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

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

Published on: July 25, 2013

An efficient parallel algorithm for accelerating computational protein design.

Yichao Zhou¹, Wei Xu¹, Bruce R Donald²

¹Institute for Theoretical Computer Science (ITCS), Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, P. R. China, Department of Computer Science, Duke University, Durham, NC 27708, USA and Department of Biochemistry, Duke University Medical Center, Durham, NC 27708, USA.

Bioinformatics (Oxford, England)

|June 17, 2014

Summary

This summary is machine-generated.

This study enhances structure-based computational protein design (SCPR) by optimizing the A* search algorithm for faster, parallelized computations on GPUs. The improved method significantly speeds up protein design, enabling efficient exploration of conformations.

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Published on: September 15, 2010

Area of Science:

Computational biology
Protein engineering
Bioinformatics

Background:

Structure-based computational protein design (SCPR) is crucial for protein engineering.
Existing methods often combine dead-end elimination (DEE) and A* search for global minimum energy conformation (GMEC).
These methods rely on assumptions of rigid backbones and discrete side-chain conformations.

Purpose of the Study:

To improve the efficiency of A* heuristic functions in protein design.
To develop a massively parallel A* algorithm suitable for GPU implementation.
To address memory limitations encountered in A* search.

Main Methods:

Optimized computation of A* heuristic functions.
Developed a GPU-accelerated, massively parallel A* variant.
Implemented strategies to mitigate memory exceeding issues.
Integrated the parallel A* algorithm with the iMinDEE criterion.

Main Results:

Achieved a four-orders-of-magnitude speedup for A*-based protein design on large datasets.
Maintained acceptable memory overhead despite significant performance gains.
Demonstrated successful combination with iMinDEE for enhanced rotamer pruning.
Enabled consideration of continuous side-chain flexibility in SCPR.

Conclusions:

The enhanced parallel A* algorithm offers substantial speed improvements for SCPR.
The approach effectively addresses computational and memory challenges in protein design.
This work facilitates more efficient and flexible computational protein design.