Biology subjects

Pritisanac, I.

Publications and source records attributed to Pritisanac, I..

17 recordsLinked to original sources

bioRxiv2026

A histidine switch controls the pH-responsive self-assembly of a helical protein filament

Self-assembling helical protein filaments underlie diverse biological processes, from signaling pathways to cell motility. Encoding tunable self-assembly into the sequences of filamentous proteins remains a major challenge. Here, we discovered that the caspase-9 CARD can natively self-assemble into helical filaments in a pH-regulated manner. We defined the determinants of filament assembly using an integrative structural, biophysical, and computational approach. Using NMR spectroscopy, we found that the protonation of a single histidine residue near an N-terminal helix dipole, H38, regulates the pH-dependent self-assembly process. Charge-altering mutations at this site tune thermodynamic stability and filament self-assembly across solution and pH conditions. We solved 3.3- and 3.5-[A] cryo-EM structures of the wild-type and H38R filaments, respectively, which show H38 positioned directly at a filament interface. Molecular dynamics simulations show that H38 functions as a molecular switch, whereby protonation rotates its positively charged side-chain toward solvent and away from the partial-positive charge at the N-terminal helix dipole. This reflects a fine balance between stabilizing intermolecular association and a destabilizing intramolecular electrostatic clash at the helix dipole. More broadly, across 350 helix-containing protein domains we identified electrostatic contributions to protein stability near helix dipoles by integrating AlphaFold2 predictions with deep mutational scanning data. Together, our results identify a native, pH-sensitive histidine switch that regulates a self-assembling helical protein filament. Our results establish a mechanism by which charge-altering mutations near helical N-termini can be engineered to control side-chain rotamers, protein stability, and self-assembly.

biophysics↗

bioRxiv2026

Proteome-wide identification and modeling of interactions between transactivation domains and arginine-glycine-rich regions

Transcription factors (TFs) and RNA-binding proteins (RBPs) coordinate gene expression across transcriptional and post-transcriptional layers, yet the principles that govern their direct physical coupling, especially through intrinsically disordered regions, remain unclear. Here we combine proteome-scale interaction mapping, disordered-region annotation, coarse-grained simulations and sequence-based prediction to dissect a prevalent TF-RBP interface mediated by acidic/hydrophobic transactivation domains (TADs) and arginine-glycine-rich (RG/RGG) regions. Network analysis reveals a global enrichment of RBP partners among TF interactions and identifies TF and RBP hubs that bridge transcriptional regulation with RNA-centered pathways. Using a sequence grammar enriched in acidic and aromatic residues, we define 230 RG/RGG-binding TAD-like segments across 190 TFs and we map 1,008 compact RG/RGG regions across 823 RBPs based on proteome-wide motif spacing. Coarse-grained simulations (CALVADOS) of representative TAD-RGG pairs quantify interaction propensities and indicate that association is primarily driven by electrostatic complementarity and charge patterning, with sequence "stickiness" modulating interaction strength. Using a hybrid machine-learning model we predicted simulated interaction strengths from a compact, interpretable set of features and extrapolate these rules to the full combinatorial space, enabling systematic prioritization of candidate TF-RBP couplings. To validate these predictions experimentally, we used NMR titration experiments on a subset of TAD-RGG pairs spanning the predicted affinity range, which showed agreement between predicted affinities and NMR-derived dissociation constants. Together, our results support a predominantly electrostatic mode of association and establish a quantitative framework for identifying and prioritising TF-RBP partnerships, revealing how complementary sequence grammars within disordered regions couple transcriptional regulation to RNA processing and transport.

biophysics↗

bioRxiv2026

The sequence context of RG/RGG motifs determines condensate formation, transportin-1 binding and chaperoning

