Structural Insight Into Jasmonic Acid Signalling Repression by Insect HARP1 Effector Yaguang Zhang, Baoyu He, Tingting Ran, Bo Ouyang, Shaobo Cui, et al. Plant Cell and Environment, 2026 Through long-term natural selection, a co-evolutionary relationship has formed between plants and pests. However, pathogens and pests can also undermine plant resistance by releasing certain substances such as effectors. Helicoverpa armigera R-like protein 1 (HARP1), an effector in oral secretions, is capable of interacting with JASMONATE-ZIM DOMAIN (JAZ) protein. This interaction inhibits the degradation of JAZ and prevents the activation of jasmonic acid (JA) signalling in response to biotic stress. Nevertheless, the mechanism by which HARP1 interacts with JAZ to suppress JA signalling remains elusive. In this study, we first confirm that the ZIM domain within JAZ is sufficient for the HARP1-JAZ interaction. To gain mechanistic insight, we determined the crystal structure of HARP1 and utilised AlphaFold2 to predict its binding mode with JAZ3. The structure analysis reveals that HARP1 is a β-sandwich fold composed of seven strands, which directly binds to JAZ homo- or hetero-dimers. This binding prevents the degradation of the JAZ repressor, consequently ensuring the repressed JA signalling pathway in the plant. Our structural and functional studies provide new insights into the JA signalling transcriptional repression mechanism by effectors released by pests that suppress JA signalling.
Unveiling the Activation Mechanism of Glucagon-Like Peptide-1 Receptor by an Ago-Allosteric Modulator via Molecular Dynamics Simulations Yue Chen, Junhao Li, Lucie Delemotte, Yuguang Mu Journal of Chemical Information and Modeling, 2026 The glucagon-like peptide-1 receptor (GLP-1R) is a key therapeutic target for metabolic disorders, particularly type 2 diabetes and obesity. Although current treatments are effective, their unavoidable side effects continue to drive the search for novel therapeutic strategies. Ago-allosteric modulators (ago-PAMs), which act as agonists on their own while enhancing the affinity and efficacy of orthosteric agonists, represent a promising avenue to overcome limitations associated with traditional peptide-based therapies. However, the molecular mechanisms by which ago-PAMs modulate GLP-1R activation remain poorly understood. In this work, we selected compound 2, a validated ago-PAM of GLP-1R, as a probe to explore these mechanisms at the atomic level. Using molecular dynamics (MD) simulations, we elucidate how compound 2 stabilizes the active conformation of GLP-1R through allosteric binding and reveal distinct pathways by which it enhances the binding of both peptide and non-peptide orthosteric agonists. Enhanced sampling simulations further provided a comprehensive conformational landscape of GLP-1R activation, identifying two intermediate states that bridge inactive and active conformations. Compound 2 was found to bias the receptor toward active-like ensembles, consistent with its intrinsic agonist activity. Together, our findings provide mechanistic insights into ago-allosteric modulation of GLP-1R, offering useful information for the rational design of small-molecule modulators with improved therapeutic profiles.
zERExtractor: An Automated Platform for Enzyme-Catalyzed Reaction Data Extraction from Scientific Literature Rui Zhou, Haohui Ma, Tianle Xin, Qiuchen Miao, Lixin Zou, Qiuyue Hu, Hongxi Cheng, Jingjing Guo, Yuguang Mu, Sheng Wang, Guoqing Zhang, Yanjie Wei, Liangzhen Zheng Journal of Chemical Information and Modeling, 2026 The rapid expansion of enzyme reaction literature has created a major bottleneck in database curation, leaving vast amounts of enzyme–substrate–condition relationships unstructured and inaccessible for DL-driven modeling. How to fully utilize the enzymatic reaction data has been an important task for future accurate enzyme activity prediction models. Current deep learning (DL)-based data extraction models heavily rely on large language models (LLMs) without a fidelity check and the ability to continuously evolve. To address these issues, we developed zERExtractor (Zelixir’s Enzyme Reaction Data Extractor), an accuracy-oriented and extensible platform for extracting enzyme-catalyzed reaction data from scientific publications. This system offers a unified multimodal information extraction framework (covering molecular reaction diagrams, tables, and texts) to integrate enzymatic reaction descriptors into structured storage. We employ fine-tuned large LLMs together with DL in a human-in-the-loop pipeline that evolves through data fidelity validation by experts and active learning. Also, zERExtractor achieves 89.9% accuracy in table recognition and over 98% accuracy in molecular image recognition on synthetic data sets, outperforming the strongest baseline by more than 2% and consistently maintaining above 95% on realistic benchmarks. zERExtractor bridges the data gap in enzyme reaction data with a scalable framework for accurate multimodal extraction, advancing DL-driven enzyme modeling and enabling future applications in computational enzymology and biotechnology. The platform is publicly accessible online at https://zpaper.zelixir.com/.
