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J R Bieńkowska

Publications and source records attributed to J R Bieńkowska.

2 recordsLinked to original sources

Eliminating superfluous neighbor pairs while threading fold models.

In this paper we address the problem of identifying which of various possible spatial residue-residue neighbor pairs are plausible physical contacts without reference to the native structure side chain geometry. We propose an algorithm that eliminates most of the implausible physical contacts from the fold models. This algorithm exploits the correlations between the amino acid side chain rotamers and the direction of the physical contacts between the amino acid side chains. We use this algorithm to "filter" the score of the sequence-to-structure alignment. Filtering is dynamic, in the sense that the set of neighbor pairs contributing to the alignment score varies during threading. Whether or not a neighbor pair contributes to the score depends on the threaded amino acids. This score filtering improves the accuracy of the predicted sequence-to-structure alignment.

Algorithms↗

Performance of threading scoring functions designed using new optimization method.

We present a new procedure for optimization of a threading scoring function. A scoring function is usually formulated in terms of the structural environment states that describe the protein fold model. We propose a method for the optimal selection of those structural environment states that naturally follows from the probabilistic description of the threading problem and is done prior to threading experiments. We demonstrate the selection of the optimal structural environment states for the solvent exposure of the amino acid position, and present the results of threading experiments performed using scoring functions designed with and without the optimization of the structural environment states. These results confirm that the optimal scoring function predicts the sequence-to-structure alignments most accurately. Threading experiments performed with 15 optimally designed scoring functions show that the correlation coefficient between the information content of the amino acid distribution that determines the scoring function and the accuracy of the optimal sequence-to-structure alignment is 0.94.

Amino Acid Sequence↗