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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

Applying biophysical models to understand the role of white matter in cognitive development

Diffusion MRI (dMRI) holds great promise for illuminating the biological changes that underpin cognitive development. The diffusion of water molecules probes the cellular structure of brain tissue, and biophysical modeling of the diffusion signal can be used to make inferences about specific tissue properties that vary over development or predict cognitive performance. However, applying these models to study development requires that the parameters can be reliably estimated given the constraints of data collection with children. Here we collect repeated scans using a multi-shell diffusion MRI protocol in a group of children (ages 7-12) and use two popular biophysical models to characterize axonal properties. We first assess the scan-rescan reliability of model parameters and show that axon water faction can be reliably estimated from a relatively fast acquisition, without applying spatial smoothing or de-noising. We then investigate developmental changes in the white matter, and individual differences in white matter that correlate with reading skill. Specifically, we test the hypothesis that previously reported correlations between reading skill and diffusion anisotropy in the corpus callosum reflect increased axon density in poor readers. Both models support this interpretation, highlighting the utility of biophysical models for testing specific hypotheses about cognitive development.

neuroscience↗

bioRxiv2018

PLUG (Pruning of Local Unrealistic Geometries) removes restrictions on biophysical modeling for protein design

Protein design algorithms must search an enormous conformational space to identify favorable conformations. As a result, those that perform this search with guarantees of accuracy generally start with a conformational pruning step, such as dead-end elimination (DEE). However, the mathematical assumptions of DEE-based pruning algorithms have up to now severely restricted the biophysical model that can feasibly be used in protein design. To lift these restrictions, I propose to prune local unrealistic geometries (PLUG) using a linear programming-based method. PLUGs biophysical model consists only of well-known lower bounds on interatomic distances. PLUG is intended as pre-processing for energy-based protein design calculations, whose biophysical model need not support DEE pruning. Based on 96 test cases, PLUG is at least as effective at pruning as DEE for larger protein designs--the type that most require pruning. When combined with the LUTE protein design algorithm, PLUG greatly facilitates designs that account for continuous entropy, large multistate designs with continuous flexibility, and designs with extensive continuous backbone flexibility and advanced non-pairwise energy functions. Many of these designs are tractable only with PLUG, either for empirical reasons (LUTEs machine learning step achieves an accurate fit only after PLUG pruning), or for theoretical reasons (many energy functions are fundamentally incompatible with DEE).

bioinformatics↗

bioRxiv2019

Biophysical models of cis-regulation as interpretable neural networks

The adoption of deep learning techniques in genomics has been hindered by the difficulty of mechanistically interpreting the models that these techniques produce. In recent years, a variety of post-hoc attribution methods have been proposed for addressing this neural network interpretability problem in the context of gene regulation. Here we describe a complementary way of approaching this problem. Our strategy is based on the observation that two large classes of biophysical models of cis-regulatory mechanisms can be expressed as deep neural networks in which nodes and weights have explicit physiochemical interpretations. We also demonstrate how such biophysical networks can be rapidly inferred, using modern deep learning frameworks, from the data produced by certain types of massively parallel reporter assays (MPRAs). These results suggest a scalable strategy for using MPRAs to systematically characterize the biophysical basis of gene regulation in a wide range of biological contexts. They also highlight gene regulation as a promising venue for the development of scientifically interpretable approaches to deep learning.

bioinformatics↗

bioRxiv2025

Signaling induced biophysical disruption of repressed chromatin domains drives immune cell fate

Cell fate transitions require signal-induced chromatin derepression, yet mechanisms governing transitions from repressed to active chromatin states are poorly understood. We discover, at fate-defining genes across immune cell types, a signal-induced histone code, and describe domains of H3 serine 28 phosphorylation (H3S28ph) spanning architectural features, often coincident with repressive H3 lysine 27 trimethylation (H3K27me3). Employing biophysical, single cell, and functional approaches to study signal-induced cell differentiation in the immune system, we uncover epigenomic transitions and cell fate choices precipitated by histone phosphorylation (H3ph). Mechanistically, H3ph overrides Polycomb Repressive Complex 2 (PRC2) chromatin repression, biophysically disrupts polynucleosome compaction, and promotes loss of H3K27me3, while increasing activating H3K27 acetylation and H3K36 dimethylation to drive domain interactivity and stabilize transcription. We demonstrate the activity of H3ph in several cell fate transitions and illuminate biophysical mechanisms enabling rapid signal-activated chromatin derepression, processes with general relevance for cellular differentiation and activation.

