Life is movement. Thus, to understand how living things function, one must understand the movement and reorganization of the atoms and molecules that make them up. The approach, called “molecular dynamics simulations,” enables scientists to use computer programs to simulate the dynamic motion of all the atoms in a molecular system as a function of time.
In a new paper in EPJ Historical Perspectives in Contemporary PhysicsDaniele Macoglia of Peking University in Beijing, China, Benoit Rowe of the University of Chicago in Chicago, USA, and Giovanni Cicotti of the University of Rome in Rome, Italy, explain how theoretical chemist Martin Karpels and his first team carried out molecular dynamics simulations of a large biological molecule, a protein, and affects In depth on biology and the physical sciences in 20The tenth and 21Street Centuries. Currently, machine learning researchers are using biomolecular simulations to better understand their time-dependent motions and the function that governs the forces between them.
In the early 1970s, physicists and physical chemists began using molecular dynamics simulations to study the behavior of simple substances such as water and liquids formed from the noble gases. Martin Karplus and his team took this method even further by applying it for the first time to a large biological molecule – a protein.
Proteins can be thought of as miniature machines whose function arises in part from the way they fold and warp into different shapes over time. Because of its complexity, simulating changes over time was a particular challenge. Karplus first announced this approach with a 1977 paper showing the first molecular dynamics simulation of a protein. Recently, the profound impact of his contribution to the accurate modeling of chemical reactions has been recognized by the awarding of the 2013 Nobel Prize in Chemistry with Aryeh Warsel and Michael Levitt.
The authors conclude that Karplus’s 1977 article opened the door to pursue a series of papers that led him to successfully integrate computational statistical mechanics with biochemistry.