Biology subjects

Zelezniak, A.

Publications and source records attributed to Zelezniak, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Cross-family learning transfers thermal adaptation between enzymes

Thermal adaptation has evolved independently across diverse protein families, yet whether the information underlying this adaptation can be transferred between evolutionarily unrelated proteins remains unknown. Here, we demonstrate that thermostability can be learned from divergent protein families and transferred to enzymes without relying on their evolutionary ancestry. We developed THOR (THermally Optimised Representations), a generative framework that learns background-specific sequence transformations between unpaired mesophilic and thermophilic sequence populations while preserving the identity of the starting protein. Across evolutionarily divergent enzyme families, THOR transferred thermostability without exposure to thermophilic homologues during training. In malate dehydrogenase, excluded from cross-family training, zero-shot designs increased apparent melting temperature by up to 15 , with gains reaching 36 following limited family-specific fine-tuning. Majority of designed variants retained activity following prolonged thermal challenge, while selected variants exhibited nearly threefold higher catalytic turnover than wild type. Molecular dynamics simulations revealed that comparable thermal phenotypes emerge through distinct, background-dependent changes in protein flexibility and stabilising interactions. Together, these findings demonstrate that thermal adaptation encodes transferable information beyond lineage-specific evolutionary solutions, revealing evolution as a source of reusable adaptive principles for engineering phenotypes in proteins whose evolutionary lineages have never acquired them.

evolutionary biology↗

bioRxiv2026

Joint Variable Selection for Omic Biomarkers in Time-to-Event Data

The incidence of the vast majority of neurodegenerative, cancer, and metabolic diseases generally increases exponentially with age. In large-scale biobanks, linking time-to-diagnosis information in electronic health records to multiple genomic ("multiomics") measures has the potential to reveal the genes and biological pathways involved in the disease onset and progression. To date, association testing has commonly been conducted by testing one variable at a time using semiparametric Cox proportional hazards (CoxPH) models, which ignores correlation structure and increases the risk of false discoveries. To address these issues, we introduce a novel fully parametric Bayesian computational method, vampW, based on the Vector Approximate Message Passing framework applied to a Weibull model. vampW jointly models correlated features, while providing an interpretable hazard structure, producing a continuous survival curve, and incorporating prior knowledge. In an extensive simulation study, we demonstrate that joint modeling of omics data and time-to-event outcomes with vampW, substantially reduces false discoveries in comparison to marginal testing and other forms of joint CoxPH models. In 53,018 individuals from the UK Biobank, vampW identifies 219 protein associations with 24 disease outcomes, most of which are not among the top marginal discoveries. We further correct protein levels for exponential age effects, identifying 1,308 associations and highlighting the sensitivity of the analysis to age-correction methodology. Our findings replicate in independent cohorts using different measurement technologies, within data from Iceland and a novel Generation Scotland proteomics dataset. vampW also achieves significant improvement in the prediction of disease onset times: across 14 outcomes, it reduces the root mean squared error by over 32% and 26%, when compared to CoxPH variants and the deep learning approach DeepSurv, respectively, while maintaining predictive utility in minority populations. In summary, vampW offers accurate and interpretable variable selection and out-of-sample prediction within a single computational framework, making it a powerful tool for dissecting the genomic architecture of common complex disease onset.

