Biology subjects

Beining, M.

Publications and source records attributed to Beining, M..

5 recordsLinked to original sources

bioRxiv2026

FrustraMPNN: An ultra-fast deep learning tool for proteome-scale analysis of deep mutational single-residue local energetic frustration in proteins

Energetic frustration, characterized by conflicting local interactions, is a key determinant of protein dynamics, allostery, protein-protein interactions, enzyme catalysis, and overall protein function. Although the principle of minimal frustration describes an evolutionary bias towards a reduction of energetic conflicts for efficient protein folding, local violations are selectively embedded to encode the complex energy landscapes necessary for protein functions. However, the computational cost of traditional single-residue frustration analysis has prevented the calculation of these energetic conflicts at the proteome scale, creating a significant bottleneck in structural biology. Here, we introduce FrustraMPNN, a message-passing neural network retrained via transfer learning that predicts a complete per-residue frustration-index mutation profile (full mutational scanning per position for a whole protein) orders-of-magnitude faster than existing methods while maintaining high accuracy. This is demonstrated by calculating the single-residue frustration of the E. coli proteome, reducing the calculation time from years to 12 hours. We validate FrustraMPNN on a diverse external set of over 3,400 human protein structures, achieving a Spearman correlation of up to 0.80 across all frustration categories, demonstrating robust generalizability. By conducting a thorough dataset ablation study, we find that the models performance improves when trained on a broader range of protein sizes, highlighting an inherent limitation of datasets like Megascale. We provide FrustraMPNN as an open-source tool, expecting it will enable exploration of new hypotheses in fields such as personalized structural biology and allow analysis of local energetic frustration patterns involved in protein function at an unprecedented proteomic scale.

bioinformatics↗

bioRxiv2025

Computational design and experimental characterization of mini-protein binders targeting Nipah, Langya and Measles virus receptor-binding domains

A lack of reagents represents a major bottleneck in pandemic preparedness and rapid vaccine development. It is therefore important to enable the design of reagents for use in the treatment and diagnosis of emerging viral diseases. Ideally, the design and identification platform is fast, can be performed by testing only a small number of candidates and enables a generally applicable strategy. In this study, we assessed the ability of recently developed computational protein design tools to establish such a workflow for validating paramyxovirus receptor-binding protein de novo binders as such reagents. The family Paramyxoviridae includes various members that cause severe disease and exhibit re-occurring zoonotic spillover events, with documented human infections over the past decades. We successfully designed, identified, and characterized mini-proteins targeting the receptor binding proteins of Nipah virus, Langya virus, and Measles virus while screening as few as 10-16 designs per target. The resulting functional binders have moderate to low nanomolar affinities and display high on-target specificity. We further showed that our most promising Nipah virus receptor-binding protein binder is able to inhibit human receptor binding in vitro and competes for an epitope that overlaps with that of the neutralizing antibody HENV-117. However, despite these promising results, this Nipah binder is only weakly neutralizing, preventing therapeutic applications. Nevertheless, we established a platform, applicable to rapidly generate diagnostically relevant proteins from only a small number of candidates, and developed novel reagents for the Paramyxoviridae family.

bioinformatics↗

bioRxiv2024

HyperMPNN - A general strategy to design thermostable proteins learned from hyperthermophiles

Stability is a key factor to enable the use of recombinant proteins in therapeutic or biotechnological applications. Deep learning protein design approaches like ProteinMPNN have shown strong performance both in creating novel proteins or stabilizing existing ones. However, it is unlikely that the stability of the designs will significantly exceed that of the natural proteins in the training set, which are biophysically only marginally stable. Therefore, we collected predicted protein structures from hyperthermophiles, which differ substantially in their amino acid composition from mesophiles. Notably, ProteinMPNN fails to recover their unique amino acid composition. Here we show that a retrained network on predicted proteins from hyperthermophiles, termed HyperMPNN, not only recovers this unique amino acid composition but can also be applied to proteins from non-hyperthermophiles. Using this novel approach on a protein nanoparticle with a melting temperature of 65{degrees}C resulted in designs remaining stable at 95{degrees}C. In conclusion, we created a new way to design highly thermostable proteins through self-supervised learning on data from hyperthermophiles.

