Abstract
Both experimental and computational methods are available to gather information about a protein’s conformational space and interpret changes in protein structure. However, experimentally observing and computationally modeling large proteins remain critical challenges for structural biology. Our work aims at addressing these challenges by combining computational and experimental techniques relying on each other to overcome their respective limitations. Indeed, despite its advantages, an experimental technique such as hydrogen-exchange monitoring cannot produce structural models because of its low resolution. Additionally, the computational methods that can generate such models suffer from the curse of dimensionality when applied to large proteins. Adopting a common solution to this issue, we have recently proposed a framework in which our computational method for protein conformational sampling is biased by experimental hydrogen-exchange data. In this paper, we present our latest application of this computational framework: generating an atomic-resolution structural model for an unknown protein state. For that, starting from an available protein structure, we explore the conformational space of this protein, using hydrogen-exchange data on this unknown state as a guide. We have successfully used our computational framework to generate models for three proteins of increasing size, the biggest one undergoing large-scale conformational changes.
Highlights
A protein’s function is known to be modulated by changes in the protein’s three-dimensional structure [1]
By guiding the conformational exploration with experimental hydrogen-exchange data collected for these proteins, we have generated atomic-resolution structural models describing the states in which these proteins were when these data were collected
The conformation of this monomer form of IL-8 differs from the conformation of the dimer form, mostly in that its C-terminus is partly disordered: the all-atom root-mean-squared deviation (RMSD) between these two conformations is 4.6 Å
Summary
A protein’s function is known to be modulated by changes in the protein’s three-dimensional structure [1]. Understanding how a protein shifts between conformations is essential to treat or prevent diseases caused by its dysfunction [2]. This requires gathering information about the protein’s conformational space, i.e., the space of all possible states in which the protein can be [3]. Some information can be obtained experimentally, using techniques such as X-ray crystallography, which has produced many of the structures reported in the Protein Data Bank (PDB) [4]. Experimental techniques have been complemented by various computational methods, such as molecular dynamics [5]. When dealing with large proteins or molecular complexes, both experimental techniques and computational methods suffer from their respective limitations. Studying large proteins and molecular complexes remains a critical challenge for structural biology
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