FRETpredict: A Python package for FRET efficiency predictions using rotamer libraries

Insights into Molecular Diversity within the FUS/EWS/TAF15 Protein Family: Unraveling Phase Separation of the N-Terminal Low-Complexity Domain from RNA-Binding Protein EWS

The FET protein family, comprising FUS, EWS, and TAF15, plays crucial roles in mRNA maturation, transcriptional regulation, and DNA damage response. Clinically, they are linked to Ewing family tumors and neurodegenerative diseases such as amyotrophic lateral sclerosis. The fusion protein EWS::FLI1, the causative mutation of Ewing sarcoma, arises from a genomic translocation that fuses a portion of the low-complexity domain (LCD) of EWS (EWSLCD) with the DNA binding domain of the ETS transcription factor FLI1. This fusion protein modifies transcriptional programs and disrupts native EWS functions, such as splicing. The exact role of the intrinsically disordered EWSLCD remains a topic of active investigation, but its ability to phase separate and form biomolecular condensates is believed to be central to EWS::FLI1's oncogenic properties. Here, we used paramagnetic relaxation enhancement NMR, microscopy, and all-atom molecular dynamics (MD) simulations to better understand the self-association and phase separation tendencies of the EWSLCD. Our NMR data and mutational analysis suggest that a higher density and proximity of tyrosine residues amplify the likelihood of condensate formation. MD simulations revealed that the tyrosine-rich termini exhibit compact conformations with unique contact networks and provided critical input on the relationship between contacts formed within a single molecule (intramolecular) and inside the condensed phase (intermolecular). These findings enhance our understanding of FET proteins' condensate-forming capabilities and underline differences between EWS, FUS, and TAF15.

Hierarchical Assembly of Single-Stranded RNA

Single-stranded RNA (ssRNA) plays a major role in the flow of genetic information-most notably, in the form of messenger RNA (mRNA)-and in the regulation of biological processes. The highly dynamic nature of chains of unpaired nucleobases challenges structural characterizations of ssRNA by experiments or molecular dynamics (MD) simulations alike. Here, we use hierarchical chain growth (HCG) to construct ensembles of ssRNA chains. HCG assembles the structures of protein and nucleic acid chains from fragment libraries created by MD simulations. Applied to homo- and heteropolymeric ssRNAs of different lengths, we find that HCG produces structural ensembles that overall are in good agreement with diverse experiments, including nuclear magnetic resonance (NMR), small-angle X-ray scattering (SAXS), and single-molecule Förster resonance energy transfer (FRET). The agreement can be further improved by ensemble refinement using Bayesian inference of ensembles (BioEn). HCG can also be used to assemble RNA structures that combine base-paired and base-unpaired regions, as illustrated for the 5' untranslated region (UTR) of SARS-CoV-2 RNA.

Conformational ensembles of the human intrinsically disordered proteome

Intrinsically disordered proteins and regions (collectively, IDRs) are pervasive across proteomes in all kingdoms of life, help to shape biological functions and are involved in numerous diseases. IDRs populate a diverse set of transiently formed structures and defy conventional sequence-structure-function relationships1. Developments in protein science have made it possible to predict the three-dimensional structures of folded proteins at the proteome scale2. By contrast, there is a lack of knowledge about the conformational properties of IDRs, partly because the sequences of disordered proteins are poorly conserved and also because only a few of these proteins have been characterized experimentally. The inability to predict structural properties of IDRs across the proteome has limited our understanding of the functional roles of IDRs and how evolution shapes them. As a supplement to previous structural studies of individual IDRs3, we developed an efficient molecular model to generate conformational ensembles of IDRs and thereby to predict their conformational properties from sequences4,5. Here we use this model to simulate nearly all of the IDRs in the human proteome. Examining conformational ensembles of 28,058 IDRs, we show how chain compaction is correlated with cellular function and localization. We provide insights into how sequence features relate to chain compaction and, using a machine-learning model trained on our simulation data, show the conservation of conformational properties across orthologues. Our results recapitulate observations from previous studies of individual protein systems and exemplify how to link-at the proteome scale-conformational ensembles with cellular function and localization, amino acid sequence, evolutionary conservation and disease variants. Our freely available database of conformational properties will encourage further experimental investigation and enable the generation of hypotheses about the biological roles and evolution of IDRs.