title: Algorithms for protein structural motif recognition.
abstract: The identification of protein sequences that fold into certain known three-dimensional (3D) structures, or motifs, is evaluated through a probabilistic analysis of their one-dimensional (1D) sequences. We present a correlation method that runs in linear time and incorporates pairwise dependencies between amino acid residues at multiple distances to assess the conditional probability that a given residue is part of a given 3D structure. This method is generalized to multiple motifs, where a dynamic programming approach leads to an efficient algorithm that runs in linear time for practical problems. By this approach, we were able to distinguish (2-stranded) coiled-coil from non-coiled-coil domains and globins from nonglobins. When tested on the Brookhaven X-ray crystal structure database, the method does not produce any false-positive or false-negative predictions of coiled coils.
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D018360:Crystallography, X-Ray; D016208:Databases, Factual; D005188:False Negative Reactions; D005189:False Positive Reactions; D008390:Markov Chains; D008433:Mathematics; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated; D011336:Probability; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results
chemical_list: D011506:Proteins