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SAXS-Restrained Ensemble Simulations of Intrinsically Disordered Proteins with Commitment to the Principle of Maximum Entropy
被引:44
作者:
Hermann, Markus R.
[1
]
Hub, Jochen S.
[2
,3
]
机构:
[1] Georg August Univ Gottingen, Inst Microbiol & Genet, D-37077 Gottingen, Germany
[2] Saarland Univ, Theoret Phys, Campus E2 6, D-66123 Saarbrucken, Germany
[3] Saarland Univ, Ctr Biophys, Campus E2 6, D-66123 Saarbrucken, Germany
关键词:
X-RAY-SCATTERING;
MOLECULAR-DYNAMICS SIMULATIONS;
PARTICLE MESH EWALD;
FORCE-FIELD;
UNSTRUCTURED PROTEINS;
NMR;
INFORMATION;
REFINEMENT;
EFFICIENT;
ORDER;
D O I:
10.1021/acs.jctc.9b00338
中图分类号:
O64 [物理化学(理论化学)、化学物理学];
学科分类号:
070304 ;
081704 ;
摘要:
Intrinsically disordered proteins (IDPs) play key roles in biology and disease, rationalizing the wide interest in deriving accurate solution ensembles of IDPs. Molecular dynamics (MD) simulations of IDPs often suffer from force-field inaccuracies, suggesting that simulations must be complemented by experimental data to obtain physically correct ensembles. We present a method for integrating small-angle X-ray scattering (SAXS) data on-the-fly into MD simulations of disordered systems, with the aim to bias the conformational sampling toward agreement with ensemble-averaged SAXS data. By coupling a set of parallel replicas to the data and following the principle of maximum entropy, this method applies only a minimal bias. Using the RS peptide as a test case, we analyze the influence of (i) the number of parallel replicas, (ii) the scaling of the force constant for the SAXS-derived biasing energy with the number of parallel replicas, and (iii) the force field. The refined ensembles are cross-validated against experimental (3)J(HN-H alpha) couplings and further compared in terms of C-alpha distance maps and secondary structure content. Remarkably, we find that the applied force field only has a small influence on the SAXS-refined ensemble, suggesting that incorporating SAXS data into MD simulations may greatly reduce the force-field bias.
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页码:5103 / 5115
页数:13
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