Rational screening and mechanistic elucidation of surfactants for mitigating phenolic inhibition in lignocellulose enzymatic hydrolysis: Combining experimental and computational approaches Xiaoxiao Jiang, Zhanyu Wang, Yujie Wang, Lai Heng Tan, Xu Yang, Shuyi Jin, Yuguang Mu, Rui Zhai, Tao Wei, Mingjie Jin Journal of Bioresources and Bioproducts, 2026 Lignin-derived phenolic compounds pose a critical bottleneck in the sustainable enzymatic hydrolysis of lignocellulose by causing severe enzyme inhibition. While surfactants can significantly alleviate this inhibition, their structure-function relationships and underlying molecular mechanisms remain unclear. In this study, the mitigating effects of various surfactants were quantitatively characterized, revealing that hydrophobicity, hydrogen bonding ability, and electrophilicity are the key structural descriptors for their efficacy. Experimental analysis confirmed that selected surfactants significantly mitigated phenolic-induced enzyme deactivation and precipitation. Circular dichroism spectroscopy further revealed that surfactants effectively restored the secondary structure (specifically α-helix content) and stabilized the enzyme conformation against phenolic denaturation. Molecular docking simulations demonstrated that surfactants preferentially bind within the catalytic tunnel of cellulase with stronger affinities (from –20.08 to –29.71 kJ/mol) compared to phenolics, driven by hydrogen bond anchoring reinforced by extensive hydrophobic and π-π stacking interactions with key tunnel residues. Collectively, these findings support a competitive stabilization mechanism, providing new insights into the surfactant-mediated protection of cellulase, facilitating more efficient lignocellulose enzymatic hydrolysis.
Proton-Activated Artificial Channels for pH-Selective Cancer Therapy Daoxin Luo, Chunyan Jia, Yuchao Lin, Jin Zhou, Congrui Ren, Xiaopan Xie, Tong Chen, Zhiping Zeng, Weifeng Li, Yuguang Mu, Changliang Ren Angewandte Chemie International Edition, 2026 Proton‐activated ion channels mediate ion transport in response to extracellular acidification, enabling cellular adaptation to acidic microenvironments. Despite their biological importance, mimicking proton‐activated functionality in artificial ion channels remains a significant challenge. Here, we present a novel class of proton‐activated artificial ion channels built from self‐assembled peptide chains integrated into a pH‐responsive 2,2′‐bipyridine scaffold. Protonation induces a conformational switch in the channel‐forming units, promoting one‐dimensional self‐assembly and subsequent hydrophobic packing into functional channels capable of transporting small molecules. As extracellular pH decreases from 7.4 to 6.5, C‐FF exhibits a 10.3‐fold enhancement in cytotoxicity against human colorectal carcinoma cells, boosting an IC 50 of 2.8 µM, mediated through apoptosis induction and cell cycle arrest resulting from disruption of the autophagic process. Significantly, C‐FF demonstrates exceptional selectivity for cancer cells, achieving a selectivity index of 8.5, surpassing that of doxorubicin by one order of magnitude while maintaining comparable potency, highlighting its potential as a pH‐responsive platform for selective anticancer therapy in acidic tumor microenvironments.
