The organization of membraneless organelles (MLOs) via liquid-liquid phase separation (LLPS) is a versatile phenomenon observed in biological cells. These MLOs, also known as biological condensate, are liquid-like droplets that perform different biological cell functions efficiently. Most MLOs, consisting of intrinsically disordered proteins (IDPs), engage in dynamic assembly and disassembly to compartmentalize to sequestrate different molecules, including RNA. These MLOs have potential applications and developments in biomaterials and drug delivery systems. While experimental investigations drive real-world outcomes, computational modeling can guide them to rational design with better theoretical understanding. However, in-silico modeling of IDPs is challenging due to the lack of well-...[ Read more ]

The organization of membraneless organelles (MLOs) via liquid-liquid phase separation (LLPS) is a versatile phenomenon observed in biological cells. These MLOs, also known as biological condensate, are liquid-like droplets that perform different biological cell functions efficiently. Most MLOs, consisting of intrinsically disordered proteins (IDPs), engage in dynamic assembly and disassembly to compartmentalize to sequestrate different molecules, including RNA. These MLOs have potential applications and developments in biomaterials and drug delivery systems. While experimental investigations drive real-world outcomes, computational modeling can guide them to rational design with better theoretical understanding. However, in-silico modeling of IDPs is challenging due to the lack of well-defined three-dimensional structures and computational limitations. The study and work in this thesis presented the development of a unique multi-scaled computational model to estimate the propensity for LLPS to get the essential idea of the sticker-spacer architecture of IDPs. The computational pipeline includes a series of all atomistic molecular dynamics (MD) simulations followed by coarse-grained lattice Monte Carlo (MC) simulations to understand and predict the LLPS phenomena.

The second chapter of the thesis covers the development of a multiscale computational pipeline and verifying its application using a well-known IDP, Fused in Sarcoma (FUS), and its two other variants, the prion-like domain and RNA binding domain, as illustrative examples. The computational protocol includes identifying potential interaction sites (stickers) from the IDP sequence, estimation of their binding energies, representation of the IDP sequence as CG beads of stickers and spacers, and lattice MC simulations for phase separation propensity estimation using progressive assembly and disassembly events in the simulation.

The third chapter contains the extended applications of the computational model using three FUS mutants, which are the glutamic acid substitution for glycine at 156th position (G156E), arginine substitution for cysteine at 244th position (R244C), and deletion of nuclear localization signal (ΔNLS). This chapter highlights the applicability of the computational framework for different scales of phase separation, including aggregation-induced liquid-solid phase separation. The results emphasize the ability of the computational model to predict the effect of mutation down to a single amino acid substitution. The study also showed temporal changes in LLPS via expanding simulations time scale nearly two times compared to wild-type (WT) FUS simulations.

The fourth chapter covers the further extension of utilizing the model for simulating the LLPS behavior of IDP mimicking polymer-oligopeptide hybrids (IPHs), which is a novel class of synthetic mimic for MLOs designed by drafting interactive oligopeptide sequences to dextran. They can undergo different levels of LLPS via modulating backbone molecular weight and different interactive oligopeptides. The results underline the ability of the multiscale model to track different levels of LLPS that depend on the interaction strength of stickers and the molecular weight of backbone spacers MLO mimetics.

In summary, the research findings of this work provide a theoretical framework for a comprehensive understanding of LLPS and a reliable computational pipeline to predict the phase separation ability of IDPs, their mutants, and MLOs mimetics. It also provides a fast and cost-effective approach to facilitate the rational design of synthetic bio-inspired materials efficiently.