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.

Quickstart

Flag known PTM motifs for a given sequence

Predict propensities for Asn deamidation and Met oxidation for a given structure and return results with residue number offset by 12

Predict propensities for Asn deamidation and Met oxidation and include raw prediction data in output

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 FASTA

    • sequence - input sequence

    • deamidation_score - score of deamidation motif counts weighted by known motif hierarchy

    • deamidation_count - number of Asn deamidation motifs found in sequence

    • deamidation_hits - list of detected deamidation motifs and sequence position

    • deamidation_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 hierarchy

    • isomerization_count - number of Asp isomerization motifs found in sequence

    • isomerization_hits - list of detected isomerization motifs and sequence position

    • glycosylation_count - number of N-linked glycosylation motifs found in sequence

    • glycosylation_hits - list of detected glycosylation motifs and sequence position

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

• 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:

• 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

• 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

References

Running PyMOL Scripts