Beschreibung:
<jats:title>Abstract</jats:title>
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<jats:title>Motivation</jats:title>
<jats:p>Secondary structure classification is one of the most important issues in structure-based analyses due to its impact on secondary structure prediction, structural alignment and protein visualization. There are still open challenges concerning helix and sheet assignments which are currently not addressed by a single multi-purpose software.</jats:p>
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<jats:title>Results</jats:title>
<jats:p>We introduce SCOT (Secondary structure Classification On Turns) as a novel secondary structure element assignment software which supports the assignment of turns, right-handed α-, 310- and π-helices, left-handed α- and 310-helices, 2.27- and polyproline II helices, β-sheets and kinks. We demonstrate that the introduction of helix Purity values enables a clear differentiation between helix classes. SCOT’s unique strengths are highlighted by comparing it to six state-of-the-art methods (DSSP, STRIDE, ASSP, SEGNO, DISICL and SHAFT). The assignment approaches were compared concerning geometric consistency, protein structure quality and flexibility dependency and their impact on secondary structure element-based structural alignments. We show that only SCOT’s combination of hydrogen bonds, geometric criteria and dihedral angles enables robust assignments independent of the structure quality and flexibility. We demonstrate that this combination and the elaborate kink detection lead to SCOT’s clear superiority for protein alignments. As the resulting helices and strands are provided in a PDB conform output format, they can immediately be used for structure alignment algorithms. Taken together, the application of our new method and the straight-forward visualization using the accompanying PyMOL scripts enable the comprehensive analysis of regular backbone geometries in proteins.</jats:p>
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<jats:title>Availability and implementation</jats:title>
<jats:p>https://this-group.rocks</jats:p>
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<jats:title>Supplementary information</jats:title>
<jats:p>Supplementary data are available at Bioinformatics online.</jats:p>
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