Identifying sequence-structure pairs undetected by sequence alignments.
We examine how effectively simple potential functions previously developed can identify compatibilities between sequences and structures of proteins for database searches. The potential function consists of pairwise contact energies, repulsive packing potentials of residues for overly dense arrangement and short-range potentials for secondary structures, all of which were estimated from statistical preferences observed in known protein structures. Each potential energy term was modified to represent compatibilities between sequences and structures for globular proteins. Pairwise contact interactions in a sequence-structure alignment are evaluated in a mean field approximation on the basis of probabilities of site pairs to be aligned. Gap penalties are assumed to be proportional to the number of contacts at each residue position, and as a result gaps will be more frequently placed on protein surfaces than in cores. In addition to minimum energy alignments, we use probability alignments made by successively aligning site pairs in order by pairwise alignment probabilities. The results show that the present energy function and alignment method can detect well both folds compatible with a given sequence and, inversely, sequences compatible with a given fold, and yield mostly similar alignments for these two types of sequence and structure pairs. Probability alignments consisting of most reliable site pairs only can yield extremely small root mean square deviations, and including less reliable pairs increases the deviations. Also, it is observed that secondary structure potentials are usefully complementary to yield improved alignments with this method. Remarkably, by this method some individual sequence-structure pairs are detected having only 5-20% sequence identity.
Miyazawa
S
S
Faculty of Technology, Gunma University, Kiryu, Gunma 376, Japan and Room B-116, Bldg 12B, MSC 5677, Laboratory of Experimental and Computational Biology, DBS, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892-5677,USA.
Jernigan
R L
RL
D000465:Algorithms; D000595:Amino Acid Sequence; D016208:Databases, Factual; D004734:Energy Metabolism; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011336:Probability; D011487:Protein Conformation; D017510:Protein Folding; D017433:Protein Structure, Secondary; D016415:Sequence Alignment; D017386:Sequence Homology, Amino Acid