immunology↗

bioRxiv2026

Neuronal microscale biophysical instability mediates macroscale network dynamics shaping pathological manifestations

Subtle changes in membrane excitability may contribute to neurological disease, but disease-relevant dynamical signatures that generalize across models remain poorly defined. Here, we quantified variability in action potential initiation in Drosophila neurons expressing tauopathy- or epilepsy-associated mutations and in human iPSC-derived neurons from patients with Alzheimers disease or epilepsy. Across these models, disease-associated neurons exhibited increased instability in spike timing relative to controls. In Drosophila neurons, this phenotype was accompanied by increased variability in voltage-gated sodium currents during non-stationary inactivation, identifying a candidate biophysical contributor to altered spike initiation. Antiepileptic drugs reduced sodium-current variability and stabilized spike initiation in fly neurons, and similarly improved spike-timing instability in patient-derived human neurons. In the fly models, neuronal instability was also associated with altered circuit- and brain-state readouts. Together, these findings identify unstable spike initiation as a conserved electrophysiological phenotype across distinct neurological disease models and suggest that sodium-channel-dependent variability may contribute to this phenotype. Rather than establishing a complete multiscale causal framework, our study defines a tractable cellular and dynamical entry point for investigating how subtle perturbations in intrinsic excitability may scale toward circuit dysfunction and disease-relevant phenotypes. Significance StatementLinking microscale neuronal changes to macroscale disease phenotypes remains a key challenge in neuroscience biophysics. Here, we show that neurons from Drosophila models of tauopathy and epilepsy and human iPSC-derived neurons from patients with Alzheimers disease and epilepsy share increased biophysical instability in their local neural activities. In fly neurons, this phenotype is associated with increased variability in voltage-gated sodium currents and is reduced by antiepileptic treatment. These findings define unstable local spike variability as a conserved dynamical signature across distinct disease models and nominate sodium-current variability as a mechanistically testable, pharmacologically reversible contributor to pathological excitability.

neuroscience↗

bioRxiv2026

Mutation-induced biophysical destabilization as a key contributor to cancer-driving potential in the human structural protein interactome

A systems-level investigation of mutation-induced perturbations in the human structural protein interactome, the network of structurally resolved protein-protein interactions in human cells, provides mechanistic insight into the complexity underlying oncogenesis. Mutations can destabilize protein folding or specific protein-protein interactions, resulting in loss-of-function effects within the interactome. Although such network-level loss-of-function consequences may contribute to oncogenesis, previous studies have largely examined either folding or binding destabilization in isolation. Here, we performed structural and free-energy calculations to assess the impact of interactome-wide cancer-associated missense mutations on protein-folding stability and protein-binding stability. We assessed the cancer-driving potential of destabilizing mutations using a "fold difference" metric, defined as the ratio between the fractions of destabilizing mutations in datasets of cancer-associated and non-pathogenic mutations. We observed a strong positive correlation between biophysical destabilization and cancer-driving potential, with folding-destabilizing ("quasi-null") and binding-destabilizing ("edgetic") mutations within cancer-driving genes causing stronger structural and functional effects than those across the entire cancer genome. Our findings align with the expectation that cancer-driving genes are enriched in driver mutations and suggest that biophysical destabilization is a key contributor to cancer-driving potential. Overall, our study provides a biophysical perspective on the loss-of-function implications of destabilizing mutations across the interactome, enhancing comprehension of the intricate processes driving oncogenesis.

bioinformatics↗

bioRxiv2026

Investigation of Protein Melting Temperature Prediction with Cross-Method Validation on Biophysical Data

