Biology subjects

Li, R.

Publications and source records attributed to Li, R..

At least 289 records · Page 16Linked to original sources

bioRxiv2024

Single-Nuclei RNA Sequencing Identifies Type C Low-Threshold Mechanoreceptors as Key Players in Paclitaxel-Induced Peripheral Neuropathy

Neuropathic pain triggered by chemotherapy poses a significant clinical challenge. Investigating cell type-specific alterations through single-cell transcriptome analysis holds promise in understanding symptom development and pathogenesis. In this study, we performed single nuclei RNA (snRNA) sequencing of dorsal root ganglions (DRG) to explore the molecular mechanism underlying paclitaxel-induced neuropathic pain. Mouse exposed to repeated paclitaxel doses developed persistent pain hypersensitivity lasting at least 21 days. The snRNA sequencing unveiled seven major cell types within DRGs, with neurons further subdivided into 12 distinct subclusters using known markers. Notably, type C low-threshold mechanoreceptors (C_LTMR) exhibited the most pronounced transcriptomic changes post-paclitaxel administration. Differential gene expression and Gene Ontology (GO) analysis highlighted suppressed potassium-related currents, microtubule transport, and mitochondrial functions in C_LTMR following paclitaxel treatment. Meanwhile, Gene Set Enrichment Analysis (GSEA) suggested increased Interleukin 17 production in C_LTMR after paclitaxel exposure. Pseudo-time analysis uncovered nine distinct states (state 1 to 9) of C_LTMR. State 1 exhibits higher prevalence in paclitaxel-treated mice and altered neurotransmission properties, likely contributing to paclitaxel-induced pain hypersensitivity. This comprehensive exploration sheds light on the molecular mechanisms driving paclitaxel-induced neuropathic pain, offering potential avenues for therapeutic intervention.

neuroscience↗

bioRxiv2023

DNA Nanostar Structures with Tunable Auxetic Properties

Auxetic structures are unique with a negative Poissons ratio. Unlike regular materials, they response to external loading with simultaneous expansion or compression in all directions, rendering powerful properties advantageous in diverse applications from manufacturing to space engineering. The auxetic behaviors are determined by structural design and architecture. Such structures have been discovered in natural crystals and demonstrated synthetically with bulk materials. Recent development of DNA-based structures has pushed the unit cell size to nanometer scale. DNA nanotechnology utilizes sequence complementarity between nucleotides. By combining sequence designs with programmable self-assembly, it is possible to construct complex structures with nanoscale accuracy and to perform dynamic reconfigurations. Herein, we report a novel design of auxetic nanostars with sliding behaviors using DNA origami. Our proposed structure, inspired by an Islamic pattern, demonstrates a unit cell with two distinct reconfigurations by programming directed sliding mechanisms. Compared to previous metamaterials, the DNA nanostars show an architecture with tunable auxetic properties for the first time. We envision that this strategy may form the basis of novel metastructures with adaptability and open new possibilities in bioengineering.

biophysics↗

bioRxiv2023

COMPUTATIONAL IDENTIFICATION OF ANTIBODY-BINDING EPITOPES FROM MIMOTOPE DATASETS

A fundamental challenge in computational vaccinology is that most B-cell epitopes are conformational and therefore hard to predict from sequence alone. Another significant challenge is that a great deal of the amino acid sequence of a viral surface protein might not in fact be antigenic. Thus, identifying the regions of a protein that are most promising for vaccine design based on the degree of surface exposure may not lead to a clinically relevant immune response. Linear peptides selected by phage display experiments that have high affinity to the monoclonal antibody of interest ("mimotopes") usually have similar physicochemical properties to the antigen epitope corresponding to that antibody. The sequences of these linear peptides can be used to find possible epitopes on the surface of the antigen structure or a homology model of the antigen in the absence of an antigen-antibody complex structure. Herein we describe two novel methods for mapping mimotopes to epitopes. The first is an ensemble approach, which combines the prediction results from two existing methods. The second is a novel algorithm named MimoTree, that allows for gaps in the mimotopes and epitopes on the antigen. More specifically, a mimotope may have a gap that does not match to the epitope to allow it to adopt a conformation relevant for binding to an antibody and residues may similarly be discontinuous in conformational epitopes. MimoTree is a fully automated epitope detection algorithm suitable for the identification of conformational as well as linear epitopes.

