Source 1primary paper2016Physical Chemistry Chemical Physics
Toolkit/conventional REMD
conventional REMD
Also known as: REMD
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
Conventional replica exchange molecular dynamics (REMD) is a molecular simulation method used for statistical sampling of biomolecular conformational ensembles. In the cited evidence, it functions as the benchmark comparator against two coarse kMC-based replica exchange methods.
Usefulness & Problems
Why this is useful
The evidence supports its use as a reference standard for evaluating statistical sampling behavior in protein-related simulations. Its utility here is specifically as a baseline against which newer replica exchange approaches were assessed.
Source:
We apply this approach on folding of 2 different β-stranded peptides: the C-terminal β-hairpin fragment of GB1 and TrpZip4. Additionally, we use the new simulation technique to study the folding of TrpCage, a small fast folding α-helical peptide. Subsequently, we apply the new methodology on conformation changes in signaling of the light-oxygen voltage (LOV) sensitive domain from Avena sativa (AsLOV2).
Problem solved
Conventional REMD addresses the problem of sampling conformational space in molecular dynamics simulations. In the supplied study, it specifically served as the comparator for performance and sampling analysis in dialanine simulations.
Source:
We apply this approach on folding of 2 different β-stranded peptides: the C-terminal β-hairpin fragment of GB1 and TrpZip4. Additionally, we use the new simulation technique to study the folding of TrpCage, a small fast folding α-helical peptide. Subsequently, we apply the new methodology on conformation changes in signaling of the light-oxygen voltage (LOV) sensitive domain from Avena sativa (AsLOV2).
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete computational method used to design, rank, or analyze an engineered system.
Target processes
No target processes tagged yet.
Implementation Constraints
cofactor dependency: cofactor requirement unknownencoding mode: genetically encodedimplementation constraint: context specific validationoperating role: builder
The evidence identifies conventional REMD as a molecular dynamics-based replica exchange method, but gives no further practical details on temperature ladders, exchange criteria, force fields, solvent models, or software. No construct design or experimental delivery considerations are relevant from the supplied text.
The provided evidence does not describe implementation details, system requirements, or quantitative limitations of conventional REMD. It also does not report its direct application results in the listed protein folding or AsLOV2 signaling systems, only that new methods were applied there.
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
The new simulation technique was applied to folding of the C-terminal beta-hairpin fragment of GB1, TrpZip4, and TrpCage, and to conformational changes in signaling of AsLOV2.
We apply this approach on folding of 2 different β-stranded peptides: the C-terminal β-hairpin fragment of GB1 and TrpZip4. Additionally, we use the new simulation technique to study the folding of TrpCage, a small fast folding α-helical peptide. Subsequently, we apply the new methodology on conformation changes in signaling of the light-oxygen voltage (LOV) sensitive domain from Avena sativa (AsLOV2).
The new simulation technique was applied to folding of the C-terminal beta-hairpin fragment of GB1, TrpZip4, and TrpCage, and to conformational changes in signaling of AsLOV2.
We apply this approach on folding of 2 different β-stranded peptides: the C-terminal β-hairpin fragment of GB1 and TrpZip4. Additionally, we use the new simulation technique to study the folding of TrpCage, a small fast folding α-helical peptide. Subsequently, we apply the new methodology on conformation changes in signaling of the light-oxygen voltage (LOV) sensitive domain from Avena sativa (AsLOV2).
The new simulation technique was applied to folding of the C-terminal beta-hairpin fragment of GB1, TrpZip4, and TrpCage, and to conformational changes in signaling of AsLOV2.
We apply this approach on folding of 2 different β-stranded peptides: the C-terminal β-hairpin fragment of GB1 and TrpZip4. Additionally, we use the new simulation technique to study the folding of TrpCage, a small fast folding α-helical peptide. Subsequently, we apply the new methodology on conformation changes in signaling of the light-oxygen voltage (LOV) sensitive domain from Avena sativa (AsLOV2).
The new simulation technique was applied to folding of the C-terminal beta-hairpin fragment of GB1, TrpZip4, and TrpCage, and to conformational changes in signaling of AsLOV2.
