Structural basis for cytoplasmic dynein-1 regulation by Lis1

The lissencephaly 1 gene, LIS1, is mutated in patients with the neurodevelopmental disease lissencephaly. The Lis1 protein is conserved from fungi to mammals and is a key regulator of cytoplasmic dynein-1, the major minus-end-directed microtubule motor in many eukaryotes. Lis1 is the only dynein regulator known to bind directly to dynein's motor domain, and by doing so alters dynein's mechanochemistry. Lis1 is required for the formation of fully active dynein complexes, which also contain essential cofactors: dynactin and an activating adaptor. Here, we report the first high-resolution structure of the yeast dynein–Lis1 complex. Our 3.1Å structure reveals, in molecular detail, the major contacts between dynein and Lis1 and between Lis1's ß-propellers. Structure-guided mutations in Lis1 and dynein show that these contacts are required for Lis1's ability to form fully active human dynein complexes and to regulate yeast dynein's mechanochemistry and in vivo function.

Periodic synchronization of isolated network elements facilitates simulating and inferring gene regulatory networks including stochastic molecular kinetics

Background The temporal progression of many fundamental processes in cells and organisms, including homeostasis, differentiation and development, are governed by gene regulatory networks (GRNs). GRNs balance fluctuations in the output of their genes, which trace back to the stochasticity of molecular interactions. Although highly desirable to understand life processes, predicting the temporal progression of gene products within a GRN is challenging when considering stochastic events such as transcription factor–DNA interactions or protein production and degradation. Results We report a method to simulate and infer GRNs including genes and biochemical reactions at molecular detail. In our approach, we consider each network element to be isolated from other elements during small time intervals, after which we synchronize molecule numbers across all network elements. Thereby, the temporal behaviour of network elements is decoupled and can be treated by local stochastic or deterministic solutions. We demonstrate the working principle of this modular approach with a repressive gene cascade comprising four genes. By considering a deterministic time evolution within each time interval for all elements, our method approaches the solution of the system of deterministic differential equations associated with the GRN. By allowing genes to stochastically switch between on and off states or by considering stochastic production of gene outputs, we are able to include increasing levels of stochastic detail and approximate the solution of a Gillespie simulation. Thereby, CaiNet is able to reproduce noise-induced bi-stability and oscillations in dynamically complex GRNs. Notably, our modular approach further allows for a simple consideration of deterministic delays. We further infer relevant regulatory connections and steady-state parameters of a GRN of up to ten genes from steady-state measurements by identifying each gene of the network with a single perceptron in an artificial neuronal network and using a gradient decent method originally designed to train recurrent neural networks. To facilitate setting up GRNs and using our simulation and inference method, we provide a fast computer-aided interactive network simulation environment, CaiNet. Conclusion We developed a method to simulate GRNs at molecular detail and to infer the topology and steady-state parameters of GRNs. Our method and associated user-friendly framework CaiNet should prove helpful to analyze or predict the temporal progression of reaction networks or GRNs in cellular and organismic biology. CaiNet is freely available at https://gitlab.com/GebhardtLab/CaiNet.

Molecular plasticity of the native mouse skeletal sarcomere revealed by cryo-ET

Sarcomeres are the force-generating and load-bearing devices of muscles. A precise molecular understanding of how the entire sarcomere is built is required to understand its role in health, disease and ageing. Here, we determine the in situ molecular architecture of vertebrate skeletal sarcomeres through electron cryo-tomography of cryo-focused ion beam-milled native myofibrils. The reconstructions reveal the three-dimensional organisation and interaction of actin and myosin filaments in the A-band, I-band and Z-disc and demonstrate how α -actinin cross-links antiparallel actin filaments to form a mesh-like structure in the Z-disc at an unprecedented level of molecular detail. A prominent feature is a so-far undescribed doublet of α-actinin cross-links with ∼ 6 nm spacing. Sub-volume averaging shows the interaction between myosin, tropomyosin and actin in molecular detail at ∼ 10 Å resolution and reveals two coexisting conformations of actin-bound heads. The flexible orientation of the lever arm and the essential and regulatory light chains allow the two heads of the “double-headed” myosin not only to interact with the same actin filament but also to split between two actin filaments. Our results provide new insights into the conformational plasticity and fundamental organisation of vertebrate skeletal muscle and serve as a strong foundation for future in situ investigations of muscle diseases.