bioinformatics↗

bioRxiv2024

Extensive PFAS accumulation by human gut bacteria

Per- and polyfluoroalkyl Substances (PFAS) - the so-called forever chemicals - are a major cause of environmental and health concern due to their toxicity and long-term persistence1,2. Yet, no efficient mechanisms for their removal have been identified. Here we report bioaccumulation of PFAS by several gut bacterial species over a wide range of concentrations from nanomolar up to 500 M. For bioaccumulating Bacteroides uniformis, a highly prevalent species, we estimate intracellular PFAS concentration in the mM range - above that of most native metabolites. Despite this high bioaccumulation, B. uniformis cells could grow appreciably up to 250 M perfluorononanoic acid (PFNA) exposure. Escherichia coli, which accumulated PFAS to a much lesser extent, substantially increased PFAS bioaccumulation when lacking TolC efflux pump indicating trans-membrane transport in PFAS bioaccumulation. Electron microscopy and cryogenic Focused Ion Beam-Secondary Ion Mass-spectrometry revealed distinct morphological changes and intracellular localisation of PFNA aggregates. Bioaccumulation of PFAS and transmembrane transport is also evident in proteomics, metabolomics, thermal proteome profiling, and mutations following adaptive laboratory evolution. In an in vivo context, mice colonized with human gut bacteria showed, compared to germ-free controls or those colonized with low-bioaccumulating bacteria, higher PFNA levels in excreted feces. As the gut microbiota is a critical interface between exposure and human body, our results have implications for understanding and utilizing microbial contribution to PFAS clearance.

microbiology↗

bioRxiv2024

Single Distal Mutation Enhances Activity of known PETases via Stabilisation of PET-binding

As a major source of plastic pollution, PET has attracted significant interest for biodegradation due to its potential in the circular economy. Finding effective enzymes still remains a challenge as screening methods are limited by either the low throughput or dependence on alternative non-PET substrates due to PETs insolubility. Here, we report a highly active, stable and robust enzyme, Fast_2.9, identified while directly screening for PET-degrading activity in mesophilic conditions using droplet-based encapsulation of PET nanoparticles with the throughput above 1 kHz. We identified a distal S269T mutation that improves activity in the majority of all known PETases with up to 400 times over wildtype, and more than twice of known engineered PETases, as tested on untreated post-consumer plastics. Microsecond time scale molecular dynamics analyses indicate that this distant mutation possibly influences residues near the substrate-binding cleft via a common mechanism across PETases. Compared to the state-of-the-art FastPETase and LCCICCG enzymes, the engineered Fast_2.9 enzyme requires up to 8 and 42 times lower enzyme concentrations to reach the same enzymatic activity, ultimately requiring significantly less enzyme. As such our engineered enzyme degrades multiple post-consumer PET substrates, including polyester textiles, within as least as just 2 days with up to nearly 100% terephthalic acid conversion using as little as 0.72 mgenzyme/gPET at 50. Our study presents a universal methodology for direct screening of insoluble substrates at ultra-high-throughput and highlights the techno-economic potential of Fast_2.9 for PET depolymerisation.

synthetic biology↗

bioRxiv2024

The Role of Metabolism in Shaping Enzyme Structures Over 400 Million Years of Evolution

The functions of cells and proteins depend on their biochemical microenvironment. To understand how biochemical constraints shaped protein structural evolution, we coupled the extensive genetic and metabolic data from the Saccharomycotina subphylum with the capability of AlphaFold2 to systematically predict protein structures from sequence. Determining how 11,269 enzyme structures catalysing 361 different metabolic reactions evolved over 400 million years alongside their molecular functions, we report that metabolism has shaped the structural evolution of enzymes at different levels: the organisms overall metabolism; the topological organisation of the metabolic network; and each enzymes molecular properties. For example, structural evolution depends on each enzymes reaction mechanism, on the variability rather than the amount of metabolic flux, and on biosynthetic cost. Evolutionary cost-optimization is stronger on highly abundant enzymes and acts differently on different structural domains, with the exception of small-molecule binding sites, which are prioritised over other structural domains and lack cost-optimisation. Finally, while enzyme surfaces are less constrained, surface residues can also be exposed to positive selection for the co-evolution of protein-protein interaction sites. Accessing AlphaFolds power to predict protein structures systematically and across species barriers, facilitating the integration of protein structures with functional genomics, we were thus able to map biological constraints which shape protein structural evolution at scale and over long timelines.

systems biology↗

bioRxiv2023

The amino acid sequence determines protein abundance through its conformational stability and reduced synthesis cost.

