Wenbo Yu

Weber Group

Contact

Email: wenbo@outerbanks.umaryland.edu

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Education

  • Postdoctoral Fellow, University of Maryland, Baltimore, 2016
  • Ph.D., Condensed Matter Physics, University of Science and Technology, China, 2009
  • B.S., Physics, University of Science and Technology, China, 2003

Profile

Dr. Wenbo Yu develops and uses computational methods to study the conformation changes, thermodynamic properties and interaction profiles of protein-protein interactions and complexes of proteins with small molecules. Dr. Yu focuses on development and application of computer-aided drug design (CADD) approaches for drug discovery, especially for cancer-related targets. In particular, he pursues new CADD methods development and coding; force field development; and molecular level simulation to investigate phenomena of biological and medicinal interest. In collaboration with experimentalists, Dr. Yu is studying new therapeutic targets, improving drug performance, and exploring structural level behaviors of drugs and macromolecules.

Illustration of a typical virtual screening approach used to search for new hit compounds for a target.

CURRENT RESEARCH

Computer-aided drug design (CADD) approaches are powerful tools that can complement experimental methods to expedite the process of drug discovery. For a new, experimentally identified target, a CADD approach can quickly screen millions of compounds that are available from commercial vendors. Using a sophisticated CADD technique can help to enhance the true positive rate and suggest a list of compounds for testing. Once hit compounds are identified, the CADD approach can be used to optimize lead compounds and improve binding affinities.

FragMaps and protein probability grids from molecular dynamics simulations are used to represent for both receptor and ligand protein in fast Fourier transform based docking in the new PPI prediction approach.

Using advanced CADD tools, Dr. Yu participates in a wide range of collaborative ongoing research projects within the Center of Biomolecular Therapeutics (CBT) to search for small molecule binders, optimize existing lead compounds, and explain their dynamic behaviors in targeting cancer, bacterial infections, and other diseases.

In addition to applying existing CADD approaches, Dr. Yu also develops new CADD methods validated through iterative coding and testing. He developed a new pharmacophore-based CADD approach to facilitate virtual screening of drugs for new targets. The new method builds pharmacophore models using energetic information from molecular dynamics simulations, rather than from static binding orientations of ligands. It also incorporates more information about conformational flexibility and the desolvation effect at the target binding pockets, which may benefit the virtual screening results. He is currently working on the development of a new protein-protein interaction (PPI) method for more accurate and robust predictions. Unlike existing methods, which are based on empirical functions, the new method is based on a new scoring function deduced from molecular dynamics simulations and is expected to provide a more accurate physical representation of PPI.

Dr. Yu is also working on structure-function studies of proteins and nucleic acids. He recently used molecular dynamics simulations to explain histone 3 (H3) methyltransferase NSD2 mutation effects on interactions of NSD2 with DNA/H3 complexes (both H3.3 and H3.1 variants). Understanding these interactions may provide new avenues for therapeutic interventions in NSD2 dysregulated malignancies, including relapsing acute lymphocytic leukemia (ALL), the most common pediatric cancer in the US.

Publications
2024
Perioperative Toripalimab Plus Chemotherapy for Patients With Resectable Non-Small Cell Lung Cancer: The Neotorch Randomized Clinical Trial.
2023
Dendritic Cell-Mediated Cross-Priming by a Bispecific Neutralizing Antibody Boosts Cytotoxic T Cell Responses and Protects Mice against SARS-CoV-2.
Integrated Covalent Drug Design Workflow Using Site Identification by Ligand Competitive Saturation.
Structure-Based Design of Potent Iminosugar Inhibitors of Endoplasmic Reticulum α-Glucosidase I with Anti-SARS-CoV-2 Activity.
hERG Blockade Prediction by Combining Site Identification by Ligand Competitive Saturation and Physicochemical Properties.
2022
Computer-Aided Drug Design: An Update.
Scaffold hopping from indoles to indazoles yields dual MCL-1/BCL-2 inhibitors from MCL-1 selective leads.
Cholecalciferol complexation with hydroxypropyl-β-cyclodextrin (HPBCD) and its molecular dynamics simulation.
Application of Site-Identification by Ligand Competitive Saturation in Computer-Aided Drug Design.
2021
Development of CHARMM Additive Potential Energy Parameters for α-Methyl Amino Acids.
Rapid and accurate estimation of protein-ligand relative binding affinities using site-identification by ligand competitive saturation.
Profiling the Tox21 Chemical Collection for Acetylcholinesterase Inhibition.
Specificity of Molecular Fragments Binding to S100B versus S100A1 as Identified by NMR and Site Identification by Ligand Competitive Saturation (SILCS).
Discovery of beta-lactamase CMY-10 inhibitors for combination therapy against multi-drug resistant Enterobacteriaceae.
Small molecules inhibitors of the heterogeneous ribonuclear protein A18 (hnRNP A18): a regulator of protein translation and an immune checkpoint.
2020
The SKI complex is a broad-spectrum, host-directed antiviral drug target for coronaviruses, influenza, and filoviruses.
Impact of electronic polarizability on protein-functional group interactions.
Optimization of a Benzothiazole Indolene Scaffold Targeting Bacterial Cell Wall Assembly.
Identification and characterization of fragment binding sites for allosteric ligand design using the site identification by ligand competitive saturation hotspots approach (SILCS-Hotspots).
Structure of the cell-binding component of the Clostridium difficile binary toxin reveals a di-heptamer macromolecular assembly.