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Introducing Pep McConst-A user-friendly peptide modeler for biophysical applications.

Fabian SchuhmannVasili KorolIlia A Solov'yov
Published in: Journal of computational chemistry (2021)
We are introducing Pep McConst-a software that employs a Monte-Carlo algorithm to construct 3D structures of polypeptide chains which could subsequently be studied as stand-alone macromolecules or complement the structure of known proteins. Using an approach to avoid steric clashes, Pep McConst allows to create multiple structures for a predefined primary sequence of amino acids. These structures could then effectively be used for further structural analysis and investigations. The article introduces the algorithm and describes its user-friendly approach that was made possible through the VIKING online platform. Finally, the manuscript provides several highlight examples where Pep McConst was used to predict the structure of the C-terminal of a known protein, generate a missing bit of already crystallized protein structures and simply generate short polypeptide chains.
Keyphrases
  • amino acid
  • high resolution
  • monte carlo
  • machine learning
  • deep learning
  • protein protein
  • high throughput
  • social media
  • binding protein
  • health information
  • mass spectrometry
  • data analysis