Biomolecular condensates are an active area of research that offers tremendous promise, and research is helping to uncover their function in cellular biology and the potential for therapeutic intervention in pathological condensates. Experiments and simulations have led to continuous improvement in our understandings of biomolecular condensate thermodynamics, but the kinetic properties of condensation remain under-explored. In this project, we explore the nucleation properties of the intrinsically-disordered region (IDR) of human Ribonucleoprotein A1, also known as A1LCD. This protein’s thermodynamic properties are well-characterized by experimental studies, but its energetic barrier to nucleation is unknown. We propose a workflow to broadly resolve nucleation barriers for A1LCD from simulations alone, taking advantage of the Mpipi coarse-grained intrinsically disordered protein (IDP) model’s accuracy and performance to run microsecond-long simulations enabling the calculation of kinetic barriers to rare events. We make predictions of condensate nucleation barriers and correlate nucleation barrier height with known critical temperature values across six A1LCD mutants. We find that energetic barriers to nucleation are relatively constant across these mutant strains that condense at different critical temperatures, and additionally see that nucleation barriers rise as we approach the critical temperature for low system densities. In addition, we offer insights to future calculation of nucleation barriers for other IDPs.

Biomolecular condensates are membraneless organelles inside living cells that primarily comprise proteins and nucleic acids. The thermodynamic process of liquid-liquid phase separation has been proposed as a primary driver of biomolecular condensation, and it is recognized that phase separation is maintained by networks of biomolecular interactions within these liquid droplets. Canonical examples of condensing biomolecules include prion-like low-complexity domains (LCDs) of proteins, and simulations of single-component LCD condensates have predicted the presence of small-world topologies in the interaction networks underlying condensate stability. Recent experimental and theoretical works have also demonstrated inhomogeneities in single-molecule conformation, orientation, and dynamics within biomolecular condensates. Here, we systematically characterize the molecular networks underlying both LCD condensates and condensates formed by generic associative heteropolymers. Further, we investigate the relationship between network topologies and single-molecule properties within condensates. To probe LCD condensates, we employ a chemically specific, coarse-grained model of disordered proteins designed to reproduce phase separation statistics. We generalize our findings by varying sequence hydrophobicities using a generic binary model of associative heteropolymers, dubbed the “hydrophobic–polar” (HP) model. In both model systems, we find persistent small-world topologies underlying single-component condensates. These topologies feature molecular “hubs” with high network betweenness centrality and molecular “cliques” that represent densely interacting clusters of biomolecules; distal cliques in condensate volumes all localize to phase interfaces and are bridged by elongated hubs that remain near condensate centers. Strikingly, we find that relationships between network connectivity and biomolecular structure and dynamics are governed by power laws. Our work demonstrates that inhomogeneous single-molecule behaviors within biomolecular condensates can be well predicted from condensate network connectivities. Furthermore, we find that network cliques have substantially longer lifetimes than molecular hubs, and that the motion of molecules within cliques is spatially constrained. Together, these results reveal a dynamic hub-clique architecture underlying condensates and suggest that the physicochemical characteristics and material properties of phase interfaces are critical to pathological gelation and fibrillization processes observed in condensate aging.

TDP-43 is an RNA-binding protein pathophysiologically implicated in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Experiments by Mann et al. have demonstrated doses of "bait oligonucleotides" with high affinity for TDP-43 can abolish neurotoxicity by dissolving pathological TDP-43 condensates in the cytosol [1]. To increase the odds of translational success for this technology, growing and diversifying the group of sequences known to bind TDP-43 with high affinity is desirable. Computational screening is a promising method for prudent identification of sequences to test experimentally, saving time and money. In this thesis, we explore the use of observables from all-atom molecular dynamics simulations to score the bound poses of TDP-43 to various RNA sequences. Coarse-grained unbinding simulations and AlphaFold uncertainty measurements were also examined. The results indicate that all-atom RMSF correlates the best with experimental Kd (Pearson approx. 0.7). AlphaFold also weakly correlated (Pearson approx. 0.5), while coarse-grained simulations possessed no correlation. Temperature replica exchange simulations suggested that vanilla simulations do not fully sample the range of bound conformations for RNA, which could interfere with correlation of observables and Kd. Notably, vanilla simulations were able to persistently model residue interactions of known experimental significance (pi stacking and hydrogen bonding between TDP-43 and RNA), which may have contributed signal to the RSMF correlation. Our results suggest the RMSF has potential for use in a pose scoring function, but that it is insufficient alone to predict Kd. Due to noise in experimental affinity values and poor sampling in vanilla simulations, more high-quality experimental Kd data is imperative, in order to build more complex MD-based scoring functions while avoiding overfitting.

Caldicellulosiruptor is a unique genus of bacteria that produces lignocellulose-degrading Carbohydrate Active enZymes (CAZymes). CAZymes are of particular interest because of their potential utility in processing cellulose for sustainable biofuel pathways. However, studying the CAZyme efficiently has been difficult both experimentally and computationally due to the enzyme’s large size, over 1700 amino acid residues in length. Previous computational studies have studied the CAZyme in a limited capacity, simulating only small sections of the protein with atomistic approaches. In this paper, I present Mpipi- Cellulose, the extension of the coarse-grained Mpipi force field to cellulose. Mpipi-Cellulose represents each monomer of cellulose as a single interaction site (or bead), allowing for more efficient computations yet still accurately capturing the cellulose-protein interactions of CAZyme binding to cellulose. Through a series of umbrella sampling simulations, potentials of mean force were constructed for interactions between cellulose and a variety of ligands, including peptide fragments and subdomains of CelA, the primary CAZyme produced by the Caldicellulosiruptor genus of bacteria. Mpipi-Cellulose demonstrated strong qualitative agreement with Martini3, a higher resolution coarse-grained model optimized for cellulose, across these simulations. Mpipi-Cellulose was then used to simulate the entire CelA interacting with a slab of crystalline cellulose. Using the newly developed model, I tracked the number of contacts between the enzyme and the cellulose, showing that the majority of the residues that interact with the cellulose come from the binding domains as predicted by experiments. Further simulations were performed with various CelA mutants, removing different binding domains and exploring the impact on interaction with the cellulose. These simulations demonstrated that the second and third binding domains in CelA are particularly important for proper binding to the cellulose surface. With this knowledge and further use of the Mpipi-Cellulose model, a wide number of mutants can be screened to optimize CAZyme performance for degradation of cellulose to produce biofuels.