Could statistical potential models achieve comparable or better performance than deep learning models? Zhihao Wang, Sheng Wang, Jingjing Guo, Yuguang Mu, Xiangdong Liu, Liangzhen Zheng, Weifeng Li Briefings in Bioinformatics, 2026 Accurately predicting protein–ligand interactions is vital for structure-based drug discovery. Although deep learning (DL) models have shown strong performance, the potential of traditional statistical potentials under data-limited conditions remains underexplored. Here, we systematically assess several statistical potential models in docking and virtual screening. We find that docking benefits from distance-dependent pairwise atom–atom potentials with clear physical meanings, while screening relies more on orientation-dependent atom–residue potentials that capture local chemical environments. Based on these findings, we propose HybridSP, a hybrid potential combining distance-dependent atom–atom, atom–residue, and orientation-dependent atom–residue terms. An affinity-weighted scheme is applied to correct biases in statistical distributions. On the CASF-2016 benchmark, HybridSP achieves a 91.6% docking success rate and an enrichment factor of 29.35 at the top 1%, rivaling and even surpassing state-of-the-art DL models. Its strong screening ability is further validated on directory of useful decoys-enhanced and directory of useful decoys-adjusted. These results demonstrate that well-designed statistical potentials can achieve high performance and interpretability without complex DL architectures, offering an efficient alternative for scoring function design. The models are available at: https://github.com/zelixirSH/HybridSP.git.
Discussions on the generalization of HybridSP on more equivalent benchmarks Zhihao Wang, Sheng Wang, Jingjing Guo, Yuguang Mu, Xiangdong Liu, Liangzhen Zheng, Weifeng Li Briefings in Bioinformatics, 2026 We thank the reviewers for their thoughtful comments and we have carefully addressed the concerns raised. In our response, we clarify key methodological aspects and provide additional analyses to improve the transparency and rigor of our work. Importantly, we further evaluate the model on more diverse and fair benchmarks beyond the original setting, demonstrating its robustness and generalization ability. These additions strengthen the overall conclusions and provide a more comprehensive assessment of HybridSP.
Revealing the intricate mechanism governing the pH-dependent activity of a quintessential representative of flavoproteins, glucose oxidase Tao Tu, Yunju Zhang, Yaru Yan, Lanxue Li, Xiaoqing Liu, Nina Hakulinen, Wei Zhang, Yuguang Mu, Huiying Luo, Bin Yao, Weifeng Li, Huoqing Huang Fundamental Research, 2026 Glucose oxidase (Gox), a prototypical flavoprotein, exhibits diverse industrial applications in glucose sensing and gluconic acid production. Its enzymatic activity is pH-dependent, with maximum activity observed at approximately neutral pH but less than 5% of peak activity at pH ≤ 3.0. However, the underlying mechanism governing these pH-dependent changes in activity remains elusive. Therefore, our objective was to investigate conformational alterations in Gox across different pH levels for engineering purposes. Our mutagenesis results suggest that protein degradation does not primarily contribute to the enzyme's pH-dependent activity. Fluorescence spectroscopy findings reveal subtle influences of pH on Gox's conformation while maintaining a similar overall microenvironment. Furthermore, the crystal structure and molecular dynamics simulations reveal that alterations in pH have a significant impact on the conformation of His514, a crucial catalytic residue for Gox function. These changes also result in structural variations within the substrate-binding pocket for both flavin adenine dinucleotide (cofactor) and β-d-glucose (substrate) between pH 6.0 and 2.5. Consequently, under acidic conditions (pH 2.5), β-d-glucose exhibits unstable binding within this pocket, leading to rapid dissociation from the active site. In summary, our findings underscore the intimate relationship between the conformational dynamics of His514 and the pH-dependent reaction mechanism, offering valuable insights for engineering acid-active Gox variants.