bioinformatics↗

bioRxiv2023

Camel milk affects serum metabolites by modulating the intestinal microflora

Gut microbes play a vital role in human health and are influenced by numerous factors including diet, genetics, and environment. (Fermented) Camel milk, which is abundant in nutrients and lacks allergenic proteins, has been consumed for its edible and medicinal properties for centuries. Research on camel milks impact on gut microbiota and host metabolism is still limited. The results found that sour camel milk contained various beneficial bacteria such as Lactobacillus helveticus, Acinetobacter lwoffii, Eubacterium coprostanoligenes group, Lachnospiraceae, which could be transported to the recipients intestines by diet. This study specified that the transportation of microbiome happened both intra- and inter-species and played a principal role in the formation of progeny gut microflora. An investigation on type 2 diabetic rats revealed that the composition of gut microflora and serum metabolites of those fed with high-dose camel whey was closer to that of the normal. Eubacterium limnetica, which can reduce the risk of diseases by producing MtcB protein, was found in the gut microflora of the ones taking camel milk. These results evidenced the high potential of camel milk as a functional food.

microbiology↗

bioRxiv2023

Individual variations in McGurk illusion susceptibility reflect different integration-segregation strategies of audiovisual speech perception

The McGurk illusion is a widely used indicator of audiovisual speech integration. In this illusion, incongruent visual articulations biases the auditory speech percepts, resulting in illusory percepts that different from both auditory and visual inputs. Despite its widespread use, its validity for measuring audiovisual integration has been questioned due to substantial individual variations. Classical forced fusion theories propose that variations in McGurk illusion susceptibility reflect differences in multisensory integration ability, whereas Bayesian causal inference (BCI) theory proposes that these variations reflect different audiovisual integration-segregation strategies used according to unisensory accuracy and inferred casual structures of the auditory and visual signals. To test these two proposals, we investigated the relationships between variations in McGurk illusion susceptibility and unisensory accuracy across testing time (Experiment 1, N =161), task type (Experiment 2, N = 88), and stimuli (Experiment 3, N = 37). We found stable negative correlations between McGurk illusion susceptibility and unisensory accuracy both at the group level and single participant level. Participants with weak illusion susceptibility had higher unisensory accuracy than participants with strong illusion susceptibility. Conversely, participants with strong illusion susceptibility were more likely to incorrectly categorize the auditory and visual stimuli as illusory percepts. Moreover, participants with similar unisensory accuracy showed similar McGurk illusion susceptibility. Consistent with the BCI theory, our results indicate that individual variations in McGurk illusion susceptibility reflect different integration-segregation strategies used by participants due to variations in unisensory accuracy. Failing to perceive an illusion does not indicate a failure to integrate audiovisual signals; instead, it indicates a successful segregation of incongruent signals. These findings suggest that caution is necessary when generalizing variations in McGurk illusion susceptibility to differences in audiovisual integration ability.

neuroscience↗

bioRxiv2023

Multifocal, multiphenotypic tumours arising from an MTOR mutation acquired in early embryogenesis

Embryogenesis is a vulnerable time. Mutations in developmental cells can result in the wide dissemination of cells predisposed to disease within mature organs. We characterised the evolutionary history of four synchronous renal tumours from a 14-year-old girl, timing their shared origin to a multipotent embryonic cell committed to the right kidney, around 4 weeks post-conception. Their shared MTOR mutation, absent from normal tissues, enhances protein flexibility, which enables a FAT domain hinge to dramatically increase activity of mTORC1 and mTORC2. Developmental mutations, not usually detected in traditional genetic screening, have vital clinical importance in guiding prognosis, targeted treatment, and family screening decisions for paediatric tumours.

cancer biology↗

bioRxiv2023

scDiffEq: drift-diffusion modeling of single-cell dynamics with neural stochastic differential equations

