Validation experiment designed to validate causal mechanisms targeting AREA in human. Primary outcome: Development and validation of a multiscale computational model that accurately predicts α-synuclein
Description
Multiscale Computational Modeling of Protein Aggregation Kinetics
Background and Rationale
Protein aggregation is a hallmark of neurodegenerative diseases including Parkinson's disease, where alpha-synuclein forms pathological Lewy bodies. Understanding the molecular mechanisms governing aggregation kinetics is crucial for therapeutic development. This computational validation study employs multiscale molecular dynamics (MD) simulations to model protein aggregation processes across temporal and spatial scales. The approach integrates all-atom MD simulations with enhanced sampling methods to capture rare conformational transitions that drive aggregation. We focus on three key aggregation-prone proteins: tau PHF6 domain (associated with Alzheimer's), alpha-synuclein NAC region (Parkinson's hallmark), and TDP-43 C-terminal domain (ALS/FTD pathology). The study design encompasses multiple computational phases: initial equilibration simulations, enhanced sampling using metadynamics and replica exchange methods, and free energy calculations for dimerization and higher-order oligomerization events....
Multiscale Computational Modeling of Protein Aggregation Kinetics
Background and Rationale
Protein aggregation is a hallmark of neurodegenerative diseases including Parkinson's disease, where alpha-synuclein forms pathological Lewy bodies. Understanding the molecular mechanisms governing aggregation kinetics is crucial for therapeutic development. This computational validation study employs multiscale molecular dynamics (MD) simulations to model protein aggregation processes across temporal and spatial scales. The approach integrates all-atom MD simulations with enhanced sampling methods to capture rare conformational transitions that drive aggregation. We focus on three key aggregation-prone proteins: tau PHF6 domain (associated with Alzheimer's), alpha-synuclein NAC region (Parkinson's hallmark), and TDP-43 C-terminal domain (ALS/FTD pathology). The study design encompasses multiple computational phases: initial equilibration simulations, enhanced sampling using metadynamics and replica exchange methods, and free energy calculations for dimerization and higher-order oligomerization events. Key measurements include conformational free energy landscapes, aggregation rate constants, critical nucleus sizes, and thermodynamic stability of oligomeric species. Advanced sampling techniques will overcome kinetic barriers typically inaccessible to conventional MD timescales, enabling characterization of aggregation pathways and intermediates. The innovation lies in the multiscale approach that bridges atomic-level interactions with mesoscopic aggregation phenomena, providing mechanistic insights into disease-relevant protein misfolding. This computational framework will validate experimental kinetic data and generate testable predictions about aggregation inhibition strategies. The significance extends beyond Parkinson's disease, offering a generalizable computational platform for understanding protein aggregation across neurodegenerative conditions and informing structure-based drug design efforts targeting pathological protein assemblies.
This experiment directly tests predictions arising from the following hypotheses:
Phase 1: System Preparation (Days 1-7) - Generate initial protein structures from PDB database for alpha-synuclein NAC region (residues 61-95), tau PHF6 domain, and TDP-43 C-terminal fragment. Perform energy minimization and 100ns equilibration simulations in explicit water using AMBER force field. Phase 2: Enhanced Sampling (Days 8-45) - Execute metadynamics simulations with collective variables including radius of gyration, inter-protein contacts, and secondary structure content. Run 12 replica exchange simulations per protein system spanning 300-400K temperature range. Perform 500ns production runs per replica with coordinate saving every 10ps. Phase 3: Free Energy Calculations (Days 46-75) - Calculate dimerization free energies using umbrella sampling with protein-protein distance as reaction coordinate. Sample 40 windows with 50ns simulation per window. Compute oligomerization thermodynamics for trimers and tetramers using potential of mean force calculations. Phase 4: Kinetic Analysis (Days 76-90) - Extract aggregation rate constants from transition state theory using free energy barriers. Analyze conformational transitions using Markov state models with 1μs lag time. Calculate nucleation rates and critical cluster sizes using classical nucleation theory framework. Phase 5: Validation (Days 91-105) - Compare computed aggregation rates with experimental thioflavin-T fluorescence kinetics. Validate structural predictions against cryo-EM fibril structures and NMR oligomer data.
