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Graph-Directed Approach for Downselecting Toxins for Experimental Structure Determination.

Rachael A Mansbach1, Srirupa Chakraborty1,2, Timothy Travers1,2

  • 1Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.

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|May 20, 2020
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Summary

This study presents a new computational method to predict conotoxin structures, significantly expanding the available data for high-throughput screening (HTS). This approach aids in identifying potential therapeutic leads and biological threats from conotoxin sequences.

Keywords:
conotoxinshomology modelingnetwork analysisprotein structure determination

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Area of Science:

  • Biochemistry
  • Structural Biology
  • Computational Chemistry

Background:

  • Conotoxins are peptides with therapeutic potential and toxicity, crucial for neuromuscular transmission.
  • Limited structural data (3%) for ~6000 conotoxins hinders high-throughput screening (HTS).

Purpose of the Study:

  • To develop a computational method for expanding conotoxin structure libraries.
  • To identify valuable conotoxin sequences for experimental structural characterization.
  • To generalize the approach for other short, cysteine-rich venoms.

Main Methods:

  • Combined graph-based approaches with homology modeling.
  • Developed and validated a venom structure modeling and experimental guidance approach.

Main Results:

  • Generated a 290% larger library of approximate conotoxin structures for HTS.
  • Provided ranked conotoxin sequences for experimental structure determination.

Conclusions:

  • The novel computational approach effectively expands conotoxin structural diversity.
  • This method accelerates the discovery of therapeutic leads and identification of toxic conotoxins.
  • The approach is applicable to other related venomous peptides.