Biology subjects

Toth-Petroczy, A.

Publications and source records attributed to Toth-Petroczy, A..

16 recordsLinked to original sources

bioRxiv2026

How are evolutionarily young and old proteins distributed in sequence space?

Protein sequence space is vast due to the combinatorial diversity of 20 amino acids. However, evolution has generated a limited set of "old" canonical protein families sharing evolutionary ancestry, structures and functions. It remains unclear how canonical sequences are placed in sequence space, how recently evolved "young" proteins compare to them, and whether random, young, and canonical sequences can interconvert along evolutionarily plausible paths, and which biophysical properties distinguish or link these sequences. Here, we analyse naturally occurring de novo proteins from yeast and flies, which originate from non-coding DNA and thus have experienced limited evolutionary selection. They serve as a model for examining the relationships between young de novo and intergenic proteins, older canonical proteins, and their randomized counterparts. Because de novo and randomized sequences lack detectable homology, we use an alignment-free k-mer-based distance approach. Randomization shifts distance distributions toward expected random behaviour in all classes, but natural, non-randomized sequence classes remain distinct, indicating non-random residue organization. Each class exhibits characteristic k-mer patterns, with de novo proteins clearly separated from both canonical and all randomized sequences. Sequences bridging these classes are frequently predicted to contain transmembrane helices. De novo proteins are thus not random samples of sequence space. Instead, they occupy constrained yet evolutionarily accessible regions defined by residue order and biophysical constraints, suggesting a plausible pathway for the emergence and diversification of new proteins. Significance StatementDespite the vast combinatorial potential of amino acids, evolution has produced only a limited repertoire of canonical proteins with conserved structure and function. How evolutionarily young proteins relate to older canonical proteins, and whether the sequence space between them is traversable, remain unclear. Here, we decompose canonical proteins, intergenic sequences, and recently emerged yeast and fly de novo proteins, together with randomized controls, into short, interpretable fragments (k-mers) and compare them using alignment-free distances. De novo proteins are markedly distinct from both randomized and canonical sequences. Notwithstanding their evolutionary distance, sequences are connected by stepwise paths comprising bridge sequences, often enriched for low-complexity motifs and transmembrane helices, connecting disordered and structured regions of sequence space.

evolutionary biology↗

bioRxiv2026

Regulatory landscape of widespread stop codon readthrough in Drosophila

In stop codon readthrough (SCR), ribosomes continue to translate past the stop. While programmed SCR is rare in mammals, it appears remarkably widespread in insects, yet its regulation and biological significance remain poorly understood. Here, we systematically characterize SCR in Drosophila melanogaster by integrating ribosome profiling, comparative genomics, proteomics, and functional screening. We identify [~]1,400 SCR genes, reveal sequence features associated with programmed SCR, and distinguish constitutive and tissue-restricted modes. We identify conserved cis-elements sufficient to stimulate constitutive SCR and demonstrate that many remain functional in mammalian cells. Unexpectedly, the major distinction between insect and mammalian cells was not termination efficiency, but insect responsiveness to SCR-promoting regulatory elements. SCR was further associated with increased overall protein output, suggesting a function beyond proteome diversification. Our results establish SCR as a pervasive regulatory mechanism in Drosophila and provide a new framework for its study through the Drosophila Stop Codon Readthrough Atlas.

genomics↗

bioRxiv2026

Pinc: a simple probabilistic AlphaFold interaction score

AlphaFold has enabled large-scale prediction of protein-protein and protein-nucleic acid complexes, but ranking and assessing the quality of predicted models remain challenging. Existing confidence scores are often highly parametrised and provide limited interpretability. We introduce a simple geometric framework that converts AlphaFold predicted aligned error (PAE) into conditional contact probability. We show that these probabilities are well calibrated to the fraction of native contacts observed across experimentally determined structures. Motivated by this, we define the Pinc score (Probability of interface native contacts) as the mean contact probability between interacting chains. Because the probabilistic interpretation extends to individual residues, Pinc captures local structural constraint beyond interfacial burial, enabling residue-level prioritisation of hotspot positions for mutational studies. Depending solely on a single empirically fixed contact radius, Pinc offers an interpretable path from PAE to interface confidence, matching or exceeding the classification performance of more complex methods across five independent benchmark sets. We provide a portable, dependency-free C program and a Google Colab notebook for calculating Pinc scores for AlphaFold models at https://git.mpi-cbg.de/tothpetroczylab/Pinc.

