Profile Hidden Markov Model trained on UDP-diNAcBac PGT cluster, multiple sequence alignment and tree of 4,693 bacterial monotopic PGTs
Published: 20 July 2023| Version 1 | DOI: 10.17632/b57wtpx78y.1
Contributors:
, , , Description
File: smPGT_diNAcBac_hmm_102522.hmm This is a Profile Hidden Markov Model which can be applied to monotopic bacterial phosphoglycosyl transferases to assign substrate specificity. This model was generated using HMMER 3.3 using a randomly selected half-set of PGTs which were found in the diNAcBac cluster of the attached tree. PGT sequences which score > 200 using this model will likely utilize UDP-diNAcBac as their preferred substrate. File: sm_PGT_alignment_012323.fasta This is a multiple sequence alignment of 4,693 bacterial monotopic PGTs. File: smPGT_tree_012323.newick This is a tree generated in geneious prime of the above multiple sequence alignment.
Files
Institutions
Massachusetts Institute of Technology, Boston University
Categories
Hidden Markov Models, Sequence Analysis
Funding
National Institutes of Health
GM131627, GM039334, GM134576