Biology subjects

Shek, R.

Publications and source records attributed to Shek, R..

6 recordsLinked to original sources

bioRxiv2026

Fractionated ionising radiation affects cellular functions, and gene expression associated to subpopulation of F11 dorsal root ganglia neurons without inducing oxidative stress

Radiotherapy is common practice to treat cancer but produces significant side effects such as chronic pain. Cancer survivors report developing chronic pain due to their treatment even long after the cancer is cured. To understand the mechanisms underlying the radiotherapy-induced chronic pain, we assessed how ionising X-ray radiation exposure during 4 consecutive days of 5 Gy (total radiation dose of 20 Gy) affected dorsal root ganglia (DRG) sensory neurons (rodent F11 cell line). On the 5th day, we assessed known impacts of ionising radiation (senescence, oxidative stress, cellular metabolism, mitochondrial copy number, and mitochondrial respiration) followed by assessing expression of genes associated with populations of DRG neuronal fibres. We discovered that fractionated exposure to ionising radiation increased senescence, mitochondrial copy number, and modulated the NAD+/NADH pathway, but did not change the oxygen consumption rate nor induce oxidative stress 24 hours after the last irradiation exposure. Additionally, ionising radiation altered the expression of genes associated with mechanoreceptor fibres, known to have pro-nociceptive properties in the context of injury and chronic pain.

neuroscience↗

bioRxiv2025

De Novo Computational Design of VHH Nanobodies Against LGR5

VHH discovery traditionally relies on animal immunization or large-scale library screening, methods that are slow, costly, and often ineffective for challenging targets such as GPCRs. We present a fully de novo computational pipeline for epitope-directed VHH design, integrating generative backbone modeling, deep learning-based sequence optimization, and iterative experimental feedback. Using LGR5 as a model, we progressed from in silico design to functional binders without structural templates. Across three design-test-learn cycles, millions of candidates were reduced to epitope-specific binders with nanomolar affinity and high thermal stability (melting temperature [Tm] > 65 {degrees}C). Cryogenic electron microscopy (cryo-EM) confirmed atomic-level agreement (RMSD {approx} 2.2 [A]). This structure-validated approach accelerates timelines, reduces cost, and is broadly applicable to GPCRs and other membrane proteins, enabling "on-demand" therapeutic antibody generation.

bioengineering↗

bioRxiv2025

AI-guided design of common light chains to enable manufacturable bispecific antibodies

Bispecific antibodies (BsAbs) offer therapeutic advantages but face manufacturing bottlenecks from light chain mispairing, which can generate a substantial fraction of incorrect products and increase manufacturing complexity and cost of goods. Common light chains (cLC) eliminate mispairing, yet existing approaches require screening thousands of variants per target. We present an AI-driven framework that computationally designs cLCs through structure-guided pairing of non-cognate VH-VL interfaces, reducing experimental screening by three orders of magnitude. The platform successfully engineers therapeutic antibodies lacking experimental structures, expanding applicability beyond crystallographic databases. Among 10 therapeutic targets, we successfully generated designs for 7 targets, comprising 55 unique BsAb pairs. Of these, 43.6% (24/55) were successfully as BsAb cLCs. Three bispecific antibodies reached production-ready specifications: >90% purity and 1.6-1.8 g/L titers. This platform democratizes bispecific antibody development, expanding access beyond well-resourced programs.

