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Opportunities and challenges in design and optimization of protein function

Nature Reviews Molecular Cell Biology (2024)Cite this article

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Abstract

The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calculations, as well as machine learning tools, have dramatically improved protein engineering and design. In this Review, we discuss how these methods have enabled the design of increasingly complex structures and therapeutically relevant activities. Additionally, protein optimization methods have improved the stability and activity of complex eukaryotic proteins. Thanks to their increased reliability, computational design methods have been applied to improve therapeutics and enzymes for green chemistry and have generated vaccine antigens, antivirals and drug-delivery nano-vehicles. Moreover, the high success of design methods reflects an increased understanding of basic rules that govern the relationships among protein sequence, structure and function. However, de novo design is still limited mostly to α-helix bundles, restricting its potential to generate sophisticated enzymes and diverse protein and small-molecule binders. Designing complex protein structures is a challenging but necessary next step if we are to realize our objective of generating new-to-nature activities.

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Fig. 1: Goals of protein design methodologies.
Fig. 2: Computational design of stable proteins.
Fig. 3: Design of enhanced protein activity.
Fig. 4: Design methods must navigate an astronomically large sequence space that is extremely sparse in functional proteins.
Fig. 5: Comparison of structural features in natural and de novo-designed proteins.
Fig. 6: Applications of de novo protein design.

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Acknowledgements

We thank A. Tennenhouse for critical reading. Work in the Fleishman lab was funded by the Volkswagen Foundation grant 9474, the Israel Science Foundation grant 1844, the European Research Council through a Consolidator Award grant 815379, the Dr. Barry Sherman Institute for Medicinal Chemistry, and a donation in memory of Sam Switzer. Work in the Correia lab was supported by the Swiss National Foundation, the National Center of Competence in Molecular Systems Engineering and Fondation Leenaards.

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Authors and Affiliations

  1. Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel

    Dina Listov & Sarel Jacob Fleishman

  2. Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Casper A. Goverde & Bruno E. Correia

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  1. Dina Listov
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Contributions

D.L. and C.A.G. researched data for the article. All authors contributed substantially to discussion of the content, wrote the article, and reviewed and/or edited the manuscript before submission.

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Correspondence to Bruno E. Correia or Sarel Jacob Fleishman.

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Competing interests

S.J.F. and B.E.C. are named inventors on patents relating to methods and designs described in the manuscript and consult on the application of protein design methods.

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Related links

Protein Data Bank: https://www.rcsb.org/

Supplementary information

Glossary

Backbone designability

The ability of amino acid sequences to fold into the desired backbone. A backbone that has many solutions is highly designable.

Backbone generation

Generating a spatial arrangement of the protein backbone excluding the amino acid side chains.

Epistasis

Non-additive effects of combinatorial mutations; for instance, when mutations are tolerated in combination but not individually, or vice versa.

Fold design

Design of a protein backbone that shares no significant sequence homology with natural proteins. Sometimes denoted de novo design.

Function design

Implementing a new function into a protein scaffold.

Idealized topologies

Simplified geometric representation of protein structure, mostly comprising secondary structure elements connected by short linkers.

Negative design

Designing elements that destabilize undesired (for example, non-functional or aggregation-prone) structural states.

Physics-based methods

Computational methods that apply the laws of physics, usually in the form of forcefields, to minimize protein structures and analyse or design three-dimensional protein structures.

Positive design

Designing protein elements that improve the stability of a desired structural state.

Protein backbone

The protein mainchain of amino acids connected through covalent amide linkages. Also known as protein scaffold.

Protein optimization

Design with the goal of optimizing desired protein functional aspects such as thermodynamic and kinetic stabilities, production yields, catalytic efficiency, binding affinity, and specificity.

Protein switches

Proteins that toggle several different conformations by interacting with a specific molecule or environment.

Relative contact order

Represents the relative complexity of a protein fold. Computed as the extent to which amino acids that are far in the primary sequence are physically close in the 3D structure.

Sequence space

The theoretical space of possible combinations of protein sequence changes. This space is often too large for experimental or computational enumeration, and design methods must find ways to restrict and sample it efficiently.

Stability design

Design with the goal of improving protein thermodynamic and kinetic stabilities.

Structure-based design

Design based on computed or experimentally determined molecular structures using physical principles.

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Listov, D., Goverde, C.A., Correia, B.E. et al. Opportunities and challenges in design and optimization of protein function. Nat Rev Mol Cell Biol (2024). https://doi.org/10.1038/s41580-024-00718-y

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