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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key...
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A deep learning model to detect novel pore-forming proteins.

Theju Jacob1, Theodore W Kahn2

  • 1BASF Corporation, 3500 Paramount Parkway, Morrisville, NC, 27560, USA. theju.jacob@gmail.com.

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|February 8, 2022
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A new deep learning model can identify novel pore-forming proteins for pest control. This approach surpasses traditional methods, offering a promising tool against pesticide resistance in agricultural pests.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Agricultural Science

Background:

  • Pore-forming proteins from bacteria are vital components in current pesticidal products.
  • Pest resistance to existing pesticides necessitates the discovery of novel protein agents.
  • Current methods for identifying pore-forming proteins are limited to sequence homology, hindering novel discovery.

Purpose of the Study:

  • To develop and evaluate a novel deep learning model for identifying pore-forming proteins.
  • To compare the efficacy of the deep learning model against traditional sequence homology approaches.
  • To assess the model's capability in discovering novel pore-forming proteins beyond sequence similarity.

Main Methods:

  • A deep learning model was trained using a dataset of known pore-forming proteins.
  • Various protein information encoding strategies were explored during model training.
  • The model's performance was benchmarked against conventional sequence homology-based methods.

Main Results:

  • The deep learning model successfully identified known pore-forming proteins.
  • The model demonstrated proficiency in recognizing pore formers lacking sequence similarity to the training data.
  • The proposed model offers a significant advancement over traditional identification techniques.

Conclusions:

  • The developed deep learning model shows strong potential for discovering novel pore-forming proteins.
  • This approach can aid in developing new pesticidal products effective against a broader range of pests.
  • The model offers a valuable tool to combat the growing issue of pest resistance to existing agricultural treatments.