DeltaGzip: Computing Biopolymer-Ligand Binding Affinity via Kolmogorov Complexity and Lossless Compression.

The design of biosequences for biosensing and therapeutics is a challenging multistep search and optimization task. In principle, computational modeling may speed up the design process by virtual screening of sequences based on their binding affinities to target molecules. However, in practice, existing machine-learned models trained to predict binding affinities lack the flexibility with respect to reaction conditions, and molecular dynamics simulations that can incorporate reaction conditions suffer from high computational costs. Here, we describe a computational approach called DeltaGzip that evaluates the free energy of binding in biopolymer-ligand complexes from ultrashort equilibrium molecular dynamics simulations. The entropy of binding is evaluated using the Kolmogorov complexity definition of entropy and approximated using a lossless compression algorithm, Gzip. We benchmark the method on a well-studied data set of protein-ligand complexes comparing the predictions of DeltaGzip to the free energies of binding obtained using Jarzynski equality and experimental measurements.

Amodiaquine Nonspecifically Binds Double Stranded and Three-Way Junction DNA Structures.

The 4-aminoquinoline class of compounds includes the important antimalarial compounds amodiaquine and chloroquine. Despite their medicinal importance, the mode of action of these compounds is poorly understood. In a previous study we observed these compounds, as well as quinine and mefloquine, tightly bind the DNA cocaine-binding aptamer. Here, we further explore the range of nucleic acid structures bound by these compounds. To gauge a wide range of binding affinities, we used isothermal titration calorimetry to explore high affinity binding (nM to tens of µM) and NMR spectroscopy to assay weak binding biding in the hundreds of micromolar range. We find that amodiaquine tightly binds all double stranded DNA structures explored. Mefloquine binds double stranded DNA duplex molecules tightly and weakly associates with a three-way junction DNA construct. Quinine and chloroquine only weakly bind duplex DNA but do not tightly bind any of the DNA constructs explored. A simulation of the free energy of binding of these ligands to the Dickerson-Drew dodecamer resulted in an excellent agreement between the simulated and experimental free energy. These results provide new insight into the DNA binding of clinically important antimalarial compounds and may play a role in future development of new antimalarials.

Simulations of disordered matter in 3D with the morphological autoregressive protocol (MAP) and convolutional neural networks.

Disordered molecular systems, such as amorphous catalysts, organic thin films, electrolyte solutions, and water, are at the cutting edge of computational exploration at present. Traditional simulations of such systems at length scales relevant to experiments in practice require a compromise between model accuracy and quality of sampling. To address this problem, we have developed an approach based on generative machine learning called the Morphological Autoregressive Protocol (MAP), which provides computational access to mesoscale disordered molecular configurations at linear cost at generation for materials in which structural correlations decay sufficiently rapidly. The algorithm is implemented using an augmented PixelCNN deep learning architecture that, as we previously demonstrated, produces excellent results in 2 dimensions (2D) for mono-elemental molecular systems. Here, we extend our implementation to multi-elemental 3D and demonstrate performance using water as our test system in two scenarios: (1) liquid water and (2) samples conditioned on the presence of pre-selected motifs. We trained the model on small-scale samples of liquid water produced using path-integral molecular dynamics simulations, including nuclear quantum effects under ambient conditions. MAP-generated water configurations are shown to accurately reproduce the properties of the training set and to produce stable trajectories when used as initial conditions in quantum dynamics simulations. We expect our approach to perform equally well on other disordered molecular systems in which structural correlations decay sufficiently fast while offering unique advantages in situations when the disorder is quenched rather than equilibrated.

Solving the Wigner equation for chemically relevant scenarios: Dynamics in 2D.

The signed particle Monte Carlo (SPMC) approach has been used in the past to model steady-state and transient dynamics of the Wigner quasi-distribution for electrons in low-dimensional semiconductors. Here, we make a step toward high-dimensional quantum phase-space simulation in chemically relevant scenarios by improving the stability and memory demands of SPMC in 2D. We do so by using an unbiased propagator for SPMC to improve trajectory stability and applying machine learning to reduce memory demands for storage and manipulation of the Wigner potential. We perform computational experiments on a 2D double-well toy model of proton transfer and demonstrate stable pico-second-long trajectories that require only a modest computational effort.

Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning.

