A pain killer without side effects: Opioids are very strong and effective pain killers. However, they also have a range of well-known side effects and can cause addiction. Painful conditions such as inflammation or trauma are often associated with localized tissue acidification. Spahn et al. designed a novel opioid receptor agonist that, unlike clinically used opioids, best activates the receptors in such acidified tissues. In rat models of inflammatory pain, the new drug exerted strong pain relief essentially without the side effects of standard opioids. Science , this issue p. 966

Multivalent ligand-receptor systems often show an enhancement in binding compared to the constituent monovalent systems. This “cooperativity effect” is often attributed to the favorable spatial preorganisation of the ligands by the connecting spacer that leads to a reduction of entropy loss at ligand binding. A different factor that has been proposed to contribute to the cooperativity effect is “rebinding”: As soon as a single ligand-receptor complex dissociates, the presence of another ligand “on coat-tails” will increase the probability of another binding event, which in turn will drive the system to a state where all ligands are bound. In this article, we derive a first quantitative description of the rebinding effect. In order to model the inherent memory effect of a spacer-connected system, we pursue a mathematical approach based on Markov state models and conformation dynamics. The theoretical investigations are illustrated by studying different prototypic ligand-receptor systems.

Dynamical reweighting methods permit to estimate kinetic observables of a stochastic process governed by a target potential $\tilde{V}(x)$ from trajectories that have been generated at a different potential V(x). In this article, we present Girsanov reweighting and square root approximation: the first method reweights path probabilities exploiting the Girsanov theorem and can be applied to Markov state models to reweight transition probabilities; the second method was originally developed to discretize the Fokker–Planck operator into a transition rate matrix, but here we implement it into a reweighting scheme for transition rates. We begin by reviewing the theoretical background of the methods and then present two applications relevant to molecular dynamics, highlighting their strengths and weaknesses.

Introduction: In 2021, the Deutsche Gesellschaft für Neurology published a new guideline on diagnosis and therapy of motor neuron disorders. Motor neuron disorders affect upper motor neurons in the primary motor cortex and/or lower motor neurons in the brain stem and spinal cord. The most frequent motor neuron disease amyotrophic lateral sclerosis (ALS) is a rapidly progressive disease with an average life expectancy of 2–4 years with a yearly incidence of 3.1/100,000 in Central Europe (Rosenbohm et al. in J Neurol 264(4):749–757, 2017. https://doi.org/10.1007/s00415-017-8413-3). It is considered a rare disease mainly due to its low prevalence as a consequence of short disease duration. Recommendations: These guidelines comprise recommendations regarding differential diagnosis, neuroprotective therapies and multidisciplinary palliative care including management of respiration and nutrition as well as provision of assistive devices and end-of-life situations. Conclusion: Diagnostic and therapeutic guidelines are necessary due the comparatively high number of cases and the aggressive disease course. Given the low prevalence and the severe impairment of patients, it is often impossible to generate evidence-based data so that ALS guidelines are partially dependent on expert opinion.

The problem of determining the rate of rare events in dynamical systems is quite well-known but still difficult to solve. Recent attempts to overcome this problem exploit the fact that dynamic systems can be represented by a linear operator, such as the Koopman operator. Mathematically, the rare event problem comes down to the difficulty in finding invariant subspaces of these Koopman operators $\mathcal{K}$ . In this article, we describe a method to learn basis functions of invariant subspaces using an artificial neural network.