Single-cell sequencing measurements facilitate the reconstruction of dynamic biology by capturing snapshot molecular profiles of individual cells. Cell fate decisions in development and disease are orchestrated through an intricate balance of deterministic and stochastic regulatory events. Drift-diffusion equations are effective in modeling single-cell dynamics from high-dimensional single-cell measurements. While existing solutions describe the deterministic dynamics associated with the drift term of these equations at the level of cell state, diffusion is modeled as a constant across cell states. To fully understand the dynamic regulatory logic in development and disease, models explicitly attuned to the balance between deterministic and stochastic biology are required. To address these limitations, we introduce scDiffEq, a generative framework for learning neural stochastic differential equations that approximate biologys deterministic and stochastic dynamics. Using lineage-traced single-cell data, we demonstrate that scDiffEq offers an improved reconstruction of cell trajectories and prediction of cell fate from multipotent progenitors during hematopoiesis. By imparting in silico perturbations to multipotent progenitor cells, we find that scDiffEq accurately recapitulates the dynamics of CRISPR-perturbed hematopoiesis. We generalize this approach beyond lineage-traced or multi-time point datasets to model the dynamics of single-cell data from a single time point. Using scDiffEq, we simulate high-resolution developmental cell trajectories, which can model their drift and diffusion, enabling us to study their time-dependent gene-level dynamics.

bioinformatics↗

bioRxiv2023

Molecular Regulation of Invasive Protrusion Formation at the Mammalian Fusogenic Synapse

Invasive membrane protrusions play a central role in a variety of cellular processes. Unlike filopodia, invasive protrusions are mechanically stiff and propelled by branched actin polymerization. However, how branched actin filaments are organized to create finger-like invasive protrusions remains a longstanding question in cell biology. Here, by examining the mammalian fusogenic synapse, where invasive protrusions are generated to promote cell membrane juxtaposition and fusion, we have uncovered the mechanism underlying invasive protrusion formation. We show that two Arp2/3 nucleation promoting factors (NPFs), WAVE and N-WASP, exhibit distinct and complementary localization patterns in the protrusions. While WAVE is at the leading edge, N-WASP is recruited by its interacting protein, WIP, to the shaft of the protrusion. During protrusion growth, new branched actin filaments are polymerized at the periphery of the shaft and crosslinked to preexisting actin bundles by the "pioneer" actin-bundling protein dynamin. The thickened actin bundles are further stabilized by WIP, which functions as a WH2 domain-mediated actin-bundling protein. Disrupting any of these components results in defective protrusions and myoblast fusion in cultured cells and/or in mouse embryos. Thus, our study has revealed the intricate spatiotemporal coordination between two NPFs and two actin-bundling proteins in creating invasive protrusions and has general implications in understanding protrusion formation in many cellular processes beyond cell-cell fusion.

cell biology↗

bioRxiv2023

Development of attenuated coxsackievirus B3 vectored intranasal pre-emptive pan-coronavirus vaccine

SARS-CoV-2 has the ability to evade immunity, resulting in breakthrough infections even after vaccination. Similarly, zoonotic coronaviruses pose a risk of spillover to humans. There is an urgent need to develop a pre-emptive pan-coronavirus vaccine that can induce systemic and mucosal immunity. Here, we employed a combination of immune-informatics approaches to identify shared immunodominant linear B- and T-cell epitopes from SARS-CoV-2 variants of concern (VOCs) and variants of interest (VOIs), as well as zoonotic coronaviruses. Epitope-guided vaccine were designed and the attenuated coxsackievirus B3 vectored intranasal vaccines rCVB3-EPI and rCVB3-RBD-trimer were constructed. The immunogenicity of these candidate vaccines was evaluated using Balb/c mice. The results demonstrated effective immune responses, including the production of SARS-CoV-2-specific IgG and sIgA antibodies, as well as T cell-mediated responses. However, further verification is required to assess cross-reactivity with various variants. Our intranasal pre-emptive pan-coronavirus vaccine design framework offers an appealing candidate for future vaccine development.

microbiology↗

bioRxiv2023

Increased spatial coupling of integrin and collagen IV in the immunoresistant clear cell renal cell carcinoma tumor microenvironment

