Source 1primary paper2010Proceedings of the National Academy of Sciences
Toolkit/likelihood maximization analysis
likelihood maximization analysis
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
Likelihood maximization analysis is a computational method for selecting reaction coordinate models for individual substeps of a conformational transition and inferring tentative transition states. In the cited application, it was applied to transition path sampling data from explicit-solvent molecular dynamics of the millisecond partial unfolding transition in the photoactive yellow protein photocycle.
Usefulness & Problems
Why this is useful
This method is useful for extracting mechanistic models of conformational transitions from existing simulation data without requiring additional simulations. The cited study used it to obtain reaction coordinate models and tentative transition states for substeps within a light-induced protein conformational change.
Problem solved
It addresses the problem of identifying plausible reaction coordinates and transition-state locations for complex, multistep conformational transitions. In the available evidence, this problem was framed in the context of the millisecond partial unfolding transition in photoactive yellow protein.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete computational method used to design, rank, or analyze an engineered system.
Techniques
Computational DesignTarget processes
No target processes tagged yet.
Implementation Constraints
The method was applied downstream of transition path sampling data generated from explicit-solvent molecular dynamics trajectories. The supplied evidence does not specify software, parameterization details, input feature design, or computational requirements beyond this analysis context.
The available evidence describes a single application to photoactive yellow protein and does not report broader benchmarking across systems. Quantitative performance, robustness, and experimental validation of the inferred transition states are not provided in the supplied evidence.
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
Transition path sampling of explicit solvent molecular dynamics trajectories was used to obtain atomistic insight into the reaction network of the millisecond partial unfolding transition in the photocycle of photoactive yellow protein.
We employ transition path sampling of explicit solvent molecular dynamics trajectories to obtain atomistic insight in the reaction network of the millisecond timescale partial unfolding transition in the photocycle of the bacterial sensor photoactive yellow protein.
Transition path sampling of explicit solvent molecular dynamics trajectories was used to obtain atomistic insight into the reaction network of the millisecond partial unfolding transition in the photocycle of photoactive yellow protein.
We employ transition path sampling of explicit solvent molecular dynamics trajectories to obtain atomistic insight in the reaction network of the millisecond timescale partial unfolding transition in the photocycle of the bacterial sensor photoactive yellow protein.
Transition path sampling of explicit solvent molecular dynamics trajectories was used to obtain atomistic insight into the reaction network of the millisecond partial unfolding transition in the photocycle of photoactive yellow protein.
We employ transition path sampling of explicit solvent molecular dynamics trajectories to obtain atomistic insight in the reaction network of the millisecond timescale partial unfolding transition in the photocycle of the bacterial sensor photoactive yellow protein.
Transition path sampling of explicit solvent molecular dynamics trajectories was used to obtain atomistic insight into the reaction network of the millisecond partial unfolding transition in the photocycle of photoactive yellow protein.
We employ transition path sampling of explicit solvent molecular dynamics trajectories to obtain atomistic insight in the reaction network of the millisecond timescale partial unfolding transition in the photocycle of the bacterial sensor photoactive yellow protein.
Transition path sampling of explicit solvent molecular dynamics trajectories was used to obtain atomistic insight into the reaction network of the millisecond partial unfolding transition in the photocycle of photoactive yellow protein.
We employ transition path sampling of explicit solvent molecular dynamics trajectories to obtain atomistic insight in the reaction network of the millisecond timescale partial unfolding transition in the photocycle of the bacterial sensor photoactive yellow protein.
Transition path sampling of explicit solvent molecular dynamics trajectories was used to obtain atomistic insight into the reaction network of the millisecond partial unfolding transition in the photocycle of photoactive yellow protein.
We employ transition path sampling of explicit solvent molecular dynamics trajectories to obtain atomistic insight in the reaction network of the millisecond timescale partial unfolding transition in the photocycle of the bacterial sensor photoactive yellow protein.
Transition path sampling of explicit solvent molecular dynamics trajectories was used to obtain atomistic insight into the reaction network of the millisecond partial unfolding transition in the photocycle of photoactive yellow protein.
We employ transition path sampling of explicit solvent molecular dynamics trajectories to obtain atomistic insight in the reaction network of the millisecond timescale partial unfolding transition in the photocycle of the bacterial sensor photoactive yellow protein.
Transition path sampling of explicit solvent molecular dynamics trajectories was used to obtain atomistic insight into the reaction network of the millisecond partial unfolding transition in the photocycle of photoactive yellow protein.
We employ transition path sampling of explicit solvent molecular dynamics trajectories to obtain atomistic insight in the reaction network of the millisecond timescale partial unfolding transition in the photocycle of the bacterial sensor photoactive yellow protein.
Transition path sampling of explicit solvent molecular dynamics trajectories was used to obtain atomistic insight into the reaction network of the millisecond partial unfolding transition in the photocycle of photoactive yellow protein.
We employ transition path sampling of explicit solvent molecular dynamics trajectories to obtain atomistic insight in the reaction network of the millisecond timescale partial unfolding transition in the photocycle of the bacterial sensor photoactive yellow protein.
Transition path sampling of explicit solvent molecular dynamics trajectories was used to obtain atomistic insight into the reaction network of the millisecond partial unfolding transition in the photocycle of photoactive yellow protein.
We employ transition path sampling of explicit solvent molecular dynamics trajectories to obtain atomistic insight in the reaction network of the millisecond timescale partial unfolding transition in the photocycle of the bacterial sensor photoactive yellow protein.
