Master of Science
Chen, Brian Y.
The exponential rise of protein structures lets us explore and visualize the sequence and structure of proteins. The structures are a great and underexplored source of insights into the physical and chemical properties of proteins. In order to process and analyze them at scale, we need to come up with a suitable digital representation which will allow structural biologists to analyze them using statistical and machine learning models. A challenge with generating these digital representations is generating them accurately and preserving their accuracy across a series of operations on the structures. This thesis presents an arbitrarily precise algorithm for constructing 3D molecular structure representations and defines a useful set of operations that preserve their accuracy. The thesis also details experiments using those digital representations proving their power and usefulness.
Georgiev, Georgi Dimitrov, "Algorithms in protein cavity classification" (2018). Theses and Dissertations. 4351.