Biology subjects

Isalan, M.

Publications and source records attributed to Isalan, M..

10 recordsLinked to original sources

bioRxiv2026

De novo design of mini-proteins as specific binders to PACAP-38, a key mediator of migraine

Pituitary adenylate cyclase-activating peptide 38 (PACAP-38) is a vasoactive peptide that is attracting attention as a potential target for next-generation migraine treatments. Antibodies against PACAP-38 have recently shown promising results as a ligand trap medicine in clinical trials. In this study, we investigated the de novo design of miniproteins to evaluate the potential for developing a medium-molecular-weight drug that binds to PACAP-38. Using Bindcraft, we designed binders against PACAP-38 and identified ICLIST-2, a 63-residue peptide with affinity for PACAP-38 (KD = 26.5 nM). Furthermore, by alanine scanning and structural analysis of ICLIST-2, we determined residues that were suitable for further optimization using ProteinMPNN. This resulted in the design of ICLIST-11, which incorporates nine amino acid substitutions relative to ICLIST-2, resulting in a KD of 4.51 nM. Interaction analysis based on a structural model suggested that three mutations in ICLIST-11 may be important for enhancing affinity. Moreover, ICLIST-11 exhibits a Tm value that is 14 {degrees}C higher, and MD simulations suggest that the substitution of three residues with hydrophobic residues (E30F, M31L, and R54W) is one reason for this. In cell-based functional assays, ICLIST-11 demonstrated potent, concentration-dependent neutralizing activity against PACAP-38-induced PAC1R activation (IC50 = 10.6 nM), significantly outperforming the parental ICLIST-2. Taken together, these results provide a framework for protein drug discovery targeting PACAP-38.

bioengineering↗

bioRxiv2026

A genetically encoded local learning rule enables physical learning in engineered bacteria

Training physical neural networks directly in matter remains difficult because most platforms do not store and update weights within the same physical substrate. Here we show that engineered Escherichia coli can implement a genetically encoded local learning rule acting on persistent biological memory. We introduce memregulons: bacterial memory elements in which coupled plasmids store an analogue weight as the relative abundance of P1 within the P1+P2 plasmid pool. Environmental inputs activate local promoters, and a shared sublethal kanamycin signal converts promoter activity into differential growth that lowers the stored P1 fraction. In single strains, flow-cytometry trajectories across distinct promoters support the predicted dependence of weight change on learning-channel activity and standing population variance. At the single-cell level, repeated negative learning reshapes the stored weight distribution as it approaches the lower boundary. In mixed populations and co-cultures, one shared negative learning signal selectively rewrites active memregulons, enabling externally routed supervised tic-tac-toe lessons. We then generalise the architecture across orthogonal chemical inputs and combinatorial promoters, and use experimentally measured updates in hybrid analyses of winner-take-all and nonlinear-classifier tasks. These results establish physical learning in living matter through local negative updates of genetically encoded analogue weights.

synthetic biology↗

bioRxiv2025

Loop engineering and activity improvement of TEV protease by a phagemid-based selection system

Loop engineering of enzymes remains challenging due to high flexibility and conformational complexity, posing a bottleneck for deep-learning-based design. Here, we constructed mutant libraries for three loops of TEV protease to assess combining directed evolution with deep learning. Using an M13 phagemid-based selection system, the three libraries were screened, resulting in a Loop 1 variant (HyperTEV60/L1) that significantly enhanced the Michaelis constant (Km) of the HyperTEV60 scaffold, a highly active mutant identified by ProteinMPNN. Structural modeling suggested that a single-residue deletion and substitution in Loop 1 expands the substrate binding pocket, accounting for the improved Km. Although the catalytic efficiency kcat/Km of HyperTEV60/L1 was only marginally higher than HyperTEV60, due to a kcat decrease, our results reveal that the phagemid-based selection system tended to find variants optimizing Km. This study demonstrates that combining deep-learning-based global optimization with localized directed evolution maximizes the probability of discovering distinct, high-performance enzyme variants.

bioengineering↗

bioRxiv2023

A three-node Turing gene circuit forms periodic spatial patterns in bacteria

Turing patterns1 are well-known self-organising systems that can form spots, stripes, or labyrinths. They represent a major theory of patterning in tissue organisation, due to their remarkable similarity to some natural patterns, such as skin pigmentation in zebrafish2, digit spacing3,4, and many others. The involvement of Turing patterns in biology has been debated because of their stringent fine-tuning requirements, where patterns only occur within a small subset of parameters5,6. This has complicated the engineering of a synthetic gene circuit for Turing patterns from first principles, even though natural genetic Turing networks have been successfully identified4,7. Here, we engineered a synthetic genetic reaction-diffusion system where three nodes interact according to a non-classical Turing network with improved parametric robustness6. The system was optimised in E. coli and reproducibly generated stationary, periodic, concentric stripe patterns in growing colonies. The patterns were successfully reproduced with a partial differential equation model, in a parameter regime obtained by fitting to experimental data. Our synthetic Turing system can contribute to novel nanotechnologies, such as patterned biomaterial deposition8,9, and provide insights into developmental patterning programs10.

