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Protein-protein Interfaces

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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...
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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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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
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Interpretable and explainable predictive machine learning models for data-driven protein engineering.

David Medina-Ortiz1, Ashkan Khalifeh2, Hoda Anvari-Kazemabad3

  • 1Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120 Halle, Germany; Departamento de Ingeniería En Computación, Universidad de Magallanes, Avenida Bulnes, 01855, Punta Arenas, Chile.; Centre for Biotechnology and Bioengineering, CeBiB, Universidad de Chile, Beauchef 851, Santiago, Chile.

Biotechnology Advances
|December 7, 2024
PubMed
Summary
This summary is machine-generated.

Explainable Artificial Intelligence (XAI) enhances protein engineering by making AI models interpretable. This approach boosts trust and guides machine learning-assisted directed evolution for better protein design.

Keywords:
Explainable artificial intelligence (XAI)Explainable machine learning (XML)Interpretable machine learning (IML)Protein designProtein engineering

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Area of Science:

  • Biotechnology
  • Computational Biology
  • Protein Engineering

Background:

  • Protein engineering utilizes directed evolution and rational design to optimize protein properties.
  • Artificial intelligence (AI) accelerates protein engineering via data-driven predictive models.
  • Current AI models lack interpretability, limiting their real-world application and trustworthiness.

Purpose of the Study:

  • To explore the principles and methodologies of Explainable Artificial Intelligence (XAI).
  • To highlight XAI's relevance and potential in biotechnology and protein design.
  • To propose theoretical pipelines integrating XAI with predictive models for protein engineering.

Main Methods:

  • Review of XAI principles and methodologies.
  • Analysis of XAI applications in biotechnology.
  • Development of theoretical pipelines for integrating XAI in protein engineering.

Main Results:

  • XAI offers insights into AI decision-making, enhancing model reliability.
  • XAI application in protein engineering is underexplored but holds significant potential.
  • Three theoretical XAI-integrated pipelines for protein design are proposed.

Conclusions:

  • XAI can significantly enhance protein engineering by improving model interpretability and trustworthiness.
  • Integrating XAI can guide machine learning-assisted directed evolution and protein design.
  • Further research is needed to address challenges and develop XAI as a support tool for traditional protein engineering.