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Register a new userMachine Learning Prediction of DNA Charge Transport
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First principle calculations of charge transfer in DNA molecules are computationally expensive given that charge carriers migrate in interaction with intra- and inter-molecular atomic motion. Screening sequences, e.g. to identify excellent electrical conductors is challenging even when adopting coarse-grained models and effective computational schemes that do not explicitly describe atomic dynamics. In this work, we present a machine learning (ML) model that allows the inexpensive prediction of the electrical conductance of millions of {it long} double-stranded DNA (dsDNA) sequences, reducing computational costs by orders of magnitude. The algorithm is trained on {it short} DNA nanojunctions with $n=3-7$ base pairs. The electrical conductance of the training set is computed with a quantum scattering method, which captures charge-nuclei scattering processes. We demonstrate that the ML method accurately predicts the electrical conductance of varied dsDNA junctions tracing different transport mechanisms: coherent (short-range) quantum tunneling, on-resonance (ballistic) transport, and incoherent site-to-site hopping. Furthermore, the ML approach supports physical observations that clusters of nucleotides regulate DNA transport behavior. The input features tested in this work could be used in other ML studies of charge transport in complex polymers, in the search for promising electronic and thermoelectric materials.
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Cytosine methylation has been found to play a crucial role in various biological processes, including a number of human diseases. The detection of this small modification remains challenging. In this work, we computationally explore the possibility of detecting methylated DNA strands through direct electrical conductance measurements. Using density functional theory and the Landauer-Buttiker method, we study the electronic properties and charge transport through an eight base-pair methylated DNA strand and its native counterpart. We first analyze the effect of cytosine methylation on the tight-binding parameters of two DNA strands and then model the transmission of the electrons and conductance through the strands both with and without decoherence. We find that the main difference of the tight-binding parameters between the native DNA and the methylated DNA lies in the on-site energies of (methylated) cytosine bases. The intra- and inter- strand hopping integrals between two nearest neighboring guanine base and (methylated) cytosine base also change with the addition of the methyl groups. Our calculations show that in the phase-coherent limit, the transmission of the methylated strand is close to the native strand when the energy is nearby the highest occupied molecular orbital level and larger than the native strand by 5 times in the bandgap. The trend in transmission also holds in the presence of the decoherence with the same rate. The lower conductance for the methylated strand in the experiment is suggested to be caused by the more stable structure due to the introduction of the methyl groups. We also study the role of the exchangecorrelation functional and the effect of contact coupling by choosing coupling strengths ranging from weak to strong coupling limit.
Charge migration along DNA molecules has attracted scientific interest for over half a century. Reports on possible high rates of charge transfer between donor and acceptor through the DNA, obtained in the last decade from solution chemistry experiments on large numbers of molecules, triggered a series of direct electrical transport measurements through DNA single molecules, bundles and networks. These measurements are reviewed and presented here. From these experiments we conclude that electrical transport is feasible in short DNA molecules, in bundles and networks, but blocked in long single molecules that are attached to surfaces. The experimental background is complemented by an account of the theoretical/computational schemes that are applied to study the electronic and transport properties of DNA-based nanowires. Examples of selected applications are given, to show the capabilities and limits of current theoretical approaches to accurately describe the wires, interpret the transport measurements, and predict suitable strategies to enhance the conductivity of DNA nanostructures.
We present an analytical model for the role of hydrogen bonding on the spin-orbit coupling of model DNA molecule. Here we analyze in detail the electric fields due to the polarization of the Hydrogen bond on the DNA base pairs and derive, within tight binding analytical band folding approach, an intrinsic Rashba coupling which should dictate the order of the spin active effects in the Chiral-Induced Spin Selectivity (CISS) effect. The coupling found is ten times larger than the intrinsic coupling estimated previously and points to the predominant role of hydrogen bonding in addition to chirality in the case of biological molecules. We expect similar dominant effects in oligopeptides, where the chiral structure is supported by hydrogen-bonding and bears on orbital carrying transport electrons.
As a secondary structure of DNA, DNA tetrahedra exhibit intriguing charge transport phenomena and provide a promising platform for wide applications like biosensors, as shown in recent electrochemical experiments. Here, we study charge transport in a multi-terminal DNA tetrahedron, finding that its charge transport properties strongly depend upon the interplay among contact position, on-site energy disorder, and base-pair mismatch. Our results indicate that the charge transport efficiency is nearly independent of contact position in the weak disorder regime, and is dramatically declined by the occurrence of a single base-pair mismatch between the source and the drain, in accordance with experimental results [J. Am. Chem. Soc. {bf 134}, 13148 (2012); Chem. Sci. {bf 9}, 979 (2018)]. By contrast, the charge transport efficiency could be enhanced monotonically by shifting the source toward the drain in the strong disorder regime, and be increased when the base-pair mismatch takes place exactly at the contact position. In particular, when the source moves successively from the top vertex to the drain, the charge transport through the tetrahedral DNA device can be separated into three regimes, ranging from disorder-induced linear decrement of charge transport to disorder-insensitive charge transport, and to disorder-enhanced charge transport. Finally, we predict that the DNA tetrahedron functions as a more efficient spin filter compared to double-stranded DNA and opposite spin polarization could be observed at different drains, which may be used to separate spin-unpolarized electrons into spin-up ones and spin-down ones. These results could be readily checked by electrochemical measurements and may help for designing novel DNA tetrahedron-based molecular nanodevices.
A subcritical load on a disordered material can induce creep damage. The creep rate in this case exhibits three temporal regimes viz. an initial decelerating regime followed by a steady-state regime and a stage of accelerating creep that ultimately leads to catastrophic breakdown. Due to the statistical regularities in the creep rate, the time evolution of creep rate has often been used to predict residual lifetime until catastrophic breakdown. However, in disordered samples, these efforts met with limited success. Nevertheless, it is clear that as the failure is approached, the damage become increasingly spatially correlated, and the spatio-temporal patterns of acoustic emission, which serve as a proxy for damage accumulation activity, are likely to mirror such correlations. However, due to the high dimensionality of the data and the complex nature of the correlations it is not straightforward to identify the said correlations and thereby the precursory signals of failure. Here we use supervised machine learning to estimate the remaining time to failure of samples of disordered materials. The machine learning algorithm uses as input the temporal signal provided by a mesoscale elastoplastic model for the evolution of creep damage in disordered solids. Machine learning algorithms are well-suited for assessing the proximity to failure from the time series of the acoustic emissions of sheared samples. We show that materials are relatively more predictable for higher disorder while are relatively less predictable for larger system sizes. We find that machine learning predictions, in the vast majority of cases, perform substantially better than other prediction approaches proposed in the literature.
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