synthetic biology↗

bioRxiv2023

Mammalian cell characterisation by non-invasive plate reader assay

Automated and non-invasive mammalian cell analysis is currently lagging behind due to a lack of methods suitable for a variety of cell lines and applications. Here, we report the development of a high throughput non-invasive method for tracking mammalian cell growth and performance based on plate reader measurements. We show the method to be suitable for both suspension and adhesion cell lines, and we demonstrate it can be adopted when cells are grown under different environmental conditions. We establish that the method can inform on effective drug treatment to be used depending on the cell line considered, and that it can support characterisation of engineered mammalian cells over time. This work provides the scientific community with a novel approach to mammalian cell screening, also contributing to the current efforts towards high throughput and automated mammalian cell engineering.

synthetic biology↗

bioRxiv2022

Engineered gene circuits with reinforcement learning allow bacteria to master gameplaying

Gene circuits enable cells to make decisions by controlling the expression of genes in reaction to specific environmental factors1. These circuits can be designed to encode logical operations2-7, but implementation of more complex algorithms has proved more challenging. Directed evolution optimizes gene circuits8 without the need for design knowledge9, but adjusting multiple genes and conditions10 in genotype searches poses challenges11. Here we show a multicellular sensor system, AdaptoCells, in Escherichia coli, that can evolve complex behavior through an accelerated adaptation to chemical environments. AdaptoCells recognize chemical patterns and act as a decision-making system. Using an iterative improvement method, we demonstrate that the AdaptoCells can evolve to achieve mastery in the game of tic-tac-toe, demonstrating an unprecedented level of complexity for engineered living cells. We provide an effective and straightforward way to encode complexity in gene circuits, allowing for fast adaptation in response to dynamic environments and leading to optimal decisions.

synthetic biology↗

bioRxiv2019

Engineering the smallest transcription factor: accelerated evolution of a 63-amino acid peptide dual activator-repressor

Transcription factors control gene expression in all life. This raises the question of what is the smallest protein that can support such activity. In nature, Cro from bacteriophage {lambda} is the smallest known repressor (66 amino acids; a.a.) but activators are typically much larger (e.g. {lambda} cI, 237 a.a.). Indeed, previous efforts to engineer a minimal activator from Cro resulted in no activity in vivo. In this study, we show that directed evolution results in a new Cro activator-repressor that functions as efficiently as {lambda} cI, in vivo. To achieve this, we develop Phagemid-Assisted Continuous Evolution: PACEmid. We find that a peptide as small as 63-a.a. functions efficiently as an activator and/or repressor. To our knowledge, this is the smallest protein gene regulator reported to date, highlighting the capacity of transcription factors to evolve from very short peptide sequences.

synthetic biology↗

bioRxiv2019

Ohm’s Law for emergent gene expression under fitness pressure

Natural selection relies on genotypic and phenotypic adaptation in response to fluctuating environmental conditions and is the key to predicting and preventing drug resistance. Whereas classic persistence is all-or-nothing, here we show for the first time that an antibiotic resistance gene displays linear dose-responsive selection for increased expression in proportion to rising antibiotic concentration in E. coli. Furthermore, we observe the general nature of an instantaneous phenotypic selection process upon bactericidal and bacteriostatic antibiotic treatment, as well as an amino acid synthesis pathway enzyme under a range of auxotrophic conditions. To explain this phenomenon, we propose an analogy to Ohms law in electricity (V=IR) where fitness pressure acts similarly to voltage (V), gene expression to current (I), and resistance (R) to cellular machinery constraints. Lastly, mathematical modelling approaches reveal that the emergent gene expression mechanism requires variation in mRNA and protein production within an isogenic population, and cell memory from positive feedbacks between growth and expression of any fitness-inducing gene.

systems biology↗

bioRxiv2018

Turing patterns are common but not robust

Turing patterns (TPs) underlie many fundamental developmental processes, but they operate over narrow parameter ranges, raising the conundrum of how evolution can ever discover them. Here we explore TP design space to address this question and to distill design rules. We exhaustively analyze 2- and 3-node biological candidate Turing systems: crucially, network structure alone neither determines nor guarantees emergent TPs. A surprisingly large fraction (>60%) of network design space can produce TPs, but these are sensitive to even subtle changes in parameters, network structure and regulatory mechanisms. This implies that TP networks are more common than previously thought, and evolution might regularly encounter prototypic solutions. Importantly, we deduce compositional rules for TP systems that are almost necessary and sufficient (96% of TP networks contain them, and 95% of networks implementing them produce TPs). This comprehensive network atlas provides the blueprints for identifying natural TPs, and for engineering synthetic systems.

developmental biology↗

bioRxiv2017

Mechanistic causes of constrained phenotypic variation revealed by synthetic gene regulatory networks

Phenotypic variation is the raw material of adaptive Darwinian evolution. The phenotypic variation found in organismal development is biased towards certain phenotypes, but the molecular mechanisms behind such restrictions are still poorly understood. Gene regulatory networks have been proposed as one cause of constrained phenotypic variation. However, most of the evidence for this is theoretical rather than experimental. Here, we study evolutionary biases in two synthetic gene regulatory circuits expressed in E. coli that produce a gene expression stripe - a pivotal pattern in embryonic development. The two parental circuits produce the same phenotype, but create it through different regulatory mechanisms. We show that mutations cause distinct novel phenotypes in the two networks and use a combination of experimental measurements, mathematical modelling and DNA sequencing to understand why mutations bring forth only some but not other novel gene expression phenotypes. Our results reveal that the regulatory mechanisms of networks restrict the possible phenotypic variation upon mutation. Consequently, seemingly equivalent networks can indeed be distinct in how they constrain the outcome of further evolution.

systems biology↗