First Report of Leaf Spot Caused by Nigrospora coryli on Tobacco in China Jingpei Zou, Liuti Cai, Yiming Geng, Mengyu Yin, Yuguang Mu, Jingjing Guo, Hancheng Wang, Feng Zhang Plant Disease, 2026 As a globally cultivated economic crop, tobacco (Nicotiana tabacum) is known for its addictive properties, which arise from the mildly irritating and psychoactive compounds it contains (Hu et al. 2010). Tobacco leaves are susceptible to a range of fungal and bacterial diseases during production and curing, including target spots, brown spots, wildfire, and powdery mildew (Guo et al. 2024). During a survey conducted in June 2025 in Zhengan (107.43° N, 28.55° E), Guizhou Province, China, tobacco (cv. Yunyan 87) plants were found affected by a leaf spot disease, with an incidence rate ranging from 41% to 47%. Initially, symptomatic leaves developed irregular, yellowish-brown spots that gradually expanded and turned necrotic, eventually acquiring a whitish appearance. To investigate the disease, six severely symptomatic plants were selected for pathogen isolation using the tissue transplanting method. From each plant, pieces (5 × 5 mm) of leaf tissue taken from the border between diseased and healthy tissue were surface-sterilized with 75% ethanol for 30 s, followed by 1% sodium hypochlorite for 1 min, and then rinsed three times with sterile distilled water before being placed on potato dextrose agar (PDA) medium. After incubating at 25°C in the dark for 7 days, a total of nine fungal isolates with similar morphology were obtained. One representative isolate, designated YB13, was selected for further identification (Fig. S1). The fungal colonies on PDA exhibited abundant aerial mycelia and were white in color, and covered the whole plates (90 mm in diameter) in seven days. After 10 days of incubation at 28°C, the fungus produced black, ovoid, smooth, and aseptate conidia with 12-15 μm in diameter. For molecular identification, genomic DNA was extracted from isolate YB13. The internal transcribed spacer (ITS) region, along with the glyceraldehyde 3-phosphate dehydrogenase (GAPDH), beta-tubulin (TUB2), and translation elongation factor 1-alpha (TEF1-α) genes were amplified using primers ITS1/ITS4 (White 1990), gpd1/gpd2 (Berbee et al. 1999), BT2Fd/BT4Rd (Li et al. 2017), and EF1-728F/EF1-986R (Carbone and Kohn 2019) respectively. The resulting sequences have been deposited in GenBank under the following accession numbers: ITS: PX736263; GAPDH: PX556631; TUB2: PX556632; and TEF1-α: PX711191. BLAST analysis of the sequences from isolate YB13 revealed high identity with those of Nigrospora coryli isolate W18. Specifically, the ITS sequence shared 99.14% identity with isolate W18 (GenBank: PP218065), the TUB2 sequence shared 99.71% identity with isolate W18 (GenBank: PP320372), and the TEF1-α sequence shared 100.00% identity with isolate W18 (GenBank: PP461302). A multilocus phylogenetic analysis based on a concatenated dataset of ITS, TEF1-α, and TUB2 genes further confirmed that isolate YB13 clusters within the N. coryli clade (Fig. S2). Pathogenicity of the isolate YB13 was confirmed on five healthy tobacco plants (cv. Yunyan 87) at seedling stage (four to five leaves). To wound the leaves, a 4 mm² area on each was lightly scratched with a sterile needle, after which a 5-mm diameter mycelial plug was placed on the wound. Control leaves were inoculated with PDA-only plugs. Following inoculation, leaves were maintained under high humidity by enclosing the treated plants in transparent plastic bags containing sterile water-soaked cotton at the base to maintain approximately 80% relative humidity. Plants were incubated in a greenhouse at 25°C. All experiments were performed in triplicate. The leaf disease development was observed and recorded daily. After 7 days, all inoculated leaves developed leaf spots consistent with symptoms observed in the field. Lesions appeared as irregular to circular spots, 5–12 mm in diameter, with a yellowish-brown color and often a chlorotic halo. As symptoms progressed, the lesions turned necrotic, developing dry, whitish centers surrounded by a darker margin and a yellow halo. In contrast, control plants remained completely asymptomatic. The pathogen was re-isolated from lesion margins and confirmed to be identical to the original inoculated strain based on colony morphology and DNA sequencing, thereby fulfilling Koch’s postulates. N. coryli has previously been reported as an endophyte within the stem of Corylus heterophylla at Mycorrhizal Seedling Cultivation Center in Guizhou, China (Wang et al. 2024). To our best of knowledge, this is the first report of N. coryli causing leaf spot on tobacco in China. These findings underscore the importance of continued pathogen surveillance and provide a basis for epidemiological studies and the development of management strategies for this emerging disease.
