Biology subjects

Lin, V.

Publications and source records attributed to Lin, V..

8 recordsLinked to original sources

bioRxiv2026

Benchmarking AlphaFold and related deep learning approaches for modeling antibody and TCR antigen recognition

Determining the structural basis of antigen recognition by antibodies and T cell receptors (TCRs) provides critical insights into effective immune targeting and can inform design of biotherapeutics and vaccines. Accurate computational modeling of antibodies and TCRs in complex with their targets poses a major challenge for predictive methods, including AlphaFold, which is generally accurate for modeling protein complexes but has shown limited success for immune recognition. In this study we assessed the performance of AlphaFold2, AlphaFold3, increased sampling protocols, and related deep learning methods for modeling antibody-protein, antibody-peptide, and TCR-peptide-major histocompatibility complex (pMHC) recognition. We show that increased sampling and AlphaFold3 generally improve performance relative to default sampling and AlphaFold2, however predictive accuracy and improvement levels varied considerably among interface classes, with antibody-peptide complexes representing a challenge despite their small antigen size. Comparing per-case success across methods showed some complementarity, indicating opportunities for increased success through model pooling approaches, for instance increasing antibody-peptide near-native success from 41% to 59%. Analysis of AlphaFold confidence scores and modeling of a noncanonical complex provided further insights into predictive performance. These results highlight considerations for predictive antibody and TCR complex modeling efforts, while revealing key distinctions among protocols, scoring, and immune complex classes.

bioinformatics↗

bioRxiv2026

CD248 activates TGF-β receptor I to promote vascular remodeling in pulmonary arterial hypertension

I.BackgroundPulmonary arterial hypertension (PAH) is a debilitating cardiopulmonary disease characterized by progressive remodeling of the pulmonary vasculature. Pathologic transforming growth factor-{beta} (TGF-{beta}) signaling is an essential driver of vascular remodeling in PAH. While global inhibitors of TGF-{beta} exist, their clinical application is limited by systemic adverse effects. Therefore, a critically unmet need in PAH is to identify pulmonary vascular-specific regulators of the TGF-{beta} axis, which would selectively enhance clinical efficacy while minimizing adverse effects. As the clinical care of PAH largely promotes vasodilation, and only one FDA-approved agent targets vascular remodeling, this study aimed to identify selective, therapeutically targetable regulators of the TGF-{beta} axis in the PAH pulmonary vasculature. MethodsCD248 was identified via liquid chromatography-tandem mass spectrometry (LC-MS/MS) proteomics in human lungs. CD248 levels were assessed across human, rat, and mouse lung tissues using western blotting, RTqPCR, and/or immunofluorescence techniques. CD248-null (CD248-/-) mice were used to study the contribution of CD248 to hypoxia-sugen (H/S)-induced PAH. The mechanistic role of CD248 in PAH vascular remodeling and TGF-{beta} signaling was assessed by genetic (siRNA knockdown; overexpression) and pharmacologic (Ontuxizumab) manipulation of primary human pulmonary vascular cells. ResultsLC-MS/MS proteomics coupled with pathway enrichment analysis of human lung tissue identified CD248 as a putative mediator of vascular remodeling that is elevated in PAH lungs. CD248 was elevated in PAH pulmonary artery smooth muscle cells (PASMCs) across human, rat, and mouse lung tissue. CD248-/- mice were protected from H/S-induced elevations in right ventricular (RV) systolic pressure (RVSP), RV hypertrophy, and pulmonary artery muscularization. CD248 knock-down reduced cell proliferation and migration of primary PAH PASMCs. CD248 was essential for phospho-activation of TGF-{beta} receptor I (T{beta}RI) at S165 and canonical phosphorylation of SMAD3 at S423/425. CD248 loss blunted TGF-{beta}-induced gene expression (FN1, Col11, -SMA) and activated expression of the vasoprotective matrix metalloprotease, MMP-8. Mechanistically, CD248 interacted with and enhanced de novo phosphorylation and stability of T{beta}RI, blocking its ubiquitin-mediated proteasomal degradation. Ontuxizumab promoted T{beta}RI instability and attenuated the production of FN1, Col11, and -SMA in primary PAH PASMCs. ConclusionsThis work identifies CD248 as a previously unrecognized co-activator of T{beta}RI in PAH. As CD248 is largely quiescent in most adult tissues yet pathologically upregulated in the PAH pulmonary vasculature, this study supports the potential of anti-CD248 therapy as a novel pulmonary vascular-specific alternative to systemic TGF-{beta} inhibition.

