Computational protein design as an optimization problem

Allouche, David; André, Isabelle; Barbe, Sophie; Davies, Jessica; Givry, Simon de; Katsirelos, George; O’Sullivan, Barry; Prestwich, Steven; Schiex, Thomas; Traoré, Seydou

doi:10.1016/j.artint.2014.03.005

Cited by 59 publications

(80 citation statements)

References 53 publications

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“…For example, in a recent study [53••], Simoncini et al compared their provable optimization algorithm implemented in the Toulbar2 program [54, 55] vs. the heuristic simulated annealing (SA) algorithm implemented in the Rosetta program using a GMEC-model. They found that, in a set of 100 test protein designs, SA often fails to compute the optimal answer even after running hundreds of times.…”

Section: Provable Vs Heuristic Algorithmsmentioning

confidence: 99%

“…Their protein design optimization algorithm exploits weighted constraint satisfaction (WCSP) techniques, including fast soft local consistencies for bounding, an advanced branch and bound implementation, and sophisticated ordering techniques, to compute the GMEC significantly faster than competing approaches [54]. These algorithms are implemented in the Toulbar2 program [53••–55], with support only for discrete flexibility models, and in the Osprey protein design program [39, 68] (with full support for continuous flexibility models, described in the next section [64•, 69••]).…”

Section: Progress In Optimization Algorithms For the Gmec-modelmentioning

confidence: 99%

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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: Provable Vs Heuristic Algorithmsmentioning

confidence: 99%

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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“…In a recent work, we have shown that the already highly challenging usual description of the CPD problem, based on rigid backbone and discrete rotamers, could be formulated and efficiently solved as a Cost Function Network (CFN) – . CFN algorithms are able to handle complex CPD combinatorial spaces which are out of reach of a broad range of combinatorial optimization technologies including the usual DEE/

A^{*}

, 0/1 Linear and Quadratic Programming, 0/1 Quadratic Optimization, Weighted Partial MaxSAT and Graphical Model optimization methods . The toulbar2 CFN solver provides speedups of several orders of magnitude both to provably find the GMEC and to exhaustively enumerate unsorted ensembles of near‐optimal solutions (within a threshold of the GMEC), offering an attractive alternative method for CPD.…”

Section: Introductionmentioning

confidence: 99%

“…To further improve this new CFN‐based method, we integrated other essential components of CFN technology, such as ordering heuristics and branching schemes. Inspired by the specific nature of the CPD problem, we further developed a new branching scheme that provides further speedups.…”

Section: Introductionmentioning

confidence: 99%

Fast search algorithms for computational protein design

et al. 2016

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One of the main challenges in Computational Protein Design (CPD) is the huge size of the protein sequence and conformational space that has to be computationally explored. Recently, we showed that state-of-the-art combinatorial optimization technologies based on Cost Function Network (CFN) processing allow to speed up provable rigid backbone protein design methods by several orders of magnitudes. Building up on this, we improved and injected CFN technology into the well-established CPD package Osprey to allow all Osprey CPD algorithms to benefit from associated speedups. Because Osprey fundamentally relies on the ability of A* to produce conformations in increasing order of energy, we defined new A* strategies combining CFN lower bounds, with new Side Chain Positioning (SCP)–based branching scheme. Beyond the speedups obtained in the new A*-CFN combination, this new branching scheme enables a much faster enumeration of sub-optimal sequences, far beyond what is reachable without it. Together with the immediate and important speedups provided by CFN technology, these developments directly benefit to all the algorithms that previously relied on the DEE/A* combination inside Osprey and make it possible to solve larger CPD problems with provable algorithms.

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“…Unfortunately, these heuristic methods can be trapped into local minima and may lead to poor quality of the final solution. On the other hand, several exact and provable search algorithms which guarantee to find the GMEC solution have been proposed, such as Dead-End Elimination (DEE) [6], A* search [21,22,7,35], tree decomposition [32], branch-and-bound (BnB) search [14,31,3], and BnB-based linear integer programming [1,18].…”

Section: Introductionmentioning

confidence: 99%

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

Zhou

Zeng

2015

Lecture Notes in Computer Science

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Abstract. The computation of the global minimum energy conformation (GMEC) is an important and challenging topic in structure-based computational protein design. In this paper, we propose a new protein design algorithm based on the AND/OR branch-and-bound (AOBB) search, which is a variant of the traditional branch-and-bound search algorithm, to solve this combinatorial optimization problem. By integrating with a powerful heuristic function, AOBB is able to fully exploit the graph structure of the underlying residue interaction network of a backbone template to significantly accelerate the design process. Tests on real protein data show that our new protein design algorithm is able to solve many problems that were previously unsolvable by the traditional exact search algorithms, and for the problems that can be solved with traditional provable algorithms, our new method can provide a large speedup by several orders of magnitude while still guaranteeing to find the global minimum energy conformation (GMEC) solution.

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Computational protein design as an optimization problem

Cited by 59 publications

References 53 publications

Algorithms for protein design

Algorithms for protein design

Fast search algorithms for computational protein design

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

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