Biology subjects

Liu, Z. H.

Publications and source records attributed to Liu, Z. H..

9 recordsLinked to original sources

bioRxiv2026

Competing effects modulate the rate of poly(A) RNA deadenylation in a biomolecular condensate

The unique solvent milieu found in biomolecular condensates can control cellular enzymatic reactions and shift reaction kinetics by modulating reactant concentrations, structural dynamics, and enzyme activities. Here we explore the interplay of multiple regulatory factors within a condensate to control poly(A) RNA deadenylation, the first and rate-limiting step in mRNA turnover. The deadenylase CNOT7, a subunit of the CCR4-NOT deadenylation complex, localizes to cytoplasmic RNA granules and shows increased degradation activity in vitro in condensates formed by the C-terminal low complexity disordered region of CAPRIN1, a component of RNA granules. We use a combination of enzymatic assays, kinetic modeling, microscopy, Nuclear Magnetic Resonance (NMR) spectroscopy, and molecular dynamics simulations to deconvolute and define the components that underlie this enhancement. We found that enzyme and RNA are concentrated in condensates relative to buffer, which increases CNOT7 activity, while the equilibrium between CNOT7s active and inactive states remains unchanged. The concentration-dependent increase in enzymatic rates is counterbalanced by a substantial decrease in the enzymes catalytic efficiency, likely due to slower diffusion of CNOT7 and RNA within the condensates, which lessens the probability of enzyme-substrate complex formation. Molecular dynamics simulations reveal CNOT7-CAPRIN1 interactions that rely on conserved CAPRIN1 sequence features, hinting at an evolutionarily conserved role for CAPRIN1 condensation. With this quantitative kinetic analysis, we describe the multifaceted mechanism behind regulation of CNOT7 deadenylation by a condensate environment. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/736149v2_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@51cd47org.highwire.dtl.DTLVardef@7bfccaorg.highwire.dtl.DTLVardef@114375org.highwire.dtl.DTLVardef@163f4ef_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2026

Conformational Ensembles of the Disordered 4E-BP2:eIF4E Complex Restrained by smFRET Experiments

Eukaryotic cap-dependent translation initiation is regulated by binding of the predominantly folded eukaryotic initiation factor 4E (eIF4E) to the intrinsically disordered eIF4E binding proteins (4E-BPs). Here, we report full-length atomistic conformational ensembles generated by IDPConformerGenerator and optimized by X-EISDv2 workflow for both apo 4E-BP2, the neuronal 4E-BP, and 4E-BP2 in complex with eIF4E, using data from single-molecule fluorescence and nuclear magnetic resonance (NMR), together with select coordinates from a 4E-BP1:eIF4E crystal structure. Structural sampling within dynamic complexes is often under-appreciated, with NMR and crystal structure data for 4E-BP:eIF4E suggesting different degrees of structural heterogeneity. Our ensemble models validated by solution spectroscopy data enable comparison of free 4E-BP2 and its complex with eIF4E. This shows a delocalization of contacts around canonical regions, which supports previous findings of unidirectional conditional occupancy of the binding sites. Two new contact regions emerged: one between the disordered N-termini of eIF4E and 4E-BP2, which may play an allosteric role in tuning the binding affinity, and the other between the C-terminus of 4E-BP2 and an extended region of eIF4E, which is consistent with the extended, dynamic binding interface that we reported previously. These results support a model of translation regulation in which the dynamic 4E-BP2:eIF4E complex facilitates accessibility of regulatory sites of 4E-BP2 when bound.

biophysics↗

bioRxiv2026

IDPForge: Deep Learning of Proteins with Global and Local Regions of Disorder

Although machine learning has transformed protein structure prediction of folded protein ground states with remarkable accuracy, intrinsically disordered proteins and regions (IDPs/IDRs) are defined by diverse and dynamical structural ensembles that are predicted with low confidence by algorithms such as AlphaFold and RoseTTAFold. We present a new machine learning method, IDPForge (Intrinsically Disordered Protein, FOlded and disordered Region GEnerator), that exploits a transformer protein language diffusion model to create all-atom IDP ensembles and IDR disordered ensembles that maintains the folded domains. IDPForge does not require sequence-specific training, back transformations from coarse-grained representations, nor ensemble reweighting, as in general the created IDP/IDR conformational ensembles show good agreement with bot NMR and SAXS solution experimental data, and options for biasing with experimental restraints are provided if desired. We envision that IDPForge with these diverse capabilities will facilitate integrative and structural studies for proteins that contain intrinsic disorder, and is available as an open source resource for general use.

biophysics↗

bioRxiv2026

PRRC2A, PRRC2B and PRRC2C are Stress Granule Proteins that Promote Translation Through Association with the eIF3 complex