Foundational Models for Personalised Mental Health Geoffrey Chern-Yee Tan, Beverly Zhiyu Wang, Hong Ming Tan, Laurence S. Pe, Alvin Keen Peng Yuen, Yuguang Mu, Bernett Lee, Sharon Lu Huixian, Ethel Siew Ee Tan, Kah Vui Fong, Jussi Keppo, Han Leong Goh, Peilun Dai Adaptation Learning and Optimization, 2026
Carbene formation as a mechanism for efficient intracellular uptake of cationic antimicrobial carbon acid polymers Chong Hui Koh, Mallikharjuna Rao Lambu, Chongyun Tan, Guangmin Wei, Zhi Yuan Kok, Kaixi Zhang, Quang Huy Nhat Vu, Muthuvel Panneerselvam, Ying Jie Ooi, Shiow Han Tan, Zheng Wang, Madhu Babu Tatina, Justin Tze Yang Ng, Aoxin Guo, Panyawut Tonanon, Tram T. Dang, Yunn-Hwen Gan, Yuguang Mu, Paula T. Hammond, Yonggui Robin Chi, Richard D. Webster, Sumod A. Pullarkat, Qingjie Li, E. Peter Greenberg, Angelika Gründling, Kevin Pethe, Mary B. Chan-Park Nature Communications, 2025
Release of frustration drives corneal amyloid disaggregation by brain chaperone Jia Yi Kimberly Low, Xiangyan Shi, Venkatraman Anandalakshmi, Dawn Neo, Gary Swee Lim Peh, Siew Kwan Koh, Lei Zhou, M. K. Abdul Rahim, Ketti Boo, JiaXuan Lee, Harini Mohanram, Reema Alag, Yuguang Mu, Jodhbir S. Mehta, Konstantin Pervushin Communications Biology, 2023
Genomic, transcriptomic, and metabolomic analysis of Oldenlandia corymbosa reveals the biosynthesis and mode of action of anti-cancer metabolites Irene Julca, Daniela Mutwil‐Anderwald, Vaishnervi Manoj, Zahra Khan, Soak Kuan Lai, Lay K. Yang, Ing T. Beh, Jerzy Dziekan, Yoon P. Lim, Shen K. Lim, Yee W. Low, Yuen I. Lam, Seth Tjia, Yuguang Mu, Qiao W. Tan, Przemyslaw Nuc, Le M. Choo, Gillian Khew, Loo Shining, Antony Kam, James P. Tam, Zbynek Bozdech, Maximilian Schmidt, Bjoern Usadel, Yoganathan Kanagasundaram, Saleh Alseekh, Alisdair Fernie, Hoi Y. Li, Marek Mutwil Journal of Integrative Plant Biology, 2023
Bladder cancer therapy using a conformationally fluid tumoricidal peptide complex Antonín Brisuda, James C. S. Ho, Pancham S. Kandiyal, Justin T-Y. Ng, Ines Ambite, Daniel S. C. Butler, Jaromir Háček, Murphy Lam Yim Wan, Thi Hien Tran, Aftab Nadeem, Tuan Hiep Tran, Anna Hastings, Petter Storm, Daniel L. Fortunati, Parisa Esmaeili, Hana Novotna, Jakub Horňák, Y. G. Mu, K. H. Mok, Marek Babjuk, Catharina Svanborg Nature Communications, 2021
Enantiomeric glycosylated cationic block co-beta-peptides eradicate Staphylococcus aureus biofilms and antibiotic-tolerant persisters Kaixi Zhang, Yu Du, Zhangyong Si, Yang Liu, Michelle E. Turvey, Cheerlavancha Raju, Damien Keogh, Lin Ruan, Subramanion L. Jothy, Sheethal Reghu, Kalisvar Marimuthu, Partha Pratim De, Oon Tek Ng, José R. Mediavilla, Barry N. Kreiswirth, Yonggui Robin Chi, Jinghua Ren, Kam C. Tam, Xue-Wei Liu, Hongwei Duan, Yabin Zhu, Yuguang Mu, Paula T. Hammond, Guillermo C. Bazan, Kevin Pethe, Mary B. Chan-Park Nature Communications, 2019
Molecular Dynamics and Simulation Liangzhen Zheng, Amr A. Alhossary, Chee-Keong Kwoh, Yuguang Mu Encyclopedia of Bioinformatics and Computational Biology Abc of Bioinformatics, 2019
Molecular dynamics and simulation Liangzhen Zheng, Amr A. Alhossary, Chee-Keong Kwoh, Yuguang Mu Encyclopedia of Bioinformatics and Computational Biology Abc of Bioinformatics, 2018
