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Searching for the Pareto frontier in multi-objective protein design

Abstract

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 multi-objective protein design, the development of Pareto optimization methods, and present a specific case study using multi-objective 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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Author information

Correspondence to Vikas Nanda.

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Conflict of interest

Vikas Nanda declares that he has no conflict of interest. Sandeep V. Belure declares that he has no conflict of interest. Ofer M. Shir declares that he has no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

This article is part of a Special Issue on ‘IUPAB Edinburgh Congress’ edited by Damien Hall.

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Nanda, V., Belure, S.V. & Shir, O.M. Searching for the Pareto frontier in multi-objective protein design. Biophys Rev 9, 339–344 (2017). https://doi.org/10.1007/s12551-017-0288-0

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Keywords

  • Computational protein design
  • Pareto optimality
  • Multi-objective optimization
  • Peptide
  • Collagen
  • Interactome