Anti-CRISPRs: Protein Inhibitors of CRISPR-Cas Systems

Clustered regularly interspaced short palindromic repeats (CRISPR) together with their accompanying cas (CRISPR-associated) genes are found frequently in bacteria and archaea, serving to defend against invading foreign DNA, such as viral genomes. CRISPR-Cas systems provide a uniquely powerful defense because they can adapt to newly encountered genomes. The adaptive ability of these systems has been exploited, leading to their development as highly effective tools for genome editing. The widespread use of CRISPR-Cas systems has driven a need for methods to control their activity. This review focuses on anti-CRISPRs (Acrs), proteins produced by viruses and other mobile genetic elements that can potently inhibit CRISPR-Cas systems. Discovered in 2013, there are now 54 distinct families of these proteins described, and the functional mechanisms of more than a dozen have been characterized in molecular detail. The investigation of Acrs is leading to a variety of practical applications and is providing exciting new insight into the biology of CRISPR-Cas systems.

Extreme biomimetics: Preservation of molecular detail in centimeter-scale samples of biological meshes laid down by sponges

Fabrication of biomimetic materials and scaffolds is usually a micro- or even nanoscale process; however, most testing and all manufacturing require larger-scale synthesis of nanoscale features. Here, we propose the utilization of naturally prefabricated three-dimensional (3D) spongin scaffolds that preserve molecular detail across centimeter-scale samples. The fine-scale structure of this collagenous resource is stable at temperatures of up to 1200°C and can produce up to 4 × 10–cm–large 3D microfibrous and nanoporous turbostratic graphite. Our findings highlight the fact that this turbostratic graphite is exceptional at preserving the nanostructural features typical for triple-helix collagen. The resulting carbon sponge resembles the shape and unique microarchitecture of the original spongin scaffold. Copper electroplating of the obtained composite leads to a hybrid material with excellent catalytic performance with respect to the reduction of p-nitrophenol in both freshwater and marine environments.

A multi-model approach to assessing local and global cryo-EM map quality

There does not currently exist a standardized indicator of how well a cryo-EM-derived model represents the density from which it was generated. We present a straightforward methodology that utilizes freely available tools to generate a suite of independent models and to evaluate their convergence in an EM density. These analyses provide both a quantitative and qualitative assessment of the precision of the models and their representation of the density, respectively, while concurrently providing a platform for assessing both global and local EM map quality. We further use standardized datasets to provide an expected model–model agreement criterion for EM maps reported to be at 5 Å resolution or better. Associating multiple atomic models with a deposited EM map provides a rapid and accessible reporter of convergence, a strong indicator of highly resolved molecular detail, and is an important step toward an FSC-independent assessment of map and model quality.

InterPred: A pipeline to identify and model protein-protein interactions

Protein-protein interactions (PPI) are crucial for protein function. There exist many techniques to identify PPIs experimentally, but to determine the interactions in molecular detail is still difficult and very time-consuming. The fact that the number of PPIs is vastly larger than the number of individual proteins makes it practically impossible to characterize all interactions experimentally. Computational approaches that can bridge this gap and predict PPIs and model the interactions in molecular detail are greatly needed. Here we present InterPred, a fully automated pipeline that predicts and model PPIs from sequence using structural modelling combined with massive structural comparisons and molecular docking. A key component of the method is the use of a novel random forest classifier that integrate several structural features to distinguish correct from incorrect protein-protein interaction models. We show that InterPred represents a major improvement in protein-protein interaction detection with a performance comparable or better than experimental high-throughput techniques. We also show that our full-atom protein-protein complex modelling pipeline performs better than state of the art protein docking methods on a standard benchmark set. In addition, InterPred was also one of the top predictors in the latest CAPRI37 experiment.InterPred source code can be downloaded from http://wallnerlab.org/InterPred