Additional file 2 of HeMoQuest: a webserver for qualitative prediction of transient heme binding to protein motifs

Additional file 2 Supplementary data Additionally, the datasets used to train and validate the application is also available for download from the “HeMoQuest Datasets” section of the webserver. A description of the files provided is given below. 1. Training data. 1a. Title: HeMoQuest KD prediction training data. 1b. Description: This comma separated values file contains 72 sequences along with their KD values used in the training of the ML algorithms of HeMoQuest. Column 1 (ID) contains a sequence identifier, column 2 (Seq) contains the sequence and column 3 (KD) contains the experimentally determined KD value of for the peptide sequence. 2. Test data. 2a. Title: HeMoQuest test data for heme binding residue and motif prediction. 2b. Description: This file contains 469 sequences in fasta format, obtained from the BioLip database, all of which are said to bind heme. This data was used to test HeMoQuest’s ability to detect heme binding residues in comparison to existing algorithms. 3. Test data. 3a. Title: HemoQuest test data with manually curated transient heme binding protein sequences. 3b. Description: This file contains 45 sequences in fasta format from 40 manually curated proteins (from Additional Table 1) that are known from literature to be transient heme binding proteins. Few of the proteins have their origins in more than one species and hence we end up with 45 sequences for 40 proteins. 4. Training features. 4a. Title: Features used in training the HeMoQuest KD prediction. 4b. Description: This comma separated values file contains 76 initial features that were generated for the KD prediction training from the R package Peptides. The final set of features used are from the columns ‘charge_vec’, ‘hydrof_vec_octanolScale_pH8’, ‘acidic’, ‘kideraFac3’, ‘vhseScale5_vec’, ‘vhseScale7_vec’, ‘protFP5_vec’ and ‘fasgaiVec4’.