Source 1primary paper2017FEBS Journal
Toolkit/molecular dynamics simulations
molecular dynamics simulations
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
Molecular dynamics simulations were used as a computational design method to guide construction of the PiL[D24] photoswitchable mPKM2-LOV2 fusion reported in the 2017 FEBS Journal study. In that context, the simulations supported engineering of a light-responsive pyruvate kinase chimera that preserved LOV2 photoreactivity and showed illumination-dependent changes in enzyme activity.
Usefulness & Problems
Why this is useful
This computational method was useful for informing design of a fusion between mammalian pyruvate kinase M2 and the LOV2 photosensory domain. The reported study links this design workflow to a construct with light-dependent biochemical and cellular effects, indicating value for engineering optically controlled allosteric proteins.
Problem solved
The method addressed the design problem of creating a functional light-responsive mPKM2-LOV2 chimera. Specifically, it was used to guide fusion design in a system where illumination altered pyruvate kinase kinetics and cellular pyruvate labeling from glucose.
Published Workflows
Objective: Integrate AlphaFold predictions with experimental structural biology workflows to accelerate structural hypothesis generation, improve cryo-EM model interpretation, and guide mutagenesis while using complementary methods to address uncertainty and dynamics.
Why it works: The abstract states that AlphaFold accelerates hypothesis generation and improves model fitting in ambiguous cryo-EM density, while MD augments static models by sampling flexibility and testing stability. Orthogonal validation is highlighted as necessary where limitations and uncertainty remain.
protein structure prediction from sequencemulti-chain complex assembly predictionconformational samplingstability testingdeep learning structure predictioncryo-EM interpretationmutagenesis guidancemolecular dynamics simulationorthogonal validation
Stages
- 1.AlphaFold structural hypothesis generation(broad_screen)
The abstract says AlphaFold provides near-experimental accuracy models directly from sequence and accelerates hypothesis generation.
Selection: Generate structural models directly from amino acid sequences to accelerate hypothesis generation.
- 2.Experimental interpretation and model fitting(functional_characterization)
The abstract explicitly states that AlphaFold predictions improve model fitting in ambiguous density regions and refine conformational hypotheses.
Selection: Use AlphaFold predictions to improve fitting within ambiguous cryo-EM density regions and refine conformational hypotheses.
- 3.Dynamic augmentation and orthogonal validation(secondary_characterization)
The abstract says MD augments static models by sampling conformational flexibility and testing stability, and it emphasizes the need for orthogonal validation.
Selection: Apply MD and orthogonal validation when static AlphaFold models are insufficient for flexibility, stability, or known limitation areas.
Objective: Characterize early lipid-driven dimerization of hepatitis C virus p7 and determine how membrane lipids guide residue alignment and binding during early oligomer assembly.
Why it works: The paper compares dimer interactions in aqueous solution versus on a lipid membrane model, and the abstract states that this comparison reveals how protein-lipid interactions guide residue alignment and binding.
protein-lipid interactions guiding inter-protein alignment and bindinghydrophobic contacts between key residues and membrane lipidshydrogen bonding with phosphatidylcholine/phosphatidylinositol lipidsfirst-helix involvement in oligomerization-promoting interactionsmolecular dynamics simulationsenvironmental comparison between aqueous solution and lipid membrane model
Objective: Use computational approaches to accelerate nanocarrier design and optimize nanodelivery system properties for improved therapeutic performance.
Why it works: The abstract states that MD provides mechanistic insight into nanoparticle-membrane interactions, while AI analyzes large chemical datasets to predict optimal structures. Together these in silico approaches are presented as enabling rapid refinement of nanoparticle composition before or alongside experimental validation.
nanoparticle interactions with biological membraneseffects of surface charge density on uptake and stabilityeffects of ligand functionalisation on uptake and stabilityeffects of nanoparticle size on uptake and stabilityMolecular Dynamics simulationsArtificial Intelligence-driven modelingin silico guided experimental validation
Stages
- 1.In silico modeling and prediction(in_silico_filter)
This stage exists to accelerate nanocarrier design and optimize properties before experimental validation by using MD for mechanistic insight and AI for structure prediction.
Selection: Mechanistic insight from MD and predictive analysis from AI are used to evaluate nanoparticle properties and candidate structures.
- 2.Experimental validation(confirmatory_validation)
The abstract explicitly states that in silico models guide experimental validation, indicating a downstream confirmation stage for computationally informed designs.
Selection: In silico-guided designs are experimentally validated.