bioinformatics↗

bioRxiv2026

Amino acid and codon usage explain amino acid misincorporation rates across the tree of life

Protein translation is an error-prone process resulting in a random population of altered protein sequences in every cell. Here, we analyzed thousands of publicly available mass spectrometry datasets to detect amino acid misincorporations and quantify error rates in 14 model organisms. We find that overall error rates and the patterns of codon to amino acid error rates correlate across species. We estimate that on average 1-2% of protein molecules in a cell harbor a misincorporation, whereas this proportion can reach 10% for long proteins. Highly expressed and very long proteins have lower error rates, indicating evolutionary selection on codon usage to reduce the cost of translation errors. While both codon-anticodon mispairing and tRNA mischarging contribute to misincorporations, we estimate that [~]70% of misincorporation events are due to mispairing. The more frequent an amino acid in the proteome, the more likely it is misincorporated (r = 0.53), likely because frequent amino acids are abundant in the cell, increasing the rate of mischarging, and have abundant tRNAs, leading to increased mispairing. Overall, we find that amino acid and codon usage explain error rates. The conserved patterns of amino acid misincorporations from bacteria to humans suggest universal mechanisms driving translational fidelity.

bioinformatics↗

bioRxiv2025

Born to Condense: Polysomes Drive Co-Translational Condensation of Biomolecular Condensate Proteins

Biomolecular condensates formed by protein liquid-liquid phase separation (LLPS) are ubiquitous in cells and play crucial roles in cellular regulation. While the physics and functions of LLPS are well studied, its interplay with protein synthesis - translation - remains largely unexplored. Here we introduce a theoretical framework for Co-Translational Condensation (CTC), in which nascent protein chains of polysomes - multiple ribosomes on one mRNA - interact with condensates, localizing translation to condensate surfaces. Using coarse-grained simulations, we show that protein domain architecture dictates the thermodynamics of CTC, consistent with a Langmuir adsorption model. Bioinformatic analysis of more than 7,500 proteins reveals that most condensate-associated proteins have architectures favoring CTC, with strong interaction regions of nascent chains exposed on polysomes. At the dynamical level, simulation and reaction-diffusion modeling reveal that CTC is kinetically feasible within typical polysome lifetimes, either through large polysomes nucleating new condensates or via diffusion to pre-existing condensates. As a case study, we demonstrate that CTC enhances post-translational modifications by minimizing unmodified intermediates. More broadly, we anticipate CTC may also influence protein folding, misfolding, and signal-integration latency. Together, our results establish CTC as a general mechanism coupling translation with phase separation, with broad implications for protein evolution, cellular organization, and synthetic biology.

biophysics↗

bioRxiv2025

Ribosomal frameshifting is a carefully tuned modifier of proteins

Ribosomal frameshifting, occurring during protein translation, is one of the most consequential recoding events, as it leads to large changes to the protein sequence. However, beyond a few well-characterized cases, its prevalence and consequences across organisms remain poorly understood. To address this, we modeled and experimentally tested frameshifting and systematically characterized its evolutionary impact. We developed SLIPPERRS, a mechanistic model that identifies "slippery" frameshift sites achieving [~]70% accuracy. Building on SLIPPERS predictions and on our mass spectrometry and fluorescence-based assay, we characterized 165 short mRNA sequences. We identified dozens of novel sites with frameshift probabilities that often exceed those of known programmed frameshift cases, and found evidence for both translocation and decoding mechanisms. In Gammaproteobacteria, an average of [~]100 genes per species harbor frameshifting sites, which alter proteins by truncating, extending, and fusing canonical proteins. We detect selection for shorter and less disordered frameshifted proteins to mitigate their deleterious impact. We found negative correlation between GC-content and the occurrence of frameshifting sites. In GC-rich bacteria, where frameshift products are expected to be particularly long and disordered, there is selection for cryptic stop codons. While frameshifting is prevalent and expands protein diversity, it is carefully tuned to avoid its harmful consequences.

