Browse
Submit Data
Databases
API
Help

Dataset Information

27 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Constraint methods that accelerate free-energy simulations of biomolecules.

ABSTRACT: Atomistic molecular dynamics simulations of biomolecules are critical for generating narratives about biological mechanisms. The power of atomistic simulations is that these are physics-based methods that satisfy Boltzmann's law, so they can be used to compute populations, dynamics, and mechanisms. But physical simulations are computationally intensive and do not scale well to the sizes of many important biomolecules. One way to speed up physical simulations is by coarse-graining the potential function. Another way is to harness structural knowledge, often by imposing spring-like restraints. But harnessing external knowledge in physical simulations is problematic because knowledge, data, or hunches have errors, noise, and combinatoric uncertainties. Here, we review recent principled methods for imposing restraints to speed up physics-based molecular simulations that promise to scale to larger biomolecules and motions.

SUBMITTER: Perez A

PROVIDER: S-EPMC4684272 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Constraint methods that accelerate free-energy simulations of biomolecules.

Perez Alberto A MacCallum Justin L JL Coutsias Evangelos A EA Dill Ken A KA

The Journal of chemical physics 20151201 24

Atomistic molecular dynamics simulations of biomolecules are critical for generating narratives about biological mechanisms. The power of atomistic simulations is that these are physics-based methods that satisfy Boltzmann's law, so they can be used to compute populations, dynamics, and mechanisms. But physical simulations are computationally intensive and do not scale well to the sizes of many important biomolecules. One way to speed up physical simulations is by coarse-graining the potential f ...[more]

PMID: 26723628

Similar Datasets

CHARMM-GUI Free Energy Calculator for Practical Ligand Binding Free Energy Simulations with AMBER.

Project description:Alchemical free energy methods, such as free energy perturbation (FEP) and thermodynamic integration (TI), become increasingly popular and crucial for drug design and discovery. However, the system preparation of alchemical free energy simulation is an error-prone, time-consuming, and tedious process for a large number of ligands. To address this issue, we have recently presented CHARMM-GUI Free Energy Calculator that can provide input and postprocessing scripts for NAMD and GENESIS FEP molecular dynamics systems. In this work, we extended three submodules of Free Energy Calculator to work with the full suite of GPU-accelerated alchemical free energy methods and tools in AMBER, including input and postprocessing scripts. The BACE1 (β-secretase 1) benchmark set was used to validate the AMBER-TI simulation systems and scripts generated by Free Energy Calculator. The overall results of relatively large and diverse systems are almost equivalent with different protocols (unified and split) and with different timesteps (1, 2, and 4 fs), with R2 > 0.9. More importantly, the average free energy differences between two protocols are small and reliable with four independent runs, with a mean unsigned error (MUE) below 0.4 kcal/mol. Running at least four independent runs for each pair with AMBER20 (and FF19SB/GAFF2.1/OPC force fields), we obtained a MUE of 0.99 kcal/mol and root-mean-square error of 1.31 kcal/mol for 58 alchemical transformations in comparison with experimental data. In addition, a set of ligands for T4-lysozyme was used to further validate our free energy calculation protocol whose results are close to experimental data (within 1 kcal/mol). In summary, Free Energy Calculator provides a user-friendly web-based tool to generate the AMBER-TI system and input files for high-throughput binding free energy calculations with access to the full set of GPU-accelerated alchemical free energy, enhanced sampling, and analysis methods in AMBER.

| S-EPMC8491128 | biostudies-literature

Protein thermostability calculations using alchemical free energy simulations.

Project description:Thermal stability of proteins is crucial for both biotechnological and therapeutic applications. Rational protein engineering therefore frequently aims at increasing thermal stability by introducing stabilizing mutations. The accurate prediction of the thermodynamic consequences caused by mutations, however, is highly challenging as thermal stability changes are caused by alterations in the free energy of folding. Growing computational power, however, increasingly allows us to use alchemical free energy simulations, such as free energy perturbation or thermodynamic integration, to calculate free energy differences with relatively high accuracy. In this article, we present an automated protocol for setting up alchemical free energy calculations for mutations of naturally occurring amino acids (except for proline) that allows an unprecedented, automated screening of large mutant libraries. To validate the developed protocol, we calculated thermodynamic stability differences for 109 mutations in the microbial Ribonuclease Barnase. The obtained quantitative agreement with experimental data illustrates the potential of the approach in protein engineering and design.

| S-EPMC2872215 | biostudies-literature

Probing Difference in Binding Modes of Inhibitors to MDMX by Molecular Dynamics Simulations and Different Free Energy Methods.

