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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
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Evaluating the accuracy of protein design using native secondary sub-structures.

Marziyeh Movahedi1, Fatemeh Zare-Mirakabad2, Seyed Shahriar Arab3

  • 1Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran.

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|September 7, 2016
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Summary

A new genetic algorithm, GAPSSIF, designs amino acid sequences for target protein secondary structures by leveraging evolutionary information. This approach significantly improves protein design efficiency and sequence quality.

Keywords:
Evolutionary informationProtein designProtein structure prediction

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Area of Science:

  • Protein bioinformatics
  • Computational biology
  • Molecular modeling

Background:

  • Protein structure-dependent function necessitates solutions for Protein Structure Prediction (PSP) and Inverse Protein Folding (IPF).
  • Inverse Protein Folding (IPF), crucial for protein design, faces challenges due to vast sequence spaces.
  • Protein Secondary Structure Inverse Folding (PSSIF) offers a reduced search space for protein design.

Purpose of the Study:

  • To introduce a novel genetic algorithm, GAPSSIF, for solving the Protein Secondary Structure Inverse Folding (PSSIF) problem.
  • To utilize evolutionary information from native protein structures to guide sequence design.
  • To reduce the computational complexity of protein design by focusing on secondary structures.

Main Methods:

  • Development of a genetic algorithm (GAPSSIF) incorporating native secondary sub-structures.
  • Construction of a repository of protein secondary sub-structures to enhance algorithm convergence.
  • Leveraging evolutionary information, solvent accessibility, and torsion angles for sequence design.

Main Results:

  • GAPSSIF demonstrates comparable secondary structure prediction accuracy to existing methods like Evolver and EvoDesign.
  • Designed sequences exhibit acceptable structural similarity to native proteins, despite not explicitly optimizing tertiary structures.
  • The algorithm benefits from evolutionary information, accelerating convergence and improving sequence quality.

Conclusions:

  • Evolutionary information from native protein structures is key to enhancing designed sequence quality in IPF.
  • Combining evolutionary data with features like solvent accessibility and torsion angles provides an efficient IPF solution.
  • The GAPSSIF algorithm and its repository are available for protein design research.