Using a molecular model generated with Macromodel in combination with caDNAno, the positions for EP modification were selected.
Our algorithm iterates between clustering modified peptides with similar modifications to form groups, which we call PTM groups, and finding the most likely modification mass and modification position for each peptide based on the grouping.
In addition, we studied the modification level distribution (the fraction of cDNA reads exhibiting the modification in a given position) for both types of modifications.
Moreover, many instances of the same modified peptide either share the same modification position (for both correct and misplaced cases) or have vastly different modification positions that point to different phosphorylation sites in the peptide.
Then, we determined the average modification position for each group (rounded to the nearest position) and computed a histogram of the modification position error.
In our endeavor, we selected C-3 position for the desirable chemical modification in order to improve its cytotoxic and pharmacological potential and prepared a library of different triazoline derivatives of BA.
The true modification position for each peptide was randomly chosen to be on one of the instances of the preassigned amino acid for that subset, and the modification positions used as input to the algorithms are set to a noisy version of the true modification positions.
We therefore took the alternative approach of asking whether any archaeal sno-like sRNAs are responsible for modification at a position in archaeal rRNA equivalent to conserved eukaryotic rRNA modifications.
However, lipases (EC 3.1.1.3) in general can also be used for such purposes, in particularly for modification at the sn-1 position.
The 3D models were exported as STL files and imported into Geomagic Studio software (version 12.0; Geomagic, Inc., Morrisville, NC, USA) for smoothing, positioning, and modification without altering the structure for further finite element modeling.