Project description:The p53-MDMX interaction has attracted extensive attention of anti-cancer drug development in recent years. This current work adopted molecular dynamics (MD) simulations and cross-correlation analysis to investigate conformation changes of MDMX caused by inhibitor bindings. The obtained information indicates that the binding cleft of MDMX undergoes a large conformational change and the dynamic behavior of residues obviously change by the presence of different structural inhibitors. Two different methods of binding free energy predictions were employed to carry out a comparable insight into binding mechanisms of four inhibitors PMI, pDI, WK23 and WW8 to MDMX. The data show that the main factor controlling the inhibitor bindings to MDMX arises from van der Waals interactions. The binding free energies were further divided into contribution of each residue and the derived information gives a conclusion that the hydrophobic interactions, such as CH-CH, CH-? and ?-? interactions, are responsible for the inhibitor associations with MDMX.

| S-EPMC4625964 | biostudies-literature

CHARMM-GUI Free Energy Calculator for Absolute and Relative Ligand Solvation and Binding Free Energy Simulations.

Project description:Alchemical free energy simulations have long been utilized to predict free energy changes for binding affinity and solubility of small molecules. However, while the theoretical foundation of these methods is well established, seamlessly handling many of the practical aspects regarding the preparation of the different thermodynamic end states of complex molecular systems and the numerous processing scripts often remains a burden for successful applications. In this work, we present CHARMM-GUI Free Energy Calculator (http://www.charmm-gui.org/input/fec) that provides various alchemical free energy perturbation molecular dynamics (FEP/MD) systems with input and post-processing scripts for NAMD and GENESIS. Four submodules are available: Absolute Ligand Binder (for absolute ligand binding FEP/MD), Relative Ligand Binder (for relative ligand binding FEP/MD), Absolute Ligand Solvator (for absolute ligand solvation FEP/MD), and Relative Ligand Solvator (for relative ligand solvation FEP/MD). Each module is designed to build multiple systems of a set of selected ligands at once for high-throughput FEP/MD simulations. The capability of Free Energy Calculator is illustrated by absolute and relative solvation FEP/MD of a set of ligands and absolute and relative binding FEP/MD of a set of ligands for T4-lysozyme in solution and the adenosine A2A receptor in a membrane. The calculated free energy values are overall consistent with the experimental and published free energy results (within ?1 kcal/mol). We hope that Free Energy Calculator is useful to carry out high-throughput FEP/MD simulations in the field of biomolecular sciences and drug discovery.

| S-EPMC7658063 | biostudies-literature

Free-energy landscape of protein oligomerization from atomistic simulations.

Project description:In the realm of protein-protein interactions, the assembly process of homooligomers plays a fundamental role because the majority of proteins fall into this category. A comprehensive understanding of this multistep process requires the characterization of the driving molecular interactions and the transient intermediate species. The latter are often short-lived and thus remain elusive to most experimental investigations. Molecular simulations provide a unique tool to shed light onto these complex processes complementing experimental data. Here we combine advanced sampling techniques, such as metadynamics and parallel tempering, to characterize the oligomerization landscape of fibritin foldon domain. This system is an evolutionarily optimized trimerization motif that represents an ideal model for experimental and computational mechanistic studies. Our results are fully consistent with previous experimental nuclear magnetic resonance and kinetic data, but they provide a unique insight into fibritin foldon assembly. In particular, our simulations unveil the role of nonspecific interactions and suggest that an interplay between thermodynamic bias toward native structure and residual conformational disorder may provide a kinetic advantage.

| S-EPMC3856838 | biostudies-literature

Blind protein structure prediction using accelerated free-energy simulations.

Project description:We report a key proof of principle of a new acceleration method [Modeling Employing Limited Data (MELD)] for predicting protein structures by molecular dynamics simulation. It shows that such Boltzmann-satisfying techniques are now sufficiently fast and accurate to predict native protein structures in a limited test within the Critical Assessment of Structure Prediction (CASP) community-wide blind competition.

| S-EPMC5106196 | biostudies-literature

Uncertainty Quantification in Alchemical Free Energy Methods.

