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A novel mixed quantum-classical approach to simulating nonadiabatic dynamics of molecules at metal surfaces is presented. The method combines the numerically exact hierarchical equations of motion approach for the quantum electronic degrees of freedom with Langevin dynamics for the classical degrees of freedom, namely, low-frequency vibrational modes within the molecule. The approach extends previous mixed quantum-classical methods based on Langevin equations to models containing strong electron-electron or quantum electronic-vibrational interactions, while maintaining a nonperturbative and non-Markovian treatment of the molecule-metal coupling. To demonstrate the approach, nonequilibrium transport observables are calculated for a molecular nanojunction containing strong interactions.

Understanding the dynamics of biomolecular complexes, e.g., of protein-ligand (un)binding, requires the understanding of paths such systems take between metastable states. In MD simulation data, paths are usually not observable per se, but need to be inferred from simulation trajectories. Here we present a novel approach to cluster trajectories based on a community detection algorithm that requires the definition of only a single free parameter. Using the streptavidin-biotin complex as benchmark system and the A\textsubscript{2a} adenosine receptor in complex with the inhibitor ZM241385 as an elaborate application, we demonstrate how such clusters of trajectories correspond to pathways, and how the approach helps in the identification of reaction coordinates for a considered (un)binding process., Comment: This preprint is the unedited version of a manuscript that has been published as peer-reviewed article by J. Chem. Theory Comput. and can be downloaded for private use only. Copyright with the journal, ACS and the authors

Segmentation of objects in microscopy images is required for many biomedical applications. We introduce object-centric embeddings (OCEs), which embed image patches such that the spatial offsets between patches cropped from the same object are preserved. Those learnt embeddings can be used to delineate individual objects and thus obtain instance segmentations. Here, we show theoretically that, under assumptions commonly found in microscopy images, OCEs can be learnt through a self-supervised task that predicts the spatial offset between image patches. Together, this forms an unsupervised cell instance segmentation method which we evaluate on nine diverse large-scale microscopy datasets. Segmentations obtained with our method lead to substantially improved results, compared to state-of-the-art baselines on six out of nine datasets, and perform on par on the remaining three datasets. If ground-truth annotations are available, our method serves as an excellent starting point for supervised training, reducing the required amount of ground-truth needed by one order of magnitude, thus substantially increasing the practical applicability of our method. Source code is available at https://github.com/funkelab/cellulus.

To sample rare events, dissipation-corrected targeted molecular dynamics (dcTMD) applies a constant velocity constraint along a one-dimensional reaction coordinate $s$, which drives an atomistic system from an initial state into a target state. Employing a cumulant approximation of Jarzynski's identity, the free energy $\Delta G (s)$ is calculated from the mean external work and dissipated work of the process. By calculating the friction coefficient $\Gamma (s)$ from the dissipated work, in a second step the equilibrium dynamics of the process can be studied by propagating a Langevin equation. While so far dcTMD has been mostly applied to study the unbinding of protein-ligand complexes, here its applicability to rare conformational transitions within a protein and the prediction of their kinetics is investigated. As this typically requires the introduction of multiple collective variables $\{x_j\}= \vec{x}$, a theoretical framework is outlined to calculate the associated free energy $\Delta G (\vec{x})$ and friction $\matrix{\Gamma}(\vec{x})$ from dcTMD simulations along coordinate $s$. Adopting the $\alpha$-$\beta$ transition of alanine dipeptide as well as the open-closed transition of T4 lysozyme as representative examples, the virtues and shortcomings of dcTMD to predict protein conformational transitions and the related kinetics are studied., Comment: This preprint is the unedited version of a manuscript that has been sent to a scientific journal for consideration as a publication and can be downloaded for private use only. Copyright with the journal and its publisher after publication

Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process. To mitigate this challenge, Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance. However, practitioners face many AL strategies for different tasks and need an empirical basis to choose between them. Surveys categorize AL strategies into taxonomies without performance indications. Presentations of novel AL strategies compare the performance to a small subset of strategies. Our contribution addresses the empirical basis by introducing a reproducible active learning evaluation (ALE) framework for the comparative evaluation of AL strategies in NLP. The framework allows the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can focus on developing new, effective AL strategies and deriving best practices for specific use cases. With best practices, practitioners can lower their annotation costs. We present a case study to illustrate how to use the framework., Comment: The Version of Record of this contribution is published in Deep Learning Theory and Applications 4th International Conference, DeLTA 2023 Proceedings, and is available online at https://doi.org/10.1007/978-3-031-39059-3_16

Protein-ligand (un)binding simulations are a recent focus of biased molecular dynamics simulations. Such binding and unbinding can occur via different pathways in and out of a binding site. We here present a theoretical framework how to compute kinetics along separate paths and to combine the path-specific rates into global binding and unbinding rates for comparison with experiment. Using dissipation-corrected targeted molecular dynamics in combination with temperature-boosted Langevin equation simulations [Nat. Commun. \textbf{11}, 2918 (2020)] applied to a two-dimensional model and the trypsin-benzamidine complex as test systems, we assess the robustness of the procedure and discuss aspects of its practical applicability to predict multisecond kinetics of complex biomolecular systems., Comment: This preprint is the unedited version of a manuscript that has been published in J. Chen. Phys. and can be downloaded for private use only. Copyright with AIP and and the authors

While allostery is of paramount importance for protein signaling and regulation, the underlying dynamical process of allosteric communication is not well understood. PDZ3 domain represents a prime example of an allosteric single-domain protein, as it features a well-established long-range coupling between the C-terminal $\alpha_3$-helix and ligand binding. In an intriguing experiment, Hamm and coworkers employed photoswitching of the $\alpha_3$-helix to initiate a conformational change of PDZ3 that propagates from the C-terminus to the bound ligand within 200 ns. Performing extensive nonequilibrium molecular dynamics simulations, the modeling of the experiment reproduces the measured timescales and reveals a detailed picture of the allosteric communication in PDZ3. In particular, a correlation analysis identifies a network of contacts connecting the $\alpha_3$-helix and the core of the protein, which move in a concerted manner. Representing a one-step process and involving direct $\alpha_3$-ligand contacts, this cooperative transition is considered as elementary step in the propagation of conformational change.

Photoproteins such as bacteriorhodopsin (bR) and rhodopsin (Rho) need to effectively dissipate photoinduced excess energy to prevent their damage. Another well-studied G protein-coupled receptor (GPCR) containing 7 transmembrane (TM) helices is the B2 adrenergic receptor (B2AR), for which energy dissipation paths have been linked with allosteric communication. To study the vibrational energy transport in the active and inactive states of these GPCRs, a master equation approach [J. Chem. Phys. 152, 045103 (2020)] is employed, which uses scaling rules that allow to calculate energy transport rates solely based on the protein structure. Despite their structural similarities, the three GPCRs reveal quite different strategies to redistribute excess energy. While bR quickly removes the energy using the TM7 helix as a "lightning rod", Rho exhibits a rather poor energy dissipation, which might eventually require the hydrolysis of the Schiff base between the protein and the retinal chromophore to prevent overheating. Heating the ligand adrenaline of B2AR, the resulting energy transport network of the protein is found to change significantly upon switching from the active to the inactive state. While the energy flow may highlight aspects of the interresidue couplings of B2AR, it seems not particularly suited to explain allosteric phenomena.