Amyloid β Protein and Alzheimer's Disease: When Computer Simulations Complement Experimental Studies Jessica Nasica-Labouze, Phuong H. Nguyen, Fabio Sterpone, Olivia Berthoumieu, Nicolae-Viorel Buchete, Sébastien Coté, Alfonso De Simone, Andrew J. Doig, Peter Faller, Angel Garcia, Alessandro Laio, Mai Suan Li, Simone Melchionna, Normand Mousseau, Yuguang Mu, Anant Paravastu, Samuela Pasquali, David J. Rosenman, Birgit Strodel, Bogdan Tarus, John H. Viles, Tong Zhang, Chunyu Wang, Philippe Derreumaux Chemical Reviews, 2015
Low-energy interaction and adsorption of C60 on diamond surfaces Yuchen Ma, Yueyuan Xia, Yuguang Mu, Suyan Li, Huadong Zhang, Mingwen Zhao, Ruijin Wang Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atoms, 2000
Energy spectra of He+ ions transmitting from thick biological targets Wuli Xuebao Acta Physica Sinica, 1999
Self-assembly growth of single-wall carbon nanotubes Yueyuan Xia, Yuguang Mu, Yuchen Ma, Suyan Li, Huadong Zhang, Chunyu Tan, Liangmo Mei Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atoms, 1999
Integrated biological and chemical strategies for sustainable management of Nigrospora coryli-induced tobacco leaf spot J Zou, L Cai, Y Geng, M Yin, Y Mu, J Guo, H Wang, F Zhang 2026
Enhancing bioactivity prediction via spatial emptiness representation of protein-ligand complex and union of multiple pockets Z Zhou, Y Yin, Y Yang, Y Mu, HY Li, AWK Kong Advances in Neural Information Processing Systems 38, 106162-106181 , 2026 2026 Citations: 1
First Report of Leaf Spot Caused by Nigrospora coryli on Tobacco in China J Zou, L Cai, Y Geng, M Yin, Y Mu, J Guo, H Wang, F Zhang Plant Disease 110 (4), 1480 , 2026 2026
Rational Screening and Mechanistic Elucidation of Surfactants for Mitigating Phenolic Inhibition in Lignocellulose Enzymatic Hydrolysis: Combining Experimental and … X Jiang, Z Wang, Y Wang, TL Heng, X Yang, S Jin, Y Mu, R Zhai, T Wei, ... Journal of Bioresources and Bioproducts, 100250 , 2026 2026
First Report of Leaf Spot Caused by Xylaria sp. ZS-2021c on Tobacco in China JP Zou, L Cai, Y Geng, M Yin, Y Mu, J Guo, H Wang, F Zhang Plant Disease , 2026 2026
Unveiling the Activation Mechanism of Glucagon-Like Peptide-1 Receptor by an Ago-Allosteric Modulator via Molecular Dynamics Simulations Y Chen, J Li, L Delemotte, Y Mu Journal of Chemical Information and Modeling 66 (7), 4161-4173 , 2026 2026
zerextractor: An automated platform for enzyme-catalyzed reaction data extraction from scientific literature R Zhou, H Ma, T Xin, Q Miao, L Zou, Q Hu, H Cheng, J Guo, Y Mu, ... Journal of Chemical Information and Modeling 66 (7), 4296-4309 , 2026 2026 Citations: 1
Discussions on the generalization of HybridSP on more equivalent benchmarks Z Wang, S Wang, J Guo, Y Mu, X Liu, L Zheng, W Li Briefings in Bioinformatics 27 (2), bbag194 , 2026 2026
Could statistical potential models achieve comparable or better performance than deep learning models? Z Wang, S Wang, J Guo, Y Mu, X Liu, L Zheng, W Li Briefings in Bioinformatics 27 (2), bbag088 , 2026 2026 Citations: 2
CrossAffinity: A Sequence-Based Protein-Protein Binding Affinity Prediction Tool Using Cross-Attention Mechanism JS Guan, Z Wang, Y Mu bioRxiv, 2026.02. 22.707318 , 2026 2026
