Data-Driven Design of Random Heteropolypeptides as Synthetic Polyclonal Antibodies.

Area of Science:

• Biotechnology
• Protein Engineering
• Immunology

Background:

• Conventional antibody development is costly and time-consuming.
• There is a need for faster, more adaptable antibody alternatives.

Purpose of the Study:

• To engineer random heteropolypeptides (RHPs) as synthetic polyclonal antibodies (SpAbs) with programmable binding.
• To demonstrate rapid SpAb discovery and characterization for therapeutic targets.

Main Methods:

• High-throughput synthesis of selenopolypeptide derivatives.
• Algorithm-assisted optimization for SpAb identification.
• Binding affinity and functional assays, including neutralization of cytotoxicity.

Main Results:

• Identified SpAbs targeting human interferon-α (IFN) and tumor necrosis factor-α (TNF-α) within 2 weeks.
• Achieved binding affinities comparable to natural antibodies (e.g., 7.9 nM Kd for TNF-α).
• Demonstrated effective neutralization of TNF-α-induced cytotoxicity and high selectivity.

Conclusions:

• Sequence-independent RHPs can mimic functional antibodies.
• This data-driven workflow enables rapid SpAb discovery and optimization.
• SpAbs show broad potential in diagnostics and therapeutics.