molecular biology↗

bioRxiv2026

Nextstrain automates real-time phylodynamic analysis of open data for endemic and emerging pathogens

MotivationGenome sequencing provides an exceptional window into the evolutionary and epidemiological dynamics of endemic and emerging pathogens, and thus allows for better, more targeted, public health interventions. Online genomic surveillance platforms can provide near real-time insight into these dynamics. ResultsNextstrain provides continually updated real-time genomic surveillance for 21 viruses and the bacterial pathogen Mycobacterium tuberculosis, with most analyses relying solely on open sequence data. Each pathogen includes steps to fetch and curate open data, classify sequences using established nomenclature systems, perform phylogenetic analyses, and share the results publicly. These analyses are automated, with most running daily to provide continually updated snapshots of pathogen evolution. Availability and ImplementationAll source code is available at https://github.com/nextstrain. Phylogenetic results can be visualized and downloaded at https://nextstrain.org/pathogens, and open sequence data and curated metadata are available at https://nextstrain.org/pathogens/files.

bioinformatics↗

bioRxiv2025

Lightweight open-source fine-tuning of SAM2 enables domain-specific microscopy segmentation

Quantitation of structures is a critical step in analyzing images. Automated segmentation of biological samples remains a central challenge in microscopy, where variations in signal/noise, intensity, texture, and edges hinder accurate delineation of cellular and tissue structures. Adaptations of foundation models such as Segment Anything Model (SAM) remain computationally intensive and require large training datasets. Here, we introduce a lightweight, open-source Google Colab pipeline that enables efficient fine-tuning of SAM2 on domain-specific datasets without additional architectural layers or specialized hardware. By coupling mask-decoder fine-tuning with biologically informed post-processing, our framework achieves robust segmentation across diverse imaging modalities. Applied to hippocampal segmentation in brain images and single-cell segmentation in cell images, fine-tuned SAM2 demonstrates substantial gains of accuracy relative to basic SAM2 and matches leading tools. This work establishes a scalable and accessible paradigm for domain-specific adaptations of SAM2 in microscopy, lowering computational and data barriers to advanced image segmentation. HighlightsO_LILightweight fine-tuning with no added architectural complexity. C_LIO_LIHigh segmentation accuracy (Dice/Jaccard scores) achieved with small datasets. C_LIO_LICross-domain generalization across tissue and cell imaging with biologically informed post-processing. C_LIO_LIComparable or superior performance to widely used tools (Cellpose, Imaris, ilastik) at substantially lower computational cost. C_LIO_LIOpen-source and executable in a single Colab notebook, ensuring reproducibility and accessibility for non-computational users. C_LIO_LITurnkey adaptability, allowing researchers to transform raw microscopy data into fine-tuned SAM2 models with minimal input. C_LI

cell biology↗

bioRxiv2025

Association between high-frequency hearing sensitivity and visual cross-modal plasticity in typical-hearing adults

Visual cross-modal plasticity after deafness describes when auditory cortical regions increase responsiveness to visual stimulation. Such neuroplasticity demonstrates that sensory cortex adapts to profound peripheral impairment by upregulating healthy sensory function. However, whether cross-modal plasticity occurs for mild, partial hearing loss is less clear. Here we investigated cross-modal plasticity in "normal" hearing adults who had varying high-frequency hearing sensitivity, and if cross-modal plasticity was linked to adaptive visual behaviours. Twenty-five participants (aged 18 to 78) were tested in a modified Sternberg task with text-based visual words. Participants viewed a five-word sentence presented word-by-word during an encoding period and were asked to hold those words in short-term memory during a retention period. After, they reported if a target word probe was present in the encoding period. Multichannel EEG recorded visual cortical activity throughout. Results showed separate effects of high-frequency hearing sensitivity and age on visual cortical responses. At scalp sensors, visual N1 amplitudes were larger with worse hearing sensitivity, and P2 latencies were longer. In contrast, age was associated with shorter P2 latencies. Source analysis found that N1 amplitude increased with worse hearing sensitivity in right auditory cortex, but no effects were found in visual cortex, or for age. Hearing sensitivity and visual encoding activity did not correlate with behavioural performance or neural oscillations during the retention period. Results suggest for the first time that even a slight hearing decline in the high-frequency range is associated with higher visual activation of right auditory cortex, consistent with cross-modal plasticity.

