[Colloquium] Reminder: Orlova/Dissertation Defense/Dec 1, 2016

[Margaret Jaffey via Colloquium]

This is a reminder about Tatiana's dissertation defense tomorrow.

       Department of Computer Science/The University of Chicago

                     *** Dissertation Defense ***


Candidate:  Tatiana Orlova

Date:  Thursday, December 1, 2016

Time:  10:00 AM

Place:  Eckhart 129

Title: Solvation Signature in Hydrogen Bond Geometry of Protein
Helices

Abstract:
As the most abundant type of protein secondary structure helices play
an essential role within a protein and in various protein-protein
interactions. Thus it is especially important to understand what
criteria influences the geometry of helices and helps successfully
perform their functions. There are many computational tools used by
protein biophysicists. However, it is rare for them to use computer
algebra systems. Thus we explored the use of such systems to show how
they could be used to study structural properties of proteins. As an
example, we chose the geometry of helical structure.

We begin by considering two types of protein helices, α- and
310-helices stabilized by 1-5 and 1-4 hydrogen bonding pattern
respectively and study the relationship between hydrogen bond
geometrical requirements and stability of protein helices via
mathematical optimization. In particular, we take two major hydrogen
bond requirements: linearity and length constraints and ask whether
the most common α- and 310-helix motifs in protein folds result
from optimization with respect to a linear combination of these two
criteria. We show that these criteria are not sufficient to explain
the observed angles. Instead, we suggest that maximizing the solvation
of the protein backbone has a significant effect on the observed
(φ, ψ) angles.

The above work suggested a completely unexpected “solvation signature”
should be observable in protein structure. There are many tools that
can be used to study this suggestion. Since “data science” is a theme
of significant current interest, we explored this approach to see what
issues arise with these techniques. So, as a next step, we
investigated the effects of solvation by collecting and analyzing a
high quality protein dataset. We found that that helical backbone
actively interacts with water irrespective of whether it is located at
the surface or buried inside protein. This interaction, as expected,
highly correlates with larger φ angles and larger distances
between neighboring main-chain carbonyl oxygens. Moreover, we observe
a distinct periodic backbone solvation pattern in α-helices,
suggesting that most helices have a very specific orientation and
position specific residue preferences.

We have seen that new tools can enhance the study of protein
biophysics. The success of data mining depends strongly on the quality
of the questions being addressed as well as the quality and quantity
of the data. This suggests that data science (1) needs to have a firm
foundation in basic science and (2) needs to have appropriate analytic
tools to examine the data faithfully.

Other tools, such as molecular dynamics and density functional theory
have also been used to study protein-water interactions. Given the
right questions to ask, these too can be potentially useful and would
be interesting to consider in the future.

Tatiana's advisor is Prof. L. Ridgway Scott

Login to the Computer Science Department website for details,
including a draft copy of the dissertation:

 https://www.cs.uchicago.edu/phd/phd_announcements#orlova

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Margaret P. Jaffey            margaret at cs.uchicago.edu
Department of Computer Science
Student Support Rep (Ry 156)               (773) 702-6011
The University of Chicago      http://www.cs.uchicago.edu
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