Project description:Alchemical free energy methods have gained much importance recently from several reports of improved ligand-protein binding affinity predictions based on their implementation using molecular dynamics simulations. A large number of variants of such methods implementing different accelerated sampling techniques and free energy estimators are available, each claimed to be better than the others in its own way. However, the key features of reproducibility and quantification of associated uncertainties in such methods have barely been discussed. Here, we apply a systematic protocol for uncertainty quantification to a number of popular alchemical free energy methods, covering both absolute and relative free energy predictions. We show that a reliable measure of error estimation is provided by ensemble simulation-an ensemble of independent MD simulations-which applies irrespective of the free energy method. The need to use ensemble methods is fundamental and holds regardless of the duration of time of the molecular dynamics simulations performed.

| S-EPMC6095638 | biostudies-literature

Validation of Free Energy Methods in AMBER.

Project description:Herein we provide high-precision validation tests of the latest GPU-accelerated free energy code in AMBER. We demonstrate that consistent free energy results are obtained in both the gas phase and in solution. We first show, in the context of thermodynamic integration (TI), that the results are invariant with respect to "split" (e.g., stepwise decharge-vdW-recharge) versus "unified" protocols. This brought to light a subtle inconsistency in previous versions of AMBER that was traced to the improper treatment of 1-4 vdW and electrostatic interactions involving atoms across the softcore boundary. We illustrate that under the assumption that the ensembles produced by different legs of the alchemical transformation between molecules A and B in the gas phase and aqueous phase are very small, the inconsistency in the relative hydration free energy ΔΔGhydr[A → B] = ΔGaq[A → B] - ΔGgas[A → B] is minimal. However, for general cases where the ensembles are shown to be substantially different, as expected in ligand-protein binding applications, these errors can be large. Finally, we demonstrate that results for relative hydration free energy simulations are independent of TI or multistate Bennett's acceptance ratio (MBAR) analysis, invariant to the specific choice of the softcore region, and in agreement with results derived from absolute hydration free energy values.

| S-EPMC7686255 | biostudies-literature

Use of Interaction Energies in QM/MM Free Energy Simulations.

Project description:The use of the most accurate (i.e., QM or QM/MM) levels of theory for free energy simulations (FES) is typically not possible. Primarily, this is because the computational cost associated with the extensive configurational sampling needed for converging FES is prohibitive. To ensure the feasibility of QM-based FES, the "indirect" approach is generally taken, necessitating a free energy calculation between the MM and QM/MM potential energy surfaces. Ideally, this step is performed with standard free energy perturbation (Zwanzig's equation) as it only requires simulations be carried out at the low level of theory; however, work from several groups over the past few years has conclusively shown that Zwanzig's equation is ill-suited to this task. As such, many approximations have arisen to mitigate difficulties with Zwanzig's equation. One particularly popular notion is that the convergence of Zwanzig's equation can be improved by using interaction energy differences instead of total energy differences. Although problematic numerical fluctuations (a major problem when using Zwanzig's equation) are indeed reduced, our results and analysis demonstrate that this "interaction energy approximation" (IEA) is theoretically incorrect, and the implicit approximation invoked is spurious at best. Herein, we demonstrate this via solvation free energy calculations using IEA from two different low levels of theory to the same target high level. Results from this proof-of-concept consistently yield the wrong results, deviating by ∼1.5 kcal/mol from the rigorously obtained value.

| S-EPMC6745021 | biostudies-literature

Binding specificity of SH2 domains: insight from free energy simulations.

Project description:Cellular signal transduction pathways are controlled by specific protein-protein interactions mediated by the binding of short peptides to small modular interaction domains. To gain insights into the specificity of these interactions, the association of phosphotyrosine-containing peptides to Src Homology 2 (SH2) domains is characterized using computations. Molecular dynamics simulations based on high-resolution crystal structures complemented by homology models are used to calculate the absolute binding free energies for 25 SH2-peptides pairs. The calculations are carried out using a potential of mean force free energy simulations method with restraining potentials that was developed previously (Woo and Roux, Proc Natl Acad Sci USA 2005;102:6825-6830). The method is utilized in conjunction with an implicit solvent representation to reduce the computational cost to characterize the association of five SH2 domains and five peptides. Specificity is ascertained by directly comparing the affinities of a given SH2 domain binding for any of the different peptides. For three of the five SH2 domains, the computational results rank the native peptides, as the most preferred binding motif. For the remaining two SH2 domains, high affinity binding motifs other than the native peptides are identified. This study illustrates how free energy computations can complement experiments in trying to elucidate complex protein-protein interactions networks.

| S-EPMC2635922 | 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