Biology subjects

van den Broek, D.

Publications and source records attributed to van den Broek, D..

4 recordsLinked to original sources

bioRxiv2025

Inhibition of ice recrystallization with designed twistless helical repeat proteins

Given the repetitive structure of crystalline ice, its unsurprising that highly active ice-binding proteins, often with beta-roll structures, also have repeating motifs. Here, we introduce a de novo designed family of ice-binding twistless alpha-helical repeat (iTHR) proteins. Each iTHR protein comprises two planar layers of parallel alpha-helices connected by loops--a structural topology not seen in native ice-binding proteins. The ice-binding helices feature an ordered array of TXXXAXXXAXX motifs, spaced to match the pyramidal {201} and secondary prism plane {110} ice lattice facets, and engineered with a 98.2{degrees} turn angle per residue to orient threonines uniformly towards the ice surface. iTHR proteins show high solubility, thermostability, and produce varied ice crystal morphologies depending on their intended target facet. Crucially, iTHRs exhibit ice recrystallization inhibition (IRI) at critical concentrations comparable to those of many native globular ice-binding proteins. Extensive site-specific mutagenesis shows that ice-binding activity in iTHR proteins is robust, remaining largely unaffected by changes in chemical composition. Variation in the repeat number reveals a non-monotonic relationship to IRI activity. X-ray crystal structures of two designs confirm the intended orientation of threonines, uniformly pointing toward the ice surface. The iTHR family provides a versatile platform to systematically investigate the complex structure-activity relationships underlying protein-ice interactions.

biophysics↗

bioRxiv2022

De novo designed ice-binding proteins from twist-constrained helices

Attaining molecular-level control over solidification processes is a crucial aspect of materials science. To control ice formation, organisms have evolved bewildering arrays of ice-binding proteins (IBPs) but these have poorly understood structure-activity relationships. We propose that reverse engineering using de novo computational protein design can shed light on structureactivity relationships of IBPs. We hypothesized that the model alpha-helical winter flounder antifreeze protein (wfAFP) uses an unusual under-twisting of its alpha-helix to align its putative ice-binding threonine residues in exactly the same direction. We test this hypothesis by designing a series of straight three-helix bundles with an ice-binding helix projecting threonines and two supporting helices constraining the twist of the ice-binding helix. We find that ice recrystallization inhibition by the designed proteins increases with the degree of designed under-twisting, thus validating our hypothesis and opening up new avenues for the computational design of icebinding proteins. Significance StatementIce-binding proteins (IBPs) modulate ice nucleation and growth in cold-adapted organisms so that they can survive in ice-laden environments at (sub)freezing temperatures. The functional repertoire of IBPs is diverse, ranging from inhibition of recrystallization and freezing point depression to shaping of ice crystals and ice nucleation. Precisely how these activities arise from the structure and ice-binding properties of IBPs is poorly understood. We demonstrate through de novo computational protein design that constraining the twist of an ice-binding helix is a key feature determining its ice-binding activity, opening new avenues for the design of synthetic IBPs with activities tailored to the requirements of specific applications, such as cell and tissue cryopreservation.

bioengineering↗

bioRxiv2022

The Tripod neuron: a minimal structural reduction of the dendritic tree

Neuron models with explicit dendritic dynamics have shed light on mechanisms for coincidence detection, pathway selection, and temporal filtering. However, it is still unclear which morphological and physiological features are required to capture these phenomena. In this work, we introduce the Tripod neuron model and propose a minimal structural reduction of the dendritic tree that is able to reproduce these dendritic computations. The Tripod is a three-compartment model consisting of two segregated passive dendrites and a somatic compartment modeled as an adaptive, exponential integrate-and-fire neuron. It incorporates dendritic geometry, membrane physiology, and receptor dynamics as measured in human pyramidal cells. We characterize the response of the Tripod to glutamatergic and GABAergic inputs and identify parameters that support supra-linear integration, coincidence-detection, and pathway-specific gating through shunting inhibition. Following NMDA spikes, the Tripod neuron generates plateau potentials whose duration depends on the dendritic length and the strength of synaptic input. When fitted with distal compartments, the Tripod neuron encodes previous activity into a dendritic depolarized state. This dendritic memory allows the neuron to perform temporal binding and we show that the neuron solves transition and sequence detection tasks on which a single-compartment model fails. Thus, the Tripod neuron can account for dendritic computations previously explained only with more detailed neuron models or neural networks. Due to its simplicity, the Tripod model can be used efficiently in simulations of larger cortical circuits.

neuroscience↗

bioRxiv2019

Neuronal memory for language processing

In language processing, an interpretation is computed incrementally within memory while utterances unfold in time. Here, we investigate the nature of this processing memory in a spiking network model of sentence comprehension. We show that the history dependence of neuronal responses endows circuits of biological neurons with adequate memory to assign semantic roles and resolve binding relations between words in a stream of language input. A neurobiological read-write memory is proposed where short-lived spiking activity encodes information into coupled dynamic variables that move at slower timescales. This state-dependent network does not rely on persistent activity, excitatory feedback, or synaptic plasticity for storage. Instead, information is maintained in adaptive neuronal conductances and can be accessed directly during comprehension without cued retrieval of previous input words. This work provides a step towards a computational neurobiology of language.

neuroscience↗