ArticleJournal of computational biology : a journal of computational molecular cell biology2016
BWM*: A Novel, Provable, Ensemble-based Dynamic Programming Algorithm for Sparse Approximations of Computational Protein Design.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Protocol for DesigningJournal of computational biology : a journal of computational molecular cell biology · 2024Article
- RESISTOR: A New OSPREY Module to Predict Resistance Mutations.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- Resistor: An algorithm for predicting resistance mutations via Pareto optimization over multistate protein design and mutational signatures.Cell systems · 2022Article
- Protein Design by Provable Algorithms.Communications of the ACM · 2019Article
- OSPREY 3.0: Open-source protein redesign for you, with powerful new features.Journal of computational chemistry · 2018Article
- BBK* (Branch and Bound Over K*): A Provable and Efficient Ensemble-Based Protein Design Algorithm to Optimize Stability and Binding Affinity Over Large Sequence Spaces.Journal of computational biology : a journal of computational molecular cell biology · 2018Article
- CATS (Coordinates of Atoms by Taylor Series): protein design with backbone flexibility in all locally feasible directions.Bioinformatics (Oxford, England) · 2017Article
- LUTE (Local Unpruned Tuple Expansion): Accurate Continuously Flexible Protein Design with General Energy Functions and Rigid Rotamer-Like Efficiency.Journal of computational biology : a journal of computational molecular cell biology · 2017Article
- A critical analysis of computational protein design with sparse residue interaction graphs.PLoS computational biology · 2017Article
- OSPREY Predicts Resistance Mutations Using Positive and Negative Computational Protein Design.Methods in molecular biology (Clifton, N.J.) · 2017Article
- cOSPREY: A Cloud-Based Distributed Algorithm for Large-Scale Computational Protein Design.Journal of computational biology : a journal of computational molecular cell biology · 2016Article
- Algorithms for protein design.Current opinion in structural biology · 2016Review
- BWM*: A Novel, Provable, Ensemble-based Dynamic Programming Algorithm for Sparse Approximations of Computational Protein Design.Journal of computational biology : a journal of computational molecular cell biology · 2016Article
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4 authors.
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Abstract
Sparse energy functions that ignore long range interactions between residue pairs are frequently used by protein design algorithms to reduce computational cost. Current dynamic programming algorithms that fully exploit the optimal substructure produced by these energy functions only compute the GMEC. This disproportionately favors the sequence of a single, static conformation and overlooks better binding sequences with multiple low-energy conformations. Provable, ensemble-based algorithms such as A* avoid this problem, but A* cannot guarantee better performance than exhaustive enumeration. We propose a novel, provable, dynamic programming algorithm called Branch-Width Minimization* (BWM*) to enumerate a gap-free ensemble of conformations in order of increasing energy. Given a branch-decomposition of branch-width w for an n-residue protein design with at most q discrete side-chain conformations per residue, BWM* returns the sparse GMEC in O([Formula: see text]) time and enumerates each additional conformation in merely O([Formula: see text]) time. We define a new measure, Total Effective Search Space (TESS), which can be computed efficiently a priori before BWM* or A* is run. We ran BWM* on 67 protein design problems and found that TESS discriminated between BWM*-efficient and A*-efficient cases with 100% accuracy. As predicted by TESS and validated experimentally, BWM* outperforms A* in 73% of the cases and computes the full ensemble or a close approximation faster than A*, enumerating each additional conformation in milliseconds. Unlike A*, the performance of BWM* can be predicted in polynomial time before running the algorithm, which gives protein designers the power to choose the most efficient algorithm for their particular design problem.
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