Biology subjects

Shin, W.-H.

Publications and source records attributed to Shin, W.-H..

11 recordsLinked to original sources

bioRxiv2026

MOZAIC: Compound Growth via In Silico Reactions and Global Optimization using Conformational Space Annealing

MotivationFragment-based drug discovery (FBDD) is an efficient strategy that leverages small molecular fragments to explore broader chemical space by combining them. Advances in computational methods have enabled the calculation of molecular properties and docking scores, thereby accelerating the development of algorithm- and AI-based approaches in FBDD. However, it should be noted that certain methods do not provide synthetic pathways to obtain the proposed compounds. Consequently, these molecules might not be synthesized easily. ResultsIn light of these developments, we propose MOZAIC, a novel framework that explores chemical space using a reaction-based fragment growing and Conformational Space Annealing, a powerful global optimization algorithm. Our results show that MOZAIC effectively produces chemically diverse molecules with balanced improvements in lead-like properties, including QED, synthetic accessibility, and binding affinity. Furthermore, its flexible objective function allows fine-tuning for specific design goals, such as enhancing solubility with binding affinity. These capabilities position MOZAIC as a valuable platform for advancing fragment-to-lead and lead optimization efforts in drug discovery. Availability and implementationMOZAIC is available at https://github.com/kucm-lsbi/MOZAIC/. Supplementary InformationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2026

Disruption of the hypothalamic orexin system links SARS-CoV-2 infection to persistent cortical neuronal pathology

Long COVID frequently presents with persistent neurological symptoms, including cognitive impairment, fatigue, and sleep disturbances; however, its underlying mechanisms remain unclear. Here, we show that SARS-CoV-2 infection induces lasting cortical neuronal injury and hypothalamic orexin (hypocretin) dysfunction in vivo. In K18-hACE2 and wild-type BALB/c mice, viral RNA persisted in the brain and coincided with focal loss of Neuronal Nuclei (NeuN)-positive cortical neurones beyond acute infection. SARS-CoV-2, but not the influenza A virus, triggered rapid and sustained suppression of hypothalamic orexin expression, defining a virus-specific neuropathological signature. Considering the downregulation of orexin and focal cortical NeuN expression, we showed that augmenting orexin signalling using recombinant orexin-A/B restored NeuN expression in vitro and in vivo. Overall, these findings identify the orexin system as a selective neural vulnerability to SARS-CoV-2 and define orexinergic circuit disruption as a mechanistic axis underlying the neurological manifestations of Long COVID.

microbiology↗

bioRxiv2026

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening

MotivationStructure-based virtual screening (SBVS) is limited by the rigid-receptor assumption, which is particularly problematic for kinases that adopt multiple active-site conformations but are experimentally biased toward a single state. Although ensemble screening can address this limitation, it remains computationally expensive. ResultsWe introduce KASSPer (Kinase Active Site Structure Predictor), a framework that predicts kinase active-site conformational states using protein and compound language models. Given a kinase amino acid sequence and a ligand SMILES string, KASSPer enables ligand-specific conformer selection prior to SBVS, substantially reducing the computational cost associated with ensemble screening. Benchmarking on the DUD-E kinase subset demonstrates that KASSPer-guided screening consistently outperforms ensemble-based approaches across all evaluation metrics. Availability and ImplementationThe implementation for model loading and inference is available at the GitHub repository https://github.com/kucm-lsbi/KASSPer

bioinformatics↗

bioRxiv2025

Allosteric deimmunisation of a Salmonella phosphatase enhances catalytic function

Genetically engineered Salmonella strains are promising vectors for cancer therapy, but their clinical use is limited by host immune responses. A key immunogenic antigen, the phosphatase PhoN, is metabolically essential for bacterial survival in the vitamin B6-depleted tumour microenvironment and thus cannot be simply deleted. While conventional deimmunisation targets surface residues, we present a strategy of allosteric deimmunisation by mutating non-surface, buried residues within the dominant h2 helix T-cell epitope. We hypothesised that the epitope is allosterically coupled to the distal active site gate, allowing for the simultaneous modulation of immunogenicity and enzymatic function. Using a computational pipeline, we designed deimmunised variants and used molecular dynamics (MD) simulations to predict their functional effects. The simulations revealed that mutations in the h2 helix allosterically controlled the gates flexibility. The enzymatic activities of the deimmunized variants were correlated with the MD predictions: two of the five designed variants exhibited 2[~]3-fold increases in catalytic function. This work demonstrates that non-surface epitopes can be rationally engineered to not only ablate immunogenicity but also to allosterically enhance protein function.