Steps
- 1.Model nanoparticle interactions and predict favorable formulationscomputational methods used to analyze and prioritize nanocarrier designs
Use MD to understand membrane interactions and AI to predict optimal lipid nanoparticle structures.
The abstract presents computational approaches as tools that accelerate design and guide later experimental validation, so this analysis occurs before downstream validation.
- 2.Experimentally validate in silico-guided designs
Confirm whether computationally informed nanocarrier designs support improved therapeutic performance.
The abstract explicitly states that in silico models guide experimental validation, making validation a downstream step after computational prioritization.
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.
Input: Light
Implementation Constraints
The documented implementation context is design of the PiL[D24] mPKM2-LOV2 domain fusion described in the FEBS Journal 2017 study. The available evidence indicates a light-responsive construct containing LOV2 fused to mammalian PKM2, but it does not provide practical simulation setup details or software parameters.
The supplied evidence supports use of molecular dynamics simulations in a single reported design case, rather than as a broadly benchmarked platform. The evidence does not specify simulation protocols, force fields, predictive accuracy, computational cost, or independent replication across additional targets.
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
Light exposure causes secondary structure changes in PiL[D24] that are associated with a 30% decrease in Km for phosphoenolpyruvate and increased pyruvate kinase activity.
causes secondary structure changes that are associated with a 30% decrease in the Km of the enzyme for phosphoenolpyruvate resulting in increased pyruvate kinase activity after light exposure
Km change for phosphoenolpyruvate 30 %
Light exposure causes secondary structure changes in PiL[D24] that are associated with a 30% decrease in Km for phosphoenolpyruvate and increased pyruvate kinase activity.
causes secondary structure changes that are associated with a 30% decrease in the Km of the enzyme for phosphoenolpyruvate resulting in increased pyruvate kinase activity after light exposure
Km change for phosphoenolpyruvate 30 %
Light exposure causes secondary structure changes in PiL[D24] that are associated with a 30% decrease in Km for phosphoenolpyruvate and increased pyruvate kinase activity.
causes secondary structure changes that are associated with a 30% decrease in the Km of the enzyme for phosphoenolpyruvate resulting in increased pyruvate kinase activity after light exposure
Km change for phosphoenolpyruvate 30 %
Light exposure causes secondary structure changes in PiL[D24] that are associated with a 30% decrease in Km for phosphoenolpyruvate and increased pyruvate kinase activity.
causes secondary structure changes that are associated with a 30% decrease in the Km of the enzyme for phosphoenolpyruvate resulting in increased pyruvate kinase activity after light exposure
Km change for phosphoenolpyruvate 30 %
Light exposure causes secondary structure changes in PiL[D24] that are associated with a 30% decrease in Km for phosphoenolpyruvate and increased pyruvate kinase activity.
causes secondary structure changes that are associated with a 30% decrease in the Km of the enzyme for phosphoenolpyruvate resulting in increased pyruvate kinase activity after light exposure
Km change for phosphoenolpyruvate 30 %
Light exposure causes secondary structure changes in PiL[D24] that are associated with a 30% decrease in Km for phosphoenolpyruvate and increased pyruvate kinase activity.
causes secondary structure changes that are associated with a 30% decrease in the Km of the enzyme for phosphoenolpyruvate resulting in increased pyruvate kinase activity after light exposure
Km change for phosphoenolpyruvate 30 %
Light exposure causes secondary structure changes in PiL[D24] that are associated with a 30% decrease in Km for phosphoenolpyruvate and increased pyruvate kinase activity.
causes secondary structure changes that are associated with a 30% decrease in the Km of the enzyme for phosphoenolpyruvate resulting in increased pyruvate kinase activity after light exposure
Km change for phosphoenolpyruvate 30 %
Expression of PiL[D24] in cells leads to a light-induced increase in labelling of pyruvate from glucose.
Expression of PiL[D24] in cells leads to light-induced increase in labelling of pyruvate from glucose.
Expression of PiL[D24] in cells leads to a light-induced increase in labelling of pyruvate from glucose.
Expression of PiL[D24] in cells leads to light-induced increase in labelling of pyruvate from glucose.
Expression of PiL[D24] in cells leads to a light-induced increase in labelling of pyruvate from glucose.
Expression of PiL[D24] in cells leads to light-induced increase in labelling of pyruvate from glucose.
Expression of PiL[D24] in cells leads to a light-induced increase in labelling of pyruvate from glucose.
Expression of PiL[D24] in cells leads to light-induced increase in labelling of pyruvate from glucose.
Expression of PiL[D24] in cells leads to a light-induced increase in labelling of pyruvate from glucose.