Inverse design of short single-stranded RNA and DNA sequences (aptamers) is the task of finding sequences that satisfy a set of desired criteria. Relevant criteria may be, for example, the presence of specific folding motifs, binding to molecular ligands, sensing properties, and so on. Most practical approaches to aptamer design identify a small set of promising candidate sequences using high-throughput experiments (e.g., SELEX) and then optimize performance by introducing only minor modifications to the empirically found candidates. Sequences that possess the desired properties but differ drastically in chemical composition will add diversity to the search space and facilitate the discovery of useful nucleic acid aptamers. Systematic diversification protocols are needed. Here we propose to use an unsupervised machine learning model known as the Potts model to discover new, useful sequences with controllable sequence diversity. We start by training a Potts model using the maximum entropy principle on a small set of empirically identified sequences unified by a common feature. To generate new candidate sequences with a controllable degree of diversity, we take advantage of the model's spectral feature: an "energy" bandgap separating sequences that are similar to the training set from those that are distinct. By controlling the Potts energy range that is sampled, we generate sequences that are distinct from the training set yet still likely to have the encoded features. To demonstrate performance, we apply our approach to design diverse pools of sequences with specified secondary structure motifs in 30-mer RNA and DNA aptamers.

In situ optical spectroscopy of crystallization: One crystal nucleation at a time.

While crystallization is a ubiquitous and an important process, the microscopic picture of crystal nucleation is yet to be established. Recent studies suggest that the nucleation process can be more complex than the view offered by the classical nucleation theory. Here, we implement single crystal nucleation spectroscopy (SCNS) by combining Raman microspectroscopy and optical trapping induced crystallization to spectroscopically investigate one crystal nucleation at a time. Raman spectral evolution during a single glycine crystal nucleation from water, measured by SCNS and analyzed by a nonsupervised spectral decomposition technique, uncovered the Raman spectrum of prenucleation aggregates and their critical role as an intermediate species in the dynamics. The agreement between the spectral feature of prenucleation aggregates and our simulation suggests that their structural order emerges through the dynamic formation of linear hydrogen-bonded networks. The present work provides a strong impetus for accelerating the investigation of crystal nucleation by optical spectroscopy.

Electronic Conduction through Monolayer Amorphous Carbon Nanojunctions.

In molecular electronic conduction, exotic lattice morphologies often give rise to exotic behaviors. Among 2D systems, graphene is a notable example. Recently, a stable amorphous version of graphene called monolayer amorphous carbon (MAC) was synthesized. MAC poses a new set of questions regarding the effects of disorder on conduction. In this Letter, we perform an ensemble-level computational analysis of the coherent electronic transmission through MAC nanofragments in search of defining characteristics. Our analysis, relying on a semiempirical Hamiltonian (Pariser-Parr-Pople) and Landauer theory, showed that states near the Fermi energy (EF) in MAC inherit partial characteristics of analogous surface states in graphene nanofragments. Away from EF, current is carried by a set of delocalized states that transition into a subset of insulating interior states at the extreme portions of MAC's energy spectrum. Finally, we also found that quantum interference between frontier orbitals is a common feature among MAC nanofragments.

Optoelectronic Current through Unbiased Monolayer Amorphous Carbon Nanojunctions.

Monolayer amorphous carbon (MAC) is a recently synthesized disordered 2D carbon material. An ensemble of MAC nanofragments contains diverse manifestations of lattice disorder, and because of disorder the key unifying characteristic of this ensemble is poor electronic conductance. Curiously, our computational analysis of the electronic properties of MAC nanofragments revealed an additional commonality: a robust presence of charge-transfer character for electronic transitions from the occupied to virtual orbitals. This charge-transfer property suggests possible applications in optoelectronics. In this Letter, we demonstrate computationally that a laser pulse induces directional electronic currents in unbiased MAC nanojunctions and discuss the effects of pulse intensity on the magnitude of electron transfer.

E2EDNA: Simulation Protocol for DNA Aptamers with Ligands.