biophysics↗

bioRxiv2025

CoBRA: Compound Binding Site Prediction using RNA Language Model

Ribonucleic acid (RNA) molecules perform a variety of functions within cells and thus implicated in various human diseases such as cancer. The fact that the proteins constitute a small portion of mRNAs and the ability of RNA to form highly specific three-dimensional binding pockets for small molecules have generated considerable interest in developing therapeutic agents that target RNAs as an alternative target for small-molecule drugs. Thus, like proteins, precise prediction of small-molecule binding sites across different classes of RNA targets could act as an starting point for drug discovery. In this study, we present a lightweight deep learning framework called Compound Binding Site Prediction for Ribonucleic Acid (CoBRA). This framework can predict ligand-binding nucleotides without requiring any explicit structural information. Our approach uses residue-level embeddings obtained from a pre-trained RNA language model. These embeddings encapsulate the contextual and statistical properties of each nucleotide and are used in a frozen state as the input for a multi-layer perceptron classifier performing binary classification of binding versus non-binding residues. The model was trained using combinations of ten distinct RNA language models and six different loss functions, using the TR60 and HARIBOSS datasets, and tested on four independent benchmark sets (RB9, JL10, TL12, and TE18). The performance of CoBRA demonstrates a relative improvement of 22.1% in Matthew correlation coefficient and an increase in sensitivity of 45.6% compared to existing state-of-the-art RNA- ligand binding site prediction methods with structural information. These results show that sequence-based language model embeddings alone, which do not require any explicit coordinate or distance information, can match or outperform structure-based methods. This makes it a flexible tool for predicting binding sites across diverse RNA targets. CoBRA is available at https://github.com/WonkyeongJang/CoBRA.

bioinformatics↗

bioRxiv2024

Benchmarking Sequence-Based and AlphaFold-Based Methods for pMHC-II Binding Core Prediction: Distinct Strengths and Consensus Approaches

BackgroundInteractions between peptide and MHC class II (pMHC-II) are crucial for T-cell recognition and immune responses, as MHC-II molecules present peptide fragments to T cells, enabling the distinction between self and non-self antigens. Accurately predicting the pMHC-II binding core is particularly important because it provides insights into pMHC-II interactions and T-cell receptor engagement. Given the high polymorphism and peptide-binding promiscuity of MHC-II molecules, computational prediction methods are essential for understanding pMHC-II interactions. While sequence-based methods are widely used, recent advances in AlphaFold-based structure prediction have opened new possibilities for improving pMHC-II binding core predictions. ResultsWe benchmarked four recent pMHC-II prediction methods with a focus on binding core prediction: two sequence-based methods, NetMHCIIpan and DeepMHCII, and two AlphaFold-based structure prediction methods, AlphaFold2 fine-tuned for peptide interactions (AF2-FT) and AlphaFold3 (AF3). The AlphaFold-based methods showed strong performance in predicting positive binders, with AF3 achieving the highest positive recall (0.86) and AF2-FT performing similarly (0.81). However, both methods frequently misclassified unbound peptides as binders. NetMHCIIpan excelled at identifying non-binders, achieving the highest negative recall (0.93), but had lower positive recall (0.44). In contrast, DeepMHCII demonstrated moderate performance without any notable strength. Consensus approaches combining AlphaFold-based methods for binder identification with filtering using NetMHCIIpan improved overall prediction precision (0.94 and 0.87 for known and unknown binding status, respectively). ConclusionsThis study highlights the complementary strengths of AlphaFold-based and sequence-based methods for predicting pMHC-II binding core regions. AlphaFold-based methods excel in predicting positive binders, while NetMHCIIpan is highly effective at identifying non-binders. Future research should focus on improving the prediction of unbound peptides for AlphaFold-based models. Since NetMHCIIpans binding core predictive ability is already high, future efforts should concentrate on enhancing its binding prediction to further improve overall accuracy.

bioinformatics↗

bioRxiv2024

Applying multi-state modeling using AlphaFold2 for kinases and its application for ensemble screening

