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The identification of catalytic RNAs is typically achieved through primarily experimental means. However, only a small fraction of sequence space can be analyzed even with high-throughput techniques. Methods to extrapolate from a limited data set to predict additional ribozyme sequences, particularly in a human-interpretable fashion, could be useful both for designing new functional RNAs and for generating greater understanding about a ribozyme fitness landscape. Using information theory, we express the effects of epistasis (i.e., deviations from additivity) on a ribozyme. This representation was incorporated into a simple model of the epistatic fitness landscape, which identified potentially exploitable combinations of mutations. We used this model to theoretically predict mutants of high activity for a self-aminoacylating ribozyme, identifying potentially active triple and quadruple mutants beyond the experimental data set of single and double mutants. The predictions were validated experimentally, with nine out of nine sequences being accurately predicted to have high activity. This set of sequences included mutants that form a previously unknown evolutionary ‘bridge’ between two ribozyme families that share a common motif. Individual steps in the method could be examined, understood, and guided by a human, combining interpretability and performance in a simple model to predict ribozyme sequences by extrapolation.

Intrinsically disordered proteins rich in cationic amino acid groups can undergo Liquid-Liquid Phase Separation (LLPS) in the presence of charge-balancing anionic counterparts. Arginine and Lysine are the two most prevalent cationic amino acids in proteins that undergo LLPS, with arginine-rich proteins observed to undergo LLPS more readily than lysine-rich proteins, a feature commonly attributed to arginine’s ability to form stronger cation-π interactions with aromatic groups. Here, we show that arginine’s ability to promote LLPS is independent of the presence of aromatic partners, and that arginine-rich peptides, but not lysine-rich peptides, display re-entrant phase behavior at high salt concentrations. We further demonstrate that the hydrophobicity of arginine is the determining factor giving rise to the reentrant phase behavior and tunable viscoelastic properties of the dense LLPS phase. Controlling arginine-induced reentrant LLPS behavior using temperature and salt concentration opens avenues for the bioengineering of stress-triggered biological phenomena and drug delivery systems.

Numerous biological systems contain vesicle‐like biomolecular compartments without membranes, which contribute to diverse functions including gene regulation, stress response, signaling, and skin barrier formation. Coacervation, as a form of liquid–liquid phase separation (LLPS), is recognized as a representative precursor to the formation and assembly of membrane‐less vesicle‐like structures, although their formation mechanism remains unclear. In this study, a coacervation‐driven membrane‐less vesicle‐like structure is constructed using two proteins, GG1234 (an anionic intrinsically disordered protein) and bhBMP‐2 (a bioengineered human bone morphogenetic protein 2). GG1234 formed both simple coacervates by itself and complex coacervates with the relatively cationic bhBMP‐2 under acidic conditions. Upon addition of dissolved bhBMP‐2 to the simple coacervates of GG1234, a phase transition from spherical simple coacervates to vesicular condensates occurred via the interactions between GG1234 and bhBMP‐2 on the surface of the highly viscoelastic GG1234 simple coacervates. Furthermore, the shell structure in the outer region of the GG1234/bhBMP‐2 vesicular condensates exhibited gel‐like properties, leading to the formation of multiphasic vesicle‐like compartments. A potential mechanism is proposed for the formation of the membrane‐less GG1234/bhBMP‐2 vesicle‐like compartments. This study provides a dynamic process underlying the formation of biomolecular multiphasic condensates, thereby enhancing the understanding of these biomolecular structures.