BackgroundImmunotherapy (IO) has improved survival for patients with advanced clear cell renal cell carcinoma (ccRCC), but resistance to therapy develops in most patients. We use cellular-resolution spatial transcriptomics in patients with IO naive and IO exposed primary ccRCC tumors to better understand IO resistance. Spatial molecular imaging (SMI) was obtained for tumor and adjacent stroma samples. Spatial gene set enrichment analysis (GSEA) and autocorrelation (coupling with high expression) of ligand-receptor transcript pairs were assessed. Multiplex immunofluorescence (mIF) validation was used for significant autocorrelative findings and the cancer genome atlas (TCGA) and the clinical proteomic tumor analysis consortium (CPTAC) databases were queried to assess bulk RNA expression and proteomic correlates. Results21 patient samples underwent SMI. Viable tumors following IO harbored more stromal CD8+ T cells and neutrophils than IO naive tumors. YES1 was significantly upregulated in IO exposed tumor cells. The epithelial-mesenchymal transition pathway was enriched on spatial GSEA and the associated transcript pair COL4A1-ITGAV had significantly higher autocorrelation in the stroma. Fibroblasts, tumor cells, and endothelium had the relative highest expression. More integrin V+ cells were seen in IO exposed stroma on mIF validation. Compared to other cancers in TCGA, ccRCC tumors have the highest expression of both COL4A1 and ITGAV. In CPTAC, collagen IV protein was more abundant in advanced stages of disease. ConclusionsOn spatial transcriptomics, COL4A1 and ITGAV were more autocorrelated in IO-exposed stroma compared to IO-naive tumors, with high expression amongst fibroblasts, tumor cells, and endothelium. Integrin represents a potential therapeutic target in IO treated ccRCC.

cancer biology↗

bioRxiv2023

Human Papillomavirus 16 replication converts SAMHD1 into a homologous recombination factor and promotes its recruitment to replicating viral DNA

We have demonstrated that SAMHD1 (sterile alpha motif and histidine-aspartic domain HD-containing protein 1) is a restriction factor for the HPV16 life cycle. Here we demonstrate that in HPV negative cervical cancer C33a cells and human foreskin keratinocytes immortalized by HPV16 (HFK+HPV16), SAMHD1 is recruited to E1-E2 replicating DNA. Homologous recombination (HR) factors are required for HPV16 replication and viral replication promotes phosphorylation of SAMHD1, which converts it from a dNTPase to an HR factor independent from E6/E7 expression. A SAMHD1 phosphor-mimic (SAMHD1 T592D) reduces E1-E2 mediated DNA replication in C33a cells and has enhanced recruitment to the replicating DNA. In HFK+HPV16 cells SAMHD1 T592D is recruited to the viral DNA and attenuates cellular growth, but does not attenuate growth in isogenic HFK cells immortalized by E6/E7 alone. SAMHD1 T592D also attenuates the development of viral replication foci following keratinocyte differentiation. The results indicated that enhanced SAMHD1 phosphorylation could be therapeutically beneficial in cells with HPV16 replicating genomes. Protein phosphatase 2A (PP2A) can dephosphorylate SAMHD1 and PP2A function can be inhibited by endothall. We demonstrate that endothall reduces E1-E2 replication and promotes SAMHD1 recruitment to E1-E2 replicating DNA, mimicking the SAMHD1 T592D phenotypes. Finally, we demonstrate that in head and neck cancer cell lines with HPV16 episomal genomes endothall attenuates their growth and promotes recruitment of SAMHD1 to the viral genome. The results suggest that targeting cellular phosphatases has therapeutic potential for the treatment of HPV infections and cancers. ImportanceHuman papillomaviruses are causative agents in around 5% of all human cancers. The development of anti-viral therapeutics depends upon an increased understanding of the viral life cycle. Here we demonstrate that HPV16 replication converts SAMHD1 into an HR factor via phosphorylation. This phosphorylation promotes recruitment of SAMHD1 to viral DNA to assist with replication. A SAMHD1 mutant that mimics phosphorylation is hyper-recruited to viral DNA and attenuates viral replication. Expression of this mutant in HPV16 immortalized cells attenuates the growth of these cells, but not cells immortalized by the viral oncogenes E6/E7 alone. Finally, we demonstrate that the phosphatase inhibitor endothall promotes hyper-recruitment of endogenous SAMHD1 to HPV16 replicating DNA and can attenuate the growth of both HPV16 immortalized human foreskin keratinocytes and HPV16 positive head and neck cancer cell lines. We propose that phosphatase inhibitors represent a novel tool for combating HPV infections and disease.