Structure-based virtual screening (SBVS) is a pivotal computational approach in drug discovery, enabling the identification of potential drug candidates within vast chemical libraries by predicting their interactions with target proteins. The SBVS relies on the receptor protein structures, making it sensitive to structural variations. Kinase, one of the major drug targets, is known as one of the typical examples of an active site conformation change caused by the type of binding inhibitors. Examination of human kinase structures shows that the majority of conformations have the DFGin state. Thus, SBVS using the structures might cause a favor of type of ligand type I inhibitors, bind to the DFGin state, rather than finding the diverse scaffolds. Recent advances in protein structure prediction, such as AlphaFold2 (AF2), offer promising solutions but may still be possibly influenced by the structural bias in existing templates. To address these challenges, we introduce a multi-state modeling (MSM) protocol for kinase structures. We apply MSM to AF2 by providing state-specific templates, allowing us to overcome structural biases and thus apply them to kinase SBVS. We benchmarked our MSM models in three categories: quality of predicted models, reproducibility of ligand binding poses, and identification of hit compounds by ensemble SBVS. The results demonstrate that MSM-generated models exhibit comparable or improved structural accuracy compared to standard AF2 models. We also show that MSM models enhance the accuracy of cognate docking, effectively capturing the interactions between kinases and their ligands. In virtual screening experiments using DUD-E compound libraries, our MSM approach consistently outperforms standard AF2 modeling. Notably, MSM-based ensemble screening excels in identifying diverse hit compounds for kinases with structurally diverse active sites, surpassing standard AF2 models. We highlight the potential of MSM in broadening the scope of kinase inhibitor discovery by facilitating the identification of chemically diverse inhibitors. Author SummaryOne of the main problems with structure-based virtual screening is structural flexibility. Ensemble screening is one of the conventional approaches to solving the issue. Gathering experimental structures or molecular simulations could be used to compile the receptor structures. Recent developments in algorithms for predicting protein structures, like AlphaFold2, suggest that different receptor conformations could be produced. However, the prediction approaches produce biased structures because of the bias in the structure database. In order to solve the problem, we developed a protocol called multi-state modeling for kinases. Rather than supplying multiple sequence alignments as an input, we gave the AlphaFold2 a specific template structure and the sequence alignment between the template and query. Our findings imply that our technique can yield a particular structural state of interest with an enhanced or comparable structural quality to AlphaFold2 and predict highly accurate protein-ligand complex structures. Lastly, compared to the typical AlphaFold2 models, ensemble screening using the multi-state modeling approach improves the structure-based virtual screening performance, particularly for diverse active molecular scaffolds.

bioinformatics↗

bioRxiv2024

PL-PatchSurfer3: Improved Structure-Based Virtual Screening for Structure Variation Using 3D Zernike Descriptors

Structure-based virtual screening (SBVS) is a widely used method in silico drug discovery. It necessitates a receptor structure or binding site to predict the binding pose and fitness of a ligand. Therefore, the performance of the SBVS is affected by the protein conformation. The most frequently used method in SBVS is the protein-ligand docking program, which utilizes atomic distance-based scoring functions. Hence, they are highly prone to sensitivity towards variation in receptor structure, and it is reported that the conformational change significantly drops the performance of the docking program. To address the problem, we have introduced a novel program of SBVS, named PL-PatchSurfer. This program makes use of molecular surface patches and the Zernike descriptor. The surfaces of the pocket and ligand are segmented into several patches by the program. These patches are then mapped with physico-chemical properties such as shape and electrostatic potential before being converted into the Zernike descriptor, which is rotationally invariant. A complementarity between the protein and the ligand is assessed by comparing the descriptors and geometric distribution of the patches in the molecules. A benchmarking study showed that PL-PatchSurfer2 was able to screen active molecules regardless of the receptor structure change with fast speed. However, the program could not achieve high performance for the targets that the hydrogen bonding feature is important such as nuclear hormone receptors. In this paper, we present the newer version of PL-PatchSurfer, PL-PatchSurfer3, which incorporates two new features: a change in the definition of hydrogen bond complementarity and consideration of visibility that contains curvature information of a patch. Our evaluation demonstrates that the new program outperforms its predecessor and other SBVS methods while retaining its characteristic tolerance to receptor structure changes. Interested individuals can access the program at kiharalab.org/plps3.

bioinformatics↗

bioRxiv2022

SARS-CoV-2 Infection of Microglia Elicits Pro-inflammatory Activation and Apoptotic Cell Death

