The beauty of life through the lens of physics

Martin Karplus, Michael Levitt, and Arieh Warshel pioneered molecular dynamics for biological systems earning the 2013 Nobel Prize in Chemistry. Computational biologists simulate protein folding, drug binding, membrane dynamics, and enzymatic catalysis. Pharmaceutical researchers use MD predicting drug efficacy and designing improved compounds. Supercomputing centers run millisecond-timescale simulations on specialized hardware.

Arieh Warshel developed early force fields for biological macromolecules. Norman Allinger created MM force field series. AMBER, CHARMM, and GROMOS force fields emerged from laboratories of Peter Kollman, Martin Karplus, and Wilfred van Gunsteren respectively. Computational chemists continuously refine parameters validating against quantum mechanical calculations and experimental observables.

Loup Verlet developed this numerical integration method for molecular dynamics in 1967. Computational physicists and chemists employ Verlet and related algorithms (leapfrog, velocity Verlet) universally in MD software. Algorithm developers optimize implementations for GPUs and specialized hardware achieving microsecond-per-day simulation speeds.

Herman Berendsen developed equilibration protocols and algorithms for temperature/pressure control in MD. Computational biologists establish preparation workflows preventing simulation artifacts. Software developers automate setup procedures through tools like CHARMM-GUI and AmberTools. Best practices emerge from decades of simulation experience across research groups worldwide.

Computational biologists developed RMSD (root mean square deviation) as standard metric quantifying structural differences. MD practitioners use RMSD assessing simulation convergence and stability. Structural biologists employ RMSD comparing crystal structures or conformational states. Software tools calculate RMSD automatically from trajectory data.

Network theorists apply graph theory to protein structures treating residues as nodes and contacts as edges. Computational biologists develop communicability metrics quantifying allosteric signal propagation. Ruth Nussinov pioneered allostery studies using network approaches. Researchers map communication pathways revealing how distant mutations affect active sites.