biochemistry↗

bioRxiv2023

Characterization of Alternative Splicing During Mammalian Brain Development Reveals the Magnitude of Isoform Diversity and its Effects on Protein Conformational Changes

Regulation of gene expression is critical for fate commitment of stem and progenitor cells during tissue formation. In the context of mammalian brain development, a plethora of studies have described how changes in the expression of individual genes characterize cell types across ontogeny and phylogeny. However, little attention was paid to the fact that different transcripts can arise from any given gene through alternative splicing (AS). Considered a key mechanism expanding transcriptome diversity during evolution, assessing the full potential of AS on isoform diversity and protein function has been notoriously difficult. Here we capitalize on the use of a validated reporter mouse line to isolate neural stem cells, neurogenic progenitors and neurons during corticogenesis and combine the use of short- and long-read sequencing to reconstruct the full transcriptome diversity characterizing neurogenic commitment. Extending available transcriptional profiles of the mammalian brain by nearly 50,000 new isoforms, we found that neurogenic commitment is characterized by a progressive increase in exon inclusion resulting in the profound remodeling of the transcriptional profile of specific cortical cell types. Most importantly, we computationally infer the biological significance of AS on protein structure by using AlphaFold2 and revealing how radical protein conformational changes can arise from subtle changes in isoforms sequence. Together, our study reveals that AS has a greater potential to impact protein diversity and function than previously thought independently from changes in gene expression.

developmental biology↗

bioRxiv2023

SHARK enables homology assessment in unalignable anddisordered sequences

Intrinsically disordered regions (IDRs) are structurally flexible protein segments with regulatory functions in multiple contexts, such as in the assembly of biomolecular condensates. Since IDRs undergo more rapid evolution than ordered regions, identifying homology of such poorly conserved regions remains challenging for state-of-the-art alignment-based methods that rely on position-specific conservation of residues. Thus, systematic functional annotation and evolutionary analysis of IDRs have been limited, despite comprising [~]21% of proteins. To accurately assess homology between unalignable sequences, we developed an alignment-free sequence comparison algorithm, SHARK (Similarity/Homology Assessment by Relating K-mers). We trained SHARK-dive, a machine learning homology classifier, which achieved superior performance to standard alignment in assessing homology in unalignable sequences, and correctly identified dissimilar IDRs capable of functional rescue in IDR-replacement experiments reported in the literature. SHARK-dive not only predicts functionally similar IDRs, but also identifies cryptic sequence properties and motifs that drive remote homology, thereby facilitating systematic analysis and functional annotation of the unalignable protein universe.

bioinformatics↗

bioRxiv2023

PICNIC identifies condensate-forming proteins across organisms

Biomolecular condensates are membraneless organelles that can concentrate hundreds of different proteins to operate essential biological functions. However, accurate identification of their components remains challenging and biased towards proteins with high structural disorder content with focus on self-phase separating (driver) proteins. Here, we present a machine learning algorithm, PICNIC (Proteins Involved in CoNdensates In Cells) to classify proteins involved in biomolecular condensates regardless of their role in condensate formation. PICNIC successfully predicts condensate members by identifying amino acid patterns in the protein sequence and structure in addition to the intrinsic disorder and outperforms previous methods. We performed extensive experimental validation in cellulo and demonstrated that PICNIC accurately predicts 21 out of 24 condensate-forming proteins regardless of their structural disorder content. Even though increasing disorder content was associated with organismal complexity, we found no correlation between predicted condensate proteome content and disorder content across organisms. Overall, we applied a novel machine learning classifier to interrogate condensate components at single protein and whole-proteome levels across the tree of life (picnic.cd-code.org).