Intrinsically disordered arginine-glycine-rich (RG/RGG) regions are highly abundant in the eukaryotic proteome. Proteins containing these motifs participate in fundamental cellular processes, including nuclear import, transcriptional regulation, biomolecular condensate formation, and apoptosis. Mutations or dysfunction of RG/RGG proteins have been implicated in neurodegenerative diseases and cancer. Although some RG/RGG proteins have been shown to drive condensate formation, localize to membrane-less organelles, interact with nuclear import receptors, or undergo arginine methylation, these properties are not shared uniformly across the proteome. The considerable diversity in RG/RGG motif length and amino acid composition raises the question of which sequence features determine their functional behaviour. To address this, we conducted a systematic bioinformatics and experimental analysis, combining synthetic and natural peptides with studies on the RNA-binding protein FUS as a model system. Our results reveal that the sequence composition of RG/RGG motifs is a key determinant of their capacity for RNA-mediated condensate formation, stress granule recruitment, and transportin-1-mediated chaperoning and nuclear import. These findings provide new insight into the sequence grammar of disordered RG/RGG regions and how it encodes the multifunctionality of these proteins in cellular regulation.

molecular biology↗

bioRxiv2025

A histidine switch regulates pH-dependent filament formation by the caspase-9 CARD

The caspase activation and recruitment domain (CARD) mediates protein-protein interactions in apoptotic and inflammatory signaling pathways. In humans, more than 30 proteins contain a CARD, several of which have been reported to polymerize into helical filaments. Here we found that the CARD from the apoptotic protease caspase-9 (C9CARD) self assembles into filaments in vitro at physiological pH and salt concentrations. The C9CARD more readily polymerizes under low-salt or mildly acidic conditions, suggesting a significant role for electrostatic interactions in mediating filament formation. Using NMR spectroscopy, we determined the pKa of the lone histidine residue, H38, which supports a role for histidine protonation in enhancing filament formation. Indeed, mutation of H38 to introduce a positive (H38R) or negative (H38D) charge, or to remove the pH-dependence of the side chain at this site altogether (H38N), dramatically alters the filament propensity of the domain. Using cryo-election microscopy, we determined 3.4- and 3.2-[A] structures of the wild-type and H38R C9CARD filaments, respectively, which provide new insights into the molecular basis of C9CARD polymerization and its pH dependence via H38.

biophysics↗

bioRxiv2024

A Functional Map of the Human Intrinsically Disordered Proteome

Intrinsically disordered regions (IDRs) represent at least one-third of the human proteome and defy the established structure-function paradigm. Because IDRs often have limited positional sequence conservation, the functional classification of IDRs using standard bioinformatics is generally not possible. Here, we show that evolutionarily conserved molecular features of the intrinsically disordered human proteome (IDR-ome), termed evolutionary signatures, enable classification and prediction of IDR functions. Hierarchical clustering of the human IDR-ome based on evolutionary signatures reveals strong enrichments for frequently studied functions of IDRs in transcription and RNA processing, as well as diverse, rarely studied functions, ranging from sub-cellular localization and biomolecular condensates to cellular signaling, transmembrane transport, and the constitution of the cytoskeleton. We exploit the information that is encoded within evolutionary conservation of molecular features to propose functional annotations for every IDR in the human proteome, inspect the conserved molecular features that correlate with different functions, and discover frequently co-occurring IDR functions on the proteome scale. Further, we identify patterns of evolutionary conserved molecular features of IDRs within proteins of unknown function and disease-risk genes for conditions such as cancer and developmental disorders. Our map of the human IDR-ome should be a valuable resource that aids in the discovery of new IDR biology.

bioinformatics↗

bioRxiv2024

Glutamate as a co-agonist for acid-sensing ion channels to aggravate ischemic brain damage

Glutamate is traditionally viewed as the first messenger to activate N-methyl-D-aspartate receptors (NMDARs) and downstream cell death pathways in stroke1,2, but unsuccessful clinical trials with NMDAR antagonists implicate the engagement of other NMDAR-independent mechanisms3-7. Here we show that glutamate and its structural analogs, including NMDAR antagonist L-AP5 (or APV), robustly potentiated currents mediated by acid-sensing ion channels (ASICs) which are known for driving acidosis-induced neurotoxicity in stroke4. Glutamate increased the proton affinity and open probability of ASICs, aggravating ischemic neurotoxicity in both in vitro and in vivo models. Site-directed mutagenesis and structure-based in silico molecular docking and simulations uncovered a novel glutamate binding cavity in the extracellular domain of ASIC1a. Computational drug screening of NMDAR competitive antagonist analogs identified a small molecule, LK-2, that binds to this cavity and abolishes glutamate-dependent potentiation of ASIC currents but spares NMDARs, providing strong neuroprotection efficacy comparable to that in ASIC1a or other cation ion channel knockout mouse models4-7. We conclude that glutamate serves as the first messenger for ASICs to exacerbate neurotoxicity, and that selective blockage of glutamate binding sites on ASICs without affecting NMDARs may be of strategic importance for developing effective stroke therapeutics devoid of the psychotic side effects of NMDAR antagonists.

