Algorithms and Software for Multidimensional Vibrational Spectroscopy of Coarse-Grained Protein
Models
Abstract
Alzheimer’s disease is age-related progressive irreversible neurological disorder which affects approximately 50
million people worldwide. It is ranked as the seventh leading cause of death in the United States with an
estimated annual cost of 1 trillion USD. Alzheimer’s disease is characterized by accumulation of amyloid plaques.
The failure is partially due to aggregation of A protein. The fibrillation of A occurs through A oligomers which
have substantial neurotoxicity. Therefore, there is much interest in understanding the mechanism by which A
aggregates because the aggregation pathway dictates the structures and populations of toxic intermediates.
Amyloid aggregation is a difficult problem to study for the standard structural biology techniques because it
involves kinetically evolving proteins. Two-dimensional infrared spectroscopy (2D IR) is an emerging analytical
technique that probes protein dynamics with chemical bond-specific spatial and high temporal resolution. 2D IR
spectroscopy is analogous to 2D NMR spectroscopy, except that it uses pulses of infrared light to measure
molecular vibrations rather than pulsed magnetic fields measuring nuclear spins. New methodology
improvements expand the frontiers of 2D IR spectroscopy permitting the study of amyloid aggregation and tissue
imaging in native environments. Interpreting congested 2D IR spectra is difficult without simulations that connect
spectral features to structural models. Computational spectroscopy advances alongside the improvements in
experimental 2D IR technique. With the present algorithms and software, it is possible to calculate 2D IR spectra
for a given all-atom or united-atom protein models and achieve at least qualitative agreement with experiment.
There is, however, an important technology gap—methods for calculating linear and multidimensional vibrational
spectra from coarse-grained protein and implicit solvent models do not exist. Such methods are highly desirable
because the study of protein aggregation, especially in the membrane environment, involves large length- and
timescales beyond the current capabilities of traditional all-atom molecular dynamics simulations. Instead, such
simulations require the use of coarse-grained protein and implicit solvent models. The proposed work will
address this gap. In Specific Aim 1 we will introduce a data-driven approach for calculating infrared vibrational
spectra of all-atom protein models in a coarse-grained solvent. Specific Aim 2 will focus on an implicit solvent
and coarse-grained protein models. The methods will be tested on the existing libraries of well-characterized
small proteins whose vibrational spectra have been measured. Specific Aim 3 is devoted to efficient software
implementation of the algorithms developed in Specific Aims 1 and 2. The new computational algorithms and
software developed in the proposed work will allow interpretation of linear and 2D IR spectroscopy experiments
on amyloid aggregation and will become an important technology in unravelling the fundamental molecular-level
insights not only into Alzheimer’s disease but also into other pathologies associated with the aggregation of
amyloid proteins such as Parkinson disease, type 2 diabetes, amyotrophic lateral sclerosis, and prion diseases.