A5035223959- Protein Structure and Dynamics
- Computational Drug Discovery Methods
- RNA and protein synthesis mechanisms
- Aeroelasticity and Vibration Control
- Enzyme Structure and Function
- Chemical Synthesis and Analysis
- Structural Analysis and Optimization
- DNA and Nucleic Acid Chemistry
- Composite Structure Analysis and Optimization
- Industrial Technology and Control Systems
- Estrogen and related hormone effects
- Viral Infectious Diseases and Gene Expression in Insects
- Microbial Natural Products and Biosynthesis
- Machine Learning in Materials Science
- Genomics and Phylogenetic Studies
- Prostate Cancer Treatment and Research
- Bioinformatics and Genomic Networks
- Signaling Pathways in Disease
- Vibration Control and Rheological Fluids
- Epigenetics and DNA Methylation
- Click Chemistry and Applications
- Machine Learning in Bioinformatics
- CRISPR and Genetic Engineering
- vaccines and immunoinformatics approaches
- Structural Health Monitoring Techniques
Zhejiang Lab
2020-2025
Zhejiang Pharmaceutical College
2024
Zhejiang University
2018-2023
Huazhong University of Science and Technology
2013-2020
Pharmaceutical Biotechnology (Czechia)
2019
Hangzhou Academy of Agricultural Sciences
2019
Hangzhou Xixi hospital
2019
Hebei University of Engineering
2008-2009
Abstract Protein–protein interactions (PPIs) play an important role in the different functions of cells, but accurate prediction three-dimensional structures for PPIs is still a notoriously difficult task. In this study, HawkDock, free and open accessed web server, was developed to predict analyze PPIs. HawkDock ATTRACT docking algorithm, HawkRank scoring function our group MM/GBSA energy decomposition analysis were seamlessly integrated into multi-functional platform. The predicted by...
Accurate quantification of protein–ligand interactions remains a key challenge to structure-based drug design. However, traditional machine learning (ML)-based methods based on handcrafted descriptors, one-dimensional protein sequences, and/or two-dimensional graph representations limit their capability learn the generalized molecular in 3D space. Here, we proposed novel deep representation framework named InteractionGraphNet (IGN) from structures complexes. In IGN, two independent...
The use of quantum mechanical potentials in protein–ligand affinity prediction is becoming increasingly feasible with growing computational power. To move forward, validation such on real-world challenges necessary. this end, we have collated an extensive set over a thousand galectin inhibitors known affinities and docked them into galectin-3. poses were then used to systematically evaluate several modern force fields semiempirical (SQM) methods up the tight-binding level under consistent...
A large number of protein–protein interactions (PPIs) are mediated by the between proteins and peptide segments binding partners, therefore determination protein–peptide (PpIs) is quite crucial to elucidate important biological processes design peptides or peptidomimetic drugs that can modulate PPIs. Nowadays, as a powerful computation tool, molecular docking has been widely utilized predict structures complexes. However, although programs have available, systematic study on assessment their...
A significant number of protein-protein interactions (PPIs) are mediated through the between proteins and peptide segments, therefore determination protein-peptide (PpIs) is critical to gain an in-depth understanding PPI network even design peptides or small molecules capable modulating PPIs. Computational approaches, especially molecular docking, provide efficient way model PpIs, a reliable scoring function that can recognize correct binding conformations for complexes one most important...
Enhanced sampling has been extensively used to capture the conformational transitions in protein folding, but it attracts much less attention studies of protein-protein recognition. In this study, we evaluated impact enhanced methods and solute dielectric constants on overall accuracy molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) mechanics/generalized Born (MM/GBSA) approaches for binding free energy calculations. Here, two widely methods, including aMD GaMD, conventional...
The first study to evaluate the capability of MM/PBSA and MM/GBSA predict binding affinities recognize near-native poses for RNA-ligand systems.
Nav1.7 is considered a promising target for developing next-generation analgesic drugs, given its critical role in human pain pathologies. Although most reported inhibitors with strong vitro activity and high selectivity share the aryl sulfonamide scaffold, they failed to demonstrate marked clinical efficacy. Therefore, exploring new Nav1.7-selective antagonists quite urgent development of drugs. Here, we report highly effective 1H-indole-3-propionamide inhibitor, WN2, identified through an...
Protein-protein interactions (PPIs) have been regarded as an attractive emerging class of therapeutic targets for the development new treatments. Computational approaches, especially molecular docking, extensively employed to predict binding structures PPI-inhibitors or discover novel small molecule PPI inhibitors. However, due relatively 'undruggable' features interfaces, accurate predictions ligands towards are quite challenging most docking algorithms. Here, we constructed a non-redundant...
