Budday D (2019)
Publication Language: English
Publication Type: Thesis
Publication year: 2019
Edited Volumes: Schriftenreihe Technische Dynamik
Proteins are dynamic biomolecules that perform an enormous variety of cellular
functions on a broad range of spatio-temporal scales. Their conformational ensemble
is a crucial determinant of functionality in health and disease, and thus,
its structural and dynamic characterization has been a major research focus
for the last 50 years. While experimental and computational advances have increasingly
enabled atomically detailed insights into molecular mechanisms, the
need for efficient, yet elaborate integrative computational methods to resolve
functional motions across scales remains considerable. Biophysically guided
by protein structure, this thesis lays out a robotics-inspired, kino-geometric
model that efficiently captures small- and large-scale collective motions, with
dihedral angles as torsional degrees of freedom and non-covalent interactions
as constraints. Using geometric tools, a universal theory for geometric rigidity
analysis is developed. The thesis demonstrates the equivalence to existing
topological tools for protein rigidity analysis, decomposing macromolecules into
larger rigid substructures and coordinated motions between them. Furthermore,
the geometrically derived analysis marks a major advance on existing
methods by providing an explicit basis for these coordinated motions. Thriving
on this advantage, the work develops an efficient, high-dimensional motion
planning algorithm to study molecular transitions between different stabilized
substates. The newly formulated algorithm dCC-RRT integrates the principle
of minimal frustration into a rapidly-exploring random tree (RRT), exploiting
non-native, steric contacts that emerge during the transition to redirect conformational
exploration via dynamic, Clash-avoiding Constraints (dCC). The
algorithm outperforms state-of-the-art peer methods and closely approximates
conformational transitions of several example systems known from Molecular
Dynamics simulations, intermediate crystal structures, and other experimental
data, thereby providing a structural basis for allosteric networks that
drive conformational change. Finally, a large-scale, multi-dataset benchmark
analysis demonstrates how our kino-geometric model captures highly conserved,
protein fold specific, dynamic information that often goes undetected
in comparable methods. Extending the methodology from rigidity-theory to
constraint-relaxation based collective motions, the approach bridges insights
from rigidity and normal mode based methods that agree well with a variety of
experimental data and more detailed simulations. Overall, our kino-geometric
modeling approach is a robust and efficient alternative to obtain high-level
insights into molecular mechanisms across scales, with broad applications in
protein engineering, drug design, and human health.
APA:
Budday, D. (2019). High-Dimensional Robotics at the Nanoscale — Kino-Geometric Modeling of Proteins and Molecular Mechanisms (Dissertation).
MLA:
Budday, Dominik. High-Dimensional Robotics at the Nanoscale — Kino-Geometric Modeling of Proteins and Molecular Mechanisms. Dissertation, 2019.
BibTeX: Download