At the molecular level, most biological function is expressed through the complex three dimensional structures of protein molecules. These structures are determined by the information in the linear amino acid sequence alone, information that is in turn derived from the sequence of bases in the DNA of a gene. This transformation from a linear information string to a complex three dimensional structure capable of biological function lies at the heart of all living processes. A major computational challenge is the development of methods that can calculate the spatial positions of the atoms of a folded protein, given only the amino acid sequence.
For the past thirty years, many research groups have worked on solving this problem, using the conventional organization of contemporary science, with small groups working largely in isolation, and publishing in peer reviewed journals. Although much excellent work has been done, progress has been gradual, with many false leads receiving acceptance.
The advent of universal electronic communications, particularly the web, has made it possible to consider different paradigms for the conduct of scientific research. For protein structure prediction, we have explored the use of a model in which many different research groups agree to work on predicting the structure of the same set of proteins, followed by an extensive comparison of the quality of the results obtained from the different methods. The outcome of these experiments will be described, both in terms of what we have learned about predicting protein structure, and what we have learned about the advantages and disadvantages of this way of doing research.
John Moult is a professor at the University of Maryland Biotechnology Institute and fellow of the Center for Advanced Research in Biotechnology. He received a Doctor of Philosophy from the University of Oxford in 1970. He has published frequently on the subject of protein folding and related areas, and has worked to apply mathematical models to problems of chemical analysis and prediction.
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