Metadiffusion: inference-time meta-energy biasing of biomolecular diffusion models HYI Lam, S Pujalte Ojeda, M Brezinova, J Hanke, XE Ong, Y Mu, ... bioRxiv, 2026.02. 10.704873 , 2026 2026 Citations: 2
Surfactants as process intensifiers in lignocellulosic sugar-platform biorefineries: Mechanistic insights and bioprocess implications X Jiang, Y Wang, Z Wang, X Yang, Y Mu, R Zhai, T Wei, M Jin Biotechnology Advances, 108837 , 2026 2026 Citations: 2
Proton‐Activated Artificial Channels for pH‐Selective Cancer Therapy D Luo, C Jia, Y Lin, J Zhou, C Ren, X Xie, T Chen, Z Zeng, W Li, Y Mu, ... Angewandte Chemie, e25440 , 2026 2026 Citations: 1
Foundational Models for Personalised Mental Health GCY Tan, BZ Wang, HM Tan, LS Pe, AKP Yuen, Y Mu, B Lee, SL Huixian, ... Advances in Artificial Intelligence: Efficiency, Reliability, and … , 2025 2025
Binding domains of α-synuclein receptors with monomeric/oligomeric α-synuclein: Implications for Parkinson’s disease R Elvira, JS Guan, Y Mu, EK Tan, ZD Zhou Biomedicine & Pharmacotherapy 193, 118802 , 2025 2025 Citations: 1
Design of Carbon Nanotube Inhibitors for Main Proteinase of SARS-CoV-2: A Combined Deep Learning and Molecular Dynamics Simulation Study Y Zhang, Z Wang, Y Yang, Y Qu, YQ Li, Q Zhang, M Zhao, Y Mu, W Li The Journal of Physical Chemistry B 129 (46), 11939-11948 , 2025 2025
AutoRevDock: An open‐source toolkit for scalable reverse docking Q Luo, Y Mu, L Zheng, J Guo Protein Science 34 (11), e70358 , 2025 2025
An efficient deep learning-based strategy to screen inhibitors for GluN1/GluN3A receptor Z Wang, Y Zeng, J Sun, X Chen, H Wu, Y Li, Y Mu, L Zheng, Z Gao, W Li Acta Pharmacologica Sinica 46 (11), 3099-3107 , 2025 2025 Citations: 2
Harnessing Cell Membrane-Derived Nanovesicles for Enhanced Nanoprobes in Multimodal Imaging: Progress and Perspectives L Lei, M Du, J Zhang, Y Mu BIO Integration 6 (1), 972 , 2025 2025
Structures, Interactions, and Antimicrobial Activity of the Shortest Thanatin Peptide from Anasa tristis SJ Abdullah, JS Guan, Y Mu, S Bhattacharjya International Journal of Molecular Sciences 26 (19), 9571 , 2025 2025 Citations: 1
MOST CITED SCHOLAR PUBLICATIONS
A polycationic antimicrobial and biocompatible hydrogel with microbe membrane suctioning ability P Li, YF Poon, W Li, HY Zhu, SH Yeap, Y Cao, X Qi, C Zhou, M Lamrani, ... Nature materials 10 (2), 149-156 , 2011 2011 Citations: 929
Amyloid β protein and Alzheimer’s disease: When computer simulations complement experimental studies J Nasica-Labouze, PH Nguyen, F Sterpone, O Berthoumieu, NV Buchete, ... Chemical reviews 115 (9), 3518-3563 , 2015 2015 Citations: 685
Structure of the Human Telomere in K + Solution: A Stable Basket-Type G-Quadruplex with Only Two G-Tetrad Layers KW Lim, S Amrane, S Bouaziz, W Xu, Y Mu, DJ Patel, KN Luu, AT Phan Journal of the American Chemical Society 131 (12), 4301-4309 , 2009 2009 Citations: 564
Energy landscape of a small peptide revealed by dihedral angle principal component analysis Y Mu, PH Nguyen, G Stock Proteins: Structure, Function, and Bioinformatics 58 (1), 45-52 , 2005 2005 Citations: 521
Fast, accurate, and reliable molecular docking with QuickVina 2 A Alhossary, SD Handoko, Y Mu, CK Kwoh Bioinformatics 31 (13), 2214-2216 , 2015 2015 Citations: 501
