Remote Homology Detection in Proteins Using Graphical Models.
Daniels, Noah.
2013
-
Abstract: Given the
amino acid sequence of a protein, researchers often infer its structure and function by
finding homologous, or evolutionarily-related, proteins of known structure and function.
Since structure is typically more conserved than sequence over long evolutionary
distances, recognizing remote protein homologs from their sequence poses a challenge. We
first consider all proteins ... read moreof known three-dimensional structure, and explore how they
cluster according to different levels of homology. An automatic computational method
reasonably approximates a human-curated hierarchical organization of proteins according
to their degree of homology. Next, we return to homology prediction, based only on the
one-dimensional amino acid sequence of a protein. Menke, Berger, and Cowen proposed a
Markov random field model to predict remote homology for beta-structural proteins, but
their formulation was computationally intractable on many beta-strand topologies. We
show two different approaches to approximate this random field, both of which make it
computationally tractable, for the first time, on all protein folds. One method
simplifies the random field itself, while the other retains the full random field, but
approximates the solution through stochastic search. Both methods achieve improvements
over the state of the art in remote homology detection for beta-structural protein
folds.
Thesis (Ph.D.)--Tufts University, 2013.
Submitted to the Dept. of Computer Science.
Advisor: Lenore Cowen.
Committee: Donna Slonim, Benjamin Hescott, Bonnie Berger, and Yu-Shan Lin.
Keywords: Computer science, and Bioinformatics.read less - ID:
- 6395wk52s
- Component ID:
- tufts:21888
- To Cite:
- TARC Citation Guide EndNote
