Oct 26, 2023
A Computational Method for detecting and evaluating Tankyrase-Binding Motifs
- Christopher M. Clements1,
- Samantha X Shellman2,
- Melody H Shellman3,
- Yiqun G. Shellman1,4
- 1Department of Dermatology, University of Colorado Anshutz Medical Campus, School of Medicine, Aurora, CO 80045;
- 2Department of Computer Science, University of Colorado, Boulder, CO 80309;
- 3H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30309;
- 4Charles C. Gates Regenerative Medicine and Stem Cell Biology Institute, University of Colorado Anschutz Medical Campus, School of Medicine, Aurora, CO 80045
Protocol Citation: Christopher M. Clements, Samantha X Shellman, Melody H Shellman, Yiqun G. Shellman 2023. A Computational Method for detecting and evaluating Tankyrase-Binding Motifs. protocols.io https://dx.doi.org/10.17504/protocols.io.n2bvj32xplk5/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: October 04, 2023
Last Modified: October 26, 2023
Protocol Integer ID: 88787
Keywords: Tankyrase binding, ankyrin repeat cluster, Tankyrase, Tankyrase 2, PARsylation, PARP, TBM, Tankyrase binding motif
Funders Acknowledgement:
NIH/NIAMS
Grant ID: R01AR074420
Abstract
Tankyrases are multifunctional proteins within the poly(ADP-ribose) polymerase family. Known tankyrase binders primarily interact with the scaffolding portion of tankyrases, which comprises five ankyrin repeat cluster (ARC) domains. These domains recognize a specific sequence known as the Tankyrase binding motif (TBM), typically following an octapeptide format characterized by an arginine at position 1 and a glycine at position 6. However, extended and nonconventional TBMs have also been reported. At present, there is no system in place that can easily find and score TBMs. This protocol describes how to use a web-based, public-accessible, computational method (https://shellmanlab.github.io/) we developed to locate and rank all types of potential TBMs. As interest in tankyrases continues to grow across various biological fields, our tool empowers researchers to quickly assess how tankyrases may impact their protein of interest.
Before start
A computer with access to the Internet will be required. Internet access to both https://www.uniprot.org/ and https://shellmanlab.github.io/ will be necessary.
Finding and Scoring Potential Canonical and Extended TBMs
Finding and Scoring Potential Canonical and Extended TBMs
Identify the Uniprot code for your protein of interest in the Uniprot database (https://www.uniprot.org/), which is the identifier indicated by the arrow in Figure 1.
Figure 1. Finding the Uniprot code for your protein of interest.
Enter Uniprot code(s), one per line, to the text box of the application at https://shellmanlab.github.io/ (Figure 2). For screening multiple proteins simultaneously, a list of Uniprot codes in a column of an Excel spreadsheet can also be copied and pasted into the text box.
Figure 2. Enter the Uniprot codes, one per line.
“Find and Score Motifs!” function will search the sequence of all listed proteins for potential canonical and extended TBMs and provide a score of binding strength for each TBM.
Figure 3. The function of “Find and Score Motifs!”.
The “Find and Score Motifs!” function will output the predicted canonical and then extended TBMs, with one TBM in one row. Each row will include the protein name, Uniprot code, amino acid sequence of the TBM, the starting position of the TBM in the protein, and the score of the binding strength.
Figure 4. An example of output of “Find and Score Motifs!”.
Convert output into an Excel file with delimited by commas (Figure 5-7). Copy and paste the output into an Excel file. Go to the "Data" tab (A) and select "Text to Columns" (B). Then make sure "delimited" is selected in the pop-up window and click "Next" (C).
Figure 5. Paste the output into a spreadsheet of an Excel file.
Make sure that "Comma" is selected as a delimiter and click "Finish" (Figure 6).
Figure 6. Convert text to column in Excel.
Data will now be presented as columns for analysis (Figure 7).
Figure 7. An example of output converted into an Excel file.
Manually Score any potential TBM
Manually Score any potential TBM
Paste or manually input any 8-residue protein sequence. Select “Score any 8-amino-acid sequence” to output a calculated score of the sequence no matter the sequence composition.
Figure 8. The function of "Score any 8-amino-acid sequence."
This will provide a raw score for your peptide with no constraints on selection.
Figure 9. An example of output for "Score any 8-amino-acid sequence" function.