We present E2EDNA, a simulation protocol and accompanying code for the molecular biophysics and materials science communities. This protocol is both easy to use and sufficiently efficient to simulate single-stranded (ss)DNA and small analyte systems that are important to cellular processes and nanotechnologies such as DNA aptamer-based sensors. Existing computational tools used for aptamer design focus on cost-effective secondary structure prediction and motif analysis in the large data sets produced by SELEX experiments. As a rule, they do not offer flexibility with respect to the choice of the theoretical engine or direct access to the simulation platform. Practical aptamer optimization often requires higher accuracy predictions for only a small subset of sequences suggested, e.g., by SELEX experiments, but in the absence of a streamlined procedure, this task is extremely time and expertise intensive. We address this gap by introducing E2EDNA, a computational framework that accepts a DNA sequence in the FASTA format and the structures of the desired ligands and performs approximate folding followed by a refining step, analyte complexation, and molecular dynamics sampling at the desired level of accuracy. As a case study, we simulate a DNA-UTP (uridine triphosphate) complex in water using the state-of-the-art AMOEBA polarizable force field. The code is available at https://github.com/InfluenceFunctional/E2EDNA.

Solving the Wigner equation with signed particle Monte Carlo for chemically relevant potentials.

Expanding the set of stable, accurate, and scalable methods for simulating molecular quantum dynamics is important for accelerating the computational exploration of molecular processes. In this paper, we adapt the signed particles Monte Carlo algorithm for solving the transient Wigner equation to scenarios of chemical interest. This approach was used in the past to study electronic processes in semi-conductors, but to the best of our knowledge, it had never been applied to molecular modeling. We present the algorithm and demonstrate its excellent performance on harmonic and double well potentials for electronic and nuclear systems. We explore the stability of the algorithm, discuss the choice of hyper-parameters, and cautiously speculate that it may be used in quantum molecular dynamics simulations.

Generating Multiscale Amorphous Molecular Structures Using Deep Learning: A Study in 2D.

Amorphous molecular assemblies appear in a vast array of systems: from living cells to chemical plants and from everyday items to new devices. The absence of long-range order in amorphous materials implies that precise knowledge of their underlying structures throughout is needed to rationalize and control their properties at the mesoscale. Standard computational simulations suffer from exponentially unfavorable scaling of the required compute with system size. We present a method based on deep learning that leverages the finite range of structural correlations for an autoregressive generation of disordered molecular aggregates up to arbitrary size from small-scale computational or experimental samples. We benchmark performance on self-assembled nanoparticle aggregates and proceed to simulate monolayer amorphous carbon with atomistic resolution. This method bridges the gap between the nanoscale and mesoscale simulations of amorphous molecular systems.

Predicting optical spectra for optoelectronic polymers using coarse-grained models and recurrent neural networks.

Coarse-grained modeling of conjugated polymers has become an increasingly popular route to investigate the physics of organic optoelectronic materials. While ultraviolet (UV)-vis spectroscopy remains one of the key experimental methods for the interrogation of these materials, a rigorous bridge between simulated coarse-grained structures and spectroscopy has not been established. Here, we address this challenge by developing a method that can predict spectra of conjugated polymers directly from coarse-grained representations while avoiding repetitive procedures such as ad hoc back-mapping from coarse-grained to atomistic representations followed by spectral computation using quantum chemistry. Our approach is based on a generative deep-learning model: the long-short-term memory recurrent neural network (LSTM-RNN). The latter is suggested by the apparent similarity between natural languages and the mathematical structure of perturbative expansions of, in our case, excited-state energies perturbed by conformational fluctuations. We also use this model to explore the level of sensitivity of spectra to the coarse-grained representation back-mapping protocol. Our approach presents a tool uniquely suited for improving postsimulation analysis protocols, as well as, potentially, for including spectral data as input in the refinement of coarse-grained potentials.

Electronic noise due to temperature differences in atomic-scale junctions.

