Biology subjects

Forman-Kay, J. D.

Publications and source records attributed to Forman-Kay, J. D..

27 records · Page 2Linked to original sources

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↗

bioRxiv2022

A Spatiotemporal Reconstruction of the C. elegans Pharyngeal Cuticle Reveals a Structure Rich in Phase-Separating Proteins

Roughly 4.5 million species of ecdysozoan animals repeatedly shed their old cuticle and construct a new one underneath to accommodate growth. How cuticles are constructed is not well understood. Here, we systematically mine gene expression datasets to uncover the spatiotemporal blueprint for how the chitin-based pharyngeal cuticle of the nematode Caenorhabditis elegans is built. We demonstrate that the blueprint correctly predicts expression patterns and functional relevance to cuticle development. We find that as larvae prepare to molt, catabolic enzymes are upregulated and the genes that encode chitin synthase, chitin cross-linkers, and homologs of amyloid regulators subsequently peak in expression. 48% of the gene products secreted during the molt are predicted to be intrinsically disordered proteins (IDPs), many of which belong to four distinct families that are expressed in overlapping waves. These include the IDPAs, IDPBs, and IDPCs that are introduced for the first time here. We find that all four families have sequence properties known to drive phase separation and show in vitro phase separation for one of these proteins. This systematic analysis reveals the massive contribution that IDPs make to the cuticle and highlights how reversibly phase-separating materials may facilitate cuticle disassembly and reassembly during the molt.

systems biology↗

bioRxiv2022

Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2

The AlphaFold Protein Structure Database contains predicted structures for millions of proteins. For the majority of human proteins that contain intrinsically disordered regions (IDRs), which do not adopt a stable structure, it is generally assumed these regions have low AlphaFold2 confidence scores that reflect low-confidence structural predictions. Here, we show that AlphaFold2 assigns confident structures to nearly 15% of human IDRs. By comparison to experimental NMR data for a subset of IDRs that are known to conditionally fold (i.e., upon binding or under other specific conditions), we find that AlphaFold2 often predicts the structure of the conditionally folded state. Based on databases of IDRs that are known to conditionally fold, we estimate that AlphaFold2 can identify conditionally folding IDRs at a precision as high as 88% at a 10% false positive rate, which is remarkable considering that conditionally folded IDR structures were minimally represented in its training data. We find that human disease mutations are nearly 5-fold enriched in conditionally folded IDRs over IDRs in general, and that up to 80% of IDRs in prokaryotes are predicted to conditionally fold, compared to less than 20% of eukaryotic IDRs. These results indicate that a large majority of IDRs in the proteomes of human and other eukaryotes function in the absence of conditional folding, but the regions that do acquire folds are more sensitive to mutations. We emphasize that the AlphaFold2 predictions do not reveal functionally relevant structural plasticity within IDRs and cannot offer realistic ensemble representations of conditionally folded IDRs. Significance StatementAlphaFold2 and other machine learning-based methods can accurately predict the structures of most proteins. However, nearly two-thirds of human proteins contain segments that are highly flexible and do not autonomously fold, otherwise known as intrinsically disordered regions (IDRs). In general, IDRs interconvert rapidly between a large number of different conformations, posing a significant problem for protein structure prediction methods that define one or a small number of stable conformations. Here, we found that AlphaFold2 can readily identify structures for a subset of IDRs that fold under certain conditions (conditional folding). We leverage AlphaFold2s predictions of conditionally folded IDRs to quantify the extent of conditional folding across the tree of life, and to rationalize disease-causing mutations in IDRs. Classifications: Biological Sciences; Biophysics and Computational Biology

biochemistry↗

bioRxiv2022

Multisite Phosphorylation and Binding Alter Conformational Dynamics of the 4E-BP2 Protein

Intrinsically disordered proteins (IDPs) play critical roles in regulatory protein interactions, but detailed structural/dynamics characterization of their ensembles remain challenging, both in isolation and they form dynamic fuzzy complexes. Such is the case for mRNA cap-dependent translation initiation, which is regulated by the interaction of the predominantly folded eukaryotic initiation factor 4E (eIF4E) with the intrinsically disordered eIF4E binding proteins (4E-BPs) in a phosphorylation-dependent manner. Single-molecule Forster resonance energy transfer showed that the conformational changes of 4E-BP2 induced by binding to eIF4E are non-uniform along the sequence; while a central region containing both motifs that bind to eIF4E expands and becomes stiffer, the C-terminal region is less affected. Fluorescence anisotropy decay revealed a nonuniform segmental flexibility around six different labelling sites along the chain. Dynamic quenching of these fluorescent probes by intrinsic aromatic residues measured via fluorescence correlation spectroscopy report on transient intra- and inter-molecular contacts on ns-s timescales. Upon hyperphosphorylation, which induces folding of ~40 residues in 4E-BP2, the quenching rates decreased at most labelling sites. The chain dynamics around sites in the C-terminal region far away from the two binding motifs significantly increased upon binding to eIF4E, suggesting that this region is also involved in the highly dynamic 4E-BP2:eIF4E complex. Our time-resolved fluorescence data paint a sequence-level rigidity map of three states of 4E-BP2 differing in phosphorylation or binding status and distinguish regions that form contacts with eIF4E. This study adds complementary structural and dynamics information to recent studies of 4E-BP2, and it constitutes an important step towards a mechanistic understanding of this important IDP via integrative modelling.

