TY - JOUR
T1 - Revealing unknown protein structures using computational conformational sampling guided by experimental hydrogen-exchange data
AU - Devaurs, Didier
AU - Antunes, Dinler A.
AU - Kavraki, Lydia E.
PY - 2018/10/31
Y1 - 2018/10/31
N2 - 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.
AB - 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.
KW - hydrogen exchange
KW - mass spectrometry
KW - nuclear magnetic resonance
KW - protein conformational sampling
KW - protein structure
UR - http://www.scopus.com/inward/record.url?scp=85055904033&partnerID=8YFLogxK
U2 - 10.3390/ijms19113406
DO - 10.3390/ijms19113406
M3 - Article
C2 - 30384411
AN - SCOPUS:85055904033
SN - 1661-6596
VL - 19
JO - International Journal of Molecular Sciences
JF - International Journal of Molecular Sciences
IS - 11
M1 - 3406
ER -