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Related Concept Videos

Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
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Protein Networks

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Humans continually engage with an environment rich in potentially harmful chemicals. These are introduced to our bodies through inhalation, ingestion, or skin contact. These chemicals exist in various forms, such as air and environmental pollutants, agricultural chemicals, organic solvents, and heavy metals.
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Toxicity Testing in Animals

Toxicity tests in animals are grounded on two main assumptions: first, the effects observed in laboratory animals can be extrapolated to humans, especially when adjusted for body surface area; second, high-dose exposure in animals is essential to identify potential human hazards from lower doses. This is based on the quantal dose-response concept, which faces the challenge of extrapolating results from relatively few test animals to much larger human populations. For example, a 0.01% incidence...

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ToxinPred2: an improved method for predicting toxicity of proteins.

Neelam Sharma1, Leimarembi Devi Naorem1, Shipra Jain1

  • 1Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi-110020, India.

Briefings in Bioinformatics
|May 20, 2022
PubMed
Summary

ToxinPred2 is a new tool that predicts protein toxicity, a key challenge in developing protein-based therapies. This updated method accurately identifies toxic proteins, aiding therapeutic development.

Keywords:
BLASTmachine learningmotifspredictionproteinstoxicitytoxins

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Computational Biology

Background:

  • Protein and peptide therapeutics offer significant potential for treating various diseases.
  • Protein toxicity presents a major hurdle in the clinical application of these therapeutic agents.
  • Accurate prediction of protein toxicity is crucial for safe and effective therapeutic development.

Purpose of the Study:

  • To develop and validate ToxinPred2, an updated web-based tool for predicting the toxicity of proteins.
  • To improve upon previous methods for peptide and small protein toxicity prediction.
  • To provide a generalizable method for toxicity prediction across diverse protein sources.

Main Methods:

  • Utilized three curated SwissProt datasets for training, testing, and validation.
  • Employed a hybrid approach combining Basic Local Alignment Search Tool (BLAST)-based similarity, motif searching, and machine learning models.
  • Performed rigorous internal (80%) and external (20%) validation to ensure unbiased evaluation.

Main Results:

  • Machine learning models demonstrated a balance of sensitivity and specificity with high accuracy.
  • The hybrid method achieved a maximum area under the receiver operating characteristic curve (AUC) of 0.99 and a Matthews correlation coefficient (MCC) of 0.91.
  • ToxinPred2 provides accurate toxicity predictions for proteins regardless of their origin.

Conclusions:

  • ToxinPred2 offers a robust and accurate solution for predicting protein toxicity.
  • The tool facilitates the development of safer protein-based therapeutics by identifying potential toxic agents.
  • The web server and standalone versions enhance accessibility for researchers in drug discovery and development.