Soft Matter Science - Drug Discovery


Drug Discovery


I. New Linear Interaction Method Based on Continuum Solvent Model

A central challenge in structure based rational drug design is the estimation of binding affinities for ligand-receptor complexes. A great deal of effort has been invested in this area from both academic groups and the pharmaceutical industry, and several approaches have been developed, ranging from rapid QSAR based scoring function to computationally intensive free energy perturbation (FEP) calculations, but a fully satisfactory solution has not yet been developed.

In this project, we have proposed a new linear interaction energy (LIE) method based on continuum solvent surface generalized Born (SGB) model for ligand-receptor binding affinity prediction. The SGB-LIE method uses three terms in binding free energy formula: (1) van der Waals energy between the ligand and the receptor, (2) electrostatic energy including the Coulomb energy between the ligand and the receptor and reaction field energy between the ligand and the continuum solvent, and (3) cavity energy between the ligand and the continuum solvent. The new method is much faster than the previously proposed LIE methods based on explicit solvents. Also, there is no need to add in a Born correction term for ionized systems as is done in explicit solvent models, or to keep the protein system neutral to avoid the Born correction due to the finite size of the solvent sphere. The fitting and cross-validation results show that about 1.0 kcal/mol accuracy is achievable for binding sets with as many as 20 ligands. We have also explored various techniques for the underlying LIE conformation space sampling, including molecular dynamics and hybrid Monte Carlo methods, and the final results show that comparable binding energies can be obtained no matter which sampling technique is used.


Related Publications:

  • R. H. Zhou, R.A. Friesner, A. Ghosh, R. C. Rizzo, W. L. Jorgensen, and R. M. Levy,
    New Linear Interaction Method for Binding Affinity Calculations Using a Continuum Solvent Model,
    J. Phys. Chem. B 2001, 105, 10388-10397

II. Molecular Recognition of mGluR1 Using Combination of FEP and LRM

Metabotropic glutamate receptors (mGluRs) constitute an important family of the G-protein coupled receptors. Due to their widespread distribution in the central nervous system (CNS), these receptors are attractive candidates for understanding the molecular basis of various cognitive processes as well as for designing inhibitors for relevant psychiatric and neurological disorders. Despite many studies on drugs targeting the mGluR receptors to date, the molecular level details on the ligand binding dynamics still remain unclear.

In this study, we performed in silico experiments for mGluR1 with 29 different ligands including known synthetic agonists and antagonists as well as natural amino acids. The ligand-receptor binding affinities were estimated by the use of atomistic simulations combined with the mathematically rigorous, free energy perturbation (FEP) method, which successfully recognized the native agonist L-glutamate among the highly favorable binders, and also accurately distinguished antagonists from agonists. Comparative contact analysis also revealed the binding mode differences between natural and non-natural amino acid-based ligands. Several factors potentially affecting the ligand binding affinity and specificity were identified including net charges, dipole moments, and presence of aromatic rings. Based on these findings, linear response models (LRMs) were built for different sets of ligands that showed high correlations (R2 > 0.95) to the corresponding FEP binding affinities. The combination of LRM with FEP is useful to extract key factors governing the ligand-receptor binding, thus enhancing our understanding of the receptor function and dynamics, which is useful for the de novo design of high-potency drugs for important targets such as the mGluRs.


Related Publications:

  • S. G. Kang, P. Das, S. J. McGrane, A. J. Martin, T. Huynh, A. K. Royyuru, A. J. Taylor, P. G. Jones, R. H. Zhou,
    Molecular Recognition of Metabotropic Glutamate Receptor Type 1 (mGluR1): Synergistic Understanding with Free Energy Perturbation and Linear Response Modeling
    The Journal of Physical Chemistry B 118 (24), 6393-6404, 2014

III. Endohedral Metallofullerenol Gd@C82(OH)22 and Pancreatic Cancer

Pancreatic adenocarcinoma is the most lethal of the solid tumors and the fourth most common cause of cancer-related death in North America. Inhibition of matrix metalloproteinases (MMPs) has long been viewed as a potential anticancer therapy because of MMP's seminal roles in both angiogenesis and extracellular matrix (ECM) degradation. These two processes are related to tumor survival and invasion. However, the questions of both the ability to selectively inhibit MMPs and the mechanism by which inhibition occurs remain unanswered.

In this project,we investigated the inhibition capability and underlying molecular mechanism of Gd@C82(OH)22 using a combination of in vivo, in vitro, and in silico approaches. Our nude mouse model clearly shows that Gd@C82(OH)22 effectively blocks tumor growth in human pancreatic cancer xenografts. Our in vitro assays have shown that Gd@C82(OH)22 not only depresses expressions of MMPs but also reduces their activities. Meanwhile, our molecular-dynamics simulations have revealed detailed inhibition dynamics and molecular mechanism behind the Gd@C82(OH)22 - MMP-9 interaction. Our findings provide insights for de novo design of nanomedicine for fatal diseases such as pancreatic cancer, and also imply that the pharmacokinetic action of nanoparticles could be markedly different from the traditional target-based molecular drugs.


Related Publications:

  • S. G. Kang, G. Q. Zhou, P. Yang, Y. Liu, B. Y. Sun, T. Huynh, H. Meng, L. Zhao, G. M. Xing, C. Y. Chen, Y. L. Zhao, R. H. Zhou,
    Molecular Mechanism of Pancreatic Tumor Metastases Inhibition by Metallofullerenol Gd@C82(OH)22: Implication for de novo Design of Nanomedicine,
    Proc. Natl. Acad. Sci., 109, 15431-15436, 2012 (featured article)

 

 

 

 

 

 




Members


Alumni

  • Otitoaleke G. Akinola
  • David R. Bell
  • Matteo Castelli
  • Camilo Jimenez
  • Yuxing Peng
  • Michael Pitman
  • Raul Araya Secchi
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  • Payel Das