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BackgroundComputational prediction of protein function constitutes one of the more complex problems in Bioinformatics, because of the diversity of functions and mechanisms in that proteins exert in nature. This issue is reinforced especially for proteins that share very low primary or tertiary structure similarity to existing annotated proteomes. In this sense, new alignment-free (AF) tools are needed to overcome the inherent limitations of classic alignment-based approaches to this issue. We have recently introduced AF protein-numerical-encoding programs (TI2BioP and ProtDCal), whose sequence-based features have been successfully applied to detect remote protein homologs, post-translational modifications and antibacterial peptides. Here we aim to demonstrate the applicability of 4 AF protein descriptor families, implemented in our programs, for the identification enzyme-like proteins. At the same time, the use of our novel family of 3D–structure-based descriptors is introduced for the first time. The Dobson u0026 Doig (Du0026D) benchmark dataset is used for the evaluation of our AF protein descriptors, because of its proven structural diversity that permits one to emulate an experiment within the twilight zone of alignment-based methods (pair-wise identity u003c30%). The performance of our sequence-based predictor was further assessed using a subset of formerly uncharacterized proteins which currently represent a benchmark annotation dataset.

Exploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zone