NSF Award
2107619
The hallmark of many debilitating diseases, such as Alzeimer’s disease (AD) and type II diabetes (T2D), is the presence of abnormal masses/aggregates of proteins termed “amyloids”. These amyloids, in which composition is disease dependent, are generally considered to be ideal markers for disease diagnosis and therapeutic intervention. Unfortunately, existing probes are limited in that they are only able to detect the presence of a single targeted amyloid protein. This project will develop a new class of generic, multiple-mode, multi-target amyloid probes that will detect a wide variety of proteins associated with different amyloid diseases. Design principles for the multimodal probes can be transformed to numerous molecular-recognition applications for targeted drug therapy, biomarker detection, and disease diagnostics (e.g., cancers and COVID-19). The proposed multi-disciplinary research activities will provide diverse training for students at all levels, especially from underrepresented and low-income families. The students will develop knowledge and skills in data mining, molecular simulations, neuroscience, and lab-on-chip techniques in close relation to public health problems. Finally, the integrated educational and research activities will enrich the curriculum of the Corrosion Engineering program at the University of Akron.<br/><br/>The overall objectives of this project are to (1) fully explore, identify, and engineer – with both data-driven simulations and experiments – a new family of AIE@βPs (an aggregation-induced emission (AIE) molecule conjugated with small β-sheet-forming peptides (βPs)) probes capable of early and enhanced detection of multiple pathological aggregates and co-aggregates formed by the same and different amyloid proteins, which co-exist in human body fluids across different amyloid diseases and (2) conduct fundamental sequence-structure-recognition studies on these multi-mode, multiple-target AIE@βPs probes. The AIE molecule targets the aggregated amyloids and avoids the aggregation-induced quenching, while βPs target the β-structures of amyloid aggregates via specific β-sheet interactions. The project’s objectives will be achieved via three tasks: (1) develop a machine-learning model, combined with molecular simulations and biophysical experiments, to screen, identify, and validate a library of βPs capable of self-assembling into β-sheet structures and cross-interacting with both Aβ (associated with AD) and hIAPP (associated with T2D); (2) design and synthesize a series of AIE@βPs probes to detect Aβ, hIAPP, and hybrid Aβ-hIAPP species at different aggregation states for demonstrating “conformational-specific, sequence-independent” mechanisms via synergetic AIE- and βPs-induced binding modes; and (3) transform AIE@βPs probes into different amyloid sensors via surface immobilization by controlling their packing structures, densities, and patterns of AIE@βPs. In parallel, multiscale molecular simulations will be conducted to study the structures, dynamics, and interactions of βPs and AIE@βPs with amyloid aggregates in solution and on surfaces, which will be correlated with amyloid recognition mechanisms of AIE@βPs by experiments. If successful, this work will provide new design principles and sensor systems for early amyloid detection beyond few available today.<br/><br/>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.
