UC Berkeley

Biological fluids, the most complex blends, have compositions that constantly vary andcannot be molecularly defined.[1] Despite these uncertainties, proteins fluctuate, fold, function, and evolve as programmed.[2-4] We hypothesize that in addition to the known monomeric sequence requirements, protein sequences encode multi-pair interactions at the segmental level to navigate random encounters;[5] synthetic heteropolymers capable of emulating such interactions can replicate how proteins behave in biological fluids individually and collectively. In this dissertation, I established a top-down approach to engineering RHPs to capture essential features from the whole protein sequence space. I developed a deep-learning model to extract the chemical characteristics and sequential arrangement along a protein chain as the segmental level from natural protein libraries. The information was subsequently used to design random heteropolymer ensembles. The extensive conformation/dynamics characterizations such as optical tweezers, small angle x-ray scattering, NMR and molecular dynamics simulation demonstrated the random heteropolymers that matched segment characteristics of proteins can mimic mixtures of disordered, partially folded, and folded protein. For each heteropolymer ensemble, the level of segmental similarity to that of natural proteins determines its ability to replicate multiple functions of biological fluids including assisting protein folding during translation, preserving the viability of fetal bovine serum without refrigeration and enhancing proteins’ thermal stability. Furthermore, heteropolymer ensembles can behave as synthetic cytosol under biologically relevant conditions. Heteropolymers can be designed to have liquid-liquid phase separation behavior using same design principles. The liquid droplets can sequester the oligonucleotides without compromising the duplex formation of complementary oligonucleotides. Molecular studies further translated protein sequence information at the segmental level into intermolecular interactions with a defined range, degree of diversity, and temporal and spatial availability. This framework provides valuable guiding principles to synthetically realize protein properties, engineer bio/abiotic hybrid materials, and ultimately, realize matter-to-life transformations.