Sparse Bayesian prediction of disordered residues and disordered regions based on amino-acid composition

This paper presents some initial results of an investigation into the use of machine learning methods to detect natively disordered regions in proteins from sequence information. A committee of Relevance Vector Machines is used to select the optimal window size for residue-by-residue prediction of disordered regions, based on local amino-acid composition. The minimal error rate of ≈ 15% is achieved using very long (205 residue) window lengths, with the classifier making little use of more local sequence information. This suggests that disorder arises principally due to large scale diffuse changes in mean hydropathy and to a lesser extent mean charge. We also demonstrate that the proportion of proteins having long disordered regions in operational conditions cannot be reliably estimated using a classifier trained on a balanced dataset.