The research is published in today's (Nov. 15, 2002) issue of Proteins.
Called MULTIPROSPECTOR, the new algorithm takes protein interaction prediction to a new level because it works on proteins on which little structural information exists, providing three-dimensional models of the protein-protein complex and identifying the amino acid residues that interact.
According to Skolnick, the new method takes the entire field of structural genomics an important step closer to the ultimate goal of using detailed information about genes and the proteins they encode to design more effective pharmaceuticals.
"The overall goal," he said, "is to develop personalized medicine, which is based on understanding how a drug affects you versus how it affects me."
He noted: "With this paper, we are moving toward an understanding of how the whole system works, what's known as systems biology, which is the key revolution in the post-genomic era," he explained.
According to Skolnick, complexes of interacting proteins provide exciting and novel targets for potential new drugs.
"Right now, very few drugs exist that inhibit protein-protein interactions; most work against single molecules," he said.
But, he noted, the Protein Data Bank, the international "public library" of solved protein structures from which scientists draw data, contains not just isolated molecules, but in many instances solved compounds of two or more proteins interacting.
"Lots of cellular signals are mediated by these protein-protein interactions," he said, "and we want to know exactly who's interacting with whom. Often, the function of one protein can be deduced by s
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