A topological data analytic approach for discovering biophysical signatures in protein dynamics.

Identifying structural differences among proteins can be a non-trivial task. When contrasting ensembles of protein structures obtained from molecular dynamics simulations, biologically-relevant features can be easily overshadowed by spurious fluctuations. Here, we present SINATRA Pro, a computational pipeline designed to robustly identify topological differences between two sets of protein structures. Algorithmically, SINATRA Pro works by first taking in the 3D atomic coordinates for each protein snapshot and summarizing them according to their underlying topology. Statistically significant topological features are then projected back onto a user-selected representative protein structure, thus facilitating the visual identification of biophysical signatures of different protein ensembles. We assess the ability of SINATRA Pro to detect minute conformational changes in five independent protein systems of varying complexities. In all test cases, SINATRA Pro identifies known structural features that have been validated by previous experimental and computational studies, as well as novel features that are also likely to be biologically-relevant according to the literature. These results highlight SINATRA Pro as a promising method for facilitating the non-trivial task of pattern recognition in trajectories resulting from molecular dynamics simulations, with substantially increased resolution.

Identifying sequence perturbations to an intrinsically disordered protein that determine its phase-separation behavior.

Phase separation of intrinsically disordered proteins (IDPs) commonly underlies the formation of membraneless organelles, which compartmentalize molecules intracellularly in the absence of a lipid membrane. Identifying the protein sequence features responsible for IDP phase separation is critical for understanding physiological roles and pathological consequences of biomolecular condensation, as well as for harnessing phase separation for applications in bioinspired materials design. To expand our knowledge of sequence determinants of IDP phase separation, we characterized variants of the intrinsically disordered RGG domain from LAF-1, a model protein involved in phase separation and a key component of P granules. Based on a predictive coarse-grained IDP model, we identified a region of the RGG domain that has high contact probability and is highly conserved between species; deletion of this region significantly disrupts phase separation in vitro and in vivo. We determined the effects of charge patterning on phase behavior through sequence shuffling. We designed sequences with significantly increased phase separation propensity by shuffling the wild-type sequence, which contains well-mixed charged residues, to increase charge segregation. This result indicates the natural sequence is under negative selection to moderate this mode of interaction. We measured the contributions of tyrosine and arginine residues to phase separation experimentally through mutagenesis studies and computationally through direct interrogation of different modes of interaction using all-atom simulations. Finally, we show that despite these sequence perturbations, the RGG-derived condensates remain liquid-like. Together, these studies advance our fundamental understanding of key biophysical principles and sequence features important to phase separation.

Influence of Physical Effects on the Swarming Motility of Pseudomonas aeruginosa.

Many species of bacteria can spread over a moist surface via a particular form of collective motion known as "surface swarming". This form of motility is typically studied by inoculating bacteria on a gel formed by 0.4-1.5% agar, which contains essential nutrients for their growth and proliferation. Using Pseudomonas aeruginosa and its pili-less mutant, ΔPilA, we investigate physical factors that either facilitate or restrict the swarming motility, measured by the rate of increase in area covered by a spreading bacterial colony, i.e., a swarm. The wild-type colony spreads over the agar surface in highly branched structures. The pili-less mutant fills up the area more fully as it spreads, but it also produces numerous and fragmented branches, or tendrils, at the swarm front. Whereas additional surfactants enhance swarming, increasing the agar percentage, adding extra salt or sugar or incorporating viscous agents in the agar matrix all decrease swarming, supporting the conclusion that swarming motility is restricted by the surface tension at the swarm front and swarm growth is limited by the rate of water supply from within the agar gel. The physical basis elaborated through this study provides a useful framework for understanding the swarming behavior of numerous species of bacteria.

Conformational heterogeneity and probability distributions from single-particle cryo-electron microscopy.

Single-particle cryo-electron microscopy (cryo-EM) is a technique that takes projection images of biomolecules frozen at cryogenic temperatures. A major advantage of this technique is its ability to image single biomolecules in heterogeneous conformations. While this poses a challenge for data analysis, recent algorithmic advances have enabled the recovery of heterogeneous conformations from the noisy imaging data. Here, we review methods for the reconstruction and heterogeneity analysis of cryo-EM images, ranging from linear-transformation-based methods to nonlinear deep generative models. We overview the dimensionality-reduction techniques used in heterogeneous 3D reconstruction methods and specify what information each method can infer from the data. Then, we review the methods that use cryo-EM images to estimate probability distributions over conformations in reduced subspaces or predefined by atomistic simulations. We conclude with the ongoing challenges for the cryo-EM community.

Ensemble Reweighting Using Cryo-EM Particle Images.