bioinformatics↗

bioRxiv2023

ENVIRONMENT MODULATES PROTEIN HETEROGENEITY THROUGH TRANSCRIPTIONAL AND TRANSLATIONAL STOP CODON MISCODING

Stop codon miscoding events give rise to longer proteins, which may alter the proteins function and thereby generate short-lasting phenotypic variability from a single gene. In order to systematically assess the frequency and origin of stop codon miscoding events, we designed a library of reporters. We introduced premature stop codons into mScarlet that enabled high-throughput quantification of protein synthesis termination errors in E.coli using fluorescent microscopy. We found that under stress conditions, stop codon miscoding may occur with a rate as high as 80%, depending on the nucleotide context, suggesting that evolution frequently samples stop codon miscoding events. The analysis of selected reporters by mass spectrometry and RNA-seq showed that not only translation but also transcription errors contribute to stop codon miscoding. The RNA polymerase is more likely to misincorporate a nucleotide at premature stop codons. Proteome-wide detection of stop codon miscoding by mass spectrometry revealed that temperature regulates the expression of cryptic sequences generated by stop codon miscoding in E.coli. Overall, our findings suggest that the environment influences the accuracy of protein production, which increases protein heterogeneity when the organisms need to adapt to new conditions.

systems biology↗

bioRxiv2022

Evolutionary impact of codon specific translation errors at the proteome scale

Errors in protein synthesis can lead to non-genetic phenotypic mutations, which contribute to generating a wide range of protein diversity. There are currently no methods to measure proteome-wide amino acid misincorporations in a high-throughput fashion, limiting their detection to specific sites and few codon-anticodon pairs. Therefore, it has been technically challenging to estimate the evolutionary impact of translation errors. Here, we developed a computational pipeline, integrated with a novel mechanistic model of translation errors, which can detect translation errors across organisms and conditions. We revealed hundreds of thousands of amino acid misincorporations and a rugged error landscape in datasets of E. coli and S. cerevisiae. We provide proteome-wide evidence of how codon choice can locally reduce translation errors. Our analysis indicates that the translation machinery prevents strongly deleterious misincorporations while allowing for advantageous ones, and the presence of missing tRNAs would increase codon-anticodon cross-reactivity and misincorporation error rates.

evolutionary biology↗

bioRxiv2022

A conserved and tunable mechanism for the temperature-controlled condensation of the translation factor Ded1p

Heat shock promotes the assembly of translation factors into condensates to facilitate the production of stress-protective proteins. How translation factors detect heat and assemble into condensates is not well understood. Here, we investigate heat-induced condensate assembly by the translation factor Ded1p from five different fungi, including Ded1p from Saccharomyces cerevisiae. Using targeted mutagenesis and in vitro reconstitution biochemistry, we find that heat-induced Ded1p assembly is driven by a conformational rearrangement of the folded helicase domain. This rearrangement determines the assembly temperature and the assembly of Ded1p into nanometer-sized particles, while the flanking intrinsically disordered regions engage in intermolecular interactions to promote assembly into micron-sized condensates. Using protein engineering, we identify six amino acid substitutions that determine most of the thermostability of a thermophilic Ded1p ortholog, thereby providing a molecular understanding underlying the adaptation of the Ded1p assembly temperature to the specific growth temperature of the species. We conclude that heat-induced assembly of Ded1p into translation factor condensates is regulated by a complex interplay of the structured domain and intrinsically disordered regions which is subject to evolutionary tuning.

biochemistry↗

bioRxiv2022

DeMAG predicts the effects of variants in clinically actionable genes by integrating structural and evolutionary epistatic features

Despite an increasing use of genomic sequencing in clinical practice, interpretation of rare genetic variants remains challenging even in well-studied disease genes, resulting in many patients with Variants of Uncertain Significance (VUSs). Computational Variant Effect Predictors (VEPs) are currently used to provide valuable evidence in variant classifications, but they often misclassify benign variants, contributing to potential misdiagnoses. Here, we developed Deciphering Mutations in Actionable Genes (DeMAG), a supervised classifier for interpreting missense variants in actionable disease genes with improved performance over existing VEPs (20% decrease of false positive rate). Our tool has balanced specificity (82%) and sensitivity (94%) on clinical data, and the lowest misclassification rate on putatively benign variants among evaluated tools. DeMAG takes advantage of a novel epistatic feature, the partners score, which is based on evolutionary and structural partnerships of residues as estimated by evolutionary information and AlphaFold2 structural models. The partners score as a general framework of epistatic interactions, can integrate not only clinical but functional information. We anticipate that our tool (demag.org) will facilitate the interpretation of variants and improve clinical decision-making.

