Structure, dynamics, and elasticity of free 16s rRNA helix 44 studied by molecular dynamics simulations

Réblová, Kamila; Lankaš, Filip; Rázga, Filip; Krasovska, Maryna V.; Koča, Jaroslav; Šponer, Jiřı́

doi:10.1002/bip.20503

Cited by 70 publications

(108 citation statements)

References 74 publications

(91 reference statements)

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“…However, in RNA simulations short-living reversible R/γ flips occur along the whole trajectory, with equilibrium populations of ∼10%. 82 In contrast to B-DNA, the γ-trans flips are known to occur in A-RNA X-ray structures to some extent, 83 so their sampling in A-RNA simulations is not considered as pathological. Nevertheless, it is still not clear if their force field description is not associated with some bias of stacking geometries.…”

Section: Methodsmentioning

confidence: 99%

Comparison of Intrinsic Stacking Energies of Ten Unique Dinucleotide Steps in A-RNA and B-DNA Duplexes. Can We Determine Correct Order of Stability by Quantum-Chemical Calculations?

Svozil¹,

Hobza²,

Šponer³

2010

J. Phys. Chem. B

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High level ab initio methods have been used to study stacking interactions in ten unique base pair steps both in A-RNA and in B-DNA duplexes. The protocol for selection of geometries based on molecular dynamics (MD) simulations is proposed, and its suitability is demonstrated by comparison with stacking in steps at fiber diffraction geometries. It is shown that fiber diffraction geometries are not sufficiently accurate for interaction energy calculations. In addition, the protocol for selection of geometries based on MD simulations allows for the evaluation of the variability of the intrinsic stacking energies along the MD trajectories. The uncertainty in stacking energies (difference between the most and least stable geometry) due to the dynamical nature of systems can be, in some cases, as large as 3.0 kcal · mol, which is almost 50% of the actual sequence dependence of base stacking energies (the energy difference between the most and least stable sequences). Thus, assessing the relative magnitude of the gas phase stacking energy using a single geometry for each sequence is insufficient to obtain an unambiguous order of gas phase stacking energies in canonical double helices. Though the ordering of ten unique dinucleotide steps cannot be definitive, some general conclusions were drawn. The stacking energies of base pair steps in A-RNA are more evenly separated compared to B-DNA, and their ordering is less sensitive to the dynamics of the system compared to be B-DNA. The most stable step both in B-DNA and A-RNA is the CG/CG step that is well separated from the second most stable step GC/GC. Also the least stable step (the CC/GG step) is well separated from the rest of the structures. The calculations further show that B-DNA stacking is favorable only marginally (on average by 1.14 kcal · mol -1 per base pair step) over A-RNA stacking, and this difference vanishes after subtracting the stabilizing van der Waals effect of the thymine 5-methyl group that is absent in RNA. Basically, no correlation between the sequence dependence of gas phase stacking energies and the sequence dependence of ∆G°3 7 free energies used in nearest-neighbor models was found either for B-DNA or for A-RNA. This reflects the complexity of the balance of forces that are responsible for the sequence dependence of thermodynamics stability of nucleic acids, which masks the effect of the intrinsic interactions between the stacked base pairs.

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Section: Methodsmentioning

confidence: 99%

Comparison of Intrinsic Stacking Energies of Ten Unique Dinucleotide Steps in A-RNA and B-DNA Duplexes. Can We Determine Correct Order of Stability by Quantum-Chemical Calculations?

Svozil¹,

Hobza²,

Šponer³

2010

J. Phys. Chem. B

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“…The production MD runs were carried out with constant pressure boundary conditions (relaxation time of 1.0 ps). Our methods for equilibration and production run protocols are described in greater detail elsewhere (33,74,77,78). Translational and rotational center-of-mass motions were initially removed, and periodically, simulations were interrupted to have the center-ofmass removed again by a subtraction of velocities to account for the "flying ice cube" effect (79).…”

Section: Unbiased Molecular Dynamics Simulationsmentioning

confidence: 99%

An Acrobatic Substrate Metamorphosis Reveals a Requirement for Substrate Conformational Dynamics in Trypsin Proteolysis

