to see widespread applications in early-stage drug discovery. However, many hurdles remain in making them a robust and reliable tool. In this review, we highlight key challenges of these calculations, discuss some examples of these challenges, and call for the designation of standard community benchmark test systems that will help the research community generate and evaluate progress. In our view, progress will require careful assessment and evaluation of new methods, force fields, and modeling innovations on well-characterized benchmark systems, and we lay out our vision for how this can be achieved.

| S-EPMC5544526 | biostudies-literature

Chemical Space Exploration with Active Learning and Alchemical Free Energies.

Project description:Drug discovery can be thought of as a search for a needle in a haystack: searching through a large chemical space for the most active compounds. Computational techniques can narrow the search space for experimental follow up, but even they become unaffordable when evaluating large numbers of molecules. Therefore, machine learning (ML) strategies are being developed as computationally cheaper complementary techniques for navigating and triaging large chemical libraries. Here, we explore how an active learning protocol can be combined with first-principles based alchemical free energy calculations to identify high affinity phosphodiesterase 2 (PDE2) inhibitors. We first calibrate the procedure using a set of experimentally characterized PDE2 binders. The optimized protocol is then used prospectively on a large chemical library to navigate toward potent inhibitors. In the active learning cycle, at every iteration a small fraction of compounds is probed by alchemical calculations and the obtained affinities are used to train ML models. With successive rounds, high affinity binders are identified by explicitly evaluating only a small subset of compounds in a large chemical library, thus providing an efficient protocol that robustly identifies a large fraction of true positives.

| S-EPMC9558370 | biostudies-literature

Predicting absolute ligand binding free energies to a simple model site.

Project description:A central challenge in structure-based ligand design is the accurate prediction of binding free energies. Here we apply alchemical free energy calculations in explicit solvent to predict ligand binding in a model cavity in T4 lysozyme. Even in this simple site, there are challenges. We made systematic improvements, beginning with single poses from docking, then including multiple poses, additional protein conformational changes, and using an improved charge model. Computed absolute binding free energies had an RMS error of 1.9 kcal/mol relative to previously determined experimental values. In blind prospective tests, the methods correctly discriminated between several true ligands and decoys in a set of putative binders identified by docking. In these prospective tests, the RMS error in predicted binding free energies relative to those subsequently determined experimentally was only 0.6 kcal/mol. X-ray crystal structures of the new ligands bound in the cavity corresponded closely to predictions from the free energy calculations, but sometimes differed from those predicted by docking. Finally, we examined the impact of holding the protein rigid, as in docking, with a view to learning how approximations made in docking affect accuracy and how they may be improved.

| S-EPMC2104542 | biostudies-literature

Computations of standard binding free energies with molecular dynamics simulations.

Project description:An increasing number of studies have reported computations of the standard (absolute) binding free energy of small ligands to proteins using molecular dynamics (MD) simulations and explicit solvent molecules that are in good agreement with experiments. This encouraging progress suggests that physics-based approaches hold the promise of making important contributions to the process of drug discovery and optimization in the near future. Two types of approaches are principally used to compute binding free energies with MD simulations. The most widely known is the alchemical double decoupling method, in which the interaction of the ligand with its surroundings are progressively switched off. It is also possible to use a potential of mean force (PMF) method, in which the ligand is physically separated from the protein receptor. For both of these computational approaches, restraining potentials may be activated and released during the simulation for sampling efficiently the changes in translational, rotational, and conformational freedom of the ligand and protein upon binding. Because such restraining potentials add bias to the simulations, it is important that their effects be rigorously removed to yield a binding free energy that is properly unbiased with respect to the standard state. A review of recent results is presented, and differences in computational methods are discussed. Examples of computations with T4-lysozyme mutants, FKBP12, SH2 domain, and cytochrome P450 are discussed and compared. Remaining difficulties and challenges are highlighted.

| S-EPMC3837708 | biostudies-literature

Predicting Binding Free Energies of PDE2 Inhibitors. The Difficulties of Protein Conformation.

Project description:A congeneric series of 21 phosphodiesterase 2 (PDE2) inhibitors are reported. Crystal structures show how the molecules can occupy a 'top-pocket' of the active site. Molecules with small substituents do not enter the pocket, a critical leucine (Leu770) is closed and water molecules are present. Large substituents enter the pocket, opening the Leu770 conformation and displacing the waters. We also report an X-ray structure revealing a new conformation of the PDE2 active site domain. The relative binding affinities of these compounds were studied with free energy perturbation (FEP) methods and it represents an attractive real-world test case. In general, the calculations could predict the energy of small-to-small, or large-to-large molecule perturbations. However, accurately capturing the transition from small-to-large proved challenging. Only when using alternative protein conformations did results improve. The new X-ray structure, along with a modelled dimer, conferred stability to the catalytic domain during the FEP molecular dynamics (MD) simulations, increasing the convergence and thereby improving the prediction of ΔΔG of binding for some small-to-large transitions. In summary, we found the most significant improvement in results when using different protein structures, and this data set is useful for future free energy validation studies.

| S-EPMC5861043 | biostudies-literature

Approaching protein design with multisite λ dynamics: Accurate and scalable mutational folding free energies in T4 lysozyme.