The molecular mechanics/generalized Born surface area (MM/GBSA) has been widely used in end-point binding free energy prediction structure-based drug design (SBDD). However, practice, it is usually being treated as a disputed method mostly because of its system dependence. Here, combining with machine-learning optimization, we developed novel version MM/GBSA, named variable atomic dielectric MM/GBSA (VAD-MM/GBSA), by assigning constants directly to the protein/ligand atoms. new strategy...
Compound-protein interactions (CPI) play significant roles in drug development. To avoid side effects, it is also crucial to evaluate selectivity when binding different targets. However, most prediction models are constructed for specific targets with limited data. In this study, we present a pretrained multi-functional model compound-protein interaction (PMF-CPI) and fine-tune assess selectivity. This uses recurrent neural networks process the protein embedding based on language TAPE,...
DNA methyltransferases (DNMTs), responsible for the regulation of methylation, have been regarded as promising drug targets cancer therapy. However, high structural conservation catalytic domains DNMTs poses a big challenge to design selective inhibitors specific DNMT isoform. In this study, molecular dynamics (MD) simulations, end-point free energy calculations and umbrella sampling (US) simulations were performed reveal basis binding selectivity three representative towards DNMT1 DNMT3A,...
In structure-based drug design (SBDD), the molecular mechanics generalized Born surface area (MM/GBSA) approach has been widely used in ranking binding affinity of small molecule ligands. However, an accurate estimation protein-ligand still remains a challenge due to intrinsic limitation standard (GB) model MM/GBSA. this study, we proposed and evaluated MM/GBSA based on variable dielectric (VDGB) using residue-type-based constants. VDGB model, different values were assigned for three types...
Abstract Binding of different ligands to glucocorticoid receptor (GR) may induce conformational changes and even trigger completely opposite biological functions. To understand the allosteric communication within GR ligand binding domain, folding pathway helix 12 (H12) induced by agonist dexamethasone (DEX), antagonist RU486, modulator AZD9567 are explored molecular dynamics simulations Markov state model analysis. The can regulate volume activation function‐2 through residues Phe737 Gln738....
Machine-learning (ML)-based scoring functions (MLSFs) have gradually emerged as a promising alternative for protein-ligand binding affinity prediction and structure-based virtual screening. However, clouds of doubts still been raised against the benefits this novel type (SFs). In study, to benchmark performance target-specific MLSFs on relatively unbiased dataset, trained from three representative interaction representations were assessed LIT-PCBA classical Glide SP SF types ligand-based...
Artificial intelligence has deeply revolutionized the field of medicinal chemistry with many impressive applications, but success these applications requires a massive amount training samples high-quality annotations, which seriously limits wide usage data-driven methods. In this paper, we focus on reaction yield prediction problem, assists chemists in selecting high-yield reactions new chemical space only few experimental trials. To attack challenge, first put forth MetaRF, an...
Protein kinases have been regarded as important therapeutic targets for many diseases. Currently, a total of 41 kinase inhibitors approved by the Food and Drug Administration, along with large number being evaluated in clinical preclinical trials. Among all, allosteric inhibitors, such type II attracted extensive attention owing to their potential high selectivity. Nowadays, molecular docking has become powerful tool search novel inhibitors. However, characteristics may exert deep influence...
Abstract Androgen receptor (AR) antagonists are widely used for the treatment of prostate cancer (PCa), but their therapeutic efficacy is usually compromised by rapid emergence drug resistance. However, lack detailed interaction between AR and its poses a major obstacle to design novel antagonists. Here, funnel metadynamics employed elucidate inherent regulation mechanisms three (hydroxyflutamide, enzalutamide, darolutamide) on AR. For first time it observed that binding significantly...
Abstract Inverse Protein Folding (IPF) is an important task of protein design, which aims to design sequences compatible with a given backbone structure. Despite the prosperous development algorithms for this task, existing methods tend rely on noisy predicted residues located in local neighborhood when generating sequences. To address limitation, we propose entropy-based residue selection method remove noise input context. Additionally, introduce ProRefiner, memory-efficient global graph...
Antimicrobial resistance poses a growing threat to public health, emphasizing the urgent need for novel therapeutic strategies. peptides (AMPs), short peptide sequences with diverse mechanisms of action, offer promising alternative due their broad-spectrum activity against pathogens. Recent advances in protein language models (PLMs) have revolutionized structure prediction and functional annotation, highlighting potential AMP discovery development. In this context, we present AMP-SEMiner...
Deoxyribonucleic acid (DNA) serves as a repository of genetic information in cells and is critical molecular target for various antibiotics anticancer drugs. A profound understanding small molecule interaction with DNA crucial the rational design DNA-targeted therapies. While mechanics/Poisson-Boltzmann surface area (MM/PBSA) mechanics/generalized Born (MM/GBSA) approaches have been well established predicting protein-ligand binding, their application to DNA-ligand interactions has less...