A computational-combinatorial approach identifies a protein inhibitor of superoxide dismutase 1 misfolding, aggregation, and cytotoxicity Academic Article uri icon

abstract

  • Molecular agents that specifically bind and neutralize misfolded and toxic superoxide dismutase 1 (SOD1) mutant proteins may find application in attenuating the disease progression of familial amyotrophic lateral sclerosis. However, high structural similarities between the wild-type and mutant SOD1 proteins limit the utility of this approach. Here we addressed this challenge by converting a promiscuous natural human IgG-binding domain, the hyperthermophilic variant of protein G (HTB1), into a highly specific aggregation inhibitor (designated HTB1M) of two familial amyotrophic lateral sclerosis–linked SOD1 mutants, SOD1G93A and SOD1G85R. We utilized a computational algorithm for mapping protein surfaces predisposed to HTB1 intermolecular interactions to construct a focused HTB1 library, complemented with an experimental platform based on yeast surface display for affinity and specificity screening. HTB1M displayed high binding specificity toward SOD1 mutants, inhibited their amyloid aggregation in vitro, prevented the accumulation of misfolded proteins in living cells, and reduced the cytotoxicity of SOD1G93A expressed in motor neuron–like cells. Competition assays and molecular docking simulations suggested that HTB1M binds to SOD1 via both its α-helical and β-sheet domains at the native dimer interface that becomes exposed upon mutated SOD1 misfolding and monomerization. Our results demonstrate the utility of computational mapping of the protein–protein interaction potential for designing focused protein libraries to be used in directed evolution. They also provide new insight into the mechanism of conversion of broad-spectrum immunoglobulin-binding proteins, such as HTB1, into target-specific proteins, thereby paving the way for the development of new selective drugs targeting the amyloidogenic proteins implicated in a variety of human diseases.

publication date

  • January 1, 2017