MotivationProtein melting temperature (Tm) prediction accelerates the discovery of thermostable enzymes which are crucial for industrial biotechnology often requiring harsh reaction conditions. Experimental determination of Tm remains labour-intensive and varies across techniques, motivating the development of in silico predictors. Mass-spectrometry datasets such as Meltome Atlas now enable large-scale Tm prediction with models based on deep learning, but model generalisation across diverse experimental datasets has not been systematically tested. ResultsWe evaluated the generalisability of state-of-the-art deep learning approaches and explored ESM-based embeddings for Tm prediction. To this end, we assembled the ProMelt training dataset (45 441 proteins) and five independent biophysics-based validation datasets. Our analysis revealed substantial differences between proteomics- and biophysics-based Tm measurements, highlighting the challenge of cross-domain generalisation. Existing state-of-the-art predictors trained on large-scale proteomics datasets showed reduced performance on biophysics-based validation sets. Our fine-tuned embedding-based models, particularly LoRA-adapted ESM-2 (TmProt 1.0), outperformed state-of-the-art predictors in identifying thermostable proteins (Tm[≥] 60 {degrees}C) across heterogeneous datasets, achieving AUC scores of 0.75-0.77. We also demonstrated that the available models could be used efficiently in the sequence prioritization task. AvailabilityThe TmProt web server is available at https://loschmidt.chemi.muni.cz/tmprot/. Source code and data are available at https://github.com/loschmidt/TmProt.

bioinformatics↗

bioRxiv2026

Highly diverged non-vertebrate Ki-67 proteins with conserved biophysical and functional properties

The Ki-67 protein is a widely used marker of mammalian cell proliferation that plays critical roles in heterochromatin organisation and mitotic perichromosomal layer formation. Thus, its apparent restriction to vertebrates is puzzling. However, sequences with limited homology to subdomains of Ki-67 are present in genomes from some non-vertebrate species. Here, using structural modelling, biophysical and functional analysis, we identify Ki-67-like proteins across diverse non-vertebrate eukaryotes, including tunicates, sponges, molluscs, insects and fungi. Despite extremely low sequence homology, the disordered domains of Ki-67 homologues have generally conserved biophysical and molecular properties. This is further reflected by comparison of cellular localisation, dynamics and effects on chromatin organisation of human, Ciona and Drosophila Ki-67 homologues. These results reinforce the emerging paradigm that functions may be encoded by general biophysical features in highly diverged intrinsically disordered domains. Our approach provides a blueprint for comparative biology of disordered proteins and a foundation for future studies exploring functions of Ki-67 homologues across eukaryotes.

Cell Biology↗

bioRxiv2026

Biophysical Characterization of the human Nav1.9 sodium channel in trigeminal ganglia and dorsal root ganglia neurons

In human dorsal root ganglia (DRG), and trigeminal (TG) neurons, the various voltage-gated sodium channel (Nav) isoforms play critical roles in the firing of action potentials, which drive electrical impulses that encode somatosensations including, itch, and pain. The SCN11A gene encodes the tetrodotoxin (TTX)-resistant voltage-gated sodium channel Nav1.9, characterized by unique gating properties. Unlike other isoforms, the Nav1.9 channel activates and inactivates slowly and has a hyperpolarized voltage-dependence of activation and depolarized voltage-dependence of inactivation. This leads to a large window current that has been suggested to function as a regulator of the resting membrane potential of neurons. Mutations in Nav1.9 channels lead to congenital insensitivity to pain (gain-of-function) or familial episodic pain syndrome (loss-of-function) suggesting the channel is a critical mediator of pain. Despite its relevance in pain pathophysiology, most existing data relies on rodent models or heterologous expression systems, leaving the specific pharmacology and biophysical behavior of these channels in human primary neurons largely unknown. In this study, we pharmacologically isolated and characterized native Nav1.9 channel currents in human DRG and TG neurons to compare their biophysical profiles. Our findings reveal significant kinetic and voltage-dependent differences between the two populations. Specifically, Nav1.9 channels in TG neurons exhibit a right-shifted steady-state inactivation curve, a larger window current, and faster activation kinetics compared to those in DRG neurons. In addition, conditions that simulate inflammatory states in-vivo greatly potentiates the Nav1.9 currents consistent with similar observations in rodent models. By detailing these distinct biophysical properties, this research offers crucial insights into Nav1.9 channel function relevant for drug discovery efforts aimed at developing analgesics for both acute and chronic pain.

neuroscience↗

bioRxiv2026

Biophysical modeling reveals how recording geometry shapes representations of cortical activity

