Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Exploring the Energy Landscapes of Cyclic Tetrapeptides with Discrete Path Sampling.

ABSTRACT: Cyclic tetrapeptides are an important class of biologically active molecules that exhibit interesting conformational dynamics, with slow interconversion of several different structures. We present calculations on their energy landscapes using discrete path sampling. In acyclic peptides and large cyclic peptides, isomers containing cis-peptide groups are much less stable than the all-trans isomers and separated from them by large barriers. Strain in small cyclic peptides causes the cis and trans isomers to be closer in energy and separated by much lower barriers. If d-amino acids or proline residues are introduced, isomers containing cis-peptides become more stable than the all-trans structures. We also show that changing the polarity of the solvent has a significant effect on the energy landscapes of cyclic tetrapeptides, causing changes in the orientations of the peptide groups and in the degree of intramolecular hydrogen bonding.

SUBMITTER: Oakley MT

PROVIDER: S-EPMC3624815 | biostudies-other | 2013 Jan

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Publications

Exploring the Energy Landscapes of Cyclic Tetrapeptides with Discrete Path Sampling.

Oakley Mark T MT Johnston Roy L RL

Journal of chemical theory and computation 20121105 1

Cyclic tetrapeptides are an important class of biologically active molecules that exhibit interesting conformational dynamics, with slow interconversion of several different structures. We present calculations on their energy landscapes using discrete path sampling. In acyclic peptides and large cyclic peptides, isomers containing cis-peptide groups are much less stable than the all-trans isomers and separated from them by large barriers. Strain in small cyclic peptides causes the cis and trans ...[more]

PMID: 23596359

Similar Datasets

Energy Landscape for the Membrane Fusion Pathway in Influenza A Hemagglutinin From Discrete Path Sampling.

Project description:The conformational change associated with membrane fusion for Influenza A Hemagglutinin is investigated with a model based upon pre- and post-fusion structures of the HA2 component. We employ computational methods based on the potential energy landscape framework to obtain an initial path connecting these two end points, which provides the starting point for refinement of a kinetic transition network. Here we employ discrete path sampling, which provides access to the experimental time and length scales via geometry optimization techniques to identify local minima and the transition states that connect them. We then analyse the distinct phases of the predicted pathway in terms of structure and energetics, and compare with available experimental data and previous simulations. Our results provide the foundations for future work, which will address the effect of mutations, changes in pH, and incorporation of additional components, especially the HA1 chain and the fusion peptide.

| S-EPMC7546250 | biostudies-literature

Reliable protein folding on complex energy landscapes: the free energy reaction path.

Project description:A theoretical framework is developed to study the dynamics of protein folding. The key insight is that the search for the native protein conformation is influenced by the rate r at which external parameters, such as temperature, chemical denaturant, or pH, are adjusted to induce folding. A theory based on this insight predicts that 1), proteins with complex energy landscapes can fold reliably to their native state; 2), reliable folding can occur as an equilibrium or out-of-equilibrium process; and 3), reliable folding only occurs when the rate r is below a limiting value, which can be calculated from measurements of the free energy. We test these predictions against numerical simulations of model proteins with a single energy scale.

| S-EPMC2527276 | biostudies-other

Exploring protein energy landscapes with hierarchical clustering.

Project description:In this work we present a new method for investigating local energy minima on a protein energy landscape. The CABS (CAlpha, CBeta and the center of mass of the Side chain) method was employed for generating protein models, but any other method could be used instead. Cα traces from an ensemble of models are hierarchical clustered with the HCPM (Hierarchical Clustering of Protein Models) method. The efficiency of this method for sampling and analyzing energy landscapes is shown.

| S-EPMC1366497 | biostudies-literature

Convergence and Sampling in Determining Free Energy Landscapes for Membrane Protein Association.

Project description:Potential of mean force (PMF) calculations are used to characterize the free energy landscape of protein-lipid and protein-protein association within membranes. Coarse-grained simulations allow binding free energies to be determined with reasonable statistical error. This accuracy relies on defining a good collective variable to describe the binding and unbinding transitions, and upon criteria for assessing the convergence of the simulation toward representative equilibrium sampling. As examples, we calculate protein-lipid binding PMFs for ANT/cardiolipin and Kir2.2/PIP2, using umbrella sampling on a distance coordinate. These highlight the importance of replica exchange between windows for convergence. The use of two independent sets of simulations, initiated from bound and unbound states, provide strong evidence for simulation convergence. For a model protein-protein interaction within a membrane, center-of-mass distance is shown to be a poor collective variable for describing transmembrane helix-helix dimerization. Instead, we employ an alternative intermolecular distance matrix RMS (DRMS) coordinate to obtain converged PMFs for the association of the glycophorin transmembrane domain. While the coarse-grained force field gives a reasonable Kd for dimerization, the majority of the bound population is revealed to be in a near-native conformation. Thus, the combination of a refined reaction coordinate with improved sampling reveals previously unnoticed complexities of the dimerization free energy landscape. We propose the use of replica-exchange umbrella sampling starting from different initial conditions as a robust approach for calculation of the binding energies in membrane simulations.

| S-EPMC5402295 | biostudies-literature

Exploring the energy landscapes of protein folding simulations with Bayesian computation.