Since the discovery a century ago1-3 of electronic thermal noise and shot noise, these forms of fundamental noise have had an enormous impact on science and technology research and applications. They can be used to probe quantum effects and thermodynamic quantities4-11, but they are also regarded as undesirable in electronic devices because they obscure the target signal. Electronic thermal noise is generated at equilibrium at finite (non-zero) temperature, whereas electronic shot noise is a non-equilibrium current noise that is generated by partial transmission and reflection (partition) of the incoming electrons8. Until now, shot noise has been stimulated by a voltage, either applied directly8 or activated by radiation12,13. Here we report measurements of a fundamental electronic noise that is generated by temperature differences across nanoscale conductors, which we term 'delta-T noise'. We experimentally demonstrate this noise in atomic and molecular junctions, and analyse it theoretically using the Landauer formalism8,14. Our findings show that delta-T noise is distinct from thermal noise and voltage-activated shot noise8. Like thermal noise, it has a purely thermal origin, but delta-T noise is generated only out of equilibrium. Delta-T noise and standard shot noise have the same partition origin, but are activated by different stimuli. We infer that delta-T noise in combination with thermal noise can be used to detect temperature differences across nanoscale conductors without the need to fabricate sophisticated local probes. Thus it can greatly facilitate the study of heat transport at the nanoscale. In the context of modern electronics, temperature differences are often generated unintentionally across electronic components. Taking into account the contribution of delta-T noise in these cases is likely to be essential for the design of efficient nanoscale electronics at the quantum limit.

Direct observation of backbone planarization via side-chain alignment in single bulky-substituted polythiophenes.

The backbone conformation of conjugated polymers affects, to a large extent, their optical and electronic properties. The usually flexible substituents provide solubility and influence the packing behavior of conjugated polymers in films or in bad solvents. However, the role of the side chains in determining and potentially controlling the backbone conformation, and thus the optical and electronic properties on the single polymer level, is currently under debate. Here, we investigate directly the impact of the side chains by studying the bulky-substituted poly(3-(2,5-dioctylphenyl)thiophene) (PDOPT) and the common poly(3-hexylthiophene) (P3HT), both with a defined molecular weight and high regioregularity, using low-temperature single-chain photoluminescence (PL) spectroscopy and quantum-classical simulations. Surprisingly, the optical transition energy of PDOPT is significantly (∼2,000 cm-1 or 0.25 eV) red-shifted relative to P3HT despite a higher static and dynamic disorder in the former. We ascribe this red shift to a side-chain induced backbone planarization in PDOPT, supported by temperature-dependent ensemble PL spectroscopy. Our atomistic simulations reveal that the bulkier 2,5-dioctylphenyl side chains of PDOPT adopt a clear secondary helical structural motif and thus protect conjugation, i.e., enforce backbone planarity, whereas, for P3HT, this is not the case. These different degrees of planarity in both thiophenes do not result in different conjugation lengths, which we found to be similar. It is rather the stronger electronic coupling between the repeating units in the more planar PDOPT which gives rise to the observed spectral red shift as well as to a reduced calculated electron-hole polarization.

Fluorescent Proteins Detect Host Structural Rearrangements via Electrostatic Mechanism.

The rational design of genetically encoded fluorescent biosensors, which can detect rearrangements of target proteins via interdomain allostery, is hindered by the absence of mechanistic understanding of the underlying photophysics. Here, we focus on the modulation of fluorescence by mechanical perturbation in a popular biological probe: enhanced Green Fluorescent Protein (eGFP). Using a combination of molecular dynamics (MD) simulations and quantum chemistry, and a set of physically motivated assumptions, we construct a map of fluorescence quantum yield as a function of a 2D electric field imposed by the protein environment on the fluorophore. This map is transferable between Tsien's Class 2 GFP's, and it allows one to estimate the shifts in fluorescence intensity due to mechanical perturbations directly from MD simulations. We use it in combination with steered MD simulations to put forward a hypothesis for the mechanism of a genetically encoded voltage probe (ArcLight) whose mechanism is currently under debate.

Relating Chromophoric and Structural Disorder in Conjugated Polymers.

The optoelectronic properties of amorphous conjugated polymers are sensitive to the details of the conformational disorder, and spectroscopy provides the means for structural characterization of the fragments of the chain that interact with light-"chromophores". A faithful interpretation of spectroscopic conformational signatures, however, presents a theoretical challenge. Here we investigate the relationship between the ground-state optical gaps, the properties of the excited states, and the structural features of chromophores of a single molecule poly(3-hexyl)-thiophene (P3HT) using quantum-classical atomistic simulations. Our results demonstrate that chromophoric disorder arises through the interplay between excited-state delocalization and electron-hole polarization, controlled by the torsional disorder introduced by side chains. Within this conceptual framework, we predict and explain the counterintuitive spectral behavior of P3HT, a red-shifted absorption, despite shortening of chromophores, with increasing temperature. This discussion introduces the concept of disorder-induced separation of charges in amorphous conjugated polymers.

