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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

BWM*: A Novel, Provable, Ensemble-based Dynamic Programming Algorithm for Sparse Approximations of Computational

Jonathan D Jou¹, Swati Jain^1,2,3, Ivelin S Georgiev¹

¹1 Department of Computer Science, Duke University , Durham, North Carolina.

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

|January 9, 2016

Summary

This summary is machine-generated.

A new dynamic programming algorithm, Branch-Width Minimization* (BWM*), efficiently finds optimal protein designs by considering multiple conformations. This method outperforms existing algorithms like A* by predicting performance and enabling faster, more accurate protein design.

Keywords:

OSPREY branch-decomposition dynamic programming ensemble-based algorithms protein design provable algorithms sparse residue interaction graphs

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Area of Science:

Computational biology
Bioinformatics
Protein engineering

Background:

Protein design algorithms often use simplified energy functions for computational efficiency, potentially overlooking optimal sequences.
Existing dynamic programming methods focus on the globally minimal energy conformation (GMEC), neglecting sequences with multiple low-energy states.
Provable ensemble-based algorithms like A* exist but lack guaranteed performance improvements over exhaustive search.

Purpose of the Study:

To introduce a novel, provable dynamic programming algorithm, Branch-Width Minimization* (BWM*), for protein design.
To enable the enumeration of a gap-free ensemble of protein conformations in increasing order of energy.
To develop a predictive measure for algorithm efficiency in protein design problems.

Main Methods:

Developed Branch-Width Minimization* (BWM*), a dynamic programming algorithm utilizing branch-decomposition.
Introduced Total Effective Search Space (TESS) as a computable a priori measure of problem complexity.
Applied BWM* to 67 protein design problems to evaluate its performance against A*.

Main Results:

BWM* efficiently enumerates protein conformations, with initial GMEC computation in O([Formula: see text]) and subsequent conformations in O([Formula: see text]).
TESS accurately predicted BWM*-efficient versus A*-efficient cases with 100% accuracy.
BWM* outperformed A* in 73% of tested cases, offering faster ensemble computation and predictable performance.

Conclusions:

BWM* provides a provable and efficient method for protein design, overcoming limitations of existing algorithms.
The TESS measure allows for informed selection of the most efficient algorithm (BWM* or A*) for specific protein design challenges.
BWM* enhances the ability of protein designers to discover optimal sequences by efficiently exploring conformational ensembles.