genetics↗

bioRxiv2021

The novel Rab5 effector FERRY links early endosomes with the translation machinery

Localized translation is vital to polarized cells and requires precise and robust distribution of different mRNAs and ribosomes across the cell. However, the underlying molecular mechanisms are poorly understood and important players are lacking. Here we show that the novel Rab5 effector Five-subunit Endosomal Rab5 and RNA/ribosome intermediarY, FERRY complex recruits mRNAs and ribosomes to early endosomes, through direct mRNA interaction. FERRY displays preferential binding to certain groups of transcripts, including mRNAs encoding mitochondrial proteins. Deletion of FERRY subunits reduces the endosomal localization of transcripts in cells and has a significant impact on mRNA and protein levels. Clinical studies show that genetic disruption of FERRY causes severe brain damage. We found that, in neurons, FERRY co-localizes with mRNA on early endosomes and mRNA loaded FERRY-positive endosomes are in close proximity of mitochondria. FERRY thus transforms endosomes into mRNA carriers and plays a key role in regulating mRNA distribution and transport.

cell biology↗

bioRxiv2018

Homozygous TRPV4 loss-of-function mutation causes a severe form of distal Hereditary Motor Neuropathy (dHMN)

ObjectiveThe objective of this study is to identify the genetic cause of disease in a congenital form of congenital spinal muscular atrophy and arthrogryposis (CSMAA).\n\nMethodsA 2-year-old boy was diagnosed with arthrogryposis multiplex congenita, severe skeletal abnormalities, torticollis, vocal cord paralysis and diminished lower limb movement. Whole exome sequencing was performed on the proband and family members. In silico modeling of protein structure and heterologous protein expression and cytotoxicity assays were performed to validate pathogenicity of the identified variant.\n\nResultsWhole exome sequencing revealed a homozygous mutation in the TRPV4 gene (c.281C>T; p.S94L). The identification of a recessive mutation in TRPV4 extends the spectrum of mutations in recessive forms of the TRPV4-associated disease. p.S94L and other previously identified TRPV4 variants in different protein domains were compared in structural modeling and functional studies. In silico structural modeling suggests that the p.S94L mutation is in the disordered N-terminal region proximal to important regulatory binding sites for phosphoinositides and for PACSIN3, which could lead to alterations in trafficking and/or channel sensitivity. Functional studies by western blot and immunohistochemical analysis show that p.S94L reduces TRPV4 protein stability because of increased cytotoxicity and therefore involves a gain-of-function mechanism.\n\nConclusionThis study identifies a novel homozygous mutation in TRPV4 as a cause of the recessive form of congenital spinal muscular atrophy and arthrogryposis.

genetics↗

bioRxiv2018

The EVcouplings Python framework for coevolutionary sequence analysis

SummaryCoevolutionary sequence analysis has become a commonly used technique for de novo prediction of the structure and function of proteins, RNA, and protein complexes. This approach requires extensive computational pipelines that integrate multiple tools, databases, and data processing steps. We present the EVcouplings framework, a fully integrated open-source application and Python package for coevolutionary analysis. The framework enables generation of sequence alignments, calculation and evaluation of evolutionary couplings (ECs), and de novo prediction of structure and mutation effects. The application has an easy to use command line interface to run workflows with user control over all analysis parameters, while the underlying modular Python package allows interactive data analysis and rapid development of new workflows. Through this multi-layered approach, the EVcouplings framework makes the full power of coevolutionary analyses available to entry-level and advanced users.\n\nAvailabilityhttps://github.com/debbiemarkslab/evcouplings\n\nContactsander.research@gmail.com, debbie@hms.harvard.edu

bioinformatics↗