Regulation of mRNA translation is essential for cellular homeostasis, and its dysregulation contributes to cancer, neurodegeneration, and developmental disorders. Stress granules are cytosolic condensates that form during stress-induced translation arrest and are enriched in mRNAs, translation factors, and RNA-binding proteins, but how stress granule proteins modulate translation remains poorly understood. Here, we identify the stress granule components Proline-Rich Coiled-Coil A, B, and C (PRRC2 proteins) as translation regulators. PRRC2 proteins are large, intrinsically disordered paralogs conserved across jawed vertebrates. Functional proteomics revealed that all PRRC2 proteins associate with the 48S translation initiation complex (PIC), whereas PRRC2B additionally interacts with nuclear proteins. Under stress, the proximal interaction network of PRRC2 proteins undergoes dynamic remodeling, including increased interactions with the stress granule scaffold G3BP1. Genetic perturbation shows that the PRRC2 proteins influence stress granule assembly in a context-specific manner, and are collectively required for cell growth in basal conditions due to their essential role in translation. Cells with reduced PRRC2 proteins exhibit a significant reduction in the abundance of more than half of the proteome, with a bias toward translational targets of eIF3d and eIF4G2. Interaction domain mapping and AlphaFold3 modeling revealed that an helix within the putative coiled-coil domain of PRRC2C mediates interactions with the eIF3 core complex. This modeling places the PRRC2C helix in a previously unassigned region of a published cryo-EM density map, validating the protein interaction and the mechanistic role of PRRC2C in translation control. Together, these findings establish PRRC2 proteins as components of the translation initiation machinery that regulate translation through their interactions with the eIF3 complex and other components of the 48S PIC factors, providing a direct mechanistic link between stress granule proteins and translational control.

cell biology↗

bioRxiv2025

Making invisible excited state protein structures visible by combining NMR and machine learning

NMR relaxation studies of the precursor form of the pro-inflammatory cytokine interleukin-18, pro-IL-18, show that it adopts two sparsely populated (<0.5%) and transiently formed (ms lifetimes) excited state conformations in exchange with a highly populated ground state conformer. Although NMR data localize regions undergoing exchange to a pair of short {beta}-strands that are preserved in at least one of the excited states, additional structural information is not forthcoming. Here we develop a protocol whereby the NMR data is used to select alternative conformers of pro-IL-18 from ensembles predicted by the generative ML model AlphaFlow that are then evaluated through further NMR experiments. The approach identifies distinct excited state conformers and suggests a general method for combining experiment with computation to characterize protein energy landscapes.

biophysics↗

bioRxiv2025

AlphaFlex: Ensembles of the human proteome representing disordered regions

More than two thirds of proteins in the human proteome are predicted to contain intrinsically disordered regions (IDRs), which lack stable folded structure. IDRs are critical for biological regulation and organization, as targets for post-translational modifications, and as mediators of biomolecular condensates. To address the pressing need for better structural models enabling functional insight, we developed AlphaFlex to model fully atomistic conformer ensembles for proteins predicted to have IDRs, modeled in the context of AlphaFold folded domains and an implicit bilayer for transmembrane proteins. The AlphaFlex resource provides conformational ensembles of human proteins from the AlphaFold database with identified IDRs in the Protein Ensemble Database that is mirrored in UniProt. This transformative resource of AlphaFlex ensembles provides physically and biologically relevant full-length models for IDR proteins, including scaffold proteins, those with IDR:folded-domain interactions, regulatory and condensate proteins requiring exposed binding elements, conditionally folding IDRs, and transmembrane proteins containing IDRs.

biochemistry↗

bioRxiv2023

Local Disordered Region Sampling (LDRS) for Ensemble Modeling of Proteins with Experimentally Undetermined or Low Confidence Prediction Segments

