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Motional timescale predictions by molecular dynamics simulations: case study using proline and hydroxyproline sidechain dynamics.

ABSTRACT: We propose a new approach for force field optimizations which aims at reproducing dynamics characteristics using biomolecular MD simulations, in addition to improved prediction of motionally averaged structural properties available from experiment. As the source of experimental data for dynamics fittings, we use (13) C NMR spin-lattice relaxation times T1 of backbone and sidechain carbons, which allow to determine correlation times of both overall molecular and intramolecular motions. For structural fittings, we use motionally averaged experimental values of NMR J couplings. The proline residue and its derivative 4-hydroxyproline with relatively simple cyclic structure and sidechain dynamics were chosen for the assessment of the new approach in this work. Initially, grid search and simplexed MD simulations identified large number of parameter sets which fit equally well experimental J couplings. Using the Arrhenius-type relationship between the force constant and the correlation time, the available MD data for a series of parameter sets were analyzed to predict the value of the force constant that best reproduces experimental timescale of the sidechain dynamics. Verification of the new force-field (termed as AMBER99SB-ILDNP) against NMR J couplings and correlation times showed consistent and significant improvements compared to the original force field in reproducing both structural and dynamics properties. The results suggest that matching experimental timescales of motions together with motionally averaged characteristics is the valid approach for force field parameter optimization. Such a comprehensive approach is not restricted to cyclic residues and can be extended to other amino acid residues, as well as to the backbone.

SUBMITTER: Aliev AE

PROVIDER: S-EPMC4282583 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

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Motional timescale predictions by molecular dynamics simulations: case study using proline and hydroxyproline sidechain dynamics.

Aliev Abil E AE Kulke Martin M Khaneja Harmeet S HS Chudasama Vijay V Sheppard Tom D TD Lanigan Rachel M RM

Proteins 20130917 2

We propose a new approach for force field optimizations which aims at reproducing dynamics characteristics using biomolecular MD simulations, in addition to improved prediction of motionally averaged structural properties available from experiment. As the source of experimental data for dynamics fittings, we use (13) C NMR spin-lattice relaxation times T1 of backbone and sidechain carbons, which allow to determine correlation times of both overall molecular and intramolecular motions. For struct ...[more]

PMID: 23818175

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Project description:Sinorhizobium meliloti forms N2-fixing root nodules on alfalfa, and as a free-living bacterium, it can grow on a very broad range of substrates, including l-proline and several related compounds, such as proline betaine, trans-4-hydroxy-l-proline (trans-4-l-Hyp), and cis-4-hydroxy-d-proline (cis-4-d-Hyp). Fourteen hyp genes are induced upon growth of S. meliloti on trans-4-l-Hyp, and of those, hypMNPQ encodes an ABC-type trans-4-l-Hyp transporter and hypRE encodes an epimerase that converts trans-4-l-Hyp to cis-4-d-Hyp in the bacterial cytoplasm. Here, we present evidence that the HypO, HypD, and HypH proteins catalyze the remaining steps in which cis-4-d-Hyp is converted to ?-ketoglutarate. The HypO protein functions as a d-amino acid dehydrogenase, converting cis-4-d-Hyp to ?(1)-pyrroline-4-hydroxy-2-carboxylate, which is deaminated by HypD to ?-ketoglutarate semialdehyde and then converted to ?-ketoglutarate by HypH. The crystal structure of HypD revealed it to be a member of the N-acetylneuraminate lyase subfamily of the (?/?)8 protein family and is consistent with the known enzymatic mechanism for other members of the group. It was also shown that S. meliloti can catabolize d-proline as both a carbon and a nitrogen source, that d-proline can complement l-proline auxotrophy, and that the catabolism of d-proline is dependent on the hyp cluster. Transport of d-proline involves the HypMNPQ transporter, following which d-proline is converted to ?(1)-pyrroline-2-carboxylate (P2C) largely via HypO. The P2C is converted to l-proline through the NADPH-dependent reduction of P2C by the previously uncharacterized HypS protein. Thus, overall, we have now completed detailed genetic and/or biochemical characterization of 9 of the 14 hyp genes.Hydroxyproline is abundant in proteins in animal and plant tissues and serves as a carbon and a nitrogen source for bacteria in diverse environments, including the rhizosphere, compost, and the mammalian gut. While the main biochemical features of bacterial hydroxyproline catabolism were elucidated in the 1960s, the genetic and molecular details have only recently been determined. Elucidating the genetics of hydroxyproline catabolism will aid in the annotation of these genes in other genomes and metagenomic libraries. This will facilitate an improved understanding of the importance of this pathway and may assist in determining the prevalence of hydroxyproline in a particular environment.

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Motional timescale predictions by molecular dynamics simulations: case study using proline and hydroxyproline sidechain dynamics.

Publications

Motional timescale predictions by molecular dynamics simulations: case study using proline and hydroxyproline sidechain dynamics.

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