bioengineering↗

bioRxiv2025

Computational Design of Soluble CCR8 Analogues with Preserved Antibody Binding

G protein-coupled receptors (GPCRs) represent the largest class of drug targets, yet their membrane-embedded nature poses significant challenges for structural studies and therapeutic development. Here, we report the successful computational design and experimental validation of soluble CCR8 analogues that maintain native antibody binding properties. Using an integrated pipeline combining ProteinMPNN-sol sequence design, and structure-based filtering, we generated 13 CCR8 analogues from 272 initial designs across three N-terminal truncation strategies. Experimental validation confirmed 62% success rate (8/13 designs) with protein yields of 1.19-73.72 mg/L in aqueous buffer, representing a significant improvement over traditional membrane protein production method. Surface plasmon resonance analysis demonstrated that all analogues retained mAb1 binding with dissociation constants ranging from 77-857 nM, comparable to wild-type CCR8 (KD = 190 nM). Despite extensive sequence divergence (10-13% identity with wild-type CCR8), structural integrity was preserved as evidenced by binding affinity maintenance and computational structural validation. This work demonstrates the feasibility of computationally designing functional soluble analogues of challenging membrane proteins, with implications for accelerating drug discovery, antibody development, and structural biology studies. Our approach addresses critical limitations in membrane protein accessibility while preserving native epitope presentation, opening new avenues for therapeutic target characterization and binder discovery.

bioengineering↗

bioRxiv2021

High-throughput screening of the ReFRAME, Pandemic Box, and COVID Box drug repurposing libraries against SARS-CoV2 nsp15 endoribonuclease to identify small-molecule inhibitors of viral activity.

SARS-CoV-2 has caused a global pandemic, and has taken over 1.7 million lives as of mid-December, 2020. Although great progress has been made in the development of effective countermeasures, with several pharmaceutical companies approved or poised to deliver vaccines to market, there is still an unmet need of essential antiviral drugs with therapeutic impact for the treatment of moderate-to-severe COVID-19. Towards this goal, a high-throughput assay was used to screen SARS-CoV-2 nsp15 uracil-dependent endonuclease (endoU) function against 13 thousand compounds from drug and lead repurposing compound libraries. While over 80% of initial hit compounds were pan-assay inhibitory compounds, three hits were confirmed as nsp15 endoU inhibitors in the 1-20 M range in vitro. Furthermore, Exebryl-1, a {beta}-amyloid anti-aggregation molecule for Alzheimers therapy, was shown to have antiviral activity between 10 to 66 M, in VERO, Caco-2, and Calu-3 cells. Although the inhibitory concentrations determined for Exebryl-1 exceed those recommended for therapeutic intervention, our findings show great promise for further optimization of Exebryl-1 as an nsp15 endoU inhibitor and as a SARS-CoV-2 antiviral. Author summaryDrugs to treat COVID-19 are urgently needed. To address this, we searched libraries of drugs and drug-like molecules for inhibitors of an essential enzyme of the virus that causes COVID-19, SARS-CoV-2 nonstructural protein (nsp)15. We found several molecules that inhibited the nsp15 enzyme function and one was shown to be active in inhibiting the SARS-CoV-2 virus. This demonstrates that searching for SARS-CoV-2 nsp15 inhibitors can lead inhibitors of SARS-CoV-2, and thus therapeutics for COVID-19. We are currently working to see if these inhibitors could be turned into a drug to treat COVID-19.

microbiology↗

bioRxiv2020

In silico detection of SARS-CoV-2 specific B-cell epitopes and validation in ELISA for serological diagnosis of COVID-19

Rapid generation of diagnostics is paramount to understand epidemiology and to control the spread of emerging infectious diseases such as COVID-19. Computational methods to predict serodiagnostic epitopes that are specific for the pathogen could help accelerate the development of new diagnostics. A systematic survey of 27 SARS-CoV-2 proteins was conducted to assess whether existing B-cell epitope prediction methods, combined with comprehensive mining of sequence databases and structural data, could predict whether a particular protein would be suitable for serodiagnosis. Nine of the predictions were validated with recombinant SARS-CoV-2 proteins in the ELISA format using plasma and sera from patients with SARS-CoV-2 infection, and a further 11 predictions were compared to the recent literature. Results appeared to be in agreement with 12 of the predictions, in disagreement with 3, while a further 5 were deemed inconclusive. We showed that two of our top five candidates, the N-terminal fragment of the nucleoprotein and the receptor-binding domain of the spike protein, have the highest sensitivity and specificity and signal-to-noise ratio for detecting COVID-19 sera/plasma by ELISA. Mixing the two antigens together for coating ELISA plates led to a sensitivity of 94% (N=80 samples from persons with RT-PCR confirmed SARS-CoV2 infection), and a specificity of 97.2% (N=106 control samples).

bioinformatics↗