Understanding what drives protein abundance is essential to biology, medicine, and biotechnology. Driven by evolutionary selection, the amino acid sequence is tailored to meet the required abundance of proteomes, underscoring the intricate relationship between sequence and functional demand. Yet, the specific role of amino acid sequences in determining proteome abundance remains elusive. Here, we demonstrate that the amino acid sequence predicts abundance by shaping a proteins conformational stability. We show that increasing the abundance provides metabolic cost benefits, underscoring the evolutionary advantage of maintaining a highly abundant and stable proteome. Specifically, using a deep learning model (BERT), we predict 56% of protein abundance variation in Saccharomyces cerevisiae solely based on amino acid sequence. The model reveals latent factors linking sequence features to protein stability. To probe these relationships, we introduce MGEM (Mutation Guided by an Embedded Manifold), a methodology for guiding protein abundance through sequence modifications. We find that mutations increasing abundance significantly alter protein polarity and hydrophobicity, underscoring a connection between protein stability and abundance. Through molecular dynamics simulations and in vivo experiments in yeast, we confirm that abundance-enhancing mutations result in longer-lasting and more stable protein expression. Importantly, these sequence changes also reduce metabolic costs of protein synthesis, elucidating the evolutionary advantage of cost-effective, high-abundance, stable proteomes. Our findings support the role of amino acid sequence as a pivotal determinant of protein abundance and stability, revealing an evolutionary optimization for metabolic efficiency.

evolutionary biology↗

bioRxiv2023

Computational Scoring and Experimental Evaluation of Enzymes Generated by Neural Networks

In recent years, generative protein sequence models have been developed to sample novel sequences. However, predicting whether generated proteins will fold and function remains challenging. We evaluate computational metrics to assess the quality of enzyme sequences produced by three contrasting generative models: ancestral sequence reconstruction, a generative adversarial network, and a protein language model. Focusing on two enzyme families, we expressed and purified over 440 natural and generated sequences with 70-90% identity to the most similar natural sequences to benchmark computational metrics for predicting in vitro enzyme activity. Over three rounds of experiments, we developed a computational filter that improved experimental success rates by 44-100%. Surprisingly, neither sequence identity to natural sequences nor AlphaFold2 residue-confidence scores were predictive of enzyme activity. The proposed metrics and models will drive protein engineering research by serving as a benchmark for generative protein sequence models and helping to select active variants to test experimentally.

biochemistry↗

bioRxiv2022

Learning deep representations of enzyme thermal adaptation

Temperature is a fundamental environmental factor that shapes the evolution of organisms. Learning thermal determinants of protein sequences in evolution thus has profound significance for basic biology, drug discovery, and protein engineering. Here, we use a dataset of over 3 million enzymes labeled with optimal growth temperatures (OGT) of their source organisms to train a deep neural network model (DeepET). The protein-temperature representations learned by DeepET provide a temperature-related statistical summary of protein sequences and capture structural properties that affect thermal stability. For prediction of enzyme optimal catalytic temperatures and protein melting temperatures via a transfer learning approach, our DeepET model outperforms classical regression models trained on rationally designed features and other recent deep-learning-based representations. DeepET thus holds promise for understanding enzyme thermal adaptation and guiding the engineering of thermostable enzymes.

bioinformatics↗

bioRxiv2022

Cell-cell metabolite exchange creates a pro-survival metabolic environment that extends lifespan

Metabolism is fundamentally intertwined with the ageing process. We here report that a key determinant of cellular lifespan is not only nutrient supply and intracellular metabolism, but also metabolite exchange interactions that occur between cells. Studying chronological ageing in yeast, we observed that metabolites exported by young, exponentially growing, cells are re- imported during the stationary phase when cells age chronologically, indicating the existence of cross-generational metabolic interactions. We then used self-establishing metabolically cooperating communities (SeMeCos) to boost cell-cell metabolic interactions and observed a significant lifespan extension. A search for the underlying mechanisms, coupling SeMeCos, metabolic profiling, proteomics and genome-scale metabolic modelling, attributed a specific role to methionine consumer cells. These cells were enriched over time, adopted glycolytic metabolism and increased export of protective metabolites. Glycerol, in particular, accumulated in the communal metabolic environment and extended the lifespan of all cells in the community in a paracrine fashion. Our results hence establish metabolite exchange interactions as a determinant of the ageing process and show that metabolically cooperating cells shape their metabolic environment to achieve lifespan extension.

