Using Multiobjective Optimization and Energy Minimization to Design an Isoform-Selective Ligand of the 14-3-3 Protein

Sanchez-Faddeev, Hernando; Emmerich, Michael; Verbeek, Fons J.; Henry, Andrew; Grimshaw, Simon; Spaink, Herman P.; Vlijmen, Herman W. van; Bender, Andreas

doi:10.1007/978-3-642-34032-1_3

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…Other solutions (gray) may be improved along either objective without compromising the other applied to the design of stabilizing mutations to proteins that minimally disrupt the native structure, concurrently optimizing energy and RMSD from the initial structure (Nivón et al 2013). State-of-the-art multi-objective optimization evolutionary algorithms such as SMS-EMOA have been applied to the design of peptide ligands that bind with reasonable affinity and selectivity for a specific isoform of 14-3-3 proteins (Sanchez-Faddeev et al 2012). In characterizing protein designs that are well distributed along a Pareto optimal set, one is able to evaluate the relative importance of objectives to design success.…”

Section: Pareto Optimalitymentioning

confidence: 99%

Searching for the Pareto frontier in multi-objective protein design

2017

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The goal of protein engineering and design is to identify sequences that adopt three-dimensional structures of desired function. Often, this is treated as a single-objective optimization problem, identifying the sequence-structure solution with the lowest computed free energy of folding. However, many design problems are multi-state, multi-specificity, or otherwise require concurrent optimization of multiple objectives. There may be tradeoffs among objectives, where improving one feature requires compromising another. The challenge lies in determining solutions that are part of the Pareto optimal set-designs where no further improvement can be achieved in any of the objectives without degrading one of the others. Pareto optimality problems are found in all areas of study, from economics to engineering to biology, and computational methods have been developed specifically to identify the Pareto frontier. We review progress in multiobjective protein design, the development of Pareto optimization methods, and present a specific case study using multiobjective optimization methods to model the tradeoff between three parameters, stability, specificity, and complexity, of a set of interacting synthetic collagen peptides.

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Section: Pareto Optimalitymentioning

confidence: 99%

Searching for the Pareto frontier in multi-objective protein design

2017

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“…The first paper of this ISoLA track, Using multiobjective optimization and energy minimization to design an isoform-selective ligand of the 14-3-3 protein (Hernando Sanchez-Faddeev, Michael T.M. Emmerich, Fons J. Verbeek, Andrew H. Henry, Simon Grimshaw, Herman P. Spaink, Herman W. van Vlijmen and Andreas Bender) [11], presents an approach for de novo design of protein ligands based on evolutionary multiobjective optimization. It shows that multiobjective optimization with evolutionary algorithms can be successfully employed in selective ligand design.…”

Section: Algorithms For Image Analysismentioning

confidence: 99%

Bioscientific Data Processing and Modeling

Kok

Lamprecht

Verbeek

et al. 2012

Leveraging Applications of Formal Methods, Verification and Validation. Applications and Case Studies

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“…Sanchez-Faddeev et al . [8] proposed a bi-objective optimization approach using the S-metric evolutionary multi-objective optimization (SMS-EMOA) to solve the problem of finding a peptide ligand. The results obtained show the possibility to design a peptide ligand of the γ 1 isoform of the 14-3-3 protein with predicted selectivity over the ɛ 1 isoform.…”

Section: Introductionmentioning

confidence: 99%

Solving Molecular Docking Problems with Multi-Objective Metaheuristics

et al. 2015

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Molecular docking is a hard optimization problem that has been tackled in the past with metaheuristics, demonstrating new and challenging results when looking for one objective: the minimum binding energy. However, only a few papers can be found in the literature that deal with this problem by means of a multi-objective approach, and no experimental comparisons have been made in order to clarify which of them has the best overall performance. In this paper, we use and compare, for the first time, a set of representative multi-objective optimization algorithms applied to solve complex molecular docking problems. The approach followed is focused on optimizing the intermolecular and intramolecular energies as two main objectives to minimize. Specifically, these algorithms are: two variants of the non-dominated sorting genetic algorithm II (NSGA-II), speed modulation multi-objective particle swarm optimization (SMPSO), third evolution step of generalized differential evolution (GDE3), multi-objective evolutionary algorithm based on decomposition (MOEA/D) and S-metric evolutionary multi-objective optimization (SMS-EMOA). We assess the performance of the algorithms by applying quality indicators intended to measure convergence and the diversity of the generated Pareto front approximations. We carry out a comparison with another reference mono-objective algorithm in the problem domain (Lamarckian genetic algorithm (LGA) provided by the AutoDock tool). Furthermore, the ligand binding site and molecular interactions of computed In addition, a case study of application for aeroplysinin-1 is performed, showing the effectiveness of our multi-objective approach in drug discovery.

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Using Multiobjective Optimization and Energy Minimization to Design an Isoform-Selective Ligand of the 14-3-3 Protein

Cited by 5 publications

References 32 publications

Searching for the Pareto frontier in multi-objective protein design

Searching for the Pareto frontier in multi-objective protein design

Bioscientific Data Processing and Modeling

Solving Molecular Docking Problems with Multi-Objective Metaheuristics

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