Biology subjects

Alon, N.

Publications and source records attributed to Alon, N..

5 recordsLinked to original sources

bioRxiv2026

The mitochondrial chaperone HSPD1 folds MTHFD2 independently of its co-chaperone HSPE1

Acquiring new cellular states entails metabolic reprogramming driven by changes in the expression of cytosolic and mitochondrial metabolic enzymes. Most mitochondrial proteins are synthesized in the cytosol and imported into the mitochondria in a linear form, after which they are folded by a network of mitochondrial chaperones and co-chaperones. Which mitochondrial protein is dependent upon which chaperone for its folding is largely unknown. HSPD1/HSPE1 (HSP60/HSP10) are evolutionarily conserved mammalian homologues of the bacterial proteins GroEL/GroES, forming a chamber-and-lid chaperonin to facilitate the folding of client proteins. We used gene knockdown and SILAC-based proteomics to identify HSPD1 client proteins. We found that HSPD1 supports the expression of Methylenetetrahydrofolate Dehydrogenase 2 (MTHFD2), a key essential protein in the mitochondrial one-carbon (1C) pathway, in cells and tumors, and directly folds MTHFD2, independently of its co-chaperone HSPE1. HSPD1 interacts with MTHFD2 in mitochondria, and MTHFD2 is degraded by LONP1 in HSPD1 knockdown cells. Consequently, we observed reduced nucleotide and S-adenosylmethionine (SAM) levels in HSPD1 knockdown and found minimal overlap in the transcriptional and metabolic cellular responses to HSPD1 vs. HSPE1 depletion. In C. elegans, knockout of HSP60 triggers the mitochondrial stress response in the gut, while HSP10 knockout triggers the mitochondrial stress response in muscle tissue. Our data support that HSPD1 is an MTHFD2 chaperone and that, in addition to working together, HSPD1 and HSPE1 have distinct biological functions.

cell biology↗

bioRxiv2025

StabilizeIT: An Automated Workflow for Protein Stabilization

The industrial application of enzymes is often hampered by poor stability and low expression yields. While computational tools can predict stabilizing mutations, many are bound by restrictive licenses that hinder their broader adoption. To address this, we developed StabilizeIT, a powerful, open-access webserver for enhancing protein stability and expression. StabilizeIT integrates a pipeline of curated open-source tools such as ProteinMPNN, AlphaFold2 and SaProt with our state-of-the-art model, SolvIT, which accurately predicts heterologous expression titers in E. coli. This unique combination allows for the simultaneous optimization of melting temperature (Tm) and solubility. The pipeline exhibits remarkable speed, generating dozens of high-quality candidates with predicted high titers and increased stability in under an hour, streamlining the path to experimental validation. To demonstrate its efficacy, StabilizeIT was used to engineer multiple enzymes in our novel biosynthetic pathway for Hyaluronic Acid. The resulting variants showed greatly enhanced thermal stability and expression, proving the pipelines real-world utility. StabilizeIT is now available to the community, offering an accessible and validated solution to accelerate the development of robust proteins for diverse applications. The webserver is freely available at https://stabilizeit.enzymit.com

bioinformatics↗

bioRxiv2023

Context-Dependent Design of Induced-fit Enzymes using Deep Learning Generates Well Expressed, Thermally Stable and Active Enzymes

The potential of engineered enzymes in practical applications is often constrained by limitations in their expression levels, thermal stability, and the diversity and magnitude of catalytic activities. De-novo enzyme design, though exciting, is challenged by the complex nature of enzymatic catalysis. An alternative promising approach involves expanding the capabilities of existing natural enzymes to enable functionality across new substrates and operational parameters. To this end we introduce CoSaNN (Conformation Sampling using Neural Network), a novel strategy for enzyme design that utilizes advances in deep learning for structure prediction and sequence optimization. By controlling enzyme conformations, we can expand the chemical space beyond the reach of simple mutagenesis. CoSaNN uses a context-dependent approach that accurately generates novel enzyme designs by considering non-linear relationships in both sequence and structure space. Additionally, we have further developed SolvIT, a graph neural network trained to predict protein solubility in E.Coli, as an additional optimization layer for producing highly expressed enzymes. Through this approach, we have engineered novel enzymes exhibiting superior expression levels, with 54% of our designs expressed in E.Coli, and increased thermal stability with more than 30% of our designs having a higher Tm than the template enzyme. Furthermore, our research underscores the transformative potential of AI in protein design, adeptly capturing high order interactions and preserving allosteric mechanisms in extensively modified enzymes. These advancements pave the way for the creation of diverse, functional, and robust enzymes, thereby opening new avenues for targeted biotechnological applications.

bioinformatics↗

bioRxiv2023

The fitness effects of codon composition of the horizontally transferred antibiotic resistance genes intensify at sub-lethal antibiotic levels.

The rampant variability in codon bias existing between bacterial genomes is expected to interfere with horizontal gene transfer (HGT), a phenomenon that drives bacterial adaptation. However, delineating the constraints imposed by codon bias on functional integration of the transferred genes is complicated by multiple genomic and functional barriers controlling HGT, and by the dependence of the evolutionary outcomes of HGT on the hosts environment. Here, we designed an experimental system in which codon composition of the transferred genes is the only variable triggering fitness change of the host. We replaced E. colis chromosomal folA gene encoding dihydrofolate reductase, an essential enzyme that constitutes a target for trimethoprim, with combinatorial libraries of synonymous codons of folA genes from trimethoprim-sensitive Listeria grayi and trimethoprim-resistant Neisseria sicca. The resulting populations underwent selection at a range of trimethoprim concentrations, and the ensuing changes in variant frequencies were used to infer the fitness effects of the individual combinations of codons. We found that when HGT causes overstabilization of the 5-end mRNA, the fitness contribution of mRNA folding stability dominates over that of codon optimality. The 5-end overstabilization can also lead to mRNA accumulation outside of the polysome, thus preventing the decay of the foreign transcripts despite the codon composition-driven reduction in translation efficiency. Importantly, the fitness effects of mRNA stability or codon optimality become apparent only at sub-lethal levels of trimethoprim individually tailored for each library, emphasizing the central role of the hosts environment in shaping the codon bias compatibility of horizontally transferred genes.

microbiology↗

bioRxiv2020

Testing the masculinization hypothesis in a sample of 23,935 human brains

The authors have withdrawn their manuscript because in revising it following a Reviewers comment, they realized the data no longer suit one of their requirements. Specifically, the Reviewer noted that they had a mistake in their code for correction for brain size. Following this correction, the authors realized that most of the corrected measures do not fit a Gaussian distribution, thereby rendering their approach mathematically unjustified. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

neuroscience↗