Expected Outcomes
1. Free energy barriers for alpha-synuclein dimerization will range 15-25 kT, consistent with experimental aggregation timescales of hours to days
2. Critical nucleus size for all three proteins will be 4-8 monomers, matching experimental seeding requirements
3. Aggregation rate constants will show tau > alpha-synuclein > TDP-43 hierarchy, correlating with disease progression rates
4. Beta-sheet formation will precede oligomerization by 50-100ns in metadynamics simulations
5. Computed fibril structures will match experimental cryo-EM data within 2-3Å RMSD for backbone atoms
6. Temperature-dependent aggregation rates will follow Arrhenius behavior with activation energies of 20-40 kcal/mol
Success Criteria
• Convergence of free energy landscapes within 2 kT across independent simulation replicas
• Computed dimerization free energies within 20% of experimental values from analytical ultracentrifugation
• Aggregation rate predictions within one order of magnitude of thioflavin-T kinetic measurements
• Structural models of oligomers and fibrils showing <3Å deviation from available experimental structures
• Successful identification of at least 3 distinct aggregation pathways per protein system
• Validation of temperature dependence with correlation coefficient R² > 0.8 compared to experimental data
TARGET GENE
AREA
MODEL SYSTEM
human
ESTIMATED COST
$2,280,000
TIMELINE
32 months
PATHWAY
N/A
SOURCE
wiki
PRIMARY OUTCOME
Development and validation of a multiscale computational model that accurately predicts α-synuclein aggregation kinetics and fibril morphology with ≥80% correlation to experimental ThT fluorescence and TEM data.
Phase 1: System Preparation (Days 1-7) - Generate initial protein structures from PDB database for alpha-synuclein NAC region (residues 61-95), tau PHF6 domain, and TDP-43 C-terminal fragment. Perform energy minimization and 100ns equilibration simulations in explicit water using AMBER force field. Phase 2: Enhanced Sampling (Days 8-45) - Execute metadynamics simulations with collective variables including radius of gyration, inter-protein contacts, and secondary structure content. Run 12 replica exchange simulations per protein system spanning 300-400K temperature range. Perform 500ns production runs per replica with coordinate saving every 10ps.
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Phase 1: System Preparation (Days 1-7) - Generate initial protein structures from PDB database for alpha-synuclein NAC region (residues 61-95), tau PHF6 domain, and TDP-43 C-terminal fragment. Perform energy minimization and 100ns equilibration simulations in explicit water using AMBER force field. Phase 2: Enhanced Sampling (Days 8-45) - Execute metadynamics simulations with collective variables including radius of gyration, inter-protein contacts, and secondary structure content. Run 12 replica exchange simulations per protein system spanning 300-400K temperature range. Perform 500ns production runs per replica with coordinate saving every 10ps. Phase 3: Free Energy Calculations (Days 46-75) - Calculate dimerization free energies using umbrella sampling with protein-protein distance as reaction coordinate. Sample 40 windows with 50ns simulation per window. Compute oligomerization thermodynamics for trimers and tetramers using potential of mean force calculations. Phase 4: Kinetic Analysis (Days 76-90) - Extract aggregation rate constants from transition state theory using free energy barriers. Analyze conformational transitions using Markov state models with 1μs lag time. Calculate nucleation rates and critical cluster sizes using classical nucleation theory framework. Phase 5: Validation (Days 91-105) - Compare computed aggregation rates with experimental thioflavin-T fluorescence kinetics. Validate structural predictions against cryo-EM fibril structures and NMR oligomer data.
Expected Outcomes
1. Free energy barriers for alpha-synuclein dimerization will range 15-25 kT, consistent with experimental aggregation timescales of hours to days
2. Critical nucleus size for all three proteins will be 4-8 monomers, matching experimental seeding requirements
3. Aggregation rate constants will show tau > alpha-synuclein > TDP-43 hierarchy, correlating with disease progression rates
4. Beta-sheet formation will precede oligomerization by 50-100ns in metadynamics simulations
5. Computed fibril structures will match experimental cryo-EM data within 2-3Å RMSD for backbone atoms
6.
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1. Free energy barriers for alpha-synuclein dimerization will range 15-25 kT, consistent with experimental aggregation timescales of hours to days
2. Critical nucleus size for all three proteins will be 4-8 monomers, matching experimental seeding requirements
3. Aggregation rate constants will show tau > alpha-synuclein > TDP-43 hierarchy, correlating with disease progression rates
4. Beta-sheet formation will precede oligomerization by 50-100ns in metadynamics simulations
5. Computed fibril structures will match experimental cryo-EM data within 2-3Å RMSD for backbone atoms
6. Temperature-dependent aggregation rates will follow Arrhenius behavior with activation energies of 20-40 kcal/mol
Success Criteria
• Convergence of free energy landscapes within 2 kT across independent simulation replicas
• Computed dimerization free energies within 20% of experimental values from analytical ultracentrifugation
• Aggregation rate predictions within one order of magnitude of thioflavin-T kinetic measurements
• Structural models of oligomers and fibrils showing <3Å deviation from available experimental structures
• Successful identification of at least 3 distinct aggregation pathways per protein system
• Validation of temperature dependence with correlation coefficient R² > 0.8 compared to experi
...
• Convergence of free energy landscapes within 2 kT across independent simulation replicas
• Computed dimerization free energies within 20% of experimental values from analytical ultracentrifugation
• Aggregation rate predictions within one order of magnitude of thioflavin-T kinetic measurements
• Structural models of oligomers and fibrils showing <3Å deviation from available experimental structures
• Successful identification of at least 3 distinct aggregation pathways per protein system
• Validation of temperature dependence with correlation coefficient R² > 0.8 compared to experimental data