cancer biology↗

bioRxiv2023

CLAIR: An integrated lipid database across multiple crop species

Oilseed crops, which are rich in plant lipids, provide essential fatty acids for human consumption and serve as major sources of biofuels and essential raw materials for the chemical industry. As a result of population growth and ecological changes, the demand for vegetable oil is increasingly outpacing supply. A comprehensive understanding of the genes involved in lipid metabolism in oilseed crops and the regulatory relationships among these genes is essential for improving oil content. However, current studies on lipid metabolism genes rely heavily on a decade-old database of genes involved in lipid metabolism in Arabidopsis thaliana. To address this issue, we mined the literature, integrated data from various databases and studies, and aligned homologs of lipid metabolism genes from nine oilseed crops to construct a comprehensive set of lipid metabolism genes in plants. Using this approach, we identified 221 additional lipid metabolism genes. In addition, we created a user-friendly lipid database called CLAIR (Crop Lipid-Associated Information Resource) by integrating and mining multi-omics data from nine major oil crops. The database is available at http://www.clipair.cn/. These resources should facilitate further research and exploration of lipid metabolism in oil crops, ultimately contributing to improved oil production.

plant biology↗

bioRxiv2023

LMD: Multiscale Marker Identification in Single-cell RNA-seq Data

Identifying accurate cell markers in single-cell RNA-seq data is crucial for understanding cellular diversity and function. Localized Marker Detector (LMD) is a novel tool to identify "localized genes" - genes exclusively expressed in groups of highly similar cells - thereby characterizing cellular diversity in a multi-resolution and fine-grained manner. LMD constructs a cell-cell affinity graph, diffuses the gene expression value across the cell graph, and assigns a score to each gene based on its diffusion dynamics. LMDs candidate markers can be grouped into functional gene modules, which accurately reflect cell types, subtypes, and other sources of variation such as cell cycle status. We apply LMD to mouse bone marrow and hair follicle dermal condensate datasets, where LMD facilitates cross-sample comparisons, identifying shared and sample-specific gene signatures and novel cell populations without requiring batch effect correction or integration methods. Furthermore, we assessed the performance of LMD across nine single-cell RNA sequencing datasets, compared it with six other methods aimed at achieving similar objectives, and found that LMD outperforms the other methods evaluated.

bioinformatics↗

bioRxiv2023

Cutaneous T cell lymphoma atlas reveals malignant Th2 cells supported by a B cell-rich microenvironment

Cutaneous T-cell lymphoma (CTCL) is a potentially fatal clonal malignancy of T cells primarily affecting the skin. The most common form of CTCL, mycosis fungoides (MF), can be difficult to diagnose resulting in treatment delay. The pathogenesis of CTCL is not fully understood due to limited data from patient studies. We performed single-cell RNA sequencing and spatial transcriptomics profiling of skin from patients with MF-type CTCL, and an integrated comparative analysis with human skin cell atlas datasets from healthy skin, atopic dermatitis and psoriasis. We reveal the co-optation of Th2-immune gene programmes by malignant CTCL cells and modelling of the tumour microenvironment to support their survival. We identify MHC-II+ fibroblast subsets reminiscent of lymph node T-zone reticular cells and monocyte-derived dendritic cells that can maintain Th2-like tumour cells. CTCL Th2-like tumour cells are spatially associated with B cells, forming aggregates reminiscent of tertiary lymphoid structures which are more prominent with progressive disease. Finally, we validated the enrichment of B cells in CTCL skin infiltrates and its association with disease progression across three independent patient cohorts. Our findings provide diagnostic aids, potential biomarkers for disease staging and therapeutic strategies for CTCL.

genomics↗

bioRxiv2023

Male-male interactions shape mate selection in Drosophila

Males of many species have evolved behavioral traits to both attract females and repel rivals. Here, we explore mate selection in Drosophila from both the male and female perspective to shed light on how these key components of sexual selection -- female choice and male-male competition -- work in concert to guide reproductive strategies. We find that male flies fend off competing suitors by interleaving their courtship of a female with aggressive wing flicks, which both repel competitors and generate a song that obscures the females auditory perception of other potential mates. Two higher-order circuit nodes - P1a and pC1x neurons - are coordinately recruited to allow males to flexibly interleave these agonistic actions with courtship displays, assuring they persistently pursue females until their rival falters. Together, our results suggest that female mating decisions are shaped by male-male interactions, underscoring how a males ability to subvert his rivals is central to his reproductive success.

neuroscience↗

bioRxiv2023

Epigenetic deregulation of IFN and WNT pathways in AT2 cells impairs alveolar regeneration (in COPD)

