Running and Analyzing Massively Parallel Molecular Simulations 1. This article provides a comprehensive guide on generating and analyzing massively parallel molecular dynamics (MD) simulations to explore protein conformational landscapes and their role in biological processes. 2. Massively parallel simulations overcome the limitations of single MD simulations by sampling long-timescale events and complex biological phenomena through distributed computing. 3. The paper highlights the use of platforms like Folding@home to create datasets from thousands of citizen-scientist-powered simulations, capturing entire energy landscapes of proteins at unprecedented scales. 4. It emphasizes key practical considerations, including selecting appropriate force fields, handling storage needs, and configuring simulations to maintain consistency across datasets for future research comparisons. 5. The guide introduces advanced methods such as adaptive sampling and replica exchange, which improve the exploration of rare events and enhance predictive power for therapeutic design. 6. The analysis section details how Markov state models (MSMs) and time-lagged independent component analysis (tICA) enable insights into protein dynamics, allostery, and mutational impacts relevant to health and disease. 7. Tools like OpenMM and GROMACS are recommended for running large-scale MD simulations, with detailed advice on handling complex systems such as membrane proteins or protein-ligand complexes. 8. The article also explores novel strategies for handling computational challenges, such as varying initial configurations and using adaptive seeding to ensure robust sampling. 💻Code: 📜Paper: