Proteins control nearly all of life's functions, but how they self-assemble or fold is an unsolved problem in biology. Understanding how folding goes awry could lead to cures for diseases such as Alzheimer's and Parkinson's, which are caused by protein misfolding.
One of the winners of the 2013 Nobel Prize in Chemistry, Michael Levitt, PhD, is an early pioneer in “computational biology,” the development of complex software algorithms that allow researchers to simulate and experiment with biological processes such as protein folding. In 1969, he realistically modeled tRNA, a helper molecule for building proteins inside the body. He also discovered the architectural patterns in proteins, devised a protocol for simulating how water interacts with proteins and designed the first simulations of humanized antibodies.
In this video, Levitt's Stanford colleague, Vijay Pande, PhD, shows a simulation of protein folding, and explains why computational biology is important to the future of medicine. By modeling protein folding, Pande says, "We hope to get exquisite detail and information that you might not be able to get from experiments."