bioinformatics↗

bioRxiv2019

A general principle of dendritic constancy – a neuron’s size and shape invariant excitability

Reducing neuronal size results in less cell membrane and therefore lower input conductance. Smaller neurons are thus more excitable as seen in their voltage responses to current injections in the soma. However, the impact of a neurons size and shape on its voltage responses to synaptic activation in dendrites is much less understood. Here we use analytical cable theory to predict voltage responses to distributed synaptic inputs and show that these are entirely independent of dendritic length. For a given synaptic density, a neurons response depends only on the average dendritic diameter and its intrinsic conductivity. These results remain true for the entire range of possible dendritic morphologies irrespective of any particular arborisation complexity. Also, spiking models result in morphology invariant numbers of action potentials that encode the percentage of active synapses. Interestingly, in contrast to spike rate, spike times do depend on dendrite morphology. In summary, a neurons excitability in response to synaptic inputs is not affected by total dendrite length. It rather provides a homeostatic input-output relation that specialised synapse distributions, local non-linearities in the dendrites and synaptic plasticity can modulate. Our work reveals a new fundamental principle of dendritic constancy that has consequences for the overall computation in neural circuits.\n\nIn briefWe show that realistic neuron models essentially collapse to point neurons when stimulated by randomly distributed inputs instead of by single synapses or current injection in the soma.\n\nHighlightsO_LIA simple equation that predicts voltage in response to distributed synaptic inputs.\nC_LIO_LIResponses to distributed and clustered inputs are largely independent of dendritic length.\nC_LIO_LISpike rates in various Hodgkin Huxley (HH) like or Leaky Integrate-and-Fire (LIF) models are largely independent of morphology.\nC_LIO_LIPrecise spike timing (firing pattern) depends on dendritic morphology.\nC_LIO_LINeuroMorpho.Org database-wide analysis of the relation between dendritic morphology and electrophysiology.\nC_LIO_LIOur equations set precise input-output relations in realistic dendrite models.\nC_LI

neuroscience↗

bioRxiv2018

Dense connectomic reconstruction in layer 4 of the somatosensory cortex

The dense circuit structure of the mammalian cerebral cortex is still unknown. With developments in 3-dimensional (3D) electron microscopy, the imaging of sizeable volumes of neuropil has become possible, but dense reconstruction of connectomes from such image data is the limiting step. Here, we report the dense reconstruction of a volume of about 500,000 m3 from layer 4 of mouse barrel cortex, about 300 times larger than previous dense reconstructions from the mammalian cerebral cortex. Using a novel reconstruction technique, FocusEM, we were able to reconstruct a total of 0.9 meters of dendrites and about 1.8 meters of axons investing only about 4,000 human work hours, about 10-25 times more efficient than previous dense circuit reconstructions. We find that connectomic data alone allows the definition of inhibitory axon types that show established principles of synaptic specificity for subcellular postsynaptic compartments. We find that also a fraction of excitatory axons exhibit such subcellular target specificity. Only about 35% of inhibitory and 55% of excitatory synaptic subcellular innervation can be predicted from the geometrical availability of membrane surface, revoking coarser models of random wiring for synaptic connections in cortical layer 4. We furthermore find evidence for enhanced variability of synaptic input composition between neurons at the level of primary dendrites in cortical layer 4. Finally, we obtain evidence for Hebbian synaptic weight adaptation in at least 24% of connections; at least 35% of connections show no sign of such previous plasticity. Together, these results establish an approach to connectomic phenotyping of local dense neuronal circuitry in the mammalian cortex.

neuroscience↗