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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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Published on: July 25, 2013

Computational Protein Design Using AND/OR Branch-and-Bound Search.

Yichao Zhou¹, Yuexin Wu¹, Jianyang Zeng¹

¹1 Institute for Interdisciplinary Information Sciences, Tsinghua University , Beijing, China .

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|May 12, 2016

Summary

We developed a new protein design algorithm using AND/OR branch-and-bound (AOBB) search. This method efficiently finds the global minimum energy conformation (GMEC), solving previously intractable problems and accelerating computations.

Keywords:

AND/OR branch-and-bound global minimum energy conformation mini-bucket heuristic protein design residue interaction network

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Area of Science:

Computational biology
Protein design
Bioinformatics

Background:

Finding the global minimum energy conformation (GMEC) is crucial for structure-based computational protein design.
Traditional exact search algorithms struggle with the combinatorial complexity of this problem.

Purpose of the Study:

To introduce a novel protein design algorithm.
To address the challenge of efficiently computing the GMEC.

Main Methods:

Developed a new algorithm based on AND/OR branch-and-bound (AOBB) search.
Integrated a heuristic function to leverage the residue interaction network graph structure.
Applied the algorithm to real protein data.

Main Results:

The AOBB algorithm successfully solved protein design problems previously unsolvable by exact methods.
Achieved significant speedups (orders of magnitude) for solvable problems.
Guaranteed finding the global minimum energy conformation (GMEC).

Conclusions:

The proposed AOBB algorithm offers a more efficient and powerful approach to computational protein design.
This method enhances the ability to accurately predict protein structures.