Knowledge-based potential functions in protein design.
Predicting protein sequences that fold into specific native three-dimensional structures is a problem of great potential complexity. Although the complete solution is ultimately rooted in understanding the physical chemistry underlying the complex interactions between amino acid residues that determine protein stability, recent work shows that empirical information about these first principles is embedded in the statistics of protein sequence and structure databases. This review focuses on the use of 'knowledge-based' potentials derived from these databases in designing proteins. In addition, the data suggest how the study of these empirical potentials might impact our fundamental understanding of the energetic principles of protein structure.
Russ
William P
WP
Howard Hughes Medical Institute and Department of Pharmacology, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas 75390-9050, USA.
Ranganathan
Rama
R
D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry; D001185:Artificial Intelligence; D017124:Conserved Sequence; D030562:Databases, Protein; D016247:Information Storage and Retrieval / Q000379:methods; D008969:Molecular Sequence Data; D011487:Protein Conformation; D015202:Protein Engineering / Q000379:methods*; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods
D000596:Amino Acids; D011506:Proteins