Kayode

Wang

Pendlebury

et al. 2016

Journal of Biological Chemistry

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Edited by George DeMartinoThe molecular basis of enzyme catalytic power and specificity derives from dynamic interactions between enzyme and substrate during catalysis. Although considerable effort has been devoted to understanding how conformational dynamics within enzymes affect catalysis, the role of conformational dynamics within protein substrates has not been addressed. Here, we examine the importance of substrate dynamics in the cleavage of Kunitz-bovine pancreatic trypsin inhibitor protease inhibitors by mesotrypsin, finding that the varied conformational dynamics of structurally similar substrates can profoundly impact the rate of catalysis. A 1.4-Å crystal structure of a mesotrypsinproduct complex formed with a rapidly cleaved substrate reveals a dramatic conformational change in the substrate upon proteolysis. By using long all-atom molecular dynamics simulations of acyl-enzyme intermediates with proteolysis rates spanning 3 orders of magnitude, we identify global and local dynamic features of substrates on the nanosecond-microsecond time scale that correlate with enzymatic rates and explain differential susceptibility to proteolysis. By integrating multiple enhanced sampling methods for molecular dynamics, we model a viable conformational pathway between substrate-like and productlike states, linking substrate dynamics on the nanosecond-microsecond time scale with large collective substrate motions on the much slower time scale of catalysis. Our findings implicate substrate flexibility as a critical determinant of catalysis.Protein function is determined by macromolecular geometry conferred by the folded state, as noted by Anfinsen nearly 40 years ago (1); however, recent years have brought fresh meaning to this paradigm with an increasing appreciation of the temporal dependence of protein structure. Proteins constantly sample varied conformational fluctuations about the time-averaged structures that we observe crystallographically or spectroscopically, and these conformational dynamics are in many cases closely coupled to protein function (2-4). Nowhere is this clearer than for enzymes, proteins evolved to accelerate biological chemical reactions. Varied examples have revealed that enzyme conformational dynamics can facilitate substrate binding, progression along the catalytic reaction coordinate, and product release (5-10). Most studies of protein dynamics in enzyme catalysis have naturally focused on conformational changes within the enzyme. However, for the many enzymes that catalyze reactions of protein substrates, an overlooked source of potentially relevant dynamics lies within the substrate.Trypsins are serine proteases, a class of proteolytic enzymes that have been well characterized and used to dissect and understand catalysis, most often using short oligopeptide model substrates as proxies for natural protein substrates. Following formation of a noncovalent Michaelis complex, the catalytic mechanism proceeds through two sequential steps (Scheme 1) (11). In the first step, the enzyme serine...

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“…Since most of the decoding activity in the A-site involves helix 44 of the 16S rRNA, several simulation studies on A-site containing 16S rRNA fragments in the presence and absence of antibiotics have been performed. (Reblova et al 2006;Vaiana et al 2006;Meroueh and Mobashery 2007;Romanowska et al 2008) These include classical MD as well as enhanced sampling simulations using replica exchange or targeted MD protocols. These studies have revealed changes in mobility of residues 1492 and 1493 when antibiotics are bound or when the nearby residue 1408 is changed from the prokaryotic adenine to the eukaryotic guanine.…”

Section: à4mentioning

confidence: 99%

Nucleotide modifications and tRNA anticodon–mRNA codon interactions on the ribosome

Allnér

Nilsson²

2011

RNA

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We have carried out molecular dynamics simulations of the tRNA anticodon and mRNA codon, inside the ribosome, to study the effect of the common tRNA modifications cmo 5 U34 and m 6 A37. In tRNA Val , these modifications allow all four nucleotides to be successfully read at the wobble position in a codon. Previous data suggest that entropic effects are mainly responsible for the extended reading capabilities, but detailed mechanisms have remained unknown. We have performed a wide range of simulations to elucidate the details of these mechanisms at the atomic level and quantify their effects: extensive free energy perturbation coupled with umbrella sampling, entropy calculations of tRNA (free and bound to the ribosome), and thorough structural analysis of the ribosomal decoding center. No prestructuring effect on the tRNA anticodon stem-loop from the two modifications could be observed, but we identified two mechanisms that may contribute to the expanded decoding capability by the modifications: The further reach of the cmo 5 U34 allows an alternative outer conformation to be formed for the noncognate base pairs, and the modification results in increased contacts between tRNA, mRNA, and the ribosome.

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Structure, dynamics, and elasticity of free 16s rRNA helix 44 studied by molecular dynamics simulations

Cited by 70 publications

References 74 publications

Comparison of Intrinsic Stacking Energies of Ten Unique Dinucleotide Steps in A-RNA and B-DNA Duplexes. Can We Determine Correct Order of Stability by Quantum-Chemical Calculations?

Comparison of Intrinsic Stacking Energies of Ten Unique Dinucleotide Steps in A-RNA and B-DNA Duplexes. Can We Determine Correct Order of Stability by Quantum-Chemical Calculations?

An Acrobatic Substrate Metamorphosis Reveals a Requirement for Substrate Conformational Dynamics in Trypsin Proteolysis

Nucleotide modifications and tRNA anticodon–mRNA codon interactions on the ribosome

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