The SCOP database in 2020: expanded classification of representative family and superfamily domains of known protein structures

The Structural Classification of Proteins (SCOP) database is a classification of protein domains organised according to their evolutionary and structural relationships. We report a major effort to increase the coverage of structural data, aiming to provide classification of almost all domain superfamilies with representatives in the PDB. We have also improved the database schema, provided a new API and modernised the web interface. This is by far the most significant update in coverage since SCOP 1.75 and builds on the advances in schema from the SCOP 2 prototype. The database is accessible from http://scop.mrc-lmb.cam.ac.uk.

Global sequence properties for superfamily prediction: a machine learning approach

SummaryFunctional annotation of a protein sequence in the absence of experimental data or clear similarity to a sequence of known function is difficult. In this study, a simple set of sequence attributes based on physicochemical and predicted structural characteristics were used as input to machine learning methods. In order to improve performance through increasing the data available for training, a technique of sequence enrichment was explored. These methods were used to predict membership to 24 and 49 large and diverse protein superfamiles from the SCOP database.We found the best performance was obtained using an enriched training dataset. Accuracies of 66.3% and 55.6% were achieved on datasets comprising 24 and 49 superfamilies with LibSVM and AdaBoostM1 respectively.The methods used here confirm that domains within superfamilies share global sequence properties. We show machine learning models used to predict categories within the SCOP database can be significantly improved via a simple sequence enrichment step. These approaches can be used to complement profile methods for detecting distant relationships where function is difficult to infer.