Application of artificial intelligence and machine learning techniques to the analysis of dynamic protein sequences

David C. Kombo, Matthew J. LaMarche, Chilaluck C. Konkankit, S. Rackovsky

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

We apply methods of Artificial Intelligence and Machine Learning to protein dynamic bioinformatics. We rewrite the sequences of a large protein data set, containing both folded and intrinsically disordered molecules, using a representation developed previously, which encodes the intrinsic dynamic properties of the naturally occurring amino acids. We Fourier analyze the resulting sequences. It is demonstrated that classification models built using several different supervised learning methods are able to successfully distinguish folded from intrinsically disordered proteins from sequence alone. It is further shown that the most important sequence property for this discrimination is the sequence mobility, which is the sequence averaged value of the residue-specific average alpha carbon B factor. This is in agreement with previous work, in which we have demonstrated the central role played by the sequence mobility in protein dynamic bioinformatics and biophysics. This finding opens a path to the application of dynamic bioinformatics, in combination with machine learning algorithms, to a range of significant biomedical problems.

Original languageEnglish
Pages (from-to)1234-1241
Number of pages8
JournalProteins: Structure, Function and Bioinformatics
Volume92
Issue number10
DOIs
StatePublished - Oct 2024
Externally publishedYes

Keywords

  • artificial intelligence
  • dynamics
  • folded proteins
  • intrinsically disordered proteins

Fingerprint

Dive into the research topics of 'Application of artificial intelligence and machine learning techniques to the analysis of dynamic protein sequences'. Together they form a unique fingerprint.

Cite this