Alternative Computational Protocols for Supercharging Protein Surfaces for Reversible Unfolding and Retention of Stability

Abstract

Reengineering protein surfaces to exhibit high net charge, referred to as “supercharging”, can improve reversibility of unfolding by preventing aggregation of partially unfolded states. Incorporation of charged side chains should be optimized while considering structural and energetic consequences, as numerous mutations and accumulation of like-charges can also destabilize the native state. A previously demonstrated approach deterministically mutates flexible polar residues (amino acids DERKNQ) with the fewest average neighboring atoms per side chain atom (AvNAPSA). Our approach uses Rosetta-based energy calculations to choose the surface mutations. Both protocols are available for use through the ROSIE web server. The automated Rosetta and AvNAPSA approaches for supercharging choose dissimilar mutations, raising an interesting division in surface charging strategy. Rosetta-supercharged variants of GFP (RscG) ranging from −11 to −61 and +7 to +58 were experimentally tested, and for comparison, we re-tested the previously developed AvNAPSA-supercharged variants of GFP (AscG) with +36 and −30 net charge. Mid-charge variants demonstrated ∼3-fold improvement in refolding with retention of stability. However, as we pushed to higher net charges, expression and soluble yield decreased, indicating that net charge or mutational load may be limiting factors. Interestingly, the two different approaches resulted in GFP variants with similar refolding properties. Our results show that there are multiple sets of residues that can be mutated to successfully supercharge a protein, and combining alternative supercharge protocols with experimental testing can be an effective approach for charge-based improvement to refolding.

Supercharging enables organized assembly of synthetic biomolecules

“…1). 40,41 Beyond the ability to observe fluorescent properties, the simple barrel shape of GFP potentially enables assembly into several crystalline 42, 43 and non-crystalline 44 geometries. For the positively supercharged protein we used a monomeric GFP variant with a +32 net charge at pH 7.4 (GFP+32), 40 and for the negatively charged protein we used monomeric GFP variants with net charges of -10, -17, and -31 at pH 7.4 (GFP-10, GFP-17, and GFP-31).…”

“…For the positively supercharged protein we used a monomeric GFP variant with a +32 net charge at pH 7.4 (GFP+32), 40 and for the negatively charged protein we used monomeric GFP variants with net charges of -10, -17, and -31 at pH 7.4 (GFP-10, GFP-17, and GFP-31). 41 To enable FRET measurements between positively and negatively charged proteins we introduced a Y66W mutation to blueshift the GFP+32 chromophore and three associated stabilizing mutations (F146G, N147I, H149D) 45-47 ultimately yielding "Ceru+32." Native gels ( Supplementary Fig.…”

The effect of charge mutations on the stability and aggregation of a human single chain Fv fragment

“…Aggregation resistance has been increased through the method of supercharging proteins by engineering in an excess number of acidic [25,45,85] or basic residues [58] or alternatively protein net charge can be increased by covalently attaching charged amino acid tags [83,87]. Similarly, heat resistant antibody V H domains isolated from a combinatorial library of mutations generated by phage display generally had a disproportionate number of acidic groups [3,26,39].…”

“…These studies indicate that the controlling factor is the protein net charge. However other work, based on phage display [27] and rational mutagenesis [25,65,47], indicates that the spatial location of charged mutations controls the protein stability. However, care must be taken when engineering in charged mutations.…”

“…Most rational design strategies such as Rosetta choose mutations that minimize the free energy of the native state to avoid increasing the formation of aggregation-prone partially folded or unfolded states [25,58]. The more challenging problem is identifying partially-folded regions on a protein that expose hot spots buried in nucleation or aggregate growth steps.…”