Cryo-electron microscopy (cryo-EM) has recently become a leading method for obtaining high-resolution structures of biological macromolecules. However, cryo-EM is limited to biomolecular samples with low conformational heterogeneity, where most conformations can be well-sampled at various projection angles. While cryo-EM provides single-molecule data for heterogeneous molecules, most existing reconstruction tools cannot retrieve the ensemble distribution of possible molecular conformations from these data. To overcome these limitations, we build on a previous Bayesian approach and develop an ensemble refinement framework that estimates the ensemble density from a set of cryo-EM particle images by reweighting a prior conformational ensemble, e.g., from molecular dynamics simulations or structure prediction tools. Our work provides a general approach to recovering the equilibrium probability density of the biomolecule directly in conformational space from single-molecule data. To validate the framework, we study the extraction of state populations and free energies for a simple toy model and from synthetic cryo-EM particle images of a simulated protein that explores multiple folded and unfolded conformations.

Molecular interactions contributing to FUS SYGQ LC-RGG phase separation and co-partitioning with RNA polymerase II heptads.

The RNA-binding protein FUS (Fused in Sarcoma) mediates phase separation in biomolecular condensates and functions in transcription by clustering with RNA polymerase II. Specific contact residues and interaction modes formed by FUS and the C-terminal heptad repeats of RNA polymerase II (CTD) have been suggested but not probed directly. Here we show how RGG domains contribute to phase separation with the FUS N-terminal low-complexity domain (SYGQ LC) and RNA polymerase II CTD. Using NMR spectroscopy and molecular simulations, we demonstrate that many residue types, not solely arginine-tyrosine pairs, form condensed-phase contacts via several interaction modes including, but not only sp2-π and cation-π interactions. In phases also containing RNA polymerase II CTD, many residue types form contacts, including both cation-π and hydrogen-bonding interactions formed by the conserved human CTD lysines. Hence, our data suggest a surprisingly broad array of residue types and modes explain co-phase separation of FUS and RNA polymerase II.

N-terminal acetylation modestly enhances phase separation and reduces aggregation of the low-complexity domain of RNA-binding protein fused in sarcoma.

The RNA-binding protein fused in sarcoma (FUS) assembles via liquid-liquid phase separation (LLPS) into functional RNA granules and aggregates in amyotrophic lateral sclerosis associated neuronal inclusions. Several studies have demonstrated that posttranslational modification (PTM) can significantly alter FUS phase separation and aggregation, particularly charge-altering phosphorylation of the nearly uncharged N-terminal low complexity domain of FUS (FUS LC). However, the occurrence and impact of N-terminal acetylation on FUS phase separation remains unexplored, even though N-terminal acetylation is the most common PTM in mammals and changes the charge at the N-terminus. First, we find that FUS is predominantly acetylated in two human cell types and stress conditions. Next, we show that recombinant FUS LC can be acetylated when co-expressed with the NatA complex in Escherichia coli. Using NMR spectroscopy, we find that N-terminal acetylated FUS LC (FUS LC Nt-Ac) does not notably alter monomeric FUS LC structure or motions. Despite no difference in structure, Nt-Ac-FUS LC phase separates more avidly than unmodified FUS LC. More importantly, N-terminal acetylation of FUS LC reduces aggregation. Our findings highlight the importance of N-terminal acetylation of proteins that undergo physiological LLPS and pathological aggregation.

Refining All-Atom Protein Force Fields for Polar-Rich, Prion-like, Low-Complexity Intrinsically Disordered Proteins.

Significant efforts in the past decade have given us highly accurate all-atom protein force fields for molecular dynamics (MD) simulations of folded and disordered proteins. These simulations, complemented with experimental data, provide new insights into molecular interactions that underlie the physical properties of proteins, especially for intrinsically disordered proteins (IDPs) for which defining the heterogeneous structural ensemble is hugely challenging by experiments alone. Consequently, the accuracy of these protein force fields is of utmost importance to ensure reliable simulated conformational data. Here, we first assess the accuracy of current state-of-the-art force fields for IDPs (ff99SBws and ff03ws) applied to disordered proteins of low amino acid sequence complexity that can undergo liquid-liquid phase separation. On the basis of a detailed comparison of NMR chemical shifts between simulation and experiment on several IDPs, we find that regions surrounding specific polar residues result in simulated ensembles with exaggerated helicity when compared to experiment. To resolve this discrepancy, we introduce residue-specific modifications to the backbone torsion potential of three residues (Ser, Thr, and Gln) in the ff99SBws force field. The modified force field, ff99SBws-STQ, provides a more accurate representation of helical structure propensity in these LC domains without compromising faithful representation of helicity in a region with distinct sequence composition. Our refinement strategy also suggests a path forward for integrating experimental data in the assessment of residue-specific deficiencies in the current physics-based force fields and improves these force fields further for their broader applicability.