Using Protein-Likeness to Validate Conformational Alternatives
Proteins are among the most complex entities known to science. Composed of just 20 fundamental building blocks arranged in simple linear strings, they nonetheless fold into a dizzying array of architectures that carry out the machinations of life at the molecular level.
Despite this central role in biology, we cannot reliably predict the structure of a protein from its sequence, and therefore rely on time-consuming and expensive experimental techniques to determine their structures. Although these methods can reveal equilibrium structures with great accuracy, they unfortunately mask much of the inherent molecular flexibility that enables proteins to dynamically perform biochemical tasks. As a result, much of the field of structural biology is mired in a static perspective; indeed, most attempts to naively model increased structural flexibility still end in failure.
This document details my work to validate alternative protein conformations beyond the primary or equilibrium conformation. The underlying hypothesis is that more realistic modeling of flexibility will enhance our understanding of how natural proteins function, and thereby improve our ability to design new proteins that perform desired novel functions.
During the course of my work, I used structure validation techniques to validate conformational alternatives in a variety of settings. First, I extended previous work introducing the backrub, a local, sidechain-coupled backbone motion, by demonstrating that backrubs also accompany sequence changes and therefore are useful for modeling conformational changes associated with mutations in protein design. Second, I extensively studied a new local backbone motion, helix shear, by documenting its occurrence in both crystal and NMR structures and showing its suitability for expanding conformational search space in protein design. Third, I integrated many types of local alternate conformations in an ultra-high-resolution crystal structure and discovered the combinatorial complexity that arises when adjacent flexible segments combine into networks. Fourth, I used structural bioinformatics techniques to construct smoothed, multi-dimensional torsional distributions that can be used to validate trial conformations or to propose new ones. Fifth, I participated in judging a structure prediction competition by using validation of geometrical and all-atom contact criteria to help define correctness across thousands of submitted conformations. Sixth, using similar tools plus collation of multiple comparable structures from the public database, I determined that low-energy states identified by the popular structure modeling suite Rosetta sometimes are valid conformations likely to be populated in the cell, but more often are invalid conformations attributable to artifacts in the physical/statistical hybrid energy function.
Unified by the theme of validating conformational alternatives by reference to high-quality experimental structures, my cumulative work advances our fundamental understanding of protein structural variability, and will benefit future endeavors to design useful proteins for biomedicine or industrial chemistry.
protein structure prediction
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