Extracellular electrophysiological recordings reflect both the organization of neural activity and the geometry through which that activity is sampled. Here, we used biophysical modeling and experimental recordings from the macaque auditory cortex to determine how cortical activity is transformed across neural sources, extracellular signals, and recording geometries. In a biophysically detailed thalamocortical model, four separately instantiated cortical-column conditions spanning graded thalamic recruitment exhibited a graded response hierarchy across local field potential, current-source density, and multiunit activity, despite signal-dependent differences in the contributions of cortical neuronal populations. We then arranged independently simulated cortical columns into controlled spatial configurations and sampled the same underlying sources using virtual linear laminar and Directional and Scalable (DiSc) electrode arrays. The linear geometry primarily represented variation along cortical depth, whereas DiSc additionally represented circumferential variation associated with lateral source organization, demonstrating that recording geometry determines which spatial dimensions of common underlying activity are represented in extracellular measurements. Experimental recordings obtained with the corresponding geometries were consistent with these model-derived predictions: broadband-noise responses exhibited spatial organization shared across tone-evoked responses in both multidimensional DiSc maps and laminar profiles, despite substantial variation in frequency-dependent response magnitude across penetrations. Together, these results show that extracellular measurements represent an interaction between biological source organization and recording geometry. Biophysical forward modeling therefore provides a framework for determining which features of underlying circuit organization remain identifiable after extracellular sampling and for relating neural-interface design to the dimensions of cortical activity accessible experimentally.

neuroscience↗

bioRxiv2026

Matrix biophysical cues drive transient or prolonged breast cancer dormancy programmes

Breast cancer metastasis can emerge after disease-free periods through the reawakening of dormant disseminated cancer cells. Despite evidence that the extracellular matrix (ECM) modulates this phenomenon, biophysical cues regulating dormancy fate and therapeutic responses remain poorly understood. Here, we engineered stiffness-matched gelatin methacryloyl (GelMA) and alginate-based 3D matrices to disentangle matrix stiffness from adhesion and degradability and uncover their contributions to dormancy. Deep learning-based 3D segmentation revealed that adhesive and degradable matrices supported rounded and proliferative tumour masses, whereas inert matrices favoured single cell dormancy. Within inert matrices, stiffness further determined dormancy fate, with soft matrices activating SOX9-associated transient stem-like dormancy, while stiff matrices reduced cell volume, increased intracellular macromolecular crowding of proteins and induced NR2F1-associated prolonged dormancy marked by autophagy and cellular stress. Transiently dormant cells retrieved from soft matrices retained dormancy memory and failed to form tumours in chicken chorioallantoic membranes, demonstrating persistence of matrix-induced states after removal of biophysical cues. Moreover, patient-derived cells in soft inert matrices recapitulated transient dormant states and exhibited therapy resistance. We demonstrate that tuneable biophysical cues shape distinct dormancy states, linking matrix mechanics to dormancy regulation and therapeutic response.

bioengineering↗

bioRxiv2014

A biophysical approach to predicting protein-DNA binding energetics

Sequence-specific interactions between proteins and DNA play a central role in DNA replication, repair, recombination, and control of gene expression. These interactions can be studied in vitro using microfluidics, protein-binding microarrays (PBMs), and other high-throughput techniques. Here we develop a biophysical approach to predicting protein-DNA binding specificities from high-throughput in vitro data. Our algorithm, called BindSter, accommodates multiple protein species competing for access to DNA and alternative binding modes of the same protein, while rigorously taking into account all sterically allowed configurations of DNA-bound particles. BindSter can be used with a hierarchy of protein-DNA interaction models of increasing complexity. We observe that the quality of BindSter predictions does not change significantly as some of the energy parameters vary over a sizable range. To take this degeneracy into account, we have developed a graphical representation of parameter uncertainties, called IntervalLogo. We find that our simplest model, in which each nucleotide in the binding site is treated independently, performs better than previous biophysical approaches. The extensions of this model, in which contributions of longer words are also considered, result in further improvements, underscoring the importance of higherorder effects in protein-DNA energetics. In contrast, we find little evidence for multiple binding modes for the transcription factors (TFs) in our dataset. Furthermore, there is limited consistency in predictions for the same TF utilizing microfluidics and PBM experimental platforms.

