(735a) Computational Studies on Modeling, Simulating and Designing Amyloid Biomaterials

Jonnalagadda, S. V. R., Texas A&M University
Kokotidou, C., University of Crete
Deidda, G., University of Crete
Ornithopoulou, E., Royal Institute of Technology
Orr, A. A., Texas A&M University
Mitraki, A., University of Crete
Tamamis, P., Texas A&M University
Jeong, H. K., Texas A&M University
Amyloid self-assembly refers to the tendency of specific peptides and proteins to convert from their native functional states into intractable amyloid aggregates. This phenomenon is associated with a range of increasingly common human disorders, including Alzheimer and Parkinson diseases, type II diabetes, and a number of systemic amyloidosis (1). While amyloid formation is linked to diseases, it can be exploited for the formation of novel amyloid biomaterials, as naturally occurring peptide sequences extracted from amyloid peptides and proteins, or β-sheet rich fibrous proteins, can self-assemble into amyloid fibrils outside the context of the entire sequence. Amyloid biomaterials have significantly advantageous properties, which among others include their easy fabrication, and the capacity to tune their properties by changes at their sequence level.

In this presentation we are focusing on the use of computational methods, including modeling, simulations and design, to tune the properties of amyloid forming peptides so as to discover novel functional amyloid biomaterials in three different applications. In the first application, we designed amyloid biomaterials encompassing cell-adhesion and metal-binding properties by computationally incorporating the cell-adhesive motif RGD and an exposed cysteine residue, optimally, at an amyloid scaffold (2). In the second application, we designed amyloid biomaterials encompassing cell-adhesion and cross-linking properties by computationally incorporating the cell-adhesion motif RGD and optimally placing tyrosine residues at suitable positions for cross-linking. In the third application, we designed amyloid biomaterials capable of binding to cesium ions using a novel computational design strategy of our lab to functionalize amyloid materials (3). In all applications, we used molecular dynamics simulations in CHARMM (4) with extensive structural analyses, combined with novel tools we developed to functionalize amyloid biomaterials. Our results have been experimentally validated, and thus the newly computationally modeled, simulated and designed materials in the first two applications can constitute promising agents of the future in tissue-engineering, while the materials of the third application capturing cesium ions can be potentially used to capture radioactive cesium ions from contaminated water or blood.

  1. Chiti F et al. Annu Rev Biochem. 2017;86:27-68.
  2. Deidda G et al. ACS Biomater. Sci. Eng.2017;3 (7):1404–1416.
  3. Jonnalagadda SVR et al. Syst. Des. Eng.2017;2:321-335.
  4. Brooks BR et al. J. Comput. Chem. 2009;30(10):1545-614.