Algorithms for protein structural motif recognition.

B Berger

doi:10.1089/cmb.1995.2.125

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Algorithms for protein structural motif recognition.

B Berger

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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.

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Algorithms Amino Acid Sequence Crystallography, X-Ray Databases, Factual False Negative Reactions False Positive Reactions Markov Chains Mathematics Models, Theoretical Pattern Recognition, Automated Probability Protein Conformation Proteins Reproducibility of Results Journal Article Research Support, Non-U.S. Gov't

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B Berger. 1995. Algorithms for protein structural motif recognition.. https://doi.org/10.1089/cmb.1995.2.125

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