[talks] Shilpa Nadimpalli Kobren FPO Thursday, May 31, 2018 at 10:00 am CS402

[Mike Freedman]

Shilpa Nadimpalli Kobren will present her FPO on Thursday, May 31, 2018 
at 10:00am in CS402. All are welcomed to attend.

*Committee members:* Mona Singh (adviser), Barbara Engelhardt 
(examiner), Stanislav Shvartsman (examiner), Olga Troyanskaya (reader), 
and Benjamin J. Raphael (reader)

*Title: *Detecting and Analyzing Variation in Protein Interactions

*Abstract:

*
Proteins carry out a dazzling multitude of functions by interacting with 
DNA, RNA, other proteins and various other molecules within our cells. 
Together these interactions comprise complex networks that differ 
naturally across cells within an organism, across individuals in 
a population, and across species. Although such variation is critical 
for normal organismal functioning, mutations affecting protein 
interactions are also known to underlie a wide range of human diseases. 
In this dissertation, I introduce novel computational approaches that 
explore the extent to which specific protein interactions vary across 
species, across healthy individuals, and across individuals with cancer.

To start, I focus on interaction variation across species. It is well 
established that changes in protein-DNA interactions underlie a wide 
range of observable differences across species. These differences are 
primarily thought to stem from changes in the DNA sites that 
transcription factor (TF) proteins bind to, although changes in the 
binding properties of TFs themselves have also been observed. 
Determining the prevalence of such TF changes, however, remains 
infeasible using current experimental approaches. Here, I develop and 
apply a comparative genomics framework to systematically quantify 
changes in the DNA-binding properties of orthologous TFs across species 
spanning ~45 million years of evolutionary divergence. I demonstrate 
that, contrary to expectation, cross-species regulatory network 
divergence resulting from changes in non-duplicated DNA-binding proteins 
is pervasive. These findings reveal a widespread yet largely unstudied 
source of divergence across transcriptional regulatory programs in animals.

Next, I turn my attention to interaction variation across individuals. 
In order to comprehensively quantify this, I first combine large-scale 
sequence, domain and structure information to pinpoint sites within 
protein domains---the fundamental structural units in proteins---that 
are involved in binding DNA, RNA, peptides, ions, metabolites, or other 
small molecules. This domain-based approach enables us to identify 
putative interaction sites in over 60% of human genes, representing 
a 2.4-fold improvement over comparable state-of-the-art approaches for 
this task. I next demonstrate that whereas domain-inferred interaction 
sites are significantly depleted of natural variants across ~60,000 
healthy individuals, these same sites are significantly enriched for 
cancer mutations across ~11,000 tumor samples. My analysis demonstrates 
that the cellular network variation that occurs across healthy 
individuals is unlikely to be due to changes within proteins; in 
contrast, mutations acquired in cancers appear to preferentially alter 
cellular networks by perturbing the proteins themselves.

Finally, I show how we can leverage an interaction-based viewpoint to 
uncover mutated genes that play causal roles in human cancers. In 
particular, I aim to uncover genes whose interaction interfaces are 
significantly altered in tumors. Towards this end, I develop a robust 
computational framework that integrates my per-domain-position binding 
propensities with additional sources of biological data regarding 
protein functionality. I demonstrate that by analytically computing the 
significance of patterns of mutations, my approach is able to achieve 
a dramatic improvement in runtime over atypical empirical permutation 
test for this task. Moreover, my interaction-based method not only 
recapitulates known cancer driver genes faster and with greater 
precision than previous methods, but it also uncovers relatively 
rarely-mutated genes with likely roles in cancer. Through focusing on 
the somatic alteration of protein interaction interfaces in tumors, my 
method can inform the perturbed molecular mechanisms across known and 
putative cancer genes, thereby enabling valuable insights that may help 
guide personalized cancer treatments.
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