TDP-43 α-helical structure tunes liquid-liquid phase separation and function

Abstract

Liquid-liquid phase separation (LLPS) is involved in the formation of membraneless organelles (MLOs) associated with RNA processing. Present in several MLOs, TDP-43 undergoes LLPS and is linked to the pathogenesis of amyotrophic lateral sclerosis (ALS). While some disease variants of TDP-43 disrupt self-interaction and function, here we show that designed single mutations can enhance TDP-43 assembly and function via modulating helical structure. Using molecular simulation and NMR spectroscopy, we observe large structural changes in a dimeric TDP-43. Two conserved glycine residues (G335 and G338) are potent inhibitors of helical extension and helix-helix interaction, which are removed in part by variants including the ALS-associated G335D. Substitution to helix-enhancing alanine at either of these positions dramatically enhances phase separation in vitro and decreases fluidity of phase separated TDP-43 reporter compartments in cells. Furthermore, G335A increases TDP-43 splicing function in a mini-gene assay. Therefore, TDP-43 helical region serves as a short but uniquely tunable module that shows promise as for controlling assembly and function in cellular and synthetic biology applications of LLPS.

Molecular details of protein condensates probed by microsecond-long atomistic simulations

“…We have optimized the procedure for systems with components of similar size to FUS LC and LAF-1 RGG so that similar simulations should be accessible using even general-purpose computing hardware (Table S1). We have leveraged our earlier work with CG simulations of IDP phase coexistence, 5,22,24,29,31,35,40,41,67 and atomistic studies of inter-protein interactions 5,22,24,35 to obtain important mechanistic details of the underlying molecular interactions of condensates.…”

“…7 A physical understanding of the driving forces of biomolecular phase separation is essential for uncovering the mechanistic details of MLO formation and the pathology of relevant diseases. [20][21][22][23][24][25] A frequent property of proteins involved in biomolecular phase separation is intrinsic disorder, which has been highlighted through estimates of enhanced disorder predicted within MLO-associated proteins. 26 Indeed, intrinsically disordered proteins (IDPs) have been shown to phase separate at relatively low concentrations compared to most folded proteins, 5,25,27 likely due to their polymeric nature, and consequent increased multivalent interactions.…”

“…We have previously overcome this difficulty by developing coarse-grained (CG) simulation models for LLPS. 29,31,[40][41][42][43] We have complemented CG simulations of LLPS with atomistic simulations of smaller fragments of the IDPs, 5,22,24,35,44 which yield detailed interactions occurring between the relevant proteins in dilute solution, and in principle, within the condensed phase. 31 In this work, we unify these two approaches, by using CG simulations to generate an initial, equilibrated configuration of phase-separated proteins, which is then mapped back to all-atom coordinates to investigate the details of atomic interactions occurring within a protein condensate.…”

The (un)structural biology of biomolecular liquid-liquid phase separation using NMR spectroscopy

“…1A). One study on the phase separation-prone protein TDP-43 mapped a helical subregion (residues 321-343) important for LLPS by observing chemical shift perturbations, which was then confirmed by testing the effect of mutations in this region on phase separation (9,16). In contrast, other LLPS systems display small chemical shifts with increasing protein concentrations across the entirety of the protein sequence, suggesting that the interactions that stabilize phase separation are not localized to a particular region (10).…”

Sequence dependent co-phase separation of RNA-protein mixtures elucidated using molecular simulations

“…Part of this stems from the lack of techniques that can provide in-depth information about the RNA-protein interactions driving RNP granule formation and high spatiotemporal resolution on the molecular organization of protein and RNA within the condensate (12,25,26). One can attempt to obtain such in-depth information from in silico atomic resolution simulation techniques (20,27,28) but studying a macroscopic phenomenon like phase separation would require considerable computational resources making this method quite expensive and prohibitive. This prompts us to look into computational approaches based on coarse-grained (CG) models which can allow investigations into the formation of biomolecular condensates and to provide molecular-level details necessary to develop theories of phase separation making CG models an integral part of the biophysical toolkit to study phase separation (29)(30)(31)(32).…”