Functional assessment of stem cell-mediated endogenous repair relies on animal studies. Here an in vitro assay is described that recapitulates important early steps of the in vivo skeletal muscle endogenous repair (MEndR) process. The assay is integrated with a custom semi-automated image analysis pipeline to enable high-content data analysis of donor-derived muscle fiber content and morphology. Myotube sheets, generated by infiltrating a cellulose scaffold with myoblasts, are engrafted with muscle stem cells (MuSCs), injured to induce a regenerative microenvironment, and muscle repair is assessed. Significantly, the spatiotemporal dynamics of in vitro repair closely matched those observed in vivo, when both stem cells and injury are present. By exploiting the easy imaging geometry of the engineered tissue, cellular mechanisms of action driving the MuSC response to the regenerative template are explored. In vivo outcomes of two known modulators of MuSC-mediated repair, measured by donor fiber production, MuSC niche repopulation, and response to a secondary injury, are phenocopied in the platform only when both the stem cells and injured 3D template are present. The MEndR platform represents a powerful opportunity to explore MuSC-mediated repair and potentially compress the discovery pipeline by combining drug screening and validation in one step.

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Brain organoids are self‐assembled, three‐dimensionally structured tissues that are typically derived from pluripotent stem cells. They are multicellular aggregates that more accurately recapitulate the tissue microenvironment compared to the other cell culture systems and can also reproduce organ function. They are promising models for evaluating drug leads, particularly those that target neurodegeneration, since they are genetically and phenotypically stable over prolonged durations of culturing and they reasonably reproduce critical physiological phenomena such as biochemical gradients and responses by the native tissue to stimuli. Beyond drug discovery, the use of brain organoids could also be extended to investigating early brain development and identifying the mechanisms that elicit neurodegeneration. Herein, the current state of the fabrication and use of brain organoids in drug development and medical research is summarized. Although the use of brain organoids represents a quantum leap over existing investigational tools used by the pharmaceutical industry, they are nonetheless imperfect systems that could be greatly improved through bioengineering. To this end, some key scientific challenges that would need to be addressed in order to enhance the relevance of brain organoids as model tissue are listed. Potential solutions to these challenges, including the use of bioprinting, are highlighted thereafter.

Protein complexes play vital roles in a variety of biological processes such as mediating biochemical reactions, the immune response, and cell signalling, with three-dimensional structure specifying function. Computational docking methods provide a means to determine the interface between two complexed polypeptide chains without using time-consuming experimental techniques. The docking process requires the optimal solution to be selected with a scoring function. Here we propose a novel graph-based deep learning model that utilizes mathematical graph representations of proteins to learn a scoring function (GDockScore). GDockScore was pre-trained on docking outputs generated with the Protein Data Bank (PDB) biounits and the RosettaDock protocol, and then fine-tuned on HADDOCK decoys generated on the ZDOCK Protein Docking Benchmark. GDockScore performs similarly to the Rosetta scoring function on docking decoys generated using the RosettaDock protocol. Furthermore, state-of-the-art is achieved on the CAPRI score set, a challenging dataset for developing docking scoring functions. The model implementation is available at https://gitlab.com/mcfeemat/gdockscore.

Protein complexes play vital roles in a variety of biological processes such as mediating biochemical reactions, the immune response, and cell signalling, with three-dimensional structure specifying function. Computational docking methods provide a means to determine the interface between two complexed polypeptide chains without using time-consuming experimental techniques. The docking process requires the optimal solution to be selected with a scoring function. Here we propose a novel graph-based deep learning model that utilizes mathematical graph representations of proteins to learn a scoring function (GDockScore). GDockScore was pre-trained on docking outputs generated with the Protein Data Bank (PDB) biounits and the RosettaDock protocol, and then fine-tuned on HADDOCK decoys generated on the ZDOCK Protein Docking Benchmark. GDockScore performs similarly to the Rosetta scoring function on docking decoys generated using the RosettaDock protocol. Furthermore, state-of-the-art is achieved on the CAPRI score set, a challenging dataset for developing docking scoring functions.