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Deep Learning on Local Sites for Protein Structure and Function Analysis.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Deep Learning on Local Sites for Protein Structure and Function Analysis./
Author:	Alexander William Fox Derry.
Description:	1 online resource (261 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
Subject:	Bioinformatics. -
Online resource:	click for full text (PQDT)
ISBN:	9798382235714

Deep Learning on Local Sites for Protein Structure and Function Analysis.
Alexander William Fox Derry.

Deep Learning on Local Sites for Protein Structure and Function Analysis. - 1 online resource (261 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Thesis (Ph.D.)--Stanford University, 2024.

Includes bibliographical references

Understanding how the three-dimensional structure of a protein leads to its function is important for determining disease mechanisms, developing targeted therapeutics, and engineering new proteins with desired functional characteristics. The expansion of protein structure databases due to experimental and computational advances provides an unprecedented opportunity to learn structure-function relationships in a data-driven manner. Deep learning methods that operate on protein structures have shown promise for specific tasks, but their utility for functional analysis has been limited due to inconsistencies in model training and evaluation, lack of labeled protein function data, and an inability to reconcile global predictions with local biochemical mechanisms. In this dissertation, I explore these challenges and propose a framework for protein analysis based on learning on local sites rather than the entire protein structure. First, to establish standards for model development and evaluation, I present work on (1) developing a suite of benchmark datasets, processing tools, and baseline models, and (2) quantifying the effect of differing structure compositions in the training data. I then describe a self-supervised learning method that leverages evolutionary relationships to learn general-purpose representations of local structural sites, and show how these representations enable improved performance on downstream tasks involving classification, search, and annotation of functional sites. By clustering millions of sites, I propose a framework for protein analysis based on conserved structural motifs which enables the discovery of functional relationships across protein classes. Finally, I present a method for explainable function annotation that predicts the overall function of a protein as well as the individual residues which are responsible.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382235714Subjects--Topical Terms:

583857
Bioinformatics.
Subjects--Index Terms:

Deep learning methodsIndex Terms--Genre/Form:

554714
Electronic books.

Deep Learning on Local Sites for Protein Structure and Function Analysis.
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