biochemistry↗

bioRxiv2021

Supervised generative design of regulatory DNA forgene expression control

In order to control gene expression, regulatory DNA variants are commonly designed using random synthetic approaches with mutagenesis and screening. This however limits the size of the designed DNA to span merely a part of a single regulatory region, whereas the whole gene regulatory structure including the coding and adjacent non-coding regions is involved in controlling gene expression. Here, we prototype a deep neural network strategy that models whole gene regulatory structures and generates de novo functional regulatory DNA with prespecified expression levels. By learning directly from natural genomic data, without the need for large synthetic DNA libraries, our ExpressionGAN can traverse the whole sequence-expression landscape to produce sequence variants with target mRNA levels as well as natural-like properties, including over 30% dissimilarity to any natural sequence. We experimentally demonstrate that this generative strategy is more efficient than a mutational one when using purely natural genomic data, as 57% of the newly-generated highly-expressed sequences surpass the expression levels of natural controls. We foresee this as a lucrative strategy to expand our knowledge of gene expression regulation as well as increase expression control in any desired organism for synthetic biology and metabolic engineering applications.

synthetic biology↗

bioRxiv2021

metaGEM: reconstruction of genome scale metabolic models directly from metagenomes.

Advances in genome-resolved metagenomic analysis of complex microbial communities have revealed a large degree of interspecies and intraspecies genetic diversity through the reconstruction of metagenome assembled genomes (MAGs). Yet, metabolic modeling efforts still tend to rely on reference genomes as the starting point for reconstruction and simulation of genome scale metabolic models (GEMs), neglecting the immense intra- and inter-species diversity present in microbial communities. Here we present metaGEM (https://github.com/franciscozorrilla/metaGEM), an end-to-end highly scalable pipeline enabling metabolic modeling of multi-species communities directly from metagenomic samples. The pipeline automates all steps from the extraction of context-specific prokaryotic GEMs from metagenome assembled genomes to community level flux balance simulations. To demonstrate the capabilities of the metaGEM pipeline, we analyzed 483 samples spanning lab culture, human gut, plant associated, soil, and ocean metagenomes, to reconstruct over 14 000 prokaryotic GEMs. We show that GEMs reconstructed from metagenomes have fully represented metabolism comparable to the GEMs reconstructed from reference genomes. We further demonstrate that metagenomic GEMs capture intraspecies metabolic diversity by identifying the differences between pathogenicity levels of type 2 diabetes at the level of gut bacterial metabolic exchanges. Overall, our pipeline enables simulation-ready metabolic model reconstruction directly from individual metagenomes, provides a resource of all reconstructed metabolic models, and showcases community-level modeling of microbiomes associated with disease conditions allowing generation of mechanistic hypotheses.

bioinformatics↗

bioRxiv2020

Plastic-degrading potential across the global microbiome correlates with recent pollution trends

Poor recycling has accumulated millions of tons of plastic waste in terrestrial and marine environments. While biodegradation is a plausible route towards sustainable management of plastic waste, the global diversity of plastic-degrading enzymes remains poorly understood. Taking advantage of global environmental DNA sampling projects, here we construct HMM models from experimentally-verified enzymes and mine ocean and soil metagenomes to assess the global potential of microorganisms to degrade plastics. By controlling for false positives using gut microbiome data, we compile a catalogue of over 30,000 non-redundant enzyme homologues with the potential to degrade 10 different plastic types. While differences between the ocean and soil microbiomes likely reflect the base compositions of these environments, we find that ocean enzyme abundance might increase with depth as a response to plastic pollution and not merely taxonomic composition. By obtaining further pollution measurements, we reveal that the abundance of the uncovered enzymes in both ocean and soil habitats significantly correlates with marine and country-specific plastic pollution trends. Our study thus uncovers the earth microbiomes potential to degrade plastics, providing evidence of a measurable effect of plastic pollution on the global microbial ecology as well as a useful resource for further applied research.