Accumulating evidence suggests that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causes various neurological symptoms in coronavirus disease 2019 (COVID-19) patients. The most dominant immune cells in the brain are microglia. Yet, the relationship between neurological manifestations, neuroinflammation, and host immune response of microglia to SARS-CoV-2 has not been well characterized. Here, we report that SARS-CoV-2 can directly infect human microglia, eliciting M1-like pro-inflammatory responses, followed by cytopathic effects. Specifically, SARS-CoV-2 infected human microglial clone 3 (HMC3), leading to inflammatory activation and cell death. RNA-seq analysis also revealed that ER stress and immune responses were induced in the early and apoptotic processes in the late phase of viral infection. SARS-CoV-2-infected HMC3 showed the M1 phenotype and produced pro-inflammatory cytokines such as interleukin (IL)-1{beta}, IL-6, and tumour necrosis factor (TNF-), but not the anti-inflammatory cytokine IL-10. After this pro-inflammatory activation, SARS-CoV-2 infection promoted both intrinsic and extrinsic death receptor-mediated apoptosis in HMC3. Using K18-hACE2 transgenic mice, murine microglia were also infected by intranasal inoculation of SARS-CoV-2. This infection induced the acute production of pro-inflammatory microglial IL-6 and TNF- and provoked a chronic loss of microglia. Our findings suggest that microglia are potential mediators of SARS-CoV-2-induced neurological problems and, consequently, can be targets of therapeutic strategies against neurological diseases in COVID-19 patients. IMPORTANCERecent studies reported neurological manifestations and complications in COVID-19 patients, which are associated with neuroinflammation. As microglia are the dominant immune cells in brains, it needs to be elucidate the relationship between neuroinflammation and host immune response of microglia to SARS-CoV-2. Here, we suggest that SARS-CoV-2 can directly infect human microglia with cytopathic effect (CPE) using human microglial clone 3 (HMC3). The infected microglia were promoted to pro-inflammatory activation following apoptotic cell death. This pro-inflammatory activation was accompanied by the high production of pro-inflammatory cytokines, and led to neurotoxic-M1 phenotype polarization. In vivo, murine microglia were infected and produced pro-inflammatory cytokines and provoked a chronic loss using K18-hACE2 mice. Thus, our data present that SARS-CoV-2-infected microglia are potential mediators of neurological problems in COVID-19 patients. In addition, HMC3 cells are susceptible to SARS-CoV-2 and exhibit the CPE, which can be further used to investigate cellular and molecular mechanisms of neuroinflammation reported in COVID-19 patients.

microbiology↗

bioRxiv2021

A genetic shift in an escaped transmitted/founder virus guides combinatorial vaccine design against HIV-1

A productive HIV-1 infection in humans is often established by transmission and propagation of a single transmitted/founder (T/F) virus, which then evolves into a complex mixture of variants during the lifetime of infection. An effective HIV-1 vaccine should elicit broad immune responses in order to block the entry of diverse T/F viruses. Currently, no such vaccine exists. An in-depth study of escape variants emerging under host immune pressure during very early stages of infection might provide insights into such a HIV-1 vaccine design. Here, in a rare longitudinal study involving HIV-1 infected individuals just days after infection in the absence of antiretroviral therapy, we discovered a remarkable genetic shift that resulted in near complete disappearance of the original T/F virus and appearance of a variant with H173Y mutation in the variable V2 domain of the HIV-1 envelope protein. This coincided with the disappearance of the first wave of strictly H173-specific antibodies and emergence of a second wave of Y173-specific antibodies with increased breadth. Structural analyses indicated conformational dynamism of the envelope protein which likely allowed selection of escape variants with a conformational switch in the V2 domain from an -helix (H173) to a {beta}-strand (Y173) and induction of broadly reactive antibody responses. This differential breadth due to a single mutational change was also recapitulated in a mouse model. Rationally designed combinatorial libraries containing 54 conformational variants of V2 domain around position 173 further demonstrated increased breadth of antibody responses elicited to diverse HIV-1 envelope proteins. These results offer new insights into designing broadly effective HIV-1 vaccines.

microbiology↗

bioRxiv2018

PP-2, a src-kinase inhibitor, is a potential corrector for F508del-CFTR in cystic fibrosis

Cystic fibrosis (CF) is an autosomal recessive disorder caused by mutations in the CF transmembrane conductance regulator (CFTR) gene. The most common mutation in CF, an in-frame deletion of phenylalanine 508, leads to a trafficking defect and endoplasmic reticulum retention of the protein where it becomes targeted for degradation. Successful clinical deployments of ivacaftor and ivacaftor/lumacaftor combination have been an exciting translational development in treating CF. However, their therapeutic effects are variable between subjects and remain insufficient. We used the Library of Integrated Network-based Cellular Signatures (LINCS) database as our chemical pool to screen for candidates. For in silico screening, we integrated connectivity mapping and CF systems biology to identify candidate therapeutic compounds for CF. Following in silico screening, we validated our candidate compounds with (i) an enteroid-based compound screening assay using CF ({Delta}F508/{Delta}F508-CFTR) patient-derived enteroids, (ii) short-circuit current analysis using polarized CF primary human airway epithelial cells and (iii) Western blots to measure F508-del-CFTR protein maturation. We identified 184 candidate compounds with in silico screening and tested 24 of them with enteroid-based forskolin-induced swelling (FIS) assay. The top hit compound was PP2, a known src-kinase inhibitor that induced swelling in enteroid comparable to known CF corrector (lumacaftor). Further validation with Western blot and short-circuit current analysis showed that PP-2 could correct mutant CFTR mis-folding and restore CFTR-mediated transmembrane current. We have identified PP2, a known src-kinase inhibitor, as a novel corrector of {Delta}F508-CFTR. Based on our studies and previous reports, src kinase inhibition may represent a novel paradigm of multi-action therapeutics - corrector, anti-inflammatory, and anti-infective - in CF.

systems biology↗