neuroscience↗

bioRxiv2023

Computational design of intrinsically disordered protein regions by matching bulk molecular properties

Algorithms for computational protein design usually begin with a 3D structure and design an amino acid sequence that will fold into that structure. Since intrinsically disordered protein regions lack stable 3D structures, strategies for their design have been limited. Here we describe and validate a general computational strategy for design of intrinsically disordered regions (IDRs). Our algorithm designs synthetic IDRs by minimizing the distance of an initially random amino acid sequence to a known IDR in a high-dimensional space of molecular properties, such as repeat content, charge and hydrophobicity. We tested a handful of IDRs designed to target proteins to mitochondria and heat-induced condensates, and several showed the expected patterns in cells. One sentence summaryWe design synthetic mitochondrial targeting signals and condensate forming intrinsically disordered regions

synthetic biology↗

bioRxiv2023

Flattening the curve - How to get better results with small deep-mutational-scanning datasets

Proteins are utilized in various biotechnological applications, often requiring the optimization of protein properties by introducing specific amino acid exchanges. Deep mutational scanning (DMS) is an effective high-throughput method for evaluating the effects of these exchanges on protein function. DMS data can then inform the training of a neural network to predict the impact of mutations. Most approaches employ some representation of the protein sequence for training and prediction. As proteins are characterized by complex structures and intricate residue interaction networks, directly providing structural information as input reduces the need to learn these features from the data. We introduce a method for encoding protein structures as stacked 2D contact maps, which capture residue interactions, their evolutionary conservation, and mutation-induced interaction changes. Furthermore, we explored techniques to augment neural network training performance on smaller DMS datasets. To validate our approach, we trained three neural network architectures originally used for image analysis on three DMS datasets, and we compared their performances with networks trained solely on protein sequences. The results confirm the effectiveness of the protein structure encoding in machine learning efforts on DMS data. Using structural representations as direct input to the networks, along with data augmentation and pre-training, significantly reduced demands on training data size and improved prediction performance, especially on smaller datasets, while performance on large datasets was on par with state-of-the-art sequence convolutional neural networks. The methods presented here have the potential to provide the same workflow as DMS without the experimental and financial burden of testing thousands of mutants. Additionally, we present an open-source, user-friendly software tool to make these data analysis techniques accessible, particularly to biotechnology and protein engineering researchers who wish to apply them to their mutagenesis data.

bioinformatics↗

bioRxiv2022

MIADE metadata guidelines: Minimum Information About a Disorder Experiment

An unambiguous description of an experimental setup and analysis, and the subsequent biological observation is vital for accurate data interpretation and reproducible results. Consequently, experimental analyses should be described in a concise, unequivocal, and digestible manner. The aim of minimum information guidelines is to define the fundamental complement of data that can support an unambiguous conclusion on experimental observations. In this document, we present the Minimum Information About Disorder Experiments (MIADE) guidelines to define the minimal fundamental parameters required for non-experts to understand the key findings of an experiment studying intrinsically disordered proteins (IDPs) or intrinsically disordered protein regions (IDRs). MIADE guidelines provide recommendations for data producers to describe the results of their experiments at source, for curators to annotate experimental data to community resources and for database developers maintaining community resources to disseminate the data. We give examples of the application of these guidelines in common use cases and describe the implementation of an update to the DisProt IDP database to allow MIADE-compliant annotation. The MIADE guidelines will improve the interpretability of experimental results for data consumers, facilitate direct data submission, simplify data curation, improve data exchange among repositories and standardise the dissemination of the key metadata on an IDP experiment by IDP data sources.

scientific communication and education↗

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↗

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

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↗

bioRxiv2019

Dysregulation of HSP27 oligomerization and interactions by a neuropathy-causing mutation in the C-terminal IPV motif

