Biology subjects

Gleizer, S.

Publications and source records attributed to Gleizer, S..

4 recordsLinked to original sources

bioRxiv2025

ICEPIC: A Toolkit to Discover Ice Binding Proteins from Sequence

Ice binding proteins, such as antifreeze proteins (AFPs) and ice nucleation proteins (INPs), are critical for survival in subzero environments and have wide-ranging applications in biotechnology, agriculture, and materials science. Current discovery methods for these proteins are constrained by low throughput and limited datasets that are not conducive for engineering. Here, we present a high-throughput, sequence-based model that leverages contextual embeddings from protein language models to predict ice binding potential, as well as the expression and activity potential of candidate proteins. Using a curated data corpus of over 18,000 ice binding proteins -- far larger than previous datasets -- we fine-tuned a ProtBERT-based model, achieving 99% accuracy for prediction of ice binding potential. Sensitivity analyses through targeted mutagenesis (alanine and threonine substitutions) confirmed the models biological significance, revealing functionally important residues and sequence patterns. Additionally, we developed an expression prediction model that achieved an R2 score of 0.64 and low false-negative rates in identifying highly expressible candidates in Pichia pastoris. An additional regression model trained to predict ice activity as measured by thermal hysteresis achieved an R2 score of at least 0.79 with a clear difference in prediction between ice binding and non-ice binding proteins. Our toolkit advances the predictive accuracy, interpretability, and scalability of ice binding protein discovery, offering a powerful tool for protein engineering in cold-environment applications. Significance StatementIce binding proteins enable organisms to survive freezing temperatures and are essential for applications in cryopreservation, agriculture, and materials science. However, discovering and engineering these proteins has been limited by small datasets and inadequate predictive tools. We developed a machine learning model trained on over 18,000 ice binding protein sequences to predict not only ice binding potential but also protein activity and expression in engineered hosts. This approach integrates advanced protein language models with biological context, enabling faster, more reliable discovery of ice binding proteins. Our platform advances rational protein design for real-world applications in climate resilience, biotechnology, and beyond.

synthetic biology↗

bioRxiv2023

Autotrophic growth of E. coli is achieved by a small number of genetic changes

Synthetic autotrophy is a promising avenue to sustainable bioproduction from CO2. Here, we use iterative laboratory evolution to generate several distinct autotrophic strains. Utilising this genetic diversity, we identify that just three mutations are sufficient for E. coli to grow autotrophically, when introduced alongside non-native energy (formate dehydrogenase) and carbon-fixing (RuBisCO, phosphoribulokinase, carbonic anhydrase) modules. The mutated genes are involved in glycolysis (pgi), central-carbon regulation (crp), and RNA transcription (rpoB). The pgi mutation reduces the enzymes activity, thereby stabilising the carbon-fixing cycle by capping a major branching flux. For the other two mutations, we observe down-regulation of several metabolic pathways and increased expression of native genes associated with the carbon-fixing module (rpiB) and the energy module (fdoGH), as well as an increased ratio of NADH/NAD+ - the cycles electron-donor. This study demonstrates the malleability of metabolism and its capacity to switch trophic modes using only a small number of genetic changes and could facilitate transforming other heterotrophic organisms into autotrophs.

synthetic biology↗

bioRxiv2020

Functional reconstitution of a bacterial CO2 concentrating mechanism in E. coli

Many photosynthetic organisms employ a CO2 concentrating mechanism (CCM) to increase the rate of CO2 fixation via the Calvin cycle. CCMs catalyze ≈50% of global photosynthesis, yet it remains unclear which genes and proteins are required to produce this complex adaptation. We describe the construction of a functional CCM in a non-native host, achieved by expressing genes from an autotrophic bacterium in an engineered E. coli strain. Expression of 20 CCM genes enabled E. coli to grow by fixing CO2 from ambient air into biomass, with growth depending on CCM components. Bacterial CCMs are therefore genetically compact and readily transplanted, rationalizing their presence in diverse bacteria. Reconstitution enabled genetic experiments refining our understanding of the CCM, thereby laying the groundwork for deeper study and engineering of the cell biology supporting CO2 assimilation in diverse organisms.One Sentence Summary A bacterial CO2 concentrating mechanism enables E. coli to fix CO2 from ambient air.Competing Interest StatementD.F.S. is a co-founder of Scribe Therapeutics and a scientific advisory board member of Scribe Therapeutics and Mammoth Biosciences. A.B.-E. is co-founder of b.fab. These companies were not involved in this research in any way. All other authors declare no competing interests.View Full Text

synthetic biology↗

bioRxiv2019

Point mutations in topoisomerase I alter the mutation spectrum in E. coli and impact the emergence of drug resistance genotypes

Identifying the molecular mechanisms that give rise to genetic variation is essential for the understanding of evolutionary processes. Previously, we have used adaptive laboratory evolution to enable biomass synthesis from CO2 in E. coli. Genetic analysis of adapted clones from two independently evolving populations revealed distinct enrichment for insertion and deletion mutational events. Here, we follow these observations to show that mutations in the gene encoding for DNA Topoisomerase 1 (topA) give rise to mutator phenotypes with characteristic mutational spectra. Using genetic assays and mutation accumulation lines, we show that point mutations in topA increase the rate of sequence deletion and duplication events. Interestingly, we observe that a single residue substitution (R168C) results in a high rate of head-to-tail (tandem) short sequence duplications, which are independent of existing sequence repeats. Finally, we show that the unique mutation spectrum of topA mutants enhances the emergence of antibiotic resistance in comparison to mismatch-repair (mutS) mutators, and lead to new resistance genotypes. Our findings highlight a potential link between the catalytic activity of topoisomerases and the fundamental question regarding the emergence of de novo tandem repeats, which are known modulators of bacterial evolution.

microbiology↗