Protein Sequence and Feature Data used to develop the ECemble enzyme classification methodology

This dataset relates to the development of ECemble, an approach to identifying enzymes and enzyme classes and study the human
gut metabolic pathways, using an ensemble of machine-learning methods to
accurately model and predict enzymes from protein sequences.

ECemble
has been used to predict entire complements of enzymes from ten sequenced
proteomes including the human proteome, as well as predicting enzymes encoded
by the human gut microbiome from gut metagenomic samples, and to study the role
played by the microbe-derived enzymes in the human metabolism.

This dataset includes
enzyme and non-enzyme sequence data (FASTA format and Identifiers) and sequence
features from Pfam, Prosite and Superfamily databases used to develop ECemble
enzyme classification methodology.

The files include:

Enzyme Data : 64,950 sequences (Fasta File | Sequence
ID File)

Non-enzyme Data
: 128,475 sequences
(Fasta File | Sequence ID File)

All Data : 193,425 sequences (Fasta File |
Sequence ID File)

List of Unique
Features : 14,241 features
These files are in .txt format and can be opened by any open source or standard word processing software.