We present two methods to reveal protein-ligand unbinding mechanisms in biased unbinding simulations by clustering trajectories into ensembles representing unbinding paths. The first approach is based on a contact principal component analysis for reducing the dimensionality of the input data, followed by identification of unbinding paths and training a machine learning model for trajectory clustering. The second approach clusters trajectories according to their pairwise mean Euclidean distance employing the neighbor-net algorithm, which takes into account input data bias in the distances set and is superior to dendrogram construction. Finally, we describe a more complex case where the reaction coordinate relevant for path identification is a single intra-ligand hydrogen bond, highlighting the challenges involved in unbinding path reaction coordinate detection., Comment: This preprint is the unformatted version of a manuscript that has been published as article in the Journal of Chemical Information and Modeling and can be downloaded for private use only. Copyright with ACS, the journal and the authors

The friction coefficient of fluids may become a function of the velocity at increased external driving. This non-Newtonian behavior is of general theoretical interest as well as of great practical importance, e.g., for the design of lubricants. While the effect has been observed in large-scale atomistic simulations of bulk liquids, its theoretical formulation and microscopic origin is not well understood. Here we use dissipation-corrected targeted molecular dynamics, which pulls apart two tagged liquid molecules in the presence of surrounding molecules and analyzes this nonequilibrium process via a generalized Langevin equation. The approach is based on a second-order cumulant expansion of Jarzynski's identity, which is shown to be valid for fluids and therefore allows for an exact computation of the friction profile as well of the underlying memory kernel. We show that velocity-dependent friction in fluids results from an intricate interplay of near-order structural effects and the non-Markovian behavior of the friction memory kernel. For complex fluids such as the model lubricant \alkane, the memory kernel exhibits a stretched-exponential long-time decay, which reflects the multitude of timescales of the system., Comment: This preprint is the unedited version of a manuscript that has been submitted to a scientific journal for consideration as publication and may be downloaded for personal use only. Copyright will be with the publisher after publication

Multi-band images of galaxies reveal a huge amount of information about their morphology and structure. However, inferring properties of the underlying stellar populations such as age, metallicity or kinematics from those images is notoriously difficult. Traditionally such information is best extracted from expensive spectroscopic observations. Here we present the $Painting\, IntrinsiC\, Attributes\, onto\, SDSS\, Objects$ (PICASSSO) project and test the information content of photometric multi-band images of galaxies. We train a convolutional neural network on 27,558 galaxy image pairs to establish a connection between broad-band images and the underlying physical stellar and gaseous galaxy property maps. We test our machine learning (ML) algorithm with SDSS $ugriz$ mock images for which uncertainties and systematics are exactly known. We show that multi-band galaxy images contain enough information to reconstruct 2d maps of stellar mass, metallicity, age and gas mass, metallicity as well as star formation rate. We recover the true stellar properties on a pixel by pixel basis with only little scatter, $\lesssim20\%$ compared to $\sim50\%$ statistical uncertainty from traditional mass-to-light-ratio based methods. We further test for any systematics of our algorithm with image resolution, training sample size or wavelength coverage. We find that galaxy morphology alone constrains stellar properties to better than $\sim20\%$ thus highlighting the benefits of including morphology into the parameter estimation. The machine learning approach can predict maps of high resolution, only limited by the resolution of the input bands, thus achieving higher resolution than IFU observations. The network architecture and all code is publicly available on GitHub., Comment: 14 pages main text, 9 figures, 3 pages appendix with 5 figures, submitted to MNRAS

Protein dynamics has been investigated on a wide range of time scales. Nano- and picosecond dynamics have been assigned to local fluctuations, while slower dynamics have been attributed to larger conformational changes. However, it is largely unknown how local fluctuations can lead to global allosteric changes. Here we show that molecule-spanning dynamics on the 100 ns time scale precede larger allosteric changes. We assign global real-space movements to dynamic modes on the 100 ns time scales, which became possible by a combination of single-molecule fluorescence, quasi-elastic neutron scattering and all-atom MD simulations. Additionally, we demonstrate the effect of Sba1, a co-chaperone of Hsp90, on these molecule-spanning dynamics, which implies functional importance of such dynamics. Our integrative approach provides comprehensive insights into molecule-spanning dynamics on the nanosecond time scale for a multi-domain protein and indicates that such dynamics are the molecular basis for allostery and large conformational changes in proteins., Comment: 29 main text pages, 7 main text figures; 27 SI pages, 15 SI figures