eCommons

 

Towards an end-to-end rigid protein docking model guided by evolutionary history

dc.contributor.authorGoh, Eleanor
dc.contributor.chairYu, Haiyuanen_US
dc.contributor.committeeMemberPollak, Nancyen_US
dc.date.accessioned2024-01-31T21:12:12Z
dc.date.available2024-01-31T21:12:12Z
dc.date.issued2023-05
dc.description.abstractHuman protein-protein interactions are a key area of study when it comes to understanding the causes of many diseases like cancer. In order to analyze how proteins interact with each other, traditionally scientists use expensive experimental techniques or inefficient computational methods that scan through an entire search space. Recently, computational biologists have been able to tackle the problem of rigid body protein-protein docking using graph neural networks. In this thesis, we aim to improve the results of already existing protein docking machine learning models using in-house evolutionary data. We present a coevolution vector that encapsulates the evolutionary history between a pair of protein residues. By incorporating this vector as a node feature in the protein representation graph, we both guide existing models towards more accurate results and at the same time demonstrate the general utility of our coevolution feature in describing proteins.en_US
dc.identifier.doihttps://doi.org/10.7298/n9bw-3h49
dc.identifier.otherGoh_cornell_0058O_11751
dc.identifier.otherhttp://dissertations.umi.com/cornell:11751
dc.identifier.urihttps://hdl.handle.net/1813/113890
dc.language.isoen
dc.titleTowards an end-to-end rigid protein docking model guided by evolutionary historyen_US
dc.typedissertation or thesisen_US
dcterms.licensehttps://hdl.handle.net/1813/59810.2
thesis.degree.disciplineComputer Science
thesis.degree.grantorCornell University
thesis.degree.levelMaster of Science
thesis.degree.nameM.S., Computer Science

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Goh_cornell_0058O_11751.pdf
Size:
3.4 MB
Format:
Adobe Portable Document Format