Biophysics↗

bioRxiv2021

Biophysical properties of a tau seed

Pathogenesis of tauopathies involves conversion of tau monomer into pathological tau conformers that serve as templates to recruit native tau into growing assemblies. Small soluble tau seeds have been proposed to drive pathological tau assembly in vitro, in cells and in vivo. We have previously described the isolation of monomeric pathogenic tau seeds derived from recombinant samples and tauopathy tissues but in-depth biophysical characterization of these species has not been done. Here we describe a chromatographic method to isolate recombinant soluble tau seeds derived from heparin treatment. We used biochemical and biophysical approaches to show that the seeds are predominantly monomeric and have the capacity to nucleate aggregation of inert forms of tau in vitro and in cells. Finally, we used crosslinking mass spectrometry to identify the topological changes in tau as it converts from an inert state to a pathogenic seed. Future studies will reveal the relationship between soluble seeds and structural polymorphs derived from tauopathies to help diagnose and develop therapeutics targeting specific tauopathies.

biophysics↗

bioRxiv2021

A fast and objective hidden Markov modeling for accurate analysis of biophysical data with numerous states

The hidden Markov model (HMM) is widely used to analyze biophysical chronological data with discrete states, such as binding/detachment of biomolecules, protein/nucleotide conformational changes and step-like movement of single proteins. Despite its usefulness, classical HMM fitting has practical drawbacks that it requires the determination of the number of hidden states and fine initialization of many parameters before fitting. To overcome these drawbacks, several HMM pre-analyses have been reported, but do not provide enough accuracy when data have unknown kinetics and/or low signal-to-noise ratio. Therefore, in many cases, HMM fitting needs trial-and-error manual process that can impair the objectivity of the analysis. Moreover, for data composed of numerous hidden states, such as stepping data of cytoskeletal motors, there has been difficulty in HMM analysis because the large number of parameters were hardly properly initialized. Here, by combining a statistical step-finding method and the Gaussian mixture model clustering, we developed a new algorithm for more objective HMM analysis. Our algorithm can execute accurate state number estimation and parameter optimization with fully automated way. Simulation analysis demonstrated that our algorithm accurately fit both fast- and slow-transition trajectories. Compared with the previous method, the speed of our algorithm was 10-20 times faster for standard size data. Our algorithm also showed the accurate fit of the simulated motor-stepping data with more than 10 transition states, suggesting the applicability of the method to the data with numerous states. Furthermore, the algorithm is flexible enough to cope with cases where some kinetics are known in advance. Some available prior information, such as the dwell time of each state, can be integrated into the algorithm via two user-tunable parameters. In summary, our method enables fast, accurate and objective HMM analysis, and broadens the application range of HMM fitting that can provide more accurate interpretation of a wide variety of biophysical data.

biophysics↗

bioRxiv2021

A Topological Data Analytic Approach for Discovering Biophysical Signatures in Protein Dynamics

Identifying structural differences among proteins can be a non-trivial task. When contrasting ensembles of protein structures obtained from molecular dynamics simulations, biologically-relevant features can be easily overshadowed by spurious fluctuations. Here, we present SINATRA Pro, a computational pipeline designed to robustly identify topological differences between two sets of protein structures. Algorithmically, SINATRA Pro works by first taking in the 3D atomic coordinates for each protein snapshot and summarizing them according to their underlying topology. Statistically significant topological features are then projected back onto a user-selected representative protein structure, thus facilitating the visual identification of biophysical signatures of different protein ensembles. We assess the ability of SINATRA Pro to detect minute conformational changes in five independent protein systems of varying complexities. In all test cases, SINATRA Pro identifies known structural features that have been validated by previous experimental and computational studies, as well as novel features that are also likely to be biologically-relevant according to the literature. These results highlight SINATRA Pro as a promising method for facilitating the non-trivial task of pattern recognition in trajectories resulting from molecular dynamics simulations, with substantially increased resolution. Author SummaryStructural features of proteins often serve as signatures of their biological function and molecular binding activity. Elucidating these structural features is essential for a full understanding of underlying biophysical mechanisms. While there are existing methods aimed at identifying structural differences between protein variants, such methods do not have the capability to jointly infer both geometric and dynamic changes, simultaneously. In this paper, we propose SINATRA Pro, a computational framework for extracting key structural features between two sets of proteins. SINATRA Pro robustly outperforms standard techniques in pinpointing the physical locations of both static and dynamic signatures across various types of protein ensembles, and it does so with improved resolution.

