Protein Design

Abstract

The design of proteins with new or modified characteristics was initially limited to random searches for optimal sequences or to a rational amino acid modification using our knowledge about the target. Nowadays, the advances in the development of new methodologies for directed evolution, including improved screening methods, in silico sequence selection and computational energy minimum search, together with their combination, have expanded the field to a whole new world of possibilities. Plenty of successful protein de novo design and redesign approaches can be found in the literature, including enzymatic optimisation, nonnatural protein scaffold design, metal‐binding site insertion or oligomerisation, among others.

Key Concepts

  • Protein design is an expanding field with methodological improvements in recent years and a huge list of successful achievements.
  • Rational protein design methods are fundamental to restrict the search for the best sequence candidates that might otherwise take ages, due to the 20n possible sequences, where n is the amino acid length of the protein.
  • The available methods can be divided into experimental methods and computational methods, where the difference is the use of computer simulations to find the optimal sequence.
  • The best results are being obtained through a combination of different techniques to arrive in the optimal sequence.
  • Enzymatic design has transformed manufacturing processes thanks to the creation of highly optimised enzymes that improve a vast variety of reactions designed to produce high‐value compounds with interest in industry, biotechnology and biomedicine.
  • The improvement of computational methods, mainly with the increase of computer power and the development of accurate simulation software, has enabled us to design de novo highly stable proteins with nonnatural structure, good candidates for being used as scaffolds in the design of new protein activities.
  • Interface optimisation in protein design helps to produce new oligomeric macromolecules with interesting properties (including rings, filaments, cages, layers or surfaces) and with plenty of possible applications in the future.

Keywords: protein design; protein engineering; functional design; rational design; de novo design; directed evolution; computer simulation

Figure 1. Scheme with the main methodologies used in protein design. Structural and physicochemical information is initially collected before applying one of the main methods (basic rational design, directed evolution or computer design). Finally, depending on the degree of necessary protein change (amino acid substitution, protein redesign or de novo design) we have different preferential uses. The arrows between the main methods and the corresponding applications indicate the most common uses of each technique.
Figure 2. Different methodologies and applications for protein function design. A protein example for each final category is included, both for directed evolution (subtilisin for enzyme activity optimisation (Graycar et al., ), esterase for enzyme stabilisation (Giver et al., ), phosphotriesterase for substrate specificity change (Hill et al., ), lipase for enantioselectivity modification (Reetz et al., ) and cytochrome P450 for the creation of new activities (Coelho et al., )) and for rational design (subtilisin BPN for rational mutagenesis using structure and function information (Abrahmsen et al., ), phytase for using homology comparison (Lehmann et al., ) and Kemp eliminase for computer optimisation (Rothlisberger et al., )). The mutated amino acids are indicated in red. Adapted from Graycar et al. , Giver et al. , Hill et al. , Reetz et al. , Coelho et al. , Abrahmsen et al. , Lehmann et al. , Rothlisberger et al. .
Figure 3. Examples for the different classes of protein structure design, including folded peptides rich in disulphide bonds (mainly alpha (pdb code 5TX8) (Buchko et al., ), mainly beta (pdb code 5KX2) and alpha‐beta (pdb code 5JHI) (Bhardwaj et al., ) peptides), helix bundles (bundles with three‐helices (pdb code 2A3D) (Walsh et al., ), four‐helices (pdb code 4UOS) (Huang et al., ) and four‐helices with metal binding (pdb code 2LFD)), α/β proteins (ferredoxin (pdb code 2KL8), rossmann2x2 (pdb code 2LV8) (Koga et al., ) and curve beta sheet (pdb code 5TPJ) (Marcos et al., ) folds) and symmetric structures (alpha toroid (pdb code 4YXX) (Doyle et al., ), TIM barrel (pdb code 5BVL) (Huang et al., ) and beta‐propeller (pdb code 3WW9) (Voet et al., ) proteins). Adapted from Bhardwaj et al. , Walsh et al. , Huang et al. , Koga et al. , Marcos et al. , Doyle et al. , Huang et al. , Voet et al. .
Figure 4. Examples for the different classes of oligomerisation design, including basic quaternary contacts optimisation (beta sheet connection of gamma‐adaptin (pdb code 3ZY7) (Stranges et al., ), creation of a seven‐helix coiled coil (pdb code 2HY6) (Liu et al., ) and creation of filaments (pdb code 5LP3) (Garcia‐Seisdedos et al., )), domain swapping (formation of a dimer (pdb code 4KXH) (Pica et al., )), oligomeric domains fusion (tetrahedral (pdb code 3VDX) (Lai et al., ) and cubic (pdb code 4QCC) (Lai et al., ) cages), crosslinking connections (four‐helix bundle with 2 iron atoms (pdb code 1EC5) (Lombardi et al., ), filaments with disulphide bonds (pdb code 1Y12) (Ballister et al., ) and ferritin cage assembled by copper atoms (pdb code 4DYY) (Huard et al., )) and interface optimisation (tetramer (pdb code 2V7G) (Grueninger et al., ), two component tetrahedral cage (pdb code 4NWO) (King et al., ), octameric ring connection (pdb code 2V9U) (Grueninger et al., ) and two component icosahedral cage (pdb code 5IM5) (Bale et al., )). Adapted from Stranges et al. , Liu et al. , Garcia‐Seisdedos et al. , Pica et al. , Lai et al. , Lombardi et al. , Ballister et al. , Huard et al. , Grueninger et al. , King et al. , Grueninger et al. , Bale et al. .
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Further Reading

Alberghina L (ed.) (2003) Protein Engineering for Industrial Biotechnology, vol. 388. CRC Press: Boca Raton, FL.

Arnold FH and Georgiou G (eds) (2003) Directed enzyme evolution: screening and selection methods. In: Methods in Molecular Biology, vol. 230, p 370. Humana Press Inc: Totowa, NJ.

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Keating AE (ed.) (2013) Methods in protein design. In: Methods in Enzymology, vol. 523, p 520. Academic Press: Cambridge, MA.

Park SJ and Cochran JR (eds) (2009) Protein Engineering and Design, p 416. CRC Press: Boca Raton, FL.

Reetz MT (ed.) (2017) Directed Evolution of Selective Enzymes: Catalysts for Organic Chemistry and Biotechnology, vol. 320. Wiley‐VCH Verlag GmbH & Co. KGaA: Weinheim, Germany.

Samish I (ed.) (2018) Computational protein design. In: Methods in Molecular Biology, vol. 1529, p 450. Humana Press Inc: New York, NY.

Sheehan MN (ed.) (2013) Protein Engineering: Design, Selection and Applications, p 221. Nova Science Publishers: Hauppauge, NY.

Stoddard BL (ed.) (2018) Computational design of ligand‐binding proteins. In: Methods in Molecular Biology, vol. 1414, p 375. Humana Press Inc: New York, NY.

Voynov V and Caravella JA (eds) (2016) Therapeutic proteins: methods and protocols. In: Methods in Molecular Biology, vol. 899, p 502. Humana Press Inc: New York, NY.

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Campos, Luis A(Oct 2019) Protein Design. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0003034.pub2]