biophysics↗

bioRxiv2021

Developmental Dynamics of RNA Translation in the Human Brain

The precise regulation of gene expression is fundamental to neurodevelopment, plasticity, and cognitive function. While several studies have deeply profiled mRNA dynamics in the developing human brain, there is a fundamental gap in our understanding of accompanying translational regulation. We perform ribosome profiling from more than 70 human prenatal and adult cortex samples across ontogeny and into adulthood, mapping translation events at nucleotide resolution. In addition to characterizing the translational regulation of annotated open reading frames (ORFs), we identify thousands of previously unknown translation events, including small open reading frames (sORFs) that give rise to human- and/or brain-specific microproteins, many of which we independently verify using size-selected proteomics. Ribosome profiling in stem cell-derived human neuronal cultures further corroborates these findings and shows that several neuronal activity-induced long non-coding RNAs (lncRNAs), including LINC00473, a primate-specific lncRNA implicated in depression, encode previously undescribed microproteins. Physicochemical analysis of these brain microproteinss identifies a large class harboring arginine-glycine-glycine (RGG) repeats as strong candidates for regulating RNA metabolism. Moreover, we find that, collectively, these previously unknown human brain sORFs are enriched for variants associated with schizophrenia. In addition to significantly expanding the translational landscape of the developing brain, this atlas will serve as a rich resource for the annotation and functional interrogation of thousands of previously unknown brain-specific protein products.

neuroscience↗

bioRxiv2021

Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning

A major challenge to the characterization of intrinsically disordered regions (IDRs), which are widespread in the proteome, but relatively poorly understood, is the identification of molecular features that mediate functions of these regions, such as short motifs, amino acid repeats and physicochemical properties. Here, we introduce a proteome-scale feature discovery approach for IDRs. Our approach, which we call "reverse homology", exploits the principle that important functional features are conserved over evolution. We use this as a contrastive learning signal for deep learning: given a set of homologous IDRs, the neural network has to correctly choose a held-out homologue from another set of IDRs sampled randomly from the proteome. We pair reverse homology with a simple architecture and standard interpretation techniques, and show that the network learns conserved features of IDRs that can be interpreted as motifs, repeats, or bulk features like charge or amino acid propensities. We also show that our model can be used to produce visualizations of what residues and regions are most important to IDR function, generating hypotheses for uncharacterized IDRs. Our results suggest that feature discovery using unsupervised neural networks is a promising avenue to gain systematic insight into poorly understood protein sequences.

bioinformatics↗

bioRxiv2021

O-GlcNAcylation reduces phase separation and aggregation of the EWS N-terminal low complexity region

Many membraneless organelles are thought to be biomolecular condensates formed by phase separation of proteins and other biopolymers. Post-translational modifications (PTMs) can impact protein phase separation behavior, although for many PTMs this aspect of their function is unknown. O-linked {beta}-D-N-acetylglucosaminylation (O-GlcNAcylation) is an abundant form of intracellular glycosylation whose roles in regulating biomolecular condensate assembly and dynamics have not been delineated. Using an in vitro approach, we found that O-GlcNAcylation reduces the phase separation propensity of the EWS N-terminal low complexity region (LCRN) under different conditions, including in the presence of the arginine-and glycine-rich RNA-binding domains (RBD). O-GlcNAcylation enhances fluorescence recovery after photobleaching (FRAP) within EWS LCRN condensates and causes the droplets to exhibit more liquid-like relaxation following fusion. Following extended incubation times, EWS LCRN+RBD condensates exhibit diminished FRAP, indicating a loss of fluidity, while condensates containing the O-GlcNAcylated LCRN do not. In HeLa cells, EWS is less O-GlcNAcylated following OGT knockdown and more prone to aggregation based on a filter retardation assay. Relative to the human proteome, O-GlcNAcylated proteins are enriched with regions that are predicted to phase separate, suggesting a general role of O-GlcNAcylation in regulation of biomolecular condensates. Insert Table of Contents artwork here O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=136 SRC="FIGDIR/small/443654v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@a09fb6org.highwire.dtl.DTLVardef@1556f47org.highwire.dtl.DTLVardef@1fcd76borg.highwire.dtl.DTLVardef@a2dbda_HPS_FORMAT_FIGEXP M_FIG C_FIG For Table of Contents only.

biochemistry↗

bioRxiv2020

FUS-ALS mutants alter FMRP phase separation equilibrium and impair protein translation

Mutations in the RNA binding protein (RBP) FUS cause amyotrophic lateral sclerosis (ALS) and result in its nuclear depletion and cytoplasmic mislocalisation, with cytoplasmic gain of function thought to be crucial in pathogenesis. Here, we show that expression of mutant FUS at physiological levels drives translation inhibition in both mouse and human motor neurons. Rather than acting directly on the translation machinery, we find that mutant FUS forms cytoplasmic condensates that promote the phase separation of FMRP, another RBP associated with neurodegeneration and robustly involved in translation regulation. FUS and FMRP co-partition and repress translation in vitro. In our in vivo model, FMRP RNA targets are depleted from ribosomes. Our results identify a novel paradigm by which FUS mutations favour the condensed state of other RBPs, impacting on crucial biological functions, such as protein translation.

neuroscience↗

bioRxiv2020

Identifying molecular features that are associated with biological function of intrinsically disordered protein regions

Previously, we showed that intrinsically disordered regions in proteins (IDRs) contain multiple sequence-distributed molecular features that are conserved over evolution, despite little sequence similarity that can be detected in alignments (Zarin et al. 2019). Here, we aim to use these molecular features to make specific functional predictions for individual IDRs and identify the molecular features within them that are responsible for specific functions. We find that the predictable functions are diverse, identifying previously known associated molecular features, as well as features that were previously not known to be associated with these functions. We experimentally confirm that elevated isoelectric point and hydrophobicity, features that are positively associated with mitochondrial localization, are necessary for mitochondrial targeting function. Remarkably, increasing isoelectric point in a synthetic IDR restores weak mitochondrial targeting. We believe feature analysis represents a new systematic approach to understand how biological functions of IDRs are specified by their protein sequences.

cell biology↗