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

Zhou, Yichao; Wu, Yuexin; Zeng, Jianyang

doi:10.1007/978-3-319-16706-0_36

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…Dynamic A * radically improves the performance of A * through both better bounding and by introducing dynamic residue ordering to the design process. Zeng and co-workers [65] developed an AND/OR branch-and-bound method that exploits the sparse nature of protein design biophysical models. In an AND/OR tree the protein design optimization problem can be split into different components on-the-fly, and each subtree can be solved independently, which can result in a significant performance improvement.…”

Section: Progress In Optimization Algorithms For the Gmec-modelmentioning

confidence: 99%

Algorithms for protein design

Gainza

Nisonoff

Donald

2016

Current Opinion in Structural Biology

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Computational structure-based protein design programs are becoming an increasingly important tool in molecular biology. These programs compute protein sequences that are predicted to fold to a target structure and perform a desired function. The success of a program's predictions largely relies on two components: (i) the input biophysical model, and (ii) the algorithm that computes the best sequence(s) and structure(s) according to the biophysical model. Improving both the model and the algorithm in tandem is essential to improving the success rate of current programs, and here we review recent developments in algorithms for protein design, emphasizing how novel algorithms enable the use of more accurate biophysical models. We conclude with a list of algorithmic challenges in computational protein design that we believe will be especially important for the design of therapeutic proteins and protein assemblies. Graphical Abstract*

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Section: Progress In Optimization Algorithms For the Gmec-modelmentioning

confidence: 99%

Algorithms for protein design

Gainza

Nisonoff

Donald

2016

Current Opinion in Structural Biology

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“…Examples are integer linear programming [17], branch-and-bound [18,19], tree decomposition [20], dead-end elimination [21,2], and A* tree search [1,22,23]. Among them, the combination of dead-end elimination and A* tree search has been popularly used to solve the design problem [24].…”

Section: Introductionmentioning

confidence: 99%

Parallel Computational Protein Design

Zhou

Donald

Zeng

2016

Methods in Molecular Biology

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Computational structure-based protein design (CSPD) is an important problem in computational biology, which aims to design or improve a prescribed protein function based on a protein structure template. It provides a practical tool for real-world protein engineering applications. A popular CSPD method that guarantees to find the global minimum energy solution (GMEC) is to combine both dead-end elimination (DEE) and A* tree search algorithms. However, in this framework, the A* search algorithm can run in exponential time in the worst case, which may become the computation bottleneck of large-scale computational protein design process. To address this issue, we extend and add a new module to the OSPREY program that was previously developed in the Donald lab [1] to implement a GPU-based massively parallel A* algorithm for improving protein design pipeline. By exploiting the modern GPU computational framework and optimizing the computation of the heuristic function for A* search, our new program, called , can provide up to four orders of magnitude speedups in large protein design cases with a small memory overhead comparing to the traditional A* search algorithm implementation, while still guaranteeing the optimality. In addition, can be configured to run in a bounded-memory mode to tackle the problems in which the conformation space is too large and the global optimal solution cannot be computed previously. Furthermore, the GPU-based A* algorithm implemented in the program can be combined with the state-of-the-art rotamer pruning algorithms such as iMinDEE [2] and DEEPer [3] to also consider continuous backbone and side-chain flexibility.

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Deterministic Search Methods for Computational Protein Design

Traoré

Allouche²,

André

et al. 2016

Methods in Molecular Biology

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One main challenge in Computational Protein Design (CPD) lies in the exploration of the amino-acid sequence space, while considering, to some extent, side chain flexibility. The exorbitant size of the search space urges for the development of efficient exact deterministic search methods enabling identification of low-energy sequence-conformation models, corresponding either to the global minimum energy conformation (GMEC) or an ensemble of guaranteed near-optimal solutions. In contrast to stochastic local search methods that are not guaranteed to find the GMEC, exact deterministic approaches always identify the GMEC and prove its optimality in finite but exponential worst-case time. After a brief overview on these two classes of methods, we discuss the grounds and merits of four deterministic methods that have been applied to solve CPD problems. These approaches are based either on the Dead-End-Elimination theorem combined with A* algorithm (DEE/A*), on Cost Function Networks algorithms (CFN), on Integer Linear Programming solvers (ILP) or on Markov Random Fields solvers (MRF). The way two of these methods (DEE/A* and CFN) can be used in practice to identify low-energy sequence-conformation models starting from a pairwise decomposed energy matrix is detailed in this review.

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

Cited by 5 publications

References 32 publications

Algorithms for protein design

Algorithms for protein design

Parallel Computational Protein Design

Deterministic Search Methods for Computational Protein Design

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