bioinformatics↗

bioRxiv2020

Transcriptome analysis of EPO and GFP HEK293 Cell-lines Reveal Shifts in Energy and ER Capacity Support Improved Erythropoietin Production in HEK293F Cells

Higher eukaryotic cell lines like HEK293 are the preferred hosts for production of therapeutic proteins requiring human post translational processing. However, recombinant protein production can result in severe stress on the cellular machinery, resulting in limited titre and product quality. To investigate the cellular and metabolic characteristics associated with these limitations, we compared erythropoietin (secretory) and GFP (non-secretory) protein producer HEK293 cell-lines using transcriptomics analysis. Despite the high demand for ATP in all protein producer clones, a significantly higher capacity for ATP production was observed with erythropoietin producers as evidenced by the enrichment of upregulated genes in the oxidative phosphorylation pathway. In addition, ribosomal genes exhibited specific patterns of expression depending on the recombinant protein and the production rate. In a clone displaying a dramatically increased erythropoietin secretion, we detected higher ER stress, including upregulation of the ATF6B gene. Our results are significant in recognizing key pathways for recombinant protein production and identifying potential target genes for further development of secretory power in mammalian cell factories. In BriefAlthough the protein secretion process has been widely studied, the complexity of it leaves many questions with regards to defining bottlenecks for successful protein secretion to be answered. By investigating the transcriptomic profiles of different HEK293 clones with varying translational rates producing either the secreted protein erythropoietin or the intracellular GFP, we reveal that high ATP production and improved capacity of specific post-translational pathways are key factors associated with boosting erythropoietin production. HighlightsO_LITranscriptomics analysis of a panel of HEK293 stable cell lines expressing GFP or erythropoietin (EPO) at varying translational rates C_LIO_LIExpression of mitochondrial ribosomal genes is positively correlated with EPO secretion C_LIO_LIExpression of different cytosolic ribosomal genes are correlated with productivity in a recombinant-protein specific manner C_LIO_LIHigh EPO producing clones have significant upregulation of ATF6B, potentially enabling a beneficial ER stress response to cope with high protein secretion C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/299966v1_ufig1.gif" ALT="Figure 1"> View larger version (87K): org.highwire.dtl.DTLVardef@1e35efforg.highwire.dtl.DTLVardef@71cee2org.highwire.dtl.DTLVardef@1c56d33org.highwire.dtl.DTLVardef@843a6f_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2020

Parallel factor analysis enables quantification and identification of highly-convolved data independent-acquired protein spectra

High-throughput data-independent acquisition (DIA) is the method of choice for quantitative proteomics, combining the best practices of targeted and shotgun proteomics approaches. The resultant DIA spectra are, however, highly convolved and with no direct precursor-fragment correspondence, complicating the analysis of biological samples. Here we present PARADIAS (PARAllel factor analysis of Data Independent Acquired Spectra), a GPU-powered unsupervised multiway factor analysis framework that deconvolves multispectral scans to individual analyte spectra, chromatographic profiles, and sample abundances, using the PARAFAC tensor decomposition method based on variation of informative spectral features. The deconvolved spectra can be annotated with traditional database search engines or used as a high-quality input for de novo sequencing methods. We demonstrate that spectral libraries generated with PARADIAS substantially reduce the false discovery rate underlying the validation of spectral quantification. PARADIAS covers up to 33 times more total ion current than library-based approaches, which typically use less than 5 % of total recorded ions, thus allowing the quantification and identification of signals from unexplored DIA spectra.

bioinformatics↗

bioRxiv2020

Bayesian genome scale modelling identifies thermal determinants of yeast metabolism

