Off-lattice protein structure prediction with homologous crossover

Olson, Brian; Jong, Kenneth De; Shehu, Amarda

doi:10.1145/2463372.2463407

Cited by 39 publications

(38 citation statements)

References 38 publications

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“…Each run of the hybrid DE is applied for a fixed budget of 10,000,000 energy function evaluations, the same used in [7]. As indicated, after the DE phase, for each individual, we used a probability (P R = 0.01) to decide, in each amino acid position, if a replacement (3-mer or 9-mer fragment) is checked for energy improvement.…”

Section: Resultsmentioning

confidence: 99%

“…The authors in [7] also worked with the Rosetta coarsegrained representation and applied a hybrid evolutionary algorithm (EA) combined with a local search to map each child conformation to a nearby local minimum (using fragment replacements). Their EA used the classical operators of crossover (1-point and 2-point crossover as well as a homologous crossover) and a mutation operator implemented also with the fragment replacements.…”

Section: Resultsmentioning

confidence: 99%

“…Olson et al [8] also experimented with the use of multi-objective optimization for the same problem but they reported results using the score 4 Rosetta function (used to compare the obtained decoys before the last Rosetta refinement). [7] using a hybrid EA. Column 6 shows the best energy value in 10 independent runs and the average lowest value (in parentheses) in the 10 runs using the hybrid DE.…”

Section: Resultsmentioning

confidence: 99%

“…Nevertheless, there are few works using evolutionary computing with off-lattice models and the Rosetta environment, such as the works by Olson et al [7] [8]. In this work we used the Rosetta environment and tested the use of Differential Evolution (DE) [10], a robust method in many optimization problems, adapted to our application and hybridized with the Rosetta fragment replacements.…”

Section: Introductionmentioning

confidence: 99%

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Combination of Differential Evolution and Fragment-based Replacements for Protein Structure Prediction

Varela

Santos

2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

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In this work Differential Evolution (DE) was combined with fragment replacement for improving the search of protein structure conformations with minimum energy. The Rosetta environment was used, employing some of its phases for the ab initio prediction in the initialization of the genetic population, as well as its fragment-assembly technique. DE provides a global search in the multimodal energy landscape whereas fragment replacement based on the Monte-Carlo procedure provides a useful search of short protein conformations that accelerates the DE search. Initial results with proteins from PDB with a comparison with previous works are provided.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Combination of Differential Evolution and Fragment-based Replacements for Protein Structure Prediction

Varela

Santos

2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

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“…Studies applying EAs for decoy sampling are thus limited to very small proteins or toy models; currently, EAs are not competitive against state-of-the-art methods in template-free structure prediction. In recent work we have provided a pathway for employing EAs in template-free structure prediction by incorporating coarse-grained representations and molecular fragment replacement, improving the exploration capability of single-trajectory or population-based EAs [24,28,29,32] as well as tree-based search algorithms [26,33]. In this paper we build upon this foundation and propose a more powerful novel evolutionary search algorithm.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Stochastic Search for Sampling Local Minima in the Protein Energy Surface

Olson

Shehu

2013

Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics

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We present an evolutionary stochastic search algorithm to obtain a discrete representation of the protein energy surface in terms of an ensemble of conformations representing local minima. This objective is of primary importance in protein structure modeling, whether the goal is to obtain a broad view of potentially different structural states thermodynamically available to a protein system or to predict a single representative structure of a unique functional native state. In this paper, we focus on the latter setting, and show how approaches from evolutionary computation for effective stochastic search and multi-objective analysis can be combined to result in protein conformational search algorithms with high exploration capability. From a broad computational perspective, the contributions of this paper are on how to balance global and local search of some highdimensional search space and how to guide the search in the presence of a noisy, inaccurate scoring function. From an application point of view, the contributions are demonstrated in the domain of template-free protein structure prediction on the primary subtask of sampling diverse low-energy decoy conformations of an amino-acid sequence. Comparison with the approach used for decoy sampling in the popular Rosetta protocol on 20 diverse protein sequences shows that the evolutionary algorithm proposed in this paper is able to access lower-energy regions with similar or better proximity to the known native structure.

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Toward a detailed understanding of search trajectories in fragment assembly approaches to protein structure prediction

2016

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Energy functions, fragment libraries, and search methods constitute three key components of fragment‐assembly methods for protein structure prediction, which are all crucial for their ability to generate high‐accuracy predictions. All of these components are tightly coupled; efficient searching becomes more important as the quality of fragment libraries decreases. Given these relationships, there is currently a poor understanding of the strengths and weaknesses of the sampling approaches currently used in fragment‐assembly techniques. Here, we determine how the performance of search techniques can be assessed in a meaningful manner, given the above problems. We describe a set of techniques that aim to reduce the impact of the energy function, and assess exploration in view of the search space defined by a given fragment library. We illustrate our approach using Rosetta and EdaFold, and show how certain features of these methods encourage or limit conformational exploration. We demonstrate that individual trajectories of Rosetta are susceptible to local minima in the energy landscape, and that this can be linked to non‐uniform sampling across the protein chain. We show that EdaFold's novel approach can help balance broad exploration with locating good low‐energy conformations. This occurs through two mechanisms which cannot be readily differentiated using standard performance measures: exclusion of false minima, followed by an increasingly focused search in low‐energy regions of conformational space. Measures such as ours can be helpful in characterizing new fragment‐based methods in terms of the quality of conformational exploration realized. Proteins 2016; 84:411–426. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

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Off-lattice protein structure prediction with homologous crossover

Cited by 39 publications

References 38 publications

Combination of Differential Evolution and Fragment-based Replacements for Protein Structure Prediction

Combination of Differential Evolution and Fragment-based Replacements for Protein Structure Prediction

Multi-Objective Stochastic Search for Sampling Local Minima in the Protein Energy Surface

Toward a detailed understanding of search trajectories in fragment assembly approaches to protein structure prediction

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