Intrinsically disordered proteins lack a stable tertiary structure and form dynamic
conformational ensembles due to their characteristic physicochemical properties and
amino acid composition. They are abundant in nature and responsible for a large variety
of cellular functions. While numerous bioinformatics tools have been developed for
in silico disorder prediction in the last decades, there
is a need for experimental methods to verify the disordered state. CD spectroscopy
is widely used for protein secondary structure analysis. It is usable in a wide concentration
range under various buffer conditions. Even without providing high-resolution information,
it is especially useful when NMR, X-ray, or other techniques are problematic or one
simply needs a fast technique to verify the structure of proteins. Here, we propose
an automatized binary disorder–order classification method by analyzing far-UV CD
spectroscopy data. The method needs CD data at only three wavelength points, making
high-throughput data collection possible. The mathematical analysis applies the k-nearest
neighbor algorithm with cosine distance function, which is independent of the spectral
amplitude and thus free of concentration determination errors. Moreover, the method
can be used even for strong absorbing samples, such as the case of crowded environmental
conditions, if the spectrum can be recorded down to the wavelength of 212 nm. We believe
the classification method will be useful in identifying disorder and will also facilitate
the growth of experimental data in IDP databases. The method is implemented on a webserver
and freely available for academic users.
