Our science

We develop the front-end biological computing methods, coupled with originally innovative biotechnologies, to speed up the modern drug discovery. Our team members have long-term experience in screening and designing potent drug candidates up to clinical trials. Our team members also discovered a series of active compounds for different orphan receptors, which are "me-only" drug candidates. Our successful experience pave the road to the future adventures. 

GPCR molecular research

Membrane proteins in general, in particular G protein-coupled receptors (GPCRs), are the most important drug targets. As an example, about 40% of the clinically used drugs are targeting GPCRs. In some pharmaceutical companies, this figure can be even as high as 70-80%. Therefore, the understanding of membrane protein functions is of fundamental importance for modern drug discovery.

Our team members have made important contributions to understand receptor mediated signalling at the molecular level. They have published many innovative works in top journals including Cell, Nature, Nature Communications, Chemical Science, Trends in Biochemical Sciences, Angewandte Chemie and others.

Publications

  1. The role of metal ions in GPCR signaling and drug discovery. Computational Molecular Science (2021). doi: 10.1002/WCMS.1565
  2. Structural basis of CXC chemokine receptor 2 activation and signalling. Nature (2020). (doi:10.1038/s41586-020-2492-5)
  3. Activation and Signaling Mechanism Revealed by Cannabinoid Receptor-Gi Complex Structures. Cell (2020). (doi:10.1016/j.cell.2020.01.008)
  4. Enhancing the Signaling of GPCRs via Orthosteric Ions. ACS Central Science (2020). doi:10.1021/acscentsci.9b01247
  5. New binding sites, new opportunities for GPCR drug discovery. Trends in Biochemical Sciences (2019). doi:10.1016/j.tibs.2018.11.011
  6. Exploring a new ligand binding site of G protein-coupled receptors. Chemical Science (2018). doi:10.1039/C8SC01680A
  7. New binding sites, new opportunities for GPCR drug discovery. Trends in Biochemical Sciences (2018). doi:10.1016/j.tibs.2018.11.011
  8.  5-HT2C Receptor Structures Reveal the Structural Basis of GPCR Polypharmacology. Cell (2018). doi:10.1016/j.cell.2018.01.001
  9. Designing Safer Analgesics via µ-Opioid Receptor Pathways. Trends in Pharmacological Sciences (2017). doi:10.1016/j.tips.2017.08.004
  10. Mechanistic Studies on the Stereoselectivity of the Serotonin 5-HT1A Receptor. Angewandte Chemie Int Ed Engl (2016). doi:10.1002/anie.201603766
  11. The Molecular Mechanism of P2Y1 Receptor Activation. Angewandte Chemie Int Ed Engl (2016). doi:10.1002/anie.201605147
  12. The Mechanism of Ligand-Induced Activation or Inhibition of mu- and kappa-Opioid Receptors. Angewandte Chemie Int Ed Engl (2015). doi:10.1002/anie.201501742
  13. W246 Opens a Gate for a Continuous Intrinsic Water Pathway during Activation of the Adenosine A Receptor. Angewandte Chemie Int Ed Engl (2014). doi:10.1002/anie.201409679
  14. Activation of G-protein-coupled receptors correlates with the formation of a continuous internal water pathway. Nature Communications (2014). doi:10.1038/ncomms5733
  15. Advances in GPCR modeling evaluated by the GPCR Dock 2013 assessment: meeting new challenges. Structure (2014), doi:10.1016/j.str.2014.06.012

Innovative Biochips

G protein-coupled receptors (GPCRs) are the most important drug targets, of which about 40% of the marketed drugs are targeting GPCRs.

As a membrane protein, the biological activity testing for GPCR drug discovery is still a challenging task. Many difficulties remain to be resolved including: (1) testing drug activity without labeling (2) measuring GPCR/G-protein interaction directly instead of determining that of cAMP or Ca2+ signaling in an indirect way (3) measuring kinetic properties of GPCR drug molecules and (4) differentiating G protein types inside cells.