HSP27 (HSPB1) is a systemically expressed human small heat-shock protein that forms large, dynamic oligomers and functions in various aspects of cellular homeostasis. Mutations in HSP27 cause Charcot-Marie-Tooth disease, the most common inherited disorder of the peripheral nervous system. A particularly severe form of the disease is triggered by the P182L mutation within the highly conserved IxI/V motif of HSP27. Here, we observed that the P182L variant of HSP27 lacks the ability to prevent the aggregation of client proteins and formed significantly larger oligomers both in vitro and in vivo. NMR spectroscopy revealed that the P182L IxI/V motif binds its -crystallin domain with significantly lower association rate, and thus affinity, rendering the binding site more available for other interactors. We identified 22 IxI/V-containing proteins that are known to interact with HSP27 and could therefore bind with enhanced affinity to the P182L variant. We validated this hypothesis through co-immunoprecipitation experiments, revealing that the IxI/V motif-bearing co-chaperone BAG3 indeed binds with higher affinity to the P182L variant. Our results provide a mechanistic basis for the impact of the P182L mutation on HSP27, and highlight the general importance of the IxI/V motif and its role in protein-protein interaction networks.

biochemistry↗

bioRxiv2019

Structure-based methyl resonance assignment with MethylFLYA

Methyl groups provide crucial NMR probes for investigating protein structure, dynamics and mechanisms in systems that are too large for NMR with uniform isotope labeling. This requires the assignment of methyl signals in the NMR spectra to specific methyl groups in the protein, an expensive and time-consuming endeavor that limits the use of methyl-based NMR for large proteins. To resolve this bottleneck, several methyl resonance assignment methods have been developed. These approaches remain limited with regard to complete automation and/or the extent and accuracy of the assignments. Here, we present the completely automated MethylFLYA method for the assignment of methyl groups. MethylFLYA requires as input exclusively methyl-methyl nuclear Overhauser effect spectroscopy (NOESY) peak lists. The algorithm was applied to five proteins of 28-358 kDa mass with a total of 708 isotope-labeled methyl groups. Manually made 1H/13C reference assignments were available for 674 methyls. The available experimental peak lists contained NOESY cross peaks for 614 methyls. MethylFLYA confidently assigned 488 methyls, i.e. 79% of those with NOESY data. Of these assignments, 460 agreed with the reference, 5 were different (and 23 concerned methyls without reference assignment). For three proteins of 28, 81, and 358 kDa, all confident assignments by MethylFLYA were correct. We furthermore show that, for high-quality NOESY spectra, automatic picking of NOE signals followed by resonance assignment with MethylFLYA can yield results that are comparable to those obtained for manually prepared peak lists, indicating the feasibility of unbiased, fully automatic methyl resonance assignment starting directly from the NMR spectra. This renders MethylFLYA an advantageous alternative to existing approaches for structure-based methyl assignment. MethylFLYA assigns, for most proteins, significantly more methyl groups than other algorithms, has an average error rate of 1%, modest runtimes of 0.4-1.2 h for the five proteins, and flexibility to handle arbitrary isotope labeling patterns and include data from other types of NMR spectra.

biophysics↗

bioRxiv2018

Local unfolding of the HSP27 monomer regulates chaperone activity

The small heat-shock protein HSP27 is a redox-sensitive molecular chaperone that is expressed throughout the human body. Here we describe redox-induced changes to the structure, dynamics, and function of HSP27 and its conserved -crystallin domain, and provide the first structural characterization of a small heat-shock protein monomer. While HSP27 assembles into oligomers, we show that the transiently populated monomers released upon reduction are highly active chaperones in vitro, but are kinetically unstable and susceptible to uncontrolled aggregation. By using relaxation dispersion and high-pressure nuclear magnetic resonance spectroscopy, we reveal that the pair of {beta}-strands that mediate dimerization become partially disordered in the monomer. Strikingly, we note that numerous HSP27 mutations associated with inherited neuropathies cluster to this unstructured region. The high degree of sequence conservation in the -crystallin domain amongst mammalian sHSPs suggests that partially unfolded monomers may be a general, functional feature of these molecular chaperones.

biophysics↗