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The cytoskeleton of eukaryotic cells is primarily composed of networks of filamentous proteins, F-actin, microtubules, and intermediate filaments. Interactions among the cytoskeletal components are important in determining cell structure and in regulating cell functions. For example, F-actin and microtubules work together to control cell shape and polarity, while the subcellular organization and transport of vimentin intermediate filament (VIF) networks depend on their interactions with microtubules. However, it is generally thought that F-actin and VIFs form two coexisting but separate networks that are independent due to observed differences in their spatial distribution and functions. In this paper, we present a closer investigation of both the structural and functional interplay between the F-actin and VIF cytoskeletal networks. We characterize the structure of VIFs and F-actin networks within the cell cortex using structured illumination microscopy and cryoelectron tomography. We find that VIFs and F-actin form an interpenetrating network (IPN) with interactions at multiple length scales, and VIFs are integral components of F-actin stress fibers. From measurements of recovery of cell contractility after transient stretching, we find that the IPN structure results in enhanced contractile forces and contributes to cell resilience. Studies of reconstituted networks and dynamic measurements in cells suggest direct and specific associations between VIFs and F-actin. From these results, we conclude that VIFs and F-actin work synergistically, both in their structure and in their function. These results profoundly alter our understanding of the contributions of the components of the cytoskeleton, particularly the interactions between intermediate filaments and F-actin. [ABSTRACT FROM AUTHOR]

Deep learning-based prediction of protein structure usually begins by constructing a multiple sequence alignment (MSA) containing homologs of the target protein. The most successful approaches combine large feature sets derived from MSAs, and considerable computational effort is spent deriving these input features. We present a method that greatly reduces the amount of preprocessing required for a target MSA, while producing main chain coordinates as a direct output of a deep neural network. The network makes use of just three recurrent networks and a stack of residual convolutional layers, making the predictor very fast to run, and easy to install and use. Our approach constructs a directly learned representation of the sequences in an MSA, starting from a one-hot encoding of the sequences. When supplemented with an approximate precision matrix, the learned representation can be used to produce structural models of comparable or greater accuracy as compared to our original DMPfold method, while requiring less than a second to produce a typical model. This level of accuracy and speed allows very large-scale three-dimensional modeling of proteins on minimal hardware, and we demonstrate this by producing models for over 1.3 million uncharacterized regions of proteins extracted from the BFD sequence clusters. After constructing an initial set of approximate models, we select a confident subset of over 30,000 models for further refinement and analysis, revealing putative novel protein folds. We also provide updated models for over 5,000 Pfam families studied in the original DMPfold paper. [ABSTRACT FROM AUTHOR]

Protein flexibility remains a major challenge in library docking because of difficulties in sampling conformational ensembles with accurate probabilities. Here, we use the model cavity site of T4 lysozyme L99A to test flexible receptor docking with energy penalties from molecular dynamics (MD) simulations. Crystallography with larger and smaller ligands indicates that this cavity can adopt three major conformations: open, intermediate, and closed. Since smaller ligands typically bind better to the cavity site, we anticipate an energy penalty for the cavity opening. To estimate its magnitude, we calculate conformational preferences from MD simulations. We find that including a penalty term is essential for retrospective ligand enrichment; otherwise, high-energy states dominate the docking. We then prospectively docked a library of over 900,000 compounds for new molecules binding to each conformational state. Absent a penalty term, the open conformation dominated the docking results; inclusion of this term led to a balanced sampling of ligands against each state. High ranked molecules were experimentally tested by Tm upshift and X-ray crystallography. From 33 selected molecules, we identified 18 ligands and determined 13 crystal structures. Most interesting were those bound to the open cavity, where the buried site opens to bulk solvent. Here, highly unusual ligands for this cavity had been predicted, including large ligands with polar tails; these were confirmed both by binding and by crystallography. In docking, incorporating protein flexibility with thermodynamic weightings may thus access new ligand chemotypes. The MD approach to accessing and, crucially, weighting such alternative states may find general applicability. [ABSTRACT FROM AUTHOR]

Recent advances in gene editing technologies are enabling the potential correction of devastating monogenic disorders through elimination of underlying genetic mutations. Duchenne muscular dystrophy (DMD) is an especially severe genetic disorder caused by mutations in the gene encoding dystrophin, a membraneassociated protein required for maintenance of muscle structure and function. Patients with DMD succumb to loss of mobility early in life, culminating in premature death from cardiac and respiratory failure. The disease has thus far defied all curative strategies. CRISPR gene editing has provided new opportunities to ameliorate the disease by eliminating DMD mutations and thereby restore dystrophin expression throughout skeletal and cardiac muscle. Proof-of-concept studies in rodents, large mammals, and human cells have validated the potential of this approach, but numerous challenges remain to be addressed, including optimization of gene editing, delivery of gene editing components throughout the musculature, and mitigation of possible immune responses. This paper provides an overview of recent work from our laboratory and others toward the genetic correction of DMD and considers the opportunities and challenges in the path to clinical translation. Lessons learned from these studies will undoubtedly enable further applications of gene editing to numerous other diseases of muscle and other tissues. [ABSTRACT FROM AUTHOR]

Breast cancer patients with increased expression of hypoxia-inducible factors (HIFs) in primary tumor biopsies are at increased risk of metastasis, which is the major cause of breast cancer-related mortality. The mechanisms by which intratumoral hypoxia and HIFs regulate metastasis are not fully elucidated. In this paper, we report that exposure of human breast cancer cells to hypoxia activates epidermal growth factor receptor (EGFR) signaling that is mediated by the HIF-dependent expression of a disintegrin and metalloprotease 12 (ADAM12), which mediates increased ectodomain shedding of heparin-binding EGF-like growth factor, an EGFR ligand, leading to EGFR-dependent phosphorylation of focal adhesion kinase. Inhibition of ADAM12 expression or activity decreased hypoxia-induced breast cancer cell migration and invasion in vitro, and dramatically impaired lungmetastasis after orthotopic implantation of MDA-MB-231 human breast cancer cells into the mammary fat pad of immunodeficient mice. [ABSTRACT FROM AUTHOR]