Project description:Nested sampling is a Bayesian sampling technique developed to explore probability distributions localized in an exponentially small area of the parameter space. The algorithm provides both posterior samples and an estimate of the evidence (marginal likelihood) of the model. The nested sampling algorithm also provides an efficient way to calculate free energies and the expectation value of thermodynamic observables at any temperature, through a simple post processing of the output. Previous applications of the algorithm have yielded large efficiency gains over other sampling techniques, including parallel tempering. In this article, we describe a parallel implementation of the nested sampling algorithm and its application to the problem of protein folding in a G?-like force field of empirical potentials that were designed to stabilize secondary structure elements in room-temperature simulations. We demonstrate the method by conducting folding simulations on a number of small proteins that are commonly used for testing protein-folding procedures. A topological analysis of the posterior samples is performed to produce energy landscape charts, which give a high-level description of the potential energy surface for the protein folding simulations. These charts provide qualitative insights into both the folding process and the nature of the model and force field used.

| S-EPMC3283771 | biostudies-literature

Enhanced, targeted sampling of high-dimensional free-energy landscapes using variationally enhanced sampling, with an application to chignolin.

Project description:The capabilities of molecular simulations have been greatly extended by a number of widely used enhanced sampling methods that facilitate escaping from metastable states and crossing large barriers. Despite these developments there are still many problems which remain out of reach for these methods which has led to a vigorous effort in this area. One of the most important problems that remains unsolved is sampling high-dimensional free-energy landscapes and systems that are not easily described by a small number of collective variables. In this work we demonstrate a new way to compute free-energy landscapes of high dimensionality based on the previously introduced variationally enhanced sampling, and we apply it to the miniprotein chignolin.

| S-EPMC4747745 | biostudies-literature

Exploring the free-energy landscapes of biological systems with steered molecular dynamics.

Project description:We perform steered molecular dynamics (SMD) simulations and use the Brownian dynamics fluctuation-dissipation-theorem (BD-FDT) to accurately compute the free-energy profiles for several biophysical processes of fundamental importance: hydration of methane and cations, binding of benzene to T4-lysozyme L99A mutant, and permeation of water through aquaglyceroporin. For each system, the center-of-mass of the small molecule (methane, ion, benzene, and water, respectively) is steered (pulled) at a given speed over a period of time, during which the system transitions from one macroscopic state/conformation (State A) to another one (State B). The mechanical work of pulling the system is measured during the process, sampling a forward pulling path. Then the reverse pulling is conducted to sample a reverse path from B back to A. Sampling a small number of forward and reverse paths, we are able to accurately compute the free-energy profiles for all the afore-listed systems that represent various important aspects of biological physics. The numerical results are in excellent agreement with the experimental data and/or other computational studies available in the literature.

| S-EPMC3111135 | biostudies-literature

Broad distributions of transition-path times are fingerprints of multidimensionality of the underlying free energy landscapes.

Project description:Recent single-molecule experiments have observed transition paths, i.e., brief events where molecules (particularly biomolecules) are caught in the act of surmounting activation barriers. Such measurements offer unprecedented mechanistic insights into the dynamics of biomolecular folding and binding, molecular machines, and biological membrane channels. A key challenge to these studies is to infer the complex details of the multidimensional energy landscape traversed by the transition paths from inherently low-dimensional experimental signals. A common minimalist model attempting to do so is that of one-dimensional diffusion along a reaction coordinate, yet its validity has been called into question. Here, we show that the distribution of the transition path time, which is a common experimental observable, can be used to differentiate between the dynamics described by models of one-dimensional diffusion from the dynamics in which multidimensionality is essential. Specifically, we prove that the coefficient of variation obtained from this distribution cannot possibly exceed 1 for any one-dimensional diffusive model, no matter how rugged its underlying free energy landscape is: In other words, this distribution cannot be broader than the single-exponential one. Thus, a coefficient of variation exceeding 1 is a fingerprint of multidimensional dynamics. Analysis of transition paths in atomistic simulations of proteins shows that this coefficient often exceeds 1, signifying essential multidimensionality of those systems.

| S-EPMC7959536 | biostudies-literature

Exploring the folding energy landscapes of heme proteins using a hybrid AWSEM-heme model.

Project description:Heme is an active center in many proteins. Here we explore computationally the role of heme in protein folding and protein structure. We model heme proteins using a hybrid model employing the AWSEM Hamiltonian, a coarse-grained forcefield for the protein chain along with AMBER, an all-atom forcefield for the heme. We carefully designed transferable force fields that model the interactions between the protein and the heme. The types of protein-ligand interactions in the hybrid model include thioester covalent bonds, coordinated covalent bonds, hydrogen bonds, and electrostatics. We explore the influence of different types of hemes (heme b and heme c) on folding and structure prediction. Including both types of heme improves the quality of protein structure predictions. The free energy landscape shows that both types of heme can act as nucleation sites for protein folding and stabilize the protein folded state. In binding the heme, coordinated covalent bonds and thioester covalent bonds for heme c drive the heme toward the native pocket. The electrostatics also facilitates the search for the binding site.

| S-EPMC8866609 | biostudies-literature

Self-Learning Adaptive Umbrella Sampling Method for the Determination of Free Energy Landscapes in Multiple Dimensions.

Project description:The potential of mean force describing conformational changes of biomolecules is a central quantity that determines the function of biomolecular systems. Calculating an energy landscape of a process that depends on three or more reaction coordinates might require a lot of computational power, making some of multidimensional calculations practically impossible. Here, we present an efficient automatized umbrella sampling strategy for calculating multidimensional potential of mean force. The method progressively learns by itself, through a feedback mechanism, which regions of a multidimensional space are worth exploring and automatically generates a set of umbrella sampling windows that is adapted to the system. The self-learning adaptive umbrella sampling method is first explained with illustrative examples based on simplified reduced model systems, and then applied to two non-trivial situations: the conformational equilibrium of the pentapeptide Met-enkephalin in solution and ion permeation in the KcsA potassium channel. With this method, it is demonstrated that a significant smaller number of umbrella windows needs to be employed to characterize the free energy landscape over the most relevant regions without any loss in accuracy.

| S-EPMC3694627 | 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