We apply this approach on folding of 2 different β-stranded peptides: the C-terminal β-hairpin fragment of GB1 and TrpZip4. Additionally, we use the new simulation technique to study the folding of TrpCage, a small fast folding α-helical peptide. Subsequently, we apply the new methodology on conformation changes in signaling of the light-oxygen voltage (LOV) sensitive domain from Avena sativa (AsLOV2).
The new simulation technique was applied to folding of the C-terminal beta-hairpin fragment of GB1, TrpZip4, and TrpCage, and to conformational changes in signaling of AsLOV2.
We apply this approach on folding of 2 different β-stranded peptides: the C-terminal β-hairpin fragment of GB1 and TrpZip4. Additionally, we use the new simulation technique to study the folding of TrpCage, a small fast folding α-helical peptide. Subsequently, we apply the new methodology on conformation changes in signaling of the light-oxygen voltage (LOV) sensitive domain from Avena sativa (AsLOV2).
The new simulation technique was applied to folding of the C-terminal beta-hairpin fragment of GB1, TrpZip4, and TrpCage, and to conformational changes in signaling of AsLOV2.
We apply this approach on folding of 2 different β-stranded peptides: the C-terminal β-hairpin fragment of GB1 and TrpZip4. Additionally, we use the new simulation technique to study the folding of TrpCage, a small fast folding α-helical peptide. Subsequently, we apply the new methodology on conformation changes in signaling of the light-oxygen voltage (LOV) sensitive domain from Avena sativa (AsLOV2).
The new simulation technique was applied to folding of the C-terminal beta-hairpin fragment of GB1, TrpZip4, and TrpCage, and to conformational changes in signaling of AsLOV2.
We apply this approach on folding of 2 different β-stranded peptides: the C-terminal β-hairpin fragment of GB1 and TrpZip4. Additionally, we use the new simulation technique to study the folding of TrpCage, a small fast folding α-helical peptide. Subsequently, we apply the new methodology on conformation changes in signaling of the light-oxygen voltage (LOV) sensitive domain from Avena sativa (AsLOV2).
The new simulation technique was applied to folding of the C-terminal beta-hairpin fragment of GB1, TrpZip4, and TrpCage, and to conformational changes in signaling of AsLOV2.
We apply this approach on folding of 2 different β-stranded peptides: the C-terminal β-hairpin fragment of GB1 and TrpZip4. Additionally, we use the new simulation technique to study the folding of TrpCage, a small fast folding α-helical peptide. Subsequently, we apply the new methodology on conformation changes in signaling of the light-oxygen voltage (LOV) sensitive domain from Avena sativa (AsLOV2).
The new simulation technique was applied to folding of the C-terminal beta-hairpin fragment of GB1, TrpZip4, and TrpCage, and to conformational changes in signaling of AsLOV2.
We apply this approach on folding of 2 different β-stranded peptides: the C-terminal β-hairpin fragment of GB1 and TrpZip4. Additionally, we use the new simulation technique to study the folding of TrpCage, a small fast folding α-helical peptide. Subsequently, we apply the new methodology on conformation changes in signaling of the light-oxygen voltage (LOV) sensitive domain from Avena sativa (AsLOV2).
The new simulation technique was applied to folding of the C-terminal beta-hairpin fragment of GB1, TrpZip4, and TrpCage, and to conformational changes in signaling of AsLOV2.
We apply this approach on folding of 2 different β-stranded peptides: the C-terminal β-hairpin fragment of GB1 and TrpZip4. Additionally, we use the new simulation technique to study the folding of TrpCage, a small fast folding α-helical peptide. Subsequently, we apply the new methodology on conformation changes in signaling of the light-oxygen voltage (LOV) sensitive domain from Avena sativa (AsLOV2).
In dialanine simulations, the two new techniques were compared with conventional REMD for statistical sampling and performance analysis.
In simulations of dialanine, we compare the statistical sampling of the 2 techniques with conventional REMD and analyze their performance.
In dialanine simulations, the two new techniques were compared with conventional REMD for statistical sampling and performance analysis.
In simulations of dialanine, we compare the statistical sampling of the 2 techniques with conventional REMD and analyze their performance.
In dialanine simulations, the two new techniques were compared with conventional REMD for statistical sampling and performance analysis.