STRUCTURED ABSTRACTO_ST_ABSSUMMARYC_ST_ABSThe Local Disordered Region Sampling (LDRS, pronounced loaders) tool, developed for the IDPConformerGenerator platform (Teixeira et al. 2022), provides a method for generating all-atom conformations of intrinsically disordered regions (IDRs) at N- and C-termini of and in loops or linkers between folded regions of an existing protein structure. These disordered elements often lead to missing coordinates in experimental structures or low confidence in predicted structures. Requiring only a pre-existing PDB structure of the protein with missing coordinates or with predicted confidence scores and its full-length primary sequence, LDRS will automatically generate physically meaningful conformational ensembles of the missing flexible regions to complete the full-length protein. The capabilities of the LDRS tool of IDPConformerGenerator include modeling phosphorylation sites using enhanced Monte Carlo Side Chain Entropy (MC-SCE) (Bhowmick and Head-Gordon 2015), transmembrane proteins within an all-atom bilayer, and multi-chain complexes. The modeling capacity of LDRS capitalizes on the modularity, ability to be used as a library and via command-line, and computational speed of the IDPConformerGenerator platform. AVAILABILITY AND IMPLEMENTATIONThe LDRS module is part of the IDPConformerGenerator modeling suite, which can be downloaded from GitHub at https://github.com/julie-forman-kay-lab/IDPConformerGenerator. IDPConformerGenerator is written in Python and works on Linux, Microsoft Windows, and Mac OS versions that support DSSP. Users can utilize LDRSs Python API for scripting the same way they can use any part of IDPConformerGenerators API, by importing functions from the idpconfgen.ldrs_helper library. Otherwise, LDRS can be used as a command line interface application within IDPConformerGenerator. Full documentation is available within the command-line interface (CLI) as well as on IDPConformerGenerators official documentation pages (https://idpconformergenerator.readthedocs.io/en/latest/). CONTACTFor support with LDRS please contact Zi Hao (Nemo) Liu via nemo.liu@sickkids.ca or submit an issue in the IDPConformerGenerator repository on GitHub (https://github.com/julie-forman-kay-lab/IDPConformerGenerator/issues). SUPPLEMENTARY INFORMATIONThe supplementary information document contains, or links to, all the conformer ensembles generated for this publication, the generalized Python scripts using the LDRS Python API, figures of detailed methods, fractional secondary structure information, torsion angle sampling, and the time required to generate the different protein cases.

biophysics↗

bioRxiv2023

Delineating Structural Propensities of the 4E-BP2 Protein via Integrative Modelling and Clustering

The intrinsically disordered 4E-BP2 protein regulates mRNA cap-dependent translation through the interaction with the predominantly folded eukaryotic initiation factor 4E (eIF4E). Phosphorylation of 4E-BP2 dramatically reduces eIF4E binding, in part by stabilizing a binding- incompatible folded domain (REF). Here, we used a Rosetta-based sampling algorithm optimized for IDRs to generate initial ensembles for two phospho forms of 4E-BP2, non- and five-fold phosphorylated (NP and 5P, respectively), with the 5P folded domain flanked by N- and C-terminal IDRs (N-IDR and C-IDR, respectively). We then applied an integrative Bayesian approach to obtain NP and 5P conformational ensembles that agree with experimental data from nuclear magnetic resonance, small-angle X-ray scattering and single-molecule Forster resonance energy transfer (smFRET). For the NP state, inter-residue distance scaling and 2D maps revealed the role of charge segregation and pi interactions in driving contacts between distal regions of the chain ([~]70 residues apart). The 5P ensemble shows prominent contacts of the N-IDR region with the two phosphosites in the folded domain, pT37 and pT46, and, to a lesser extent, delocalized interactions with the C-IDR region. Agglomerative hierarchical clustering led to partitioning of each of the two ensembles into four clusters, with different global dimensions and contact maps. This helped delineate an NP cluster that, based on our smFRET data, is compatible with the eIF4E-bound state. 5P clusters were differentiated by interactions of C-IDR with the folded domain and of the N-IDR with the two phosphosites in the folded domain. Our study provides both a better visualization of fundamental structural poses of 4E-BP2 and a set of falsifiable insights on intrachain interactions that bias folding and binding of this protein.

biophysics↗

bioRxiv2022

IDPConformerGenerator: A Flexible Software Suite for Sampling Conformational Space of Disordered Protein States

The power of structural information for informing biological mechanism is clear for stable folded macromolecules, but similar structure-function insight is more difficult to obtain for highly dynamic systems such as intrinsically disordered proteins (IDPs) which must be described as structural ensembles. Here we present IDPConformerGenerator, a flexible, modular open source software platform for generating large and diverse ensembles of disordered protein states that builds conformers that obey geometric, steric and other physical restraints on the input sequence. IDPConformerGenerator samples backbone phi ({varphi}), psi ({psi}), and omega ({omega}) torsion angles of relevant sequence fragments from loops and secondary structure elements extracted from folded protein structures in the RCSB Protein Data Bank, and builds side chains from robust Monte Carlo algorithms using expanded rotamer libraries. IDPConformerGenerator has many user-defined options enabling variable fractional sampling of secondary structures, supports Bayesian models for assessing agreement of IDP ensembles for consistency with experimental data, and introduces a machine learning approach to transform between internal to Cartesian coordinates with reduced error. IDPConformerGenerator will facilitate the characterization of disordered proteins to ultimately provide structural insights into these states that have key biological functions.

biophysics↗