In this regard, the team of AlphaMol developed a series of innovative biochip technologies to overcome the hurdle in GPCR drug discovery in the past 20 years. Such breakthrough facilitates the process of GPCR drug discovery in a much more accurate and efficient way. 

Publications

  1. Single-Vesicle Assays Using Liposomes and Cell-Derived Vesicles: From Modeling Complex Membrane Processes to Synthetic Biology and Biomedical Applications. Chemical Reviews (2018) doi:10.1021/acs.chemrev.7b00777
  2. Microfluidics: Microfluidic Single‐Cell Analysis with Affinity Beads. Small (2015) doi:10.1002/smll.201570128
  3. Microfluidic Single‐Cell Analysis with Affinity Beads. Small (2015) doi:10.1002/smll.201402650
  4. Probing bio-molecular interactions in attoliter volumes. Optical Sensors (2014) doi:10.1364/SENSORS.2014.SeM4C.1
  5. Single-Molecule Microscopy Deciphers the Relation between Trafficking and Signaling of the NK1 Receptor in Livings Cells. Biophysical Journal (2014) doi:10.1016/j.bpj.2013.11.629
  6. Protein-binding microarray analysis of tumor suppressor AP2α target gene specificity. PloS One (2011) doi:10.1371/journal.pone.0022895
  7. Semisynthesis of Fluorescent Metabolite Sensors on Cell Surfaces. J. Am. Chem. Soc. (2011) doi: 10.1021/ja206915m
  8. Bioanalytical procedure to detect membrane receptor activation (2010) WO2010052337 (A3)
  9. Real-time high-sensitivity impedance measurement interface for tethered BLM biosensor arrays. Sensor (2008) doi:10.1109/ICSENS.2008.4716525
  10. Mobility and signaling of single receptor proteins. Single Molecules and Nanotechnology (2008) doi:10.1007/978-3-540-73924-1_6
  11. Reversible site-selective labeling of membrane proteins in live cells. Nature Biotechnology (2004) doi:10.1038/nbt954

New drug discovery

Modern drug discovery and development is a complicated process, which costs about $2-3 billion and lasts for 12 years on average. How to decrease the costs and speed up the process is the challenge. Computational biology and Artificial Intelligence, combined with new technologies in experimental drug screening, are expected to make the hunt for new drugs quicker, cheaper and more efficient.

Our team has established a sophisticated computer-aided drug discovery platform, which combines Artificial Intelligence, WebGL, cloud computing and big data. It can perform a wide range of drug discovery tasks such as molecular generation, virtual screening, lead optimization, drug property optimization, molecular dynamics simulation, accurate binding energy prediction and others. We have applied our tools successfully to discover and design potent drug candidates for various targets.

Members of our team have the experience of advancing designed molecules into clinical trials. More recently, we discovered a series of potent compounds for different orphan receptors. These compounds could result in "me-only" drug candidates.

Publications

  1. MolADI: A Web Server for Automatic Analysis of Protein–Small Molecule Dynamic Interactions. Molecules (2021). doi: 10.3390/molecules26154625
  2. Rutin, a Natural Inhibitor of IGPD Protein, Inhibits the Formation of Biofilm in Staphylococcus xylosus ATCC700404 in vitro and in vivo, Frontiers in Pharmacology (2021). doi: 10.3389/fphar.2021.728354
  3. Clinical HDAC Inhibitors are Effective Drugs to Prevent the Entry of SARS-CoV2. ACS Pharmacology & Translational Science (2020). doi:10.1021/acsptsci.0c00163
  4. Enhancing the Signaling of GPCRs via Orthosteric Ions. ACS Central Science (2020). doi:10.1021/acscentsci.9b01247
  5. Advancing Drug Discovery via Artificial Intelligence. Trends in Pharmacological Sciences (2019). doi:10.1016/j.tips.2019.06.004
  6. New binding sites, new opportunities for GPCR drug discovery. Trends in Biochemical Sciences (2019). doi:10.1016/j.tibs.2018.11.011
  7. Computational modeling of the olfactory receptor Olfr73 suggests a molecular basis for low potency of olfactory receptor-activating compounds, Communications Biology (2019). doi:10.1038/s42003-019-0384-8
  8. Exploring a new ligand binding site of G protein-coupled receptors. Chemical Science (2018). doi:10.1039/C8SC01680A
  9. Implementing WebGL and HTML5 in macromolecular visualization and modern computer-aided drug design. Trends in Biotechnology (2017). doi:10.1016/j.tibtech.2017.03.009
  10. Using PyMOL as a platform for computational drug design. Computational Molecular Science (2017). doi:10.1002/wcms.1303