Expression of PiL[D24] in cells leads to light-induced increase in labelling of pyruvate from glucose.
Expression of PiL[D24] in cells leads to a light-induced increase in labelling of pyruvate from glucose.
Expression of PiL[D24] in cells leads to light-induced increase in labelling of pyruvate from glucose.
Expression of PiL[D24] in cells leads to a light-induced increase in labelling of pyruvate from glucose.
Expression of PiL[D24] in cells leads to light-induced increase in labelling of pyruvate from glucose.
Molecular dynamics simulations were used to guide the design of the PiL[D24] mPKM2-LOV2 fusion.
we have used molecular dynamics simulations to guide the design of mPKM2 internal light/oxygen/voltage-sensitive domain 2 (LOV2) fusion at position D24 (PiL[D24])
Molecular dynamics simulations were used to guide the design of the PiL[D24] mPKM2-LOV2 fusion.
we have used molecular dynamics simulations to guide the design of mPKM2 internal light/oxygen/voltage-sensitive domain 2 (LOV2) fusion at position D24 (PiL[D24])
Molecular dynamics simulations were used to guide the design of the PiL[D24] mPKM2-LOV2 fusion.
we have used molecular dynamics simulations to guide the design of mPKM2 internal light/oxygen/voltage-sensitive domain 2 (LOV2) fusion at position D24 (PiL[D24])
Molecular dynamics simulations were used to guide the design of the PiL[D24] mPKM2-LOV2 fusion.
we have used molecular dynamics simulations to guide the design of mPKM2 internal light/oxygen/voltage-sensitive domain 2 (LOV2) fusion at position D24 (PiL[D24])
Molecular dynamics simulations were used to guide the design of the PiL[D24] mPKM2-LOV2 fusion.
we have used molecular dynamics simulations to guide the design of mPKM2 internal light/oxygen/voltage-sensitive domain 2 (LOV2) fusion at position D24 (PiL[D24])
Molecular dynamics simulations were used to guide the design of the PiL[D24] mPKM2-LOV2 fusion.
we have used molecular dynamics simulations to guide the design of mPKM2 internal light/oxygen/voltage-sensitive domain 2 (LOV2) fusion at position D24 (PiL[D24])
Molecular dynamics simulations were used to guide the design of the PiL[D24] mPKM2-LOV2 fusion.
we have used molecular dynamics simulations to guide the design of mPKM2 internal light/oxygen/voltage-sensitive domain 2 (LOV2) fusion at position D24 (PiL[D24])
The LOV2 photoreaction is preserved in the PiL[D24] chimera.
The LOV2 photoreaction is preserved in the PiL[D24] chimera
The LOV2 photoreaction is preserved in the PiL[D24] chimera.
The LOV2 photoreaction is preserved in the PiL[D24] chimera
The LOV2 photoreaction is preserved in the PiL[D24] chimera.
The LOV2 photoreaction is preserved in the PiL[D24] chimera
The LOV2 photoreaction is preserved in the PiL[D24] chimera.
The LOV2 photoreaction is preserved in the PiL[D24] chimera
The LOV2 photoreaction is preserved in the PiL[D24] chimera.
The LOV2 photoreaction is preserved in the PiL[D24] chimera
The LOV2 photoreaction is preserved in the PiL[D24] chimera.
The LOV2 photoreaction is preserved in the PiL[D24] chimera
The LOV2 photoreaction is preserved in the PiL[D24] chimera.
The LOV2 photoreaction is preserved in the PiL[D24] chimera
PiL[D24] could provide a means to modulate cellular glucose metabolism remotely.
PiL[D24] therefore could provide a means to modulate cellular glucose metabolism in a remote manner
PiL[D24] could provide a means to modulate cellular glucose metabolism remotely.
PiL[D24] therefore could provide a means to modulate cellular glucose metabolism in a remote manner
PiL[D24] could provide a means to modulate cellular glucose metabolism remotely.
PiL[D24] therefore could provide a means to modulate cellular glucose metabolism in a remote manner
PiL[D24] could provide a means to modulate cellular glucose metabolism remotely.
PiL[D24] therefore could provide a means to modulate cellular glucose metabolism in a remote manner
PiL[D24] could provide a means to modulate cellular glucose metabolism remotely.
PiL[D24] therefore could provide a means to modulate cellular glucose metabolism in a remote manner
PiL[D24] could provide a means to modulate cellular glucose metabolism remotely.
PiL[D24] therefore could provide a means to modulate cellular glucose metabolism in a remote manner
PiL[D24] could provide a means to modulate cellular glucose metabolism remotely.
