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.
引用
收藏
页码:5103 / 5115
页数:13
相关论文
共 88 条
[1]   Using simulation to interpret experimental data in terms of protein conformational ensembles [J].
Allison, Jane R. .
CURRENT OPINION IN STRUCTURAL BIOLOGY, 2017, 43 :79-87
[2]   Bayesian inference of protein ensembles from SAXS data [J].
Antonov, L. D. ;
Olsson, S. ;
Boomsma, W. ;
Hamelryck, T. .
PHYSICAL CHEMISTRY CHEMICAL PHYSICS, 2016, 18 (08) :5832-5838
[3]   Bayesian Energy Landscape Tilting: Towards Concordant Models of Molecular Ensembles [J].
Beauchamp, Kyle A. ;
Pande, Vijay S. ;
Das, Rhiju .
BIOPHYSICAL JOURNAL, 2014, 106 (06) :1381-1390
[4]   MOLECULAR-DYNAMICS WITH COUPLING TO AN EXTERNAL BATH [J].
BERENDSEN, HJC ;
POSTMA, JPM ;
VANGUNSTEREN, WF ;
DINOLA, A ;
HAAK, JR .
JOURNAL OF CHEMICAL PHYSICS, 1984, 81 (08) :3684-3690
[5]   Structural characterization of flexible proteins using small-angle X-ray scattering [J].
Bernado, Pau ;
Mylonas, Efstratios ;
Petoukhov, Maxim V. ;
Blackledge, Martin ;
Svergun, Dmitri I. .
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY, 2007, 129 (17) :5656-5664
[6]   Conformational Space of Flexible Biological Macromolecules from Average Data [J].
Bertini, Ivano ;
Giachetti, Andrea ;
Luchinat, Claudio ;
Parigi, Giacomo ;
Petoukhov, Maxim V. ;
Pierattelli, Roberta ;
Ravera, Enrico ;
Svergun, Dmitri I. .
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY, 2010, 132 (38) :13553-13558
[7]   Determination of protein structures consistent with NMR order parameters [J].
Best, RB ;
Vendruscolo, M .
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY, 2004, 126 (26) :8090-8091
[8]   Balanced Protein-Water Interactions Improve Properties of Disordered Proteins and Non-Specific Protein Association [J].
Best, Robert B. ;
Zheng, Wenwei ;
Mittal, Jeetain .
JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2014, 10 (11) :5113-5124
[9]   A Brief Survey of State-of-the-Art BioSAXS [J].
Bizien, Thomas ;
Durand, Dominique ;
Roblin, Pierre ;
Thureau, Aurelien ;
Vachette, Patrice ;
Perez, Javier .
PROTEIN AND PEPTIDE LETTERS, 2016, 23 (03) :217-231
[10]   Principles of protein structural ensemble determination [J].
Bonomi, Massimiliano ;
Heller, Gabriella T. ;
Camilloni, Carlo ;
Vendruscolo, Michele .
CURRENT OPINION IN STRUCTURAL BIOLOGY, 2017, 42 :106-116