Post-Translational Modification Prediction API
The post-translational modification (PTM) predictor API paces industry standards for in silico liability predictions for therapeutic development. Maintaining therapeutic protein stability and potency during development continues to be a costly and significant challenge due to degradation by PTMs. Early screening for liabilities is critical in reducing development costs and enabling downstream success. The ptm-predictor
API provides both sequence and structure based predictions (See Notes for more information).
Currently, the ptm-predictor
API provides predictions for Asn deamidation, Asp isomerization, N-linked glycosylation, and Met photooxidation.
For more sophisticated N-linked glycosylation prediction, use our Glycosylation Prediction API
Examples
Flag known PTM motifs for a given sequence
lev engine run ptm-prediction --fasta-file input.fasta
Predict propensities for Asn deamidation and Met oxidation for a given structure and return results with residue number offset by 12
lev engine run ptm-prediction --pdb-file input.pdb --offset 12
Predict propensities for Asn deamidation and Met oxidation and include raw prediction data in output
lev engine run ptm-prediction --pdb-file input.pdb --raw
Inputs
Sequence PTM Prediction
--fasta-file
(str)- FASTA file containing sequence(s) of interest
Structural PTM Prediction
--pdb-file
(str)- Input PDB file for predictions
- Ideally the PDB is cleaned using the Clean PDB API and doesn’t not have regions of missing density as this would impact the quality of results
Options
--offset
(int32)- For Structural PTM prediction only
- Adjusts output residue numbering to original numbering scheme by offset provided
- The API will automatically convert a given input PDB to sequential numbering (first residue starts at 1) internally to extract necessary features for predictions.
--raw
(boolean)- For Structural PTM prediction only
- Output raw prediction data used for both deamidation and oxidation as CSVs
Outputs
Sequence PTM Prediction
sequence_ptm_report.tsv
- TSV file containing the following columns:
seqID
- header of sequence in input FASTAsequence
- input sequencedeamidation_score
- score of deamidation motif counts weighted by known motif hierarchydeamidation_count
- number of Asn deamidation motifs found in sequencedeamidation_hits
- list of detected deamidation motifs and sequence positiondeamidation_prone_hits
- highly deamidation prone motifs if detected and sequence position (See Notes for more information)isomerization_score
- score of isomerization motif counts weighted by known motif hierarchyisomerization_count
- number of Asp isomerization motifs found in sequenceisomerization_hits
- list of detected isomerization motifs and sequence positionglycosylation_count
- number of N-linked glycosylation motifs found in sequenceglycosylation_hits
- list of detected glycosylation motifs and sequence position
- TSV file containing the following columns:
Structural PTM Prediction
structure_ptm_report.csv
- CSV file containing residue numbers and corresponding predicted PTM
- Residue numbers will be offset by value provided if
--offset
is used
structure_ptm.pml
- PyMOL script generated to highlight liable residues predicted to have PTMs
- See Notes for more details and how to use this script to visualize your results
deamidation_report.csv
(if--raw
)- CSV file containing the raw structural features used to predict for Asn deamidation for all Asn residues in the provided structure
- Includes both positive and negative predictions
oxidation_report.csv
(if--raw
)- CSV file containing the raw structural features used to predict for Met oxidation for all Met residues in the provided structure
- Includes both positive and negative predictions
Notes
Visualizing Liable Residues in PyMOL
Requires updated and valid PyMOL license to run
- The PTM predictor API, when running the structural PTM predictions, will automatically generate a
.pml
script to visualize predicted liable residues on the input structure in PyMOL. - To visualize the predicted PTM residues, run the following command:
pymol structure_ptm.pml
- Ensure that line 1 in the script correctly sets the path to the input PDB file so that it may be correctly loaded into the PyMOL session.
- Residues will be color coded and shown as sticks in separate scenes as follows:
- Deamidation: color = cyan (
util.cbac
), selection/scene =deamidation_pred
- Oxidation: color = magenta (
util.cbam
), selection/scene =oxidation_pred
- Deamidation: color = cyan (
- Upon starting the session, the current view of the structure would be of all predictions (scene =
all_predictions
) - Clicking on a specific PTM scene would change views to only show residues of that specific PTM
- See References for a tutorial on running PyMOL
Asn Deamidation and Asp Isomerization
- Asparagine deamidation occurs in three potential pathways:
- Nucleophilic attack by backbone carbonyl group
- Nucleophilic attack on backbone nitrogen of N+1 residue
- Direct hydrolysis
- Canonical motifs for deamidation are NG, NS, NN and NH in order of high to low deamidation rate.
- Aspartic acid isomerization occurs through a related pathway to Asn deamidation, but typically occurs at higher rates at low pH
- Canonical motifs for isomerization are DG, DS, DD, DT, and DH in order of high to low isomerization rate
- Both deamidation and isomerization share the same structural attributes for prediction include N+1 residue, solvent accessibility, dihedral angles and nucleophilic attack distance
Detection of Asp isomerization by mass spectrometry is challenging due to the same molecular mass of IsoAsp compared to Asp resulting in limited available experimental data. Therefore, the API for isomerization prediction is currently limited to sequence based flagging.