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Broadly speaking, we apply theoretical methods - modeling and simulation - to understand disease pathways on an atomistic level and to use this information for computer-aided drug design

DNA and RNA G-Quadruplexes

Nucleic acids adopt a wide range of conformations beyond the canonical duplex states. Among these are G-quadruplexes, which form in guanine-enriched regions in DNA and RNA. These structures serve as regulators of gene expression and altered stability and formation of these structures is linked with a number of diseases, from mental retardation and neurodegeneration to several types of human cancer.

Towards a greater understanding of G-quadruplex structure and dynamics, we are applying state-of-the-art polarizable MD simulations to understand the driving forces for G-quadruplex formation and stability, and we are examining the conformational ensembles that distinct structures sample. This information can be utilized for future efforts in drug design against these distinct nucleic acid structures.

Amyloidogenic Peptides and Proteins

The pathological aggregation of proteins is associated with dozens of disease states, including Alzheimer's, Parkinson's, and type 2 diabetes. Current therapies for these conditions are largely palliative and address only symptoms. To understand the early events in the unfolding and aggregation of these deleterious proteins, we are applying polarizable MD simulations. By doing so, we aim to determine the factors that influence the initiation of the disease pathway.

Our previous work suggests that the explicit representation of electronic polarization is required to study subtle effects related to dipole-dipole interactions as a function of peptide conformation. We are expanding upon these observations by simulating larger peptides and proteins, as well as amyloid aggregates of increasing size. We aim to delineate conformations that can be targeted by small-molecule and protein-based therapeutics.

Computer-Aided Drug Design with SILCS

The application of in silico methods, including MD simulation, Monte Carlo refinement, and free energy calculations is an integral part of the drug discovery pipeline. We are engaged in several collaborations to discover, validate, and modify putative drugs against a number of biological targets.

We are applying the Site Identification by Ligand Competitive Saturation (SILCS) method to allow for small-molecule sampling over the entire surface of the biomolecular targets. This method provides real, thermodynamic information related to the competition among the probe molecules and water, as well as a fully dynamical representation of the biomolecular target. This approach allows for an atomistic examination of the factors governing ligand binding for the purposes of more effective drug design.

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