“…Thus the interaction between DNA and H 2 S is more active/variable. This behavior may be attributed to the dynamic formation of weak hydrogen bonds with H 2 O and/or DNA molecules 69 70 71 , which is consistent with the more fluctuations of the pulling force observed in both ssDNA-H 2 S and dsDNA-H 2 S systems, due to the binding competition between the solvent (H 2 O molecules) and the DNA (target nucleobase). Similar behavior was observed in the relatively small DNA-HCl systems.…”
DNA-based sensors can detect disease biomarkers, including acetone and ethanol for diabetes and H2S for cardiovascular diseases. Before experimenting on thousands of potential DNA segments, we conduct full atomistic steered molecular dynamics (SMD) simulations to screen the interactions between different DNA sequences with targeted molecules to rank the nucleobase sensing performance. We study and rank the strength of interaction between four single DNA nucleotides (Adenine (A), Guanine (G), Cytosine (C), and Thymine (T)) on single-stranded DNA (ssDNA) and double-stranded DNA (dsDNA) with acetone, ethanol, H2S and HCl. By sampling forward and reverse interaction paths, we compute the free-energy profiles of eight systems for the four targeted molecules. We find that dsDNA react differently than ssDNA to the targeted molecules, requiring more energy to move the molecule close to DNA as indicated by the potential of mean force (PMF). Comparing the PMF values of different systems, we obtain a relative ranking of DNA base for the detection of each molecule. Via the same procedure, we could generate a library of DNA sequences for the detection of a wide range of chemicals. A DNA sensor array built with selected sequences differentiating many disease biomarkers can be used in disease diagnosis and monitoring.
“…Thus the interaction between DNA and H 2 S is more active/variable. This behavior may be attributed to the dynamic formation of weak hydrogen bonds with H 2 O and/or DNA molecules 69 70 71 , which is consistent with the more fluctuations of the pulling force observed in both ssDNA-H 2 S and dsDNA-H 2 S systems, due to the binding competition between the solvent (H 2 O molecules) and the DNA (target nucleobase). Similar behavior was observed in the relatively small DNA-HCl systems.…”
DNA-based sensors can detect disease biomarkers, including acetone and ethanol for diabetes and H2S for cardiovascular diseases. Before experimenting on thousands of potential DNA segments, we conduct full atomistic steered molecular dynamics (SMD) simulations to screen the interactions between different DNA sequences with targeted molecules to rank the nucleobase sensing performance. We study and rank the strength of interaction between four single DNA nucleotides (Adenine (A), Guanine (G), Cytosine (C), and Thymine (T)) on single-stranded DNA (ssDNA) and double-stranded DNA (dsDNA) with acetone, ethanol, H2S and HCl. By sampling forward and reverse interaction paths, we compute the free-energy profiles of eight systems for the four targeted molecules. We find that dsDNA react differently than ssDNA to the targeted molecules, requiring more energy to move the molecule close to DNA as indicated by the potential of mean force (PMF). Comparing the PMF values of different systems, we obtain a relative ranking of DNA base for the detection of each molecule. Via the same procedure, we could generate a library of DNA sequences for the detection of a wide range of chemicals. A DNA sensor array built with selected sequences differentiating many disease biomarkers can be used in disease diagnosis and monitoring.
“…Our choice of methodology has proven to be very accurate for the treatment of hydrogen-bonded clusters. [37][38][39][40][41][42][43][44][45][46] Total binding energies (BE) were calculated by subtracting the sum of the energies of the constituting isolated moieties from the energy of a particular fluorocarbene-(methanol) 3 cluster. Thus, larger negative numbers correspond to larger stabilization energies.…”
Section: Equilibrium Geometriesmentioning
confidence: 99%
“…ASCEC applies a modified Metropolis acceptance test in an adapted version of the simulated annealing optimization procedure, 34,35 which allows to retain the comparative advantages of stochastic optimization over analytical methods. 36 The ASCEC method has been successfully used to treat diverse systems, [37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54] and is used in this work. Further details about this methodology can be found elsewhere.…”
In this paper, we report the geometries and properties of 48 molecular species located on the MP2/6-311++G(d,p) PES of the fluorocarbene-(methanol)3 system. The structures were found by a combination of a stochastic search method, using a modified Metropolis acceptance test, and some hand constructed very symmetrical structures. We use several theoretical descriptors to categorize these species, focusing our attention on the interaction between the carbene carbon and the methanol oxygen, CcO, because this is the key interaction in the formation of O-ylides, ether products, and O-ylidic solvation complexes. These descriptors include natural charges and natural bond orbitals (NBO), CcO bond orders, CcO distances, energetic stabilities, and properties at bond critical points. Accordingly, the isomers were divided into four groups: ethers, fluorocarbene-methanol O-ylides, O-ylidic carbene-solvent complexes and hydrogen bonded carbene-solvent complexes. We found that the possibility of forming H-bonds among solvent molecules and between the carbene carbon and the hydrogen of the solvent molecule affects the stability, structure and nature of CcO interactions in O-ylides and O-ylidic complexes to the point of generating some diffuse borderlines between these two kinds of species. We determined which set of theoretical tools is suitable to better distinguish between them. Additionally, we clarify the nature of the relevant interactions in these species.
“…The large number of occurrences (216 cases or � 14 % of the total) and the corresponding interaction energies, of the same magnitude as secondary hydrogen bonds and dihydrogen contacts, indicate that those interactions are not to be taken as mere curiosities and that they indeed play a role in the stabilization of Alanine dimers. These interactions are seldom mentioned in the scientific literature, [67][68][69][70][71] and thus we analyze them in some detail next.…”
Section: Non Conventional or Exotic Contactsmentioning
High level quantum mechanical computations and extensive stochastic searches of the potential energy surfaces of the Alanine dimers uncover rich and complex structural and interaction landscapes. A total of 416 strongly bound (up 13.4 kcal mol À 1 binding energies at the DLPNO-CCSD(T)/6-311 + + G(d,p) level corrected by the basis set superposition error and by the zero point vibrational energies over B3LYP-D3 geometries), close energy equilibrium structures were located, bonded via 32 specific types of intermolecular contacts including Y•••HÀ X primary and Y•••HÀ C secondary hydrogen bonds, H•••H dihydrogen contacts, and non conventional antielectrostatic Y dÀ � � �X dÀ interactions. The putative global minimum is triply degenerate, corresponding to the structure of the common dimer of a carboxylic acid. All quantum descriptors of chemical bonding point to a multitude of weak individual interactions within each dimer, whose cumulative effect results in large binding energies and in an attractive fluxional wall of non-covalent interactions in the interstitial region between the monomers.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.