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Computer search algorithms in protein modification and design

J R Desjarlais1, N D Clarke

  • 1Department of Chemistry, Pennsylvania State University, University Park 16802, USA. jrd@chem.psu.edu

Current Opinion in Structural Biology
|September 8, 1998
PubMed
Summary
This summary is machine-generated.

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Designing protein sequences computationally requires advanced search algorithms to manage complexity. Recent advances include dead end elimination for motifs and genetic/mean-field algorithms for hydrophobic cores.

Area of Science:

  • Computational biology
  • Protein design
  • Bioinformatics

Background:

  • Protein sequence design is computationally intensive due to vast combinatorial possibilities.
  • Various search algorithms are employed to address this complexity.
  • Algorithm choice impacts problem representation, energy terms, and global minimum identification.

Purpose of the Study:

  • To review and highlight recent advancements in search algorithms for computer-aided protein sequence design.
  • To discuss the influence of different algorithms on problem representation and optimization capabilities.

Main Methods:

  • Review of computational approaches applied to protein sequence design.
  • Analysis of algorithms like dead end elimination, genetic algorithms, and mean-field methods.

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  • Examination of how these algorithms handle discrete configurations and energy terms.

    • Main Results:

    • Dead end elimination has been successfully applied to design complete sequences for small protein motifs.
    • Genetic and mean-field algorithms are prominent for designing protein hydrophobic cores.
    • Algorithm selection critically affects the ability to find optimal (global minimum energy) configurations.

    Conclusions:

    • Efficient search algorithms are crucial for tackling the combinatorial complexity in protein sequence design.
    • Specific algorithms show promise for different aspects of protein design, such as motif or core engineering.
    • Continued development in algorithmic approaches is essential for advancing computer-aided protein design.