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Molecular mechanics and DNA replication regulation.

ABSTRACT: Magnetic and optical tweezers are providing novel insights on the structure, energetics, and functional dynamics of biological macromolecules. The modulation of DNA topology has provided very appropriate opportunities to study with these technologies the energetic and mechanistic features of the action of DNA topoisomerases, the enzymes that maintain the physiological level of negative superhelicity in the genome. Modulation of the superhelical state of the DNA replication origins is essential for the initiation of DNA synthesis in prokaryotes and eukaryotes. The results obtained recently from independent studies of two different groups combine to give new insights on the topological aspects of this process. With magnetic tweezers it was shown that the inhibition of human topoisomerase I by camptothecin freezes the drug-enzyme-DNA complex and specifically forbids the relaxation of positive supercoils; a study of the in vivo role of topoisomerase I on the activation of a human origin showed that this process is forbidden when the enzyme is frozen on the origin DNA by camptothecin. The inhibition of the relaxation of positive supercoils, probably introduced by the proteins performing origin activation, is therefore lethal for this process. Thus, the use of advanced physical technologies provides insights on an essential biological process.

SUBMITTER: Falaschi A

PROVIDER: S-EPMC2639860 | biostudies-other | 2007 Nov

REPOSITORIES: biostudies-other

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Project description:Direct molecular dynamics (MD) simulation with ab initio quantum mechanical and molecular mechanical (QM/MM) methods is very powerful for studying the mechanism of chemical reactions in a complex environment but also very time-consuming. The computational cost of QM/MM calculations during MD simulations can be reduced significantly using semiempirical QM/MM methods with lower accuracy. To achieve higher accuracy at the ab initio QM/MM level, a correction on the existing semiempirical QM/MM model is an attractive idea. Recently, we reported a neural network (NN) method as QM/MM-NN to predict the potential energy difference between semiempirical and ab initio QM/MM approaches. The high-level results can be obtained using neural network based on semiempirical QM/MM MD simulations, but the lack of direct MD samplings at the ab initio QM/MM level is still a deficiency that limits the applications of QM/MM-NN. In the present paper, we developed a dynamic scheme of QM/MM-NN for direct MD simulations on the NN-predicted potential energy surface to approximate ab initio QM/MM MD. Since some configurations excluded from the database for NN training were encountered during simulations, which may cause some difficulties on MD samplings, an adaptive procedure inspired by the selection scheme reported by Behler [ Behler Int. J. Quantum Chem. 2015 , 115 , 1032 ; Behler Angew. Chem., Int. Ed. 2017 , 56 , 12828 ] was employed with some adaptions to update NN and carry out MD iteratively. We further applied the adaptive QM/MM-NN MD method to the free energy calculation and transition path optimization on chemical reactions in water. The results at the ab initio QM/MM level can be well reproduced using this method after 2-4 iteration cycles. The saving in computational cost is about 2 orders of magnitude. It demonstrates that the QM/MM-NN with direct MD simulations has great potentials not only for the calculation of thermodynamic properties but also for the characterization of reaction dynamics, which provides a useful tool to study chemical or biochemical systems in solution or enzymes.

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Molecular mechanics and DNA replication regulation.

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