The growth of modern civilization is closely tied to controlled movement. This control is engineered by motors that extract energy from a fuel source and convert it to mechanical energy. Similarly, motors are an indispensable part of biology, and cells employ motor proteins to perform the energy conversions that sustain life. These molecular motors must minimize energy loss, be robust enough to withstand constant cycling, and display a high efficiency of energy conversion. Data science approaches, hand-in-hand with experimental work, bring forth the opportunity to learn how such features are achieved in a family of molecular assemblies that function as biological motors. The working principles revealed by this work will guide the design of better man-made, bioinspired devices that harness energy for the world's future needs, can potentially be utilized to enhance crop yields, or even tapped to understand the molecular origins of ageing. Advances in computer technology allow us to bring the excitement of research into how these powerful molecular machines work to high schools with limited computer resources by creating virtual laboratories that illustrate dynamic molecular machines in biological systems at no cost. Thus, leading edge concepts in biology can be taught and appreciated, and spur critical thinking, at otherwise underserved high schools.

Molecular motors exist as large oligomeric protein complexes. A goal of this project to determine whether the oligomeric state or copy-number of the motor proteins is responsible for their low overall light- or nutrient-to-ATP yield, underscoring a "biological energy crisis". Three different V-ATPase motors will be investigated to decipher the coupling between ATP hydrolysis, reconfigurable oligomerization and metabolic activity. Reengineering of these motors offers tangible biosynthetic alternatives (mutants or chimeras) to improve biological energy turnover. The primary method for studying the motor proteins include molecular dynamics and multi-physics simulations. These computations will be guided by ATPase models derived from experimental data by machine learning of two-dimensional cryo-electron microscopy images. The computational results will be restrained by X-ray crystallographic and single-molecule imaging experiments. As a technological product, new schemes for deriving large-scale molecular dynamics from cryo-EM and crystallographic data will be developed. Addressing a major societal need, a cloud-based remote visualization platform will be created to offer free enquiry-based online education to underserved high schools.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Agency
National Science Foundation (NSF)
Institute
Division of Molecular and Cellular Biosciences (MCB)
Application #
1942763
Program Officer
Marcia Newcomer
Project Start
Project End
Budget Start
2020-02-01
Budget End
2025-01-31
Support Year
Fiscal Year
2019
Total Cost
$257,086
Indirect Cost
Name
Arizona State University
Department
Type
DUNS #
City
Tempe
State
AZ
Country
United States
Zip Code
85281