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Area of Science:

  • Biotechnology
  • Computational Biology
  • Protein Engineering

Background:

  • Computational protein design (CPD) has transformed protein engineering since the late 1990s.
  • The field has advanced significantly, now driven by artificial intelligence (AI).

Purpose of the Study:

  • To track the evolution of computational protein design.
  • To highlight recent advancements and future potential of CPD.

Main Methods:

  • Review of CPD evolution from initial stages to AI-driven approaches.
  • Incorporation of AI, machine learning, quantum mechanics, and high-throughput virtual screening.
  • Analysis of protein backbone modeling, energy functions, sampling algorithms, and sequence optimization.

Main Results:

  • CPD has become more accurate and efficient.
  • Applications include novel therapeutics, industrial enzymes, and biomaterials.
  • Challenges remain in energy functions, structural predictions, and computational resources.

Conclusions:

  • Future directions include programmable cellular systems and self-assembling materials.
  • Multidisciplinarity and ethical considerations are crucial for addressing global challenges.
  • CPD promises novel proteins with unprecedented functions and properties.