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Assessing Protein-Drug Resistance Due to Mutations via a Rigidity Analysis in silico Approach

10 pagesPublished: March 11, 2020

Abstract

A mutation to the amino acid sequence of a protein can cause a biomolecule to be resistant to the intended effects of a drug. Assessing the changes of a drug’s efficacy in response to mutations via mutagenesis wet-lab experiments is prohibitively time consuming for even a single point mutation, let alone for all possible mutations. Existing approaches for inferring mutation-induced drug resistance are available, but all of them reason about mutations of residues at or very near the protein-drug interface. However, there are examples of mutations far away from the region where the ligand binds, but which nonetheless render a protein resistant to the effects of the drug. We present a proof-of-concept computational pipeline that generates in silico the set of all possible single point mutations in a protein-ligand complex. We assess drug resistance using a graph theoretic rigidity analysis approach. Unlike existing methods, we are able to assess the impact of mutations far away from the protein-drug interface. We introduce several visualizations for exploring how amino acid substitutions both near and far away from where the ligand interacts with a protein target have a stabilizing or destabilizing effect on the protein-drug complex. We discuss our analytical approach in the context of experimental data from the literature about clinically known protein-drug interactions.

Keyphrases: drug resistance, Protein-ligand complex, rigidity analysis

In: Qin Ding, Oliver Eulenstein and Hisham Al-Mubaid (editors). Proceedings of the 12th International Conference on Bioinformatics and Computational Biology, vol 70, pages 69--78

Links:
BibTeX entry
@inproceedings{BICOB2020:Assessing_Protein_Drug_Resistance_Due,
  author    = {Dylan Carpenter and Tess Thackray and Cecilia Kalthoff and Filip Jagodzinski},
  title     = {Assessing Protein-Drug Resistance Due to Mutations via a Rigidity Analysis in silico Approach},
  booktitle = {Proceedings of the 12th International Conference on Bioinformatics and Computational Biology},
  editor    = {Qin Ding and Oliver Eulenstein and Hisham Al-Mubaid},
  series    = {EPiC Series in Computing},
  volume    = {70},
  pages     = {69--78},
  year      = {2020},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/pDm5},
  doi       = {10.29007/7gnf}}
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