MOSAICS WP1 Tutorial
Methodologies for Optimization and SAmpling In Computational Studies
Master advanced Monte Carlo sampling methods for molecular simulation. Become an expert in Parallel Tempering and Equi-Energy Monte Carlo.
Progress: 0/6 modules complete
📖 Case Study Reference
📌 Keep this PDF open as reference throughout the tutorial. It contains essential background and experimental details.
The MOSAICS Framework
MOSAICS = Methodologies for Optimization and SAmpling In Computational Studies.Created by Prof. Peter Minary at Stanford (2007), continuously developed at Oxford. A revolutionary software framework for modeling biomolecular structures using advanced Monte Carlo sampling and optimization.
Key Innovation
Uses natural degrees of freedom instead of Cartesian coordinates
Speed Advantage
Minutes vs days compared to traditional MD simulations
🧬 Critical Need for CRISPR Research
The Data Gap: Training GNN models for CRISPR-Cas9 off-target prediction requires 3D structures of Cas9 complexes with different DNA-RNA mismatches. Current available data: ~30 structures. Needed: thousands.
Traditional MD Limitations:
- Extremely long timescales (days to weeks per structure)
- Gets trapped in local energy minima
- Cannot efficiently sample conformational space
- Prohibitively expensive for high-throughput structure generation
MOSAICS Solution: Generate relaxed, biologically relevant Cas9-DNA-RNA structures in MINUTES, not days, enabling rapid dataset creation for ML training.
🎯 Peter's Direction 3: The Pipeline
🚧 The Two Fundamental Problems
1. DIMENSIONALITY PROBLEM
Issue: Too many degrees of freedom (3N for N atoms)
Solution: WP2-4 address this with natural coordinates and constraints
Uses bond angles, dihedrals instead of x,y,z coordinates
2. ENERGY SURFACE PROBLEM
Issue: Deep local minima trap simulations
Solution: WP1 - Advanced sampling (PT, EEMC)
Temperature exchanges help escape energy traps
WP1 (this tutorial) solves the energy surface problem
📚 Essential References
Foundational Papers:
- • Minary (2007) MOSAICS Software Framework
- • Sim, Levitt, Minary (2012) PNAS 109(8):2890-2895
Core Methods:
- • Geyer (1991) Parallel Tempering
- • Metropolis et al. (1953) Monte Carlo Method