The molecular basis of how temperature affects cell metabolism has been a long-standing question in biology, where the main obstacles are the lack of high-quality data and methods to associate temperature effects on the function of individual proteins as well as to combine them at a systems level. Here we develop and apply a Bayesian modeling approach to resolve the temperature effects in genome scale metabolic models (GEM). The approach minimizes uncertainties in enzymatic thermal parameters and greatly improves the predictive strength of the GEMs. The resulting temperature constrained yeast GEM uncovered enzymes that limit growth at superoptimal temperatures, and squalene epoxidase (ERG1) was predicted to be the most rate limiting. By replacing this single key enzyme with an ortholog from a thermotolerant yeast strain, we obtained a thermotolerant strain that outgrew the wild type, demonstrating the critical role of sterol metabolism in yeast thermosensitivity. Therefore, apart from identifying thermal determinants of cell metabolism and enabling the design of thermotolerant strains, our Bayesian GEM approach facilitates modelling of complex biological systems in the absence of high-quality data and therefore shows promise for becoming a standard tool for genome scale modeling.

systems biology↗

bioRxiv2020

Benchmarking accuracy and precision of intensity-based absolute quantification of protein abundances in Saccharomyces cerevisiae

Protein quantification via label-free mass spectrometry (MS) has become an increasingly popular method for determining genome-wide absolute protein abundances. A known caveat of this approach is the poor technical reproducibility, i.e. how consistent the estimations are when the same sample is measured repeatedly. Here, we measured proteomics data for Saccharomyces cerevisiae with both biological and inter-batch technical triplicates, to analyze both accuracy and precision of protein quantification via MS. Moreover, we analyzed how these metrics vary when applying different methods for converting MS intensities to absolute protein abundances. We found that a simple normalization and rescaling approach performs as accurately yet more precisely than methods that rely on external standards. Additionally, we show that inter-batch reproducibility is worse than biological reproducibility for all evaluated methods. These results subsequently serve as a benchmark for assessing MS data quality for protein quantification, whilst also underscoring current limitations in this approach.

bioinformatics↗

bioRxiv2019

Gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

Understanding the genetic regulatory code that governs gene expression is a primary, yet challenging aspiration in molecular biology that opens up possibilities to cure human diseases and solve biotechnology problems. However, the fundamental question of how each of the individual coding and non-coding regions of the gene regulatory structure interact and contribute to the mRNA expression levels remains unanswered. Considering that all the information for gene expression regulation is already present in living cells, here we applied deep learning on over 20,000 mRNA datasets in 7 model organisms ranging from bacteria to Human. We show that in all organisms, mRNA abundance can be predicted directly from the DNA sequence with high accuracy, demonstrating that up to 82% of the variation of gene expression levels is encoded in the gene regulatory structure. Coding and non-coding regions carry both overlapping and orthogonal information and additively contribute to gene expression levels. By searching for DNA regulatory motifs present across the whole gene regulatory structure, we discover that motif interactions can regulate gene expression levels in a range of over three orders of magnitude. The uncovered co-evolution of coding and non-coding regions challenges the current paradigm that single motifs or regions are solely responsible for gene expression levels. Instead, we show that the correct combination of all regulatory regions must be established in order to accurately control gene expression levels. Therefore, the holistic system that spans the entire gene regulatory structure is required to analyse, understand, and design any future gene expression systems.

systems biology↗

bioRxiv2019

Expanding functional protein sequence space using generative adversarial networks

De novo protein design for catalysis of any desired chemical reaction is a long standing goal in protein engineering, due to the broad spectrum of technological, scientific and medical applications. Currently, mapping protein sequence to protein function is, however, neither computationionally nor experimentally tangible 1,2. Here we developed ProteinGAN, a specialised variant of the generative adversarial network 3 that is able to learn natural protein sequence diversity and enables the generation of functional protein sequences. ProteinGAN learns the evolutionary relationships of protein sequences directly from the complex multidimensional amino acid sequence space and creates new, highly diverse sequence variants with natural-like physical properties. Using malate dehydrogenase as a template enzyme, we show that 24% of the ProteinGAN-generated and experimentally tested sequences are soluble and display wild-type level catalytic activity in the tested conditions in vitro, even in highly mutated (>100 mutations) sequences. ProteinGAN therefore demonstrates the potential of artificial intelligence to rapidly generate highly diverse novel functional proteins within the allowed biological constraints of the sequence space.

synthetic biology↗