Electron transport in nanoscale junctions with local anharmonic modes.

We study electron transport in nanojunctions in which an electron on a quantum dot or a molecule is interacting with an N-state local impurity, a harmonic ("Holstein") mode, or a two-state system ("spin"). These two models, the Anderson-Holstein model and the spin-fermion model, can be conveniently transformed by a shift transformation into a form suitable for a perturbative expansion in the tunneling matrix element. We explore the current-voltage characteristics of the two models in the limit of high temperature and weak electron-metal coupling using a kinetic rate equation formalism, considering both the case of an equilibrated impurity, and the unequilibrated case. Specifically, we show that the analog of the Franck-Condon blockade physics is missing in the spin-fermion model. We complement this study by considering the low-temperature quantum adiabatic limit of the dissipative spin-fermion model, with fast tunneling electrons and a slow impurity. While a mean-field analysis of the Anderson-Holstein model suggests that nonlinear functionalities, bistability and hysteresis may develop, such effects are missing in the spin-fermion model at the mean-field level.

Landau-Zener transitions mediated by an environment: population transfer and energy dissipation.

We study Landau-Zener transitions between two states with the addition of a shared discretized continuum. The continuum allows for population decay from the initial state as well as indirect transitions between the two states. The probability of nonadiabatic transition in this multichannel model preserves the standard Landau-Zener functional form except for a shift in the usual exponential factor, reflecting population transfer into the continuum. We provide an intuitive explanation for this behavior assuming individual, independent transitions between pairs of states. In contrast, the ground state survival probability at long time shows a novel, non-monotonic, functional form with an oscillatory behavior in the sweep rate at low sweep rate values. We contrast the behavior of this open-multistate model to other generalized Landau-Zener models incorporating an environment: the stochastic Landau-Zener model and the dissipative case, where energy dissipation and thermal excitations affect the adiabatic region. Finally, we present evidence that the continuum of states may act to shield the two-state Landau-Zener transition probability from the effect of noise.

Path-integral simulations with fermionic and bosonic reservoirs: transport and dissipation in molecular electronic junctions.

We expand iterative numerically exact influence functional path-integral tools and present a method capable of following the nonequilibrium time evolution of subsystems coupled to multiple bosonic and fermionic reservoirs simultaneously. Using this method, we study the real-time dynamics of charge transfer and vibrational mode excitation in an electron conducting molecular junction. We focus on nonequilibrium vibrational effects, particularly, the development of vibrational instability in a current-rectifying junction. Our simulations are performed by assuming large molecular vibrational anharmonicity (or low temperature). This allows us to truncate the molecular vibrational mode to include only a two-state system. Exact numerical results are compared to perturbative Markovian master equation calculations demonstrating an excellent agreement in the weak electron-phonon coupling regime. Significant deviations take place only at strong coupling. Our simulations allow us to quantify the contribution of different transport mechanisms, coherent dynamics, and inelastic transport, in the overall charge current. This is done by studying two model variants: The first admits inelastic electron transmission only, while the second one allows for both coherent and incoherent pathways.

Vibrational cooling, heating, and instability in molecular conducting junctions: full counting statistics analysis.

We study current-induced vibrational cooling, heating, and instability in a donor-acceptor rectifying molecular junction using a full counting statistics approach. In our model, electron-hole pair excitations are coupled to a given molecular vibrational mode which is either harmonic or highly anharmonic. This mode may be further coupled to a dissipative thermal environment. Adopting a master equation approach, we confirm the charge and heat exchange fluctuation theorem in the steady-state limit, for both harmonic and anharmonic models. Using simple analytical expressions, we calculate the charge current and several measures for the mode effective temperature. At low bias, we observe the effect of bias-induced cooling of the vibrational mode. At higher bias, the mode effective temperature is higher than the environmental temperature, yet the junction is stable. Beyond that, once the vibrational mode (bias-induced) excitation rate overcomes its relaxation rate, instability occurs. We identify regimes of instability as a function of voltage bias and coupling to an additional phononic thermal bath. Interestingly, we observe a reentrant behavior where an unstable junction can properly behave at a high enough bias. The mechanism for this behavior is discussed.