Soft Matter Science - overview
The Soft Matter Science Group focuses on understanding the fundamental issues in soft matter, particularly biomaterials where physics, chemistry and biology meet. Our research activities cover a broad and diverse spectrum of subjects ranging from computational structural biology, biomolecular dynamics, multiscale modeling of bio-nano interface, proteomics, petrochemical modeling to lead optimization. The group pursues innovative, high-impact and practical research using high performance computing (HPC) to leverage and expand upon novel techniques developed by our team for scientific and technological advancement. Besides contributions to the scientific community, our works have brought significant business value impact to IBM, resulting with engagement of projects related to core businesses of some top Fortune 500 companies as well as high-profile non-profit organizations.
To tackle the challenge of estimating the binding affinities of ligand-receptor complexes in structure based rational drug design, 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 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.
To enhance 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 metabotropic glutamate receptors (mGluRs), we have used a combination of the mathematically rigorous, free energy perturbation (FEP) method with the linear response models (LRMs) to extract key factors governing the ligand-receptor binding. 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 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, LRMs were built for different sets of ligands that showed high correlations (R2 > 0.95) to the corresponding FEP binding affinities.
As nanomedicine has started to gather momentum in recent years, we have also conducted many research related to this emerging technology. One of these activities involves the investigation of 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 matrix metalloproteinases (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.
For more information, please visit our website at http://researcher.watson.ibm.com/researcher/view_group.php?id=961