Paracrine Wnt/b-catenin signalling is important during developmental processes, tissue regeneration and stem cell regulation. Wnt proteins are morphogens, which form concentration gradients across responsive tissues. Little is known about the transport mechanism for these lipid-modified signalling proteins in vertebrates. Here we show that Wnt8a is transported on actin-based filopodia to contact responding cells and activate signalling during neural plate formation in zebrafish. Cdc42/N-Wasp regulates the formation of these Wnt-positive filopodia. Enhanced formation of filopodia increases the effective signalling range of Wnt by facilitating spreading. Consistently, reduction in filopodia leads to a restricted distribution of the ligand and a limited signalling range. Using a simulation, we provide evidence that such a short-range transport system for Wnt has a long-range signalling function. Indeed, we show that a filopodia-based transport system for Wnt8a controls anteroposterior patterning of the neural plate during vertebrate gastrulation.

Fully understanding biomolecular function requires detailed insight into the systems' structural dynamics. Powerful experimental techniques such as single molecule Förster Resonance Energy Transfer (FRET) provide access to such dynamic information yet have to be carefully interpreted. Molecular simulations can complement these experiments but typically face limits in accessing slow time scales and large or unstructured systems. Here, we introduce a coarse-grained simulation technique that tackles these challenges. While requiring only few parameters, we maintain full protein flexibility and include all heavy atoms of proteins, linkers, and dyes. We are able to sufficiently reduce computational demands to simulate large or heterogeneous structural dynamics and ensembles on slow time scales found in, e.g., protein folding. The simulations allow for calculating FRET efficiencies which quantitatively agree with experimentally determined values. By providing atomically resolved trajectories, this work supports the planning and microscopic interpretation of experiments. Overall, these results highlight how simulations and experiments can complement each other leading to new insights into biomolecular dynamics and function.

Supplementary data are available at Bioinformatics online.

During embryogenesis, morphogens form a concentration gradient in responsive tissue, which is then translated into a spatial cellular pattern. The mechanisms by which morphogens spread through a tissue to establish such a morphogenetic field remain elusive. Here, we investigate by mutually complementary simulations and in vivo experiments how Wnt morphogen transport by cytonemes differs from typically assumed diffusion-based transport for patterning of highly dynamic tissue such as the neural plate in zebrafish. Stochasticity strongly influences fate acquisition at the single cell level and results in fluctuating boundaries between pattern regions. Stable patterning can be achieved by sorting through concentration dependent cell migration and apoptosis, independent of the morphogen transport mechanism. We show that Wnt transport by cytonemes achieves distinct Wnt thresholds for the brain primordia earlier compared with diffusion-based transport. We conclude that a cytoneme-mediated morphogen transport together with directed cell sorting is a potentially favored mechanism to establish morphogen gradients in rapidly expanding developmental systems.

The 2013 Nobel Prize in Chemistry highlights how crucial computer simulations have become for many scientific and engineering fields. Nowadays, scientific progress is not only driven by the interplay of new experimental measurements and increasingly sophisticated theoretical frameworks, but also by an incredible toolbox of complex computational models meeting ubiquitously available computing power and data storage facilities. Quantum mechanical (QM) calculations can be condensed into molecular mechanics (MM) force fields and coupled QM/MM calculations can derive atomic and molecular properties of biomolecular or materials science systems with high accuracy. Pure MM simulations driven by Monte Carlo or molecular dynamics algorithms are widely applied in biological chemistry/physics and can investigate large biomolecular systems, such as proteins, DNA, or RNA. One coarse‐grained class of these models, native‐structure‐based or Go models, are based on energy landscape theory and the principle of minimal frustration. Herein, an ensemble of converging pathways guide protein folding on a funnel‐like shape of the entire energy landscape towards the native state. Simulations based on these ideas have been tremendously successful in explaining protein folding and function. Their history and recent application highlights are reviewed.