Resolving membrane protein structures in real lipids

Membrane proteins are important drug targets which include G protein-coupled receptors (GPCRs), ion channels, transporters and others. Our team has close collaborations with leading scientist in the area of structural biology. However, most of current membrane protein structures were resolved at detergent or artificial lipid environments which are totally different from their physiological conditions. This in return result in losing the physiological functions of target membrane protein. Such structures will increase the cost the of structure-based drug discovery noticeably. 

Our team has developed a new pipeline which can resolve the membrane protein structures at real lipid environments. With such functional structures, we can design drug molecules in a more accurate way. Moreover, excellent cryoEM facilities are accessible in both China and Switzerland. 

Publications

  1. Asymmetric opening of the homopentameric 5-HT3A serotonin receptor in lipid bilayers, Nature Communications (2021).(doi:10.1038/s41467-021-21016-7)
  2. A Gating Mechanism of the Serotonin 5-HT3 Receptor, Structure (2016) (doi:10.1016/j.str.2016.03.019)
  3. The Structure of the Mouse Serotonin 5-HT3 Receptor in Lipid Vesicles, Structure (2016) (doi:10.1016/j.str.2015.11.004)
  4. X-ray structure of the mouse serotonin 5-HT3 receptor, Nature (2014) (doi:10.1038/nature13552)
  5. Molecular and dimensional profiling of highly purified extracellular vesicles by fluorescence fluctuation spectroscopy, Analytical Chemistry (2014) (doi:10.1021/ac501801m)
  6. Overcoming barriers to membrane protein structure determination. Nature Biotechnology (2011) (doi:10.1038/nbt.1833)

New computational algorithm development

In modern drug discovery, lead optimization is an essential step to obtain a pre-clinical candidate (PCC). At this stage, many properties should be improved including physical properties, binding affinity and toxicities. 

Our team has developed an absolute binding energy prediction tool,  which is known as AlphaE. With the new algorithm of AlphaE, we can predict accurately the binding activity of a compound. The RMSE (root-mean-square-energy) is as low as 1 kcal/mol in comparison with experimental data. AlphaE also overcomes many difficulties in currently available tools such as:  (1) predictions are not reliable when the molecule changes too much; (2) calculations cannot be performed if molecules bind to different pockets;  (3) predictions need large amount of CPU and GPU resources.

Furthermore, AlphaMol also developed many new computational algorithm and tools to predict the physical properties and drug-likeness of molecules. These tools demonstrated perfect correlations with experimental data, of which the R2 is up to 0.99.  

Publications

  1. Accurate Physical Property Predictions via Deep Learning. (2022) Molecules, doi: 10.3390/molecules27051668
  2. MolADI: A Web Server for Automatic Analysis of Protein–Small Molecule Dynamic Interactions. Molecules (2021). doi: 10.3390/molecules26154625
  3. Advancing Drug Discovery via Artificial Intelligence. Trends in Pharmacological Sciences (2019). doi:10.1016/j.tips.2019.06.004
  4. Implementing WebGL and HTML5 in macromolecular visualization and modern computer-aided drug design. Trends in Biotechnology (2017). doi:10.1016/j.tibtech.2017.03.009
  5. Using PyMOL as a platform for computational drug design. Computational Molecular Science (2017). doi:10.1002/wcms.1303