PiL[D24] therefore could provide a means to modulate cellular glucose metabolism in a remote manner
The light-induced change in PiL[D24] activity is reversible upon light withdrawal.
Importantly, this change in activity is reversible upon light withdrawal.
The light-induced change in PiL[D24] activity is reversible upon light withdrawal.
Importantly, this change in activity is reversible upon light withdrawal.
The light-induced change in PiL[D24] activity is reversible upon light withdrawal.
Importantly, this change in activity is reversible upon light withdrawal.
The light-induced change in PiL[D24] activity is reversible upon light withdrawal.
Importantly, this change in activity is reversible upon light withdrawal.
The light-induced change in PiL[D24] activity is reversible upon light withdrawal.
Importantly, this change in activity is reversible upon light withdrawal.
The light-induced change in PiL[D24] activity is reversible upon light withdrawal.
Importantly, this change in activity is reversible upon light withdrawal.
The light-induced change in PiL[D24] activity is reversible upon light withdrawal.
Importantly, this change in activity is reversible upon light withdrawal.
Approval Evidence
5 sources10 linked approval claimsfirst-pass slug molecular-dynamics-simulations
molecular dynamics (MD) simulations augment AlphaFold's static models by sampling conformational flexibility and testing stability.
Source:
this work characterizes early lipid-driven dimerization using molecular dynamics simulations
Source:
computational approaches-particularly Molecular Dynamics (MD) simulations and Artificial Intelligence (AI)-have emerged as transformative tools to accelerate nanocarrier design and optimise their properties
Source:
we have used molecular dynamics simulations to guide the design
Source:
as we demonstrate using molecular dynamics simulations
Source:
capabilitysupports
Molecular dynamics simulations augment AlphaFold static models by sampling conformational flexibility and testing stability.
molecular dynamics (MD) simulations augment AlphaFold's static models by sampling conformational flexibility and testing stability
Source:
capabilitysupports
Molecular Dynamics simulations and Artificial Intelligence are described as transformative computational approaches for accelerating nanocarrier design and optimizing nanocarrier properties.
Source:
design impactsupports
Computational approaches can be used to refine nanoparticle composition to improve biocompatibility, reduce toxicity, and achieve more precise drug targeting.
Source:
limitationsupports
Data scarcity and complex in vivo dynamics are identified as key challenges for integrating computational insights into next generation nanodelivery systems.
Source:
mechanistic insightsupports
Compared with aqueous solution, a lipid membrane model reveals that protein-lipid interactions critically guide inter-protein residue alignment and binding during p7 dimer interactions.
Comparing dimer interactions in aqueous solution versus on a lipid membrane model reveal that protein-lipid interactions critically guide inter-protein residue alignment and binding.
Source:
mechanistic insightsupports
Hydrophobic contacts and hydrogen bonding between key residues and phosphatidylcholine/phosphatidylinositol lipids drive helix interactions that promote p7 oligomerization, particularly involving the first helix.
Hydrophobic contacts and hydrogen bonding between key residues and phosphatidylcholine/phosphatidylinositol lipids drive essential helix interactions that promote p7 oligomerization, particularly involving the first helix.
Source:
mechanistic insightsupports
MD simulations provide atomic-to-mesoscale insight into nanoparticle interactions with biological membranes, including how surface charge density, ligand functionalisation, and nanoparticle size affect cellular uptake and stability.
Source:
mechanistic insightsupports
Molecular dynamics simulations characterize early lipid-driven dimerization of hepatitis C virus p7.
Using the hepatitis C virus p7 hexamer as a representative of proteins with complex transmembrane topology, this work characterizes early lipid-driven dimerization using molecular dynamics simulations.
Source:
workflow rolesupports
In silico models are described as guiding experimental validation, informing rational design strategies, and streamlining translation of nanodelivery systems from bench to bedside.
Source:
engineered designsupports
Molecular dynamics simulations were used to guide the design of the PiL[D24] mPKM2-LOV2 fusion.
we have used molecular dynamics simulations to guide the design of mPKM2 internal light/oxygen/voltage-sensitive domain 2 (LOV2) fusion at position D24 (PiL[D24])
Source:
Comparisons
Source-backed strengths
The main demonstrated strength is that molecular dynamics simulations were explicitly used to guide design of a successful photoswitchable enzyme construct. In the resulting PiL[D24] tool, light exposure was associated with secondary-structure changes, a 30% decrease in Km for phosphoenolpyruvate, increased pyruvate kinase activity, and increased labeling of pyruvate from glucose in cells.
Ranked Citations
- 1.