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

Jou, Jonathan; Jain, Swati; Georgiev, Ivelin S.; Donald, Bruce Randall

doi:10.1007/978-3-319-16706-0_16

Cited by 6 publications

(1 citation statement)

References 43 publications

(40 reference statements)

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“…Beyond the language of permutated submodular functions, the other important tractable class of CFN is the class of CFN with a graph of bounded tree-width. This parameter is exploited in several protein packing [45] and design [46] algorithms and is also exploited in dedicated branch and bound algorithms, also implemented in toulbar2 [47? ,48].…”

Section: Discussionmentioning

confidence: 99%

Guaranted Diversity and Optimality in Cost Function Network Based Computational Protein Design Methods

Ruffini¹,

Vucinic²,

Givry³

et al. 2021

Preprint

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Proteins are the main active molecules of Life. While natural proteins play many roles, as enzymes or antibodies for example, there is a need to go beyond the repertoire of natural proteins to produce engineered proteins that precisely meet application requirements, in terms of function, stability, activity or other protein capacities. Computational Protein Design aims at designing new proteins from first principles, using full-atom molecular models. However, the size and complexity of proteins require approximations to make them amenable to energetic optimization queries. These approximations make the design process less reliable and a provable optimal solution may fail. In practice, expensive libraries of solutions are therefore generated and tested. In this paper, we explore the idea of generating libraries of provably diverse low energy solutions by extending Cost Function Network algorithms with dedicated automaton-based diversity constraints on a large set of realistic full protein redesign problems. We observe that it is possible to generate provably diverse libraries in reasonable time and that the produced libraries do enhance the Native Sequence Recovery, a traditional measure of design methods reliability.

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Section: Discussionmentioning

confidence: 99%

Guaranted Diversity and Optimality in Cost Function Network Based Computational Protein Design Methods

Ruffini¹,

Vucinic²,

Givry³

et al. 2021

Preprint

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Fast gap-free enumeration of conformations and sequences for protein design

et al. 2015

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Despite significant successes in structure-based computational protein design in recent years, protein design algorithms must be improved to increase the biological accuracy of new designs. Protein design algorithms search through an exponential number of protein conformations, protein ensembles, and amino acid sequences in an attempt to find globally optimal structures with a desired biological function. To improve the biological accuracy of protein designs, it is necessary to increase both the amount of protein flexibility allowed during the search and the overall size of the design, while guaranteeing that the lowest-energy structures and sequences are found. DEE/A*-based algorithms are the most prevalent provable algorithms in the field of protein design and can provably enumerate a gap-free list of low-energy protein conformations, which is necessary for ensemble-based algorithms that predict protein binding. We present two classes of algorithmic improvements to the A* algorithm that greatly increase the efficiency of A*. First, we analyze the effect of ordering the expansion of mutable residue positions within the A* tree and present a dynamic residue ordering that reduces the number of A* nodes that must be visited during the search. Second, we propose new methods to improve the conformational bounds used to estimate the energies of partial conformations during the A* search. The residue ordering techniques and improved bounds can be combined for additional increases in A* efficiency. Our enhancements enable all A*-based methods to more fully search protein conformation space, which will ultimately improve the accuracy of complex biomedically-relevant designs.

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OSPREY Predicts Resistance Mutations Using Positive and Negative Computational Protein Design

Ojewole

Lowegard

Gainza

et al. 2016

Methods in Molecular Biology

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Summary Drug resistance in protein targets is an increasingly common phenomenon that reduces the efficacy of both existing and new antibiotics. However, knowledge of future resistance mutations during pre-clinical phases of drug development would enable the design of novel antibiotics that are robust against not only known resistant mutants, but also against those that have not yet been clinically observed. Computational structure-based protein design (CSPD) is a transformative field that enables the prediction of protein sequences with desired biochemical properties such as binding affinity and specificity to a target. The use of CSPD to predict previously unseen resistance mutations represents one of the frontiers of computational protein design. In a recent study (1), we used our OSPREY (Open Source Protein REdesign for You) suite of CSPD algorithms to prospectively predict resistance mutations that arise in the active site of the dihydrofolate reductase enzyme from methicillin-resistant Staphylococcus aureus (SaDHFR) in response to selective pressure from an experimental competitive inhibitor. We demonstrated that our top predicted candidates are indeed viable resistant mutants. Since that study, we have significantly enhanced the capabilities of OSPREY with not only improved modeling of backbone flexibility, but also efficient multi-state design, fast sparse approximations, partitioned rotamers for more accurate energy bounds, and a computationally efficient representation of molecular-mechanics and quantum-mechanical energy functions. Here, using SaDHFR as an example, we present a protocol for resistance prediction using the latest version of OSPREY. Specifically, we show how to use a combination of positive and negative design to predict active site escape mutations that maintain the enzyme’s catalytic function but selectively ablate binding of an inhibitor.

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

Cited by 6 publications

References 43 publications

Guaranted Diversity and Optimality in Cost Function Network Based Computational Protein Design Methods

Guaranted Diversity and Optimality in Cost Function Network Based Computational Protein Design Methods

Fast gap-free enumeration of conformations and sequences for protein design

OSPREY Predicts Resistance Mutations Using Positive and Negative Computational Protein Design

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