Design of anti-inflammatory peptides based on generation of Gaussian membrane curvature
UC Los Angeles
Characterization and Properties
Computation and Data Science
Synthesis and Formulation
The ability to turn off pore formation is a general materials problem of foundational interest. For example, heart disease is characterized by the deposition of plaques on the inner wall of arterial blood vessels. These plaques become destabilized by the death of smooth muscle cells (SMCs). We recently demonstrated that immune cells known as neutrophils can release histone H4 which kill SMCs. We have recently show H4 can kill by generating negative Gaussian curvature (NGC) in cell membranes, which is a requirement of pore formation. In this research, we use machine learning methods to aid in the design of peptides that generate the opposite curvature (positive Gaussian curvature, PGC) to turn off this process. This work will provide a general understanding of how complex distributions of membrane bound proteins or peptides can deterministically control pore formation in membranes, and impact fields of inquiry including inflammation, viral infection, and bacterial toxins.