Conformational stability as a design target to control protein aggregation

Area of Science:

• Biochemistry
• Biotechnology
• Protein Engineering

Background:

• Non-native protein aggregation is a significant challenge in biotechnology, impacting product efficacy and potentially causing disease.
• Aggregation can compromise biological activity or trigger immune responses.
• Amyloid fibril formation, linked to diseases, arises from specific aggregation pathways.

Purpose of the Study:

• To investigate computational strategies for mitigating protein aggregation by enhancing conformational stability.
• To use human γD crystallin (γD-crys) as a model to test if increasing the free energy for unfolding (ΔGunf) reduces aggregation rates.
• To explore stabilizing domain-domain interfaces and less stable domains as mutational approaches.

Main Methods:

• Utilized RosettaDesign, a computational protein design algorithm, to identify point mutations.
• Applied two mutational strategies: stabilizing the less stable domain and the domain-domain interface.
• Experimentally assessed the impact of mutations on aggregation rates and conformational stability (ΔGunf).

Main Results:

• While predicted free energies showed weak correlation with experimental ΔGunf, aggregation rates correlated well with changes in ΔGunf.
• Decreased aggregation rates were observed with increased experimental ΔGunf, supporting conformational stability as a design target.
• Identified that conformational stability is not the sole determinant of aggregation rates in natively folded proteins.

Conclusions:

• Computational design can enhance protein conformational stability to mitigate non-native aggregation.
• Targeting conformational stability is a viable strategy, though not the only factor, for controlling protein aggregation.
• Further research is needed to fully understand and control protein aggregation mechanisms in biotechnological applications.