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Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis
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Novel Descriptors and Digital Signal Processing- Based Method for Protein Sequence Activity Relationship Study.

Nicolas T Fontaine1, Xavier F Cadet1, Iyanar Vetrivel1

  • 1PEACCEL, Protein Engineering ACCELerator, 6 Square Albin Cachot, box 42, 75013 Paris, France.

International Journal of Molecular Sciences
|November 14, 2019
PubMed
Summary
This summary is machine-generated.

Predicting protein fitness from sequence alone is now possible using novel numerical descriptors and fast Fourier transformation (FFT). This method enhances modeling and prediction accuracy for polypeptide variants, aiding in protein engineering.

Keywords:
artificial intelligencedigital signal processingdirected evolutionextended sequenceinnov’SARmachine learningprotein spectrumrational screening

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Area of Science:

  • Computational Biology
  • Protein Engineering
  • Bioinformatics

Background:

  • Understanding the relationship between protein sequence and function is crucial, especially without structural data.
  • Existing methods may not fully capture the complex sequence-function correlations.

Purpose of the Study:

  • To develop a novel computational approach for predicting polypeptide fitness directly from amino acid sequences.
  • To improve the accuracy of modeling and predicting protein variant fitness values.

Main Methods:

  • Calculating elementary numerical sequences (Ele_SEQ) based on amino acid indices.
  • Generating extended numerical sequences (Ext_SEQ) by concatenating Ele_SEQ, incorporating fast Fourier transformation (FFT) spectra.
  • Applying these descriptors to model and predict fitness for various protein sets and activities.

Main Results:

  • The novel approach, utilizing physicochemical descriptors and FFT, significantly improved model and prediction quality.
  • Performance varied depending on the specific protein and fitness attribute, highlighting the method's adaptability.
  • Demonstrated effectiveness across diverse protein lengths and functions (e.g., GLP-2, TNF alpha, cytochrome P450).

Conclusions:

  • This sequence-based method offers a powerful tool for predicting protein fitness without structural information.
  • It enhances the value of existing mutant libraries by potentially identifying improved protein variants for applications.
  • The approach provides a flexible framework adaptable to different protein/fitness combinations.