Project description:The estimation of changes in free energy upon mutation is central to the problem of protein design. Modern protein design methods have had remarkable success over a wide range of design targets, but are reaching their limits in ligand binding and enzyme design due to insufficient accuracy in mutational free energies. Alchemical free energy calculations have the potential to supplement modern design methods through more accurate molecular dynamics based prediction of free energy changes, but suffer from high computational cost. Multisite λ dynamics (MSλD) is a particularly efficient and scalable free energy method with potential to explore combinatorially large sequence spaces inaccessible with other free energy methods. This work aims to quantify the accuracy of MSλD and demonstrate its scalability. We apply MSλD to the classic problem of calculating folding free energies in T4 lysozyme, a system with a wealth of experimental measurements. Single site mutants considering 32 mutations show remarkable agreement with experiment with a Pearson correlation of 0.914 and mean unsigned error of 1.19 kcal/mol. Multisite mutants in systems with up to five concurrent mutations spanning 240 different sequences show comparable agreement with experiment. These results demonstrate the promise of MSλD in exploring large sequence spaces for protein design.

| S-EPMC6225981 | biostudies-literature

Protocols utilizing constant pH molecular dynamics to compute pH-dependent binding free energies.

Project description:In protein-ligand binding, the electrostatic environments of the two binding partners may vary significantly in bound and unbound states, which may lead to protonation changes upon binding. In cases where ligand binding results in a net uptake or release of protons, the free energy of binding is pH-dependent. Nevertheless, conventional free energy calculations and molecular docking protocols typically do not rigorously account for changes in protonation that may occur upon ligand binding. To address these shortcomings, we present a simple methodology based on Wyman's binding polynomial formalism to account for the pH dependence of binding free energies and demonstrate its use on cucurbit[7]uril (CB[7]) host-guest systems. Using constant pH molecular dynamics and a reference binding free energy that is taken either from experiment or from thermodynamic integration computations, the pH-dependent binding free energy is determined. This computational protocol accurately captures the large pKa shifts observed experimentally upon CB[7]:guest association and reproduces experimental binding free energies at different levels of pH. We show that incorrect assignment of fixed protonation states in free energy computations can give errors of >2 kcal/mol in these host-guest systems. Use of the methods presented here avoids such errors, thus suggesting their utility in computing proton-linked binding free energies for protein-ligand complexes.

| S-EPMC4306499 | biostudies-literature

Accurate determination of protein:ligand standard binding free energies from molecular dynamics simulations.

Project description:Designing a reliable computational methodology to calculate protein:ligand standard binding free energies is extremely challenging. The large change in configurational enthalpy and entropy that accompanies the association of ligand and protein is notoriously difficult to capture in naive brute-force simulations. Addressing this issue, the present protocol rests upon a rigorous statistical mechanical framework for the determination of protein:ligand binding affinities together with the comprehensive Binding Free-Energy Estimator 2 (BFEE2) application software. With the knowledge of the bound state, available from experiments or docking, application of the BFEE2 protocol with a reliable force field supplies in a matter of days standard binding free energies within chemical accuracy, for a broad range of protein:ligand complexes. Limiting undesirable human intervention, BFEE2 assists the end user in preparing all the necessary input files and performing the post-treatment of the simulations towards the final estimate of the binding affinity.

| S-EPMC10082674 | biostudies-literature

Combinatorial profiling of chromatin binding modules reveals multisite discrimination.

Project description:Specific interactions between post-translational modifications (PTMs) and chromatin-binding proteins are central to the idea of a 'histone code'. Here, we used a 5,000-member, PTM-randomized, combinatorial peptide library based on the N terminus of histone H3 to interrogate the multisite specificity of six chromatin binding modules, which read the methylation status of Lys4. We found that Thr3 phosphorylation, Arg2 methylation and Thr6 phosphorylation are critical additional PTMs that modulate the ability to recognize and bind histone H3. Notably, phosphorylation of Thr6 yielded the most varied effect on protein binding, suggesting an important regulatory mechanism for readers of the H3 tail. Mass spectrometry and antibody-based evidence indicate that this previously uncharacterized modification exists on native H3, and NMR analysis of ING2 revealed the structural basis for discrimination. These investigations reveal a continuum of binding affinities in which multisite PTM recognition involves both switch- and rheostat-like properties, yielding graded effects that depend on the inherent 'reader' specificity.

| S-EPMC2922993 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data