Structure Description

PDB-ID: {{protein.pdbId}}
Chain-ID: {{protein.chainId}}
Title: {{protein.title}}

Early Folding Residues


Early folding residues are rendered in blue in the structure on the left. Hover over the name of individual early folding residues listed above to see their position in the three-dimensional structure.

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During protein folding, the denatured protein chain passes the transition state and adopts its native conformation. Early folding residues form local stable structures early during the folding process by interacting with sequentially close residues. Early folding residues tend to interact with an increased number of other residues in the native structure. What causes some residues to fold early is unclear. schematic representation of folding process

The presented classifier for early folding residues demonstrates how an easily comprehensible white box machine learning technique, called generalized matrix learning vector quantization (GMLVQ), can be employed to create a sparse, interpretable classification model. The algorithm is provided as plug-in of the popular Weka machine learning toolbox.

schematic representation of GMLVQ method

GMLVQ represents classes of the data (e.g. early folding and late folding) by a small number of prototypes. The prototype placement allows to identify discriminative features between classes. Furthermore, GMLVQ learns a linear transformation which potentially increases classification performance and can be used to compute a classification correlation matrix. This matrix identifies the most influential features as well as crucial correlations between them. This also allows to identify the most discriminative features for the given classification problem: Early folding residues are embedded in ordered secondary structure elements and participate in networks of hydrophobic residues.

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Last updated: October 25, 2018

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