Protein-ligand blind docking using QuickVina-W with inter-process spatio-temporal integration NM Hassan, AA Alhossary, Y Mu, CK Kwoh Scientific reports 7 (1), 15451 , 2017 2017 Citations: 424
Onionnet: a multiple-layer intermolecular-contact-based convolutional neural network for protein–ligand binding affinity prediction L Zheng, J Fan, Y Mu ACS omega 4 (14), 15956-15965 , 2019 2019 Citations: 399
Hydrogen-bond lifetime measured by time-resolved 2D-IR spectroscopy: N-methylacetamide in methanol S Woutersen, Y Mu, G Stock, P Hamm Chemical Physics 266 (2-3), 137-147 , 2001 2001 Citations: 361
Peptide conformational heterogeneity revealed from nonlinear vibrational spectroscopy and molecular-dynamics simulations S Woutersen, R Pfister, P Hamm, Y Mu, DS Kosov, G Stock The Journal of chemical physics 117 (14), 6833-6840 , 2002 2002 Citations: 280
Molecular Mechanism of the Inhibition of EGCG on the Alzheimer Aβ 1–42 Dimer T Zhang, J Zhang, P Derreumaux, Y Mu The journal of physical chemistry B 117 (15), 3993-4002 , 2013 2013 Citations: 262
A PELDOR-based nanometer distance ruler for oligonucleotides O Schiemann, N Piton, Y Mu, G Stock, JW Engels, TF Prisner Journal of the American Chemical Society 126 (18), 5722-5729 , 2004 2004 Citations: 257
Conformational dynamics of trialanine in water. 2. Comparison of AMBER, CHARMM, GROMOS, and OPLS force fields to NMR and infrared experiments Y Mu, DS Kosov, G Stock The Journal of Physical Chemistry B 107 (21), 5064-5073 , 2003 2003 Citations: 257
Subpicosecond conformational dynamics of small peptides probed by two-dimensional vibrational spectroscopy S Woutersen, Y Mu, G Stock, P Hamm Proceedings of the National Academy of Sciences 98 (20), 11254-11258 , 2001 2001 Citations: 256
The Toxicity of Amyloid β Oligomers LN Zhao, HW Long, Y Mu, LY Chew International journal of molecular sciences 13 (6), 7303-7327 , 2012 2012 Citations: 234
The role of pro-inflammatory S100A9 in Alzheimer’s disease amyloid-neuroinflammatory cascade C Wang, AG Klechikov, AL Gharibyan, SKTS Wärmländer, J Jarvet, ... Acta neuropathologica 127 (4), 507-522 , 2014 2014 Citations: 200
Base-specific spin-labeling of RNA for structure determination N Piton, Y Mu, G Stock, TF Prisner, O Schiemann, JW Engels Nucleic acids research 35 (9), 3128-3143 , 2007 2007 Citations: 177
Improving protein–ligand docking and screening accuracies by incorporating a scoring function correction term L Zheng, J Meng, K Jiang, H Lan, Z Wang, M Lin, W Li, H Guo, Y Wei, ... Briefings in Bioinformatics 23 (3), bbac051 , 2022 2022 Citations: 167
Enantiomeric glycosylated cationic block co-beta-peptides eradicate Staphylococcus aureus biofilms and antibiotic-tolerant persisters K Zhang, Y Du, Z Si, Y Liu, ME Turvey, C Raju, D Keogh, L Ruan, ... Nature communications 10 (1), 4792 , 2019 2019 Citations: 147
OnionNet-2: a convolutional neural network model for predicting protein-ligand binding affinity based on residue-atom contacting shells Z Wang, L Zheng, Y Liu, Y Qu, YQ Li, M Zhao, Y Mu, W Li Frontiers in chemistry 9, 753002 , 2021 2021 Citations: 145
The molecular basis of distinct aggregation pathways of islet amyloid polypeptide L Wei, P Jiang, W Xu, H Li, H Zhang, L Yan, MB Chan-Park, XW Liu, ... Journal of Biological Chemistry 286 (8), 6291-6300 , 2011 2011 Citations: 141