Chronic lung diseases, including chronic obstructive pulmonary disease (COPD), affect over 500 million people and are a leading cause of death worldwide. A common feature of both chronic and acute lung diseases is altered respiratory barrier integrity and impaired lung regeneration. We hypothesized that alveolar type 2 (AT2) cells, as alveolar epithelial progenitors, will carry molecular alterations that compromise alveolar regeneration in COPD. Sorted AT2 cells from ex-smokers with and without COPD at different disease stages were subjected to RNA sequencing and whole-genome bisulfite sequencing to generate unbiased transcriptome and DNA methylation maps of alveolar progenitors in the lung. Our analysis revealed genome-wide epigenetic changes in AT2 cells during COPD that were associated with global gene expression changes. Integrative data analysis uncovered a strong anti-correlation between gene expression and promoter methylation, suggesting that dysregulation of COPD-associated pathways in AT2 cells may be regulated by DNA methylation. Interferon (IFN) signaling was the top-upregulated pathway associated with the concomitant loss of promoter DNA methylation. Epigenetic regulation of the IFN pathway was validated in both global and targeted DNA demethylation assays in A549 cells. Notably, targeted DNA demethylation of IRF9 triggered upregulation of IFN signaling, mimicking the effects observed in COPD AT2 cells in the profiling data. Our findings suggest that COPD-triggered epigenetic alterations in AT2 cells may impair internal regeneration programs in human lung parenchyma.

cell biology↗

bioRxiv2023

Enhancing Mental Well-being: Efficacy and Neural Mechanisms of Leyi Training in Emotional and Cognitive Intervention

Leyi mental training comprises physical and mental cultivation methods, such as relaxation, breathing, meditation, koan, and cognitive regulation, with the ultimate goal of achieving enlightenment. Over a decade of practical experience has demonstrated its potential to enhance individuals mental well-being. In our study, we employed EEG and psychological scales to investigate the efficacy of Leyi training in emotional and cognitive intervention, as well as its underlying neural mechanisms. The results revealed significant improvements in both emotion and cognition following the intervention. We propose that the effectiveness of Leyi training in emotional regulation may stem from the heightened sensitivity to present experiences and the robust cognitive control ability cultivated through the practice, which enables practitioners to allocate equal, non-reactive attention to all stimuli, regardless of their emotional valence. Moreover, this training facilitates more effective allocation of cognitive resources to deal with current tasks, thus improving cognitive abilities.

neuroscience↗

bioRxiv2023

NanoBERTa-ASP: Predicting Nanobody Binding Epitopes Based on a Pretrained RoBERTa Model

Nanobodies, also known as VHH or single-domain antibodies, are a unique class of antibodies that consist only of heavy chains and lack light chains. Nanobodies possess the advantages of both small molecule drugs and conventional antibodies, making them a promising class of therapeutic biopharmaceuticals. Studying the characteristics of nanobody sequences can aid the development and design of nanobodies. An important challenge in this field is accurately predicting the binding sites between nanobodies and antigens. The binding site is the region where the nanobody interacts with the antigen. The precise prediction of these binding sites is crucial for comprehending the interaction mechanism between the nanobody and the antigen, facilitating the design of effective nanobodies, as well as gaining valuable insights into the properties of nanobodies. Nanobodies typically have smaller and more flexible binding sites than traditional antibodies, however predictive models trained on traditional antibodies may not be suitable for nanobodies. Moreover, the limited availability of antibodyderived nanobody datasets for deep learning poses challenges in constructing highly accurate predictive models that can generalize well to unseen data. To address these challenges, we propose a novel nanobody prediction model, named NanoBERTa-ASP (Antibody Specificity Prediction), which is specifically designed for predicting nanobody-antigen binding sites. The model adopts a training strategy more suitable for nanobodies by leveraging an advanced natural language processing (NLP) model called BERT (Bidirectional Encoder Representations from Transformers). The model also utilizes a masked language modeling approach to learn the contextual information of the nanobody sequence and predict its binding site. The results obtained from training NanoBERTa-ASP on nanobodies highlight its exceptional performance in Precision and AUC, underscoring its proficiency in capturing sequence information specific to nanobodies and accurately predicting their binding sites. Furthermore, our model can provide insights into the interaction mechanisms between nanobodies and antigens, contributing to a better understanding of nanobodies, as well as accelerating the development and design of nanobodies with potential therapeutic applications. To the best of our knowledge, NanoBERTa-ASP is the first nanobody language model that achieved high accuracy in predicting the binding sites. Github repositoryhttps://github.com/WangLabforComputationalBiology/NanoBERTa-ASP

bioinformatics↗