# Algorithms and Software for Multidimensional Vibrational Spectroscopy of Coarse-Grained Protein Models > **NIH NIH R21** · UNIVERSITY OF DELAWARE · 2024 · $200,000 ## Abstract 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 A𝛽 protein. The fibrillation of A𝛽 occurs through A𝛽 oligomers which have substantial neurotoxicity. Therefore, there is much interest in understanding the mechanism by which A𝛽 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 p... ## Key facts - **NIH application ID:** 10790697 - **Project number:** 1R21AG085412-01 - **Recipient organization:** UNIVERSITY OF DELAWARE - **Principal Investigator:** Alexei Kananenka - **Activity code:** R21 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $200,000 - **Award type:** 1 - **Project period:** 2024-05-15 → 2026-04-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10790697 ## Citation > US National Institutes of Health, RePORTER application 10790697, Algorithms and Software for Multidimensional Vibrational Spectroscopy of Coarse-Grained Protein Models (1R21AG085412-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10790697. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*