In simulations of dialanine, we compare the statistical sampling of the 2 techniques with conventional REMD and analyze their performance.
In dialanine simulations, the two new techniques were compared with conventional REMD for statistical sampling and performance analysis.
In simulations of dialanine, we compare the statistical sampling of the 2 techniques with conventional REMD and analyze their performance.
In dialanine simulations, the two new techniques were compared with conventional REMD for statistical sampling and performance analysis.
In simulations of dialanine, we compare the statistical sampling of the 2 techniques with conventional REMD and analyze their performance.
In dialanine simulations, the two new techniques were compared with conventional REMD for statistical sampling and performance analysis.
In simulations of dialanine, we compare the statistical sampling of the 2 techniques with conventional REMD and analyze their performance.
In dialanine simulations, the two new techniques were compared with conventional REMD for statistical sampling and performance analysis.
In simulations of dialanine, we compare the statistical sampling of the 2 techniques with conventional REMD and analyze their performance.
In dialanine simulations, the two new techniques were compared with conventional REMD for statistical sampling and performance analysis.
In simulations of dialanine, we compare the statistical sampling of the 2 techniques with conventional REMD and analyze their performance.
In dialanine simulations, the two new techniques were compared with conventional REMD for statistical sampling and performance analysis.
In simulations of dialanine, we compare the statistical sampling of the 2 techniques with conventional REMD and analyze their performance.
In dialanine simulations, the two new techniques were compared with conventional REMD for statistical sampling and performance analysis.
In simulations of dialanine, we compare the statistical sampling of the 2 techniques with conventional REMD and analyze their performance.
In dialanine simulations, the two new techniques were compared with conventional REMD for statistical sampling and performance analysis.
In simulations of dialanine, we compare the statistical sampling of the 2 techniques with conventional REMD and analyze their performance.
In dialanine simulations, the two new techniques were compared with conventional REMD for statistical sampling and performance analysis.
In simulations of dialanine, we compare the statistical sampling of the 2 techniques with conventional REMD and analyze their performance.
In dialanine simulations, the two new techniques were compared with conventional REMD for statistical sampling and performance analysis.
In simulations of dialanine, we compare the statistical sampling of the 2 techniques with conventional REMD and analyze their performance.
In dialanine simulations, the two new techniques were compared with conventional REMD for statistical sampling and performance analysis.
In simulations of dialanine, we compare the statistical sampling of the 2 techniques with conventional REMD and analyze their performance.
In dialanine simulations, the two new techniques were compared with conventional REMD for statistical sampling and performance analysis.
In simulations of dialanine, we compare the statistical sampling of the 2 techniques with conventional REMD and analyze their performance.
In dialanine simulations, the two new techniques were compared with conventional REMD for statistical sampling and performance analysis.
In simulations of dialanine, we compare the statistical sampling of the 2 techniques with conventional REMD and analyze their performance.
In dialanine simulations, the two new techniques were compared with conventional REMD for statistical sampling and performance analysis.
In simulations of dialanine, we compare the statistical sampling of the 2 techniques with conventional REMD and analyze their performance.
Approval Evidence
1 source1 linked approval claimfirst-pass slug conventional-remd
we compare the statistical sampling of the 2 techniques with conventional REMD
Source:
comparative performancesupports
In dialanine simulations, the two new techniques were compared with conventional REMD for statistical sampling and performance analysis.
In simulations of dialanine, we compare the statistical sampling of the 2 techniques with conventional REMD and analyze their performance.
Source:
Comparisons
Source-backed strengths
The supplied evidence indicates that conventional REMD is established enough to be used as the comparison method for statistical sampling analyses. No quantitative performance advantages or validated outcomes beyond this benchmark role are provided in the evidence.
Source:
In simulations of dialanine, we compare the statistical sampling of the 2 techniques with conventional REMD and analyze their performance.
Compared with free-energy calculations
conventional REMD and free-energy calculations address a similar problem space.
Shared frame: same top-level item type
Compared with mathematical model
conventional REMD and mathematical model address a similar problem space.
Shared frame: same top-level item type
Strengths here: looks easier to implement in practice.
Compared with SwiftLib
conventional REMD and SwiftLib address a similar problem space.
Shared frame: same top-level item type
Ranked Citations
- 1.