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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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A sequence space search engine for computational protein design to modulate molecular functionality.

Ayush Malik1, Anupam Banerjee2, Abantika Pal1

  • 1Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India.

Journal of Biomolecular Structure & Dynamics
|February 28, 2022
PubMed
Summary
This summary is machine-generated.

We developed a novel greedy simulated annealing Monte Carlo algorithm for de novo protein design. This method efficiently explores sequence space, achieving high accuracy and preserving core protein structures while enabling new molecular functions.

Keywords:
Monte-Carlo parallel search algorithmProtein designaltering molecular functionalityhigh native sequence recapitulationprotein sequence-structure compatibility

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Area of Science:

  • Computational biology
  • Protein engineering
  • Bioinformatics

Background:

  • De novo protein design aims to explore uncharted sequence space beyond natural evolution.
  • Efficient search algorithms are crucial for computational protein design convergence.
  • Existing methods may struggle with comprehensive sequence-structure compatibility probing.

Purpose of the Study:

  • To introduce a novel greedy simulated annealing-based Monte Carlo parallel search algorithm for de novo protein design.
  • To enhance sequence-structure compatibility assessment in protein design.
  • To improve the efficiency and convergence of computational protein design.

Main Methods:

  • Developed a greedy simulated annealing-based Monte Carlo parallel search algorithm.
  • Incorporated evolutionary profiles, a greedy approach, and a cooling schedule for search space exploration.
  • Evaluated the algorithm on 76 target scaffolds.

Main Results:

  • Achieved an average root-mean-square deviation (RMSD) of 1.07 Å and a TM-Score of 0.93 against target scaffolds.
  • Demonstrated high sequence recapitulation rates: 48.7% for all solvent-inaccessible residues and 59.4% for hydrophobic residues.
  • Preserved core hydrophobic residues (93.4% intra-group recapitulation) while allowing alternative solvent-accessible residue combinations.
  • COFACTOR analysis revealed altered and novel molecular functions in designed sequences compared to targets.

Conclusions:

  • The proposed algorithm effectively explores the protein sequence space for de novo design.
  • It ensures high sequence-structure compatibility, preserves essential protein cores, and introduces new functionalities.
  • This method represents a significant advancement in computational protein design efficiency and capability.