neuroscience↗

bioRxiv2025

Comprehensive identification of β-lactam antibiotic polypharmacology in Mycobacterium tuberculosis

Infections with Mycobacterium tuberculosis (Mtb) cause tuberculosis (TB), which requires at least six months of treatment with multiple antibiotics. There is emergent interest in using {beta}-lactam antibiotics to improve treatment outcomes for patients. These drugs target cell wall biosynthesis, but a comprehensive list of enzymes inhibited by {beta}-lactams in Mtb is lacking. In the current study, we sought to identify and characterize Mtb enzymes inhibited by {beta}-lactam antibiotics using physiological conditions representative of both acute and chronic TB disease. We used new activity-based probes based on the {beta}-lactam antibiotic meropenem due to its approval by the World Health Organization for TB treatment. Activity-based probes label enzymes based on both substrate specificity and catalytic mechanism, enabling precise identification of drug targets. We identified previously undiscovered targets of meropenem in addition to known cell wall biosynthetic enzymes. We validated {beta}-lactam binding and hydrolysis for six newly identified targets: Rv1723, Rv2257c, Rv0309, DapE (Rv1202), MurI (Rv1338), and LipD (Rv1923). Our results demonstrate that there are at least 30 enzymes in Mtb vulnerable to inhibition by meropenem. This is many more {beta}-lactam targets than historically described, suggesting that efficacy in Mtb is a direct result of polypharmacology.

biochemistry↗

bioRxiv2024

Proteomic characterization of Mycobacterium tuberculosis subjected to carbon starvation

Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis (TB), the leading cause of infectious-disease related deaths worldwide. TB infections present as a spectrum from active to latent disease. In the human host, Mtb faces hostile environments, such as nutrient deprivation, hypoxia, and low pH. Under these conditions, Mtb can enter a dormant, but viable, state characterized by a lack of cell replication and increased resistance to antibiotics. These dormant Mtb pose a major challenge to curing infections and eradicating TB globally. In the current study, we subjected Mtb to carbon starvation (CS), a culture condition that induces growth stasis and mimics nutrient-starved conditions associated with dormancy in vivo. We provide a detailed analysis of the proteome in CS compared to replicating samples. We observed extensive proteomic reprogramming, with 36% of identified proteins significantly altered in CS. Many enzymes involved in oxidative phosphorylation and lipid metabolism were retained or upregulated in CS. The cell wall biosynthetic machinery was present in CS, although numerous changes in the abundance of peptidoglycan, arabinogalactan, and mycolic acid biosynthetic enzymes likely result in pronounced remodeling of the cell wall. Many clinically approved anti-TB drugs target cell wall biosynthesis, and we found that these enzymes were largely retained in CS. Lastly, we compared our results to those of other dormancy models and propose that CS produces a physiologically-distinct state of stasis compared to hypoxia in Mtb.

microbiology↗

bioRxiv2020

Petabase-scale sequence alignment catalyses viral discovery

Public databases contain a planetary collection of nucleic acid sequences, but their systematic exploration has been inhibited by a lack of efficient methods for searching this corpus, now exceeding multiple petabases and growing exponentially [1, 2]. We developed a cloud computing infrastructure, Serratus, to enable ultra-high throughput sequence alignment at the petabase scale. We searched 5.7 million biologically diverse samples (10.2 petabases) for the hallmark gene RNA dependent RNA polymerase, identifying well over 105 novel RNA viruses and thereby expanding the number of known species by roughly an order of magnitude. We characterised novel viruses related to coronaviruses and to hepatitis{delta} virus, respectively and explored their environmental reservoirs. To catalyse a new era of viral discovery, we established a free and comprehensive database of these data and tools. Expanding the known sequence diversity of viruses can reveal the evolutionary origins of emerging pathogens and improve pathogen surveillance for the anticipation and mitigation of future pandemics.

bioinformatics↗