biophysics↗

bioRxiv2021

Reduced Dimension, Biophysical Neuron Models Constructed From Observed Data

Using methods from nonlinear dynamics and interpolation techniques from applied mathematics, we show how to use data alone to construct discrete time dynamical rules that forecast observed neuron properties. These data may come from from simulations of a Hodgkin-Huxley (HH) neuron model or from laboratory current clamp experiments. In each case the reduced dimension data driven forecasting (DDF) models are shown to predict accurately for times after the training period. When the available observations for neuron preparations are, for example, membrane voltage V(t) only, we use the technique of time delay embedding from nonlinear dynamics to generate an appropriate space in which the full dynamics can be realized. The DDF constructions are reduced dimension models relative to HH models as they are built on and forecast only observables such as V(t). They do not require detailed specification of ion channels, their gating variables, and the many parameters that accompany an HH model for laboratory measurements, yet all of this important information is encoded in the DDF model. As the DDF models use only voltage data and forecast only voltage data, they can be used in building networks with biophysical connections. Both gap junction connections and ligand gated synaptic connections among neurons involve presynaptic voltages and induce postsynaptic voltage response. Biophysically based DDF neuron models can replace other reduced dimension neuron models, say of the integrate-and-fire type, in developing and analyzing large networks of neurons. When one does have detailed HH model neurons for network components, a reduced dimension DDF realization of the HH voltage dynamics may be used in network computations to achieve computational efficiency and the exploration of larger biological networks.

biophysics↗

bioRxiv2021

Biophysical and pharmacokinetic characterization of a small-molecule inhibitor of RUNX1/ETO tetramerization with anti-leukemic effects

Acute myeloid leukemia (AML) is a malignant disease of immature myeloid cells and the most prevalent acute leukemia among adults. The oncogenic homo-tetrameric fusion protein RUNX1/ETO results from the chromosomal translocation t(8;21) and is found in AML patients. The nervy homology region 2 (NHR2) domain of ETO mediates tetramerization; this oligomerization is essential for oncogenic activity. Previously, we identified the first-in-class small-molecule inhibitor of NHR2 tetramer formation, 7.44, which was shown to specifically interfere with NHR2, restore gene expression down-regulated by RUNX1/ETO, inhibit the proliferation of RUNX1/ETO-depending SKNO-1 cells, and reduce the RUNX1/ETO-related tumor growth in a mouse model. However, no biophysical and structural characterization of 7.44 binding to the NHR2 domain has been reported. Likewise, the compound has not been characterized as to physicochemical, pharmacokinetic, and toxicological properties. Here, we characterize the interaction between the NHR2 domain of RUNX1/ETO and 7.44 by biophysical assays and show that 7.44 interferes with NHR2 tetramer stability and leads to an increase in the dimer population of NHR2. The affinity of 7.44 with respect to binding to NHR2 is Klig = 3.95 {+/-} 1.28 M. By NMR spectroscopy combined with molecular dynamics simulations, we show that 7.44 binds with both heteroaromatic moieties to NHR2 and interacts with or leads to conformational changes in the N-termini of the NHR2 tetramer. Finally, we demonstrate that 7.44 has favorable physicochemical, pharmacokinetic, and toxicological properties. Together with biochemical, cellular, and in vivo assessments, the results reveal 7.44 as a lead for further optimization towards targeted therapy of t(8;21) AML.

biophysics↗

bioRxiv2022

Biophysical fitness landscape of the SARS-CoV-2 Delta variant receptor binding domain

Among the five known SARS-CoV-2 variants of concern, Delta is the most virulent leading to severe symptoms and increased number of deaths. Our study seeks to examine how the biophysical parameters of the Delta variant correlate to the clinical observations. Receptor binding domain (RBD) is the first point of contact with the human host cells and is the immunodominant form of the spike protein. Delta variant RBD contains two novel mutations L452R and T478K. We examined the effect of single mutations as well as the double mutation on RBD expression in human Expi293 cells, RBD stability using urea and thermal denaturation, and RBD binding to angiotensin converting enzyme 2 (ACE2) receptor and to neutralizing antibodies using isothermal titration calorimetry. Delta variant RBD showed significantly higher expression compared to the wild-type RBD, and the increased expression is due to L452R mutation. Despite their non-conservative nature, none of the mutations significantly affected RBD structure and stability. All mutants showed similar binding affinity to ACE2 and to Class 1 antibodies (CC12.1 and LY-CoV016) as that of the wild-type. Delta double mutant L452R/T478K showed no binding to Class 2 antibodies (P2B-2F6 and LY-CoV555) and a hundred-fold weaker binding to a Class 3 antibody (REGN10987), and the decreased antibody binding is determined by the L452R mutation. These results indicate that the immune escape from neutralizing antibodies, rather than receptor binding, is the main biophysical parameter determining the fitness landscape of the Delta variant RBD and is determined by the L452R mutation.

biophysics↗