Sequence Design of Random Heteropolymers as Protein Mimics

Abstract

Random heteropolymers (RHPs) have been computationally designed and experimentally shown to recapitulate protein-like phase behavior and function. However, unlike proteins, RHP sequences are only statistically defined and cannot be sequenced. Recent developments in reversible-deactivation radical polymerization allowed simulated polymer sequences based on the well-established Mayo−Lewis equation to more accurately reflect ground-truth sequences that are experimentally synthesized. This led to opportunities to perform bioinformatics-inspired analysis on simulated sequences to guide the design, synthesis, and interpretation of RHPs. We compared batches on the order of 10000 simulated RHP sequences that vary by synthetically controllable and measurable RHP characteristics such as chemical heterogeneity and average degree of polymerization. Our analysis spans across 3 levels: segments along a single chain, sequences within a batch, and batch-averaged statistics. We discuss simulator fidelity and highlight the importance of robust segment definition. Examples are presented that demonstrate the use of simulated sequence analysis for in-silico iterative design to mimic protein hydrophobic/hydrophilic segment distributions in RHPs and compare RHP and protein sequence segments to explain experimental results of RHPs that mimic protein function. To facilitate the community use of this workflow, the simulator and analysis modules have been made available through an open source toolkit, the RHPapp.

Mapping Composition Evolution through Synthesis, Purification, and Depolymerization of Random Heteropolymers

“…MMA, EHMA, and OEGMA were routinely used to mimic amino acids in RHP synthesis. 18,48 3-Sulfopropyl methacrylate potassium salt (SPMA) was previously used as the fourth monomer. However, the chemical shifts of SPMA and EHMA largely overlap in the NMR spectra.…”

“…(c) Distribution of chain hydrophobicity in in silico-designed RHP1–4 . Chain hydrophobicity was estimated using previously reported HLB values (MMA: 8.45, EHMA: 5.125, OEGMA: 11.42, and NHSMA: 12.775). , …”

Sequence Patterning, Morphology, and Dispersity in Single-Chain Nanoparticles: Insights from Simulation and Machine Learning

“…Based on prior work, , we hypothesized that these parameters would enable study of a vast set of precursor chains that would result in distinct SCNPs. Although a precise connection of f and β to experimental synthetic conditions may be nontrivial, we expect that such parameters would meaningfully relate to aspects of monomer concentration and reactivity ratios. , In the long term, advances in sequence-level polymerization may enable more precise definitions. − , …”

“…Although a precise connection of f and β to experimental synthetic conditions may be nontrivial, we expect that such parameters would meaningfully relate to aspects of monomer concentration and reactivity ratios. 56,57 In the long term, advances in sequence-level polymerization may enable more precise definitions. [21][22][23][24][25]58 2.1.3.…”

Confinement of folding motifs within central blocks improves single chain polymer nanoparticle folding

“…In their work, certain sequences of hydrophobic, charged and hydrophilic monomers will cause a polymer to fold just like a protein, even to the point of replicating protein function. 16,17 Parallel computational work is helping unpick the relationship between the polymer sequence and the folded structure. 18,19 Because Xu's work uses random heteropolymers, only a tiny fraction of the total polymer population bears the correct sequence to fold into the desired conformation, but this work demonstrates the potential of simple folding motifs to drive complex folding.…”