Transition path sampling of explicit solvent molecular dynamics trajectories was used to obtain atomistic insight into the reaction network of the millisecond partial unfolding transition in the photocycle of photoactive yellow protein.
We employ transition path sampling of explicit solvent molecular dynamics trajectories to obtain atomistic insight in the reaction network of the millisecond timescale partial unfolding transition in the photocycle of the bacterial sensor photoactive yellow protein.
Transition path sampling of explicit solvent molecular dynamics trajectories was used to obtain atomistic insight into the reaction network of the millisecond partial unfolding transition in the photocycle of photoactive yellow protein.
We employ transition path sampling of explicit solvent molecular dynamics trajectories to obtain atomistic insight in the reaction network of the millisecond timescale partial unfolding transition in the photocycle of the bacterial sensor photoactive yellow protein.
Transition path sampling of explicit solvent molecular dynamics trajectories was used to obtain atomistic insight into the reaction network of the millisecond partial unfolding transition in the photocycle of photoactive yellow protein.
We employ transition path sampling of explicit solvent molecular dynamics trajectories to obtain atomistic insight in the reaction network of the millisecond timescale partial unfolding transition in the photocycle of the bacterial sensor photoactive yellow protein.
Transition path sampling of explicit solvent molecular dynamics trajectories was used to obtain atomistic insight into the reaction network of the millisecond partial unfolding transition in the photocycle of photoactive yellow protein.
We employ transition path sampling of explicit solvent molecular dynamics trajectories to obtain atomistic insight in the reaction network of the millisecond timescale partial unfolding transition in the photocycle of the bacterial sensor photoactive yellow protein.
Likelihood maximization analysis predicted reaction coordinate models for each substep and tentative transition states without further simulation.
Likelihood maximization analysis predicts the best model for the reaction coordinates of each substep as well as tentative transition states, without further simulation.
Likelihood maximization analysis predicted reaction coordinate models for each substep and tentative transition states without further simulation.
Likelihood maximization analysis predicts the best model for the reaction coordinates of each substep as well as tentative transition states, without further simulation.
Likelihood maximization analysis predicted reaction coordinate models for each substep and tentative transition states without further simulation.
Likelihood maximization analysis predicts the best model for the reaction coordinates of each substep as well as tentative transition states, without further simulation.
Likelihood maximization analysis predicted reaction coordinate models for each substep and tentative transition states without further simulation.
Likelihood maximization analysis predicts the best model for the reaction coordinates of each substep as well as tentative transition states, without further simulation.
Likelihood maximization analysis predicted reaction coordinate models for each substep and tentative transition states without further simulation.
Likelihood maximization analysis predicts the best model for the reaction coordinates of each substep as well as tentative transition states, without further simulation.
Likelihood maximization analysis predicted reaction coordinate models for each substep and tentative transition states without further simulation.
Likelihood maximization analysis predicts the best model for the reaction coordinates of each substep as well as tentative transition states, without further simulation.
Likelihood maximization analysis predicted reaction coordinate models for each substep and tentative transition states without further simulation.
Likelihood maximization analysis predicts the best model for the reaction coordinates of each substep as well as tentative transition states, without further simulation.
Likelihood maximization analysis predicted reaction coordinate models for each substep and tentative transition states without further simulation.
Likelihood maximization analysis predicts the best model for the reaction coordinates of each substep as well as tentative transition states, without further simulation.
Likelihood maximization analysis predicted reaction coordinate models for each substep and tentative transition states without further simulation.
Likelihood maximization analysis predicts the best model for the reaction coordinates of each substep as well as tentative transition states, without further simulation.
Likelihood maximization analysis predicted reaction coordinate models for each substep and tentative transition states without further simulation.
Likelihood maximization analysis predicts the best model for the reaction coordinates of each substep as well as tentative transition states, without further simulation.
Likelihood maximization analysis predicted reaction coordinate models for each substep and tentative transition states without further simulation.
Likelihood maximization analysis predicts the best model for the reaction coordinates of each substep as well as tentative transition states, without further simulation.
Likelihood maximization analysis predicted reaction coordinate models for each substep and tentative transition states without further simulation.
Likelihood maximization analysis predicts the best model for the reaction coordinates of each substep as well as tentative transition states, without further simulation.
Likelihood maximization analysis predicted reaction coordinate models for each substep and tentative transition states without further simulation.
Likelihood maximization analysis predicts the best model for the reaction coordinates of each substep as well as tentative transition states, without further simulation.
Likelihood maximization analysis predicted reaction coordinate models for each substep and tentative transition states without further simulation.
Likelihood maximization analysis predicts the best model for the reaction coordinates of each substep as well as tentative transition states, without further simulation.
Approval Evidence
1 source1 linked approval claimfirst-pass slug likelihood-maximization-analysis
Likelihood maximization analysis predicts the best model for the reaction coordinates of each substep as well as tentative transition states
Source:
predictionsupports
Likelihood maximization analysis predicted reaction coordinate models for each substep and tentative transition states without further simulation.
Likelihood maximization analysis predicts the best model for the reaction coordinates of each substep as well as tentative transition states, without further simulation.
Source:
Comparisons
Source-backed strengths
The reported strength is that it predicted best reaction coordinate models for each substep and tentative transition states without further simulation. It was used in conjunction with transition path sampling of explicit-solvent molecular dynamics trajectories, providing atomistic context for the analyzed reaction network.
Ranked Citations
- 1.