Laboratory Notes: Engineering Therapies for Alzheimer’s Disease

Collin Stultz
Computational Biophysics Group
Research Laboratory of Electronics

Alzheimer’s disease (AD) is the leading cause of dementia in the United States. Due to the growth of the elderly population, the number of cases of AD is expected to reach a staggering 13.2 million by 2050. Existing therapies do not effectively slow the rate of neurodegeneration in AD patients. As such, there is an urgent need to develop new treatments for Alzheimer’s dementia. Our approach is to take an engineering based approach to allow for novel AD therapies.

Computational models of the conformational states of tau protein provide insights into how tau aggregates into neurofibrillary tangles during Alzheimer’s disease.

Computational models of the conformational states of tau protein provide insights into how tau aggregates into neurofibrillary tangles during Alzheimer’s disease.

Traditionally, rational drug development involves the design of ligands which bind target proteins in a way that inhibits the protein’s participation in the disease process. Structural descriptions of target proteins are usually provided by x-ray crystallography and drugs are designed by first constructing ligands that bind their targets with high affinity. Unfortunately, a key protein thought to play a role in AD – tau protein – is known to adopt multiple conformations in solution, thereby making standard techniques for determining protein structure problematic. Thus, novel approaches to describing the ensemble of possible conformations adopted by this disease-related protein are required.

Our current work addresses the need for modeling the heterogeneous set of conformations taken on by proteins associated with AD. This is done by combining results from conformational sampling algorithms coupled with biochemical experiments. Molecular simulations form a powerful set of tools that can be used for conformational sampling. This method uses fundamental physical principles to generate energetically favorable candidate conformations. Experimental data obtained with NMR can then be used to optimize and validate the candidate ensembles arising from the calculations.

From this analysis, we deduce candidate structures that likely play a role in the pathologic process and then use these conformations to design novel therapies. Our long-term goal is to use this knowledge to design new therapies that will benefit patients with Alzheimer’s dementia.

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