GDockScore: a graph-based protein-protein docking scoring function.

Summary: Protein complexes play vital roles in a variety of biological processes, such as mediating biochemical reactions, the immune response and cell signalling, with 3D 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 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. Availability and implementation: The model implementation is available at https://gitlab.com/mcfeemat/gdockscore. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Computational and artificial intelligence-based methods for antibody development.

Due to their high target specificity and binding affinity, therapeutic antibodies are currently the largest class of biotherapeutics. The traditional largely empirical antibody development process is, while mature and robust, cumbersome and has significant limitations. Substantial recent advances in computational and artificial intelligence (AI) technologies are now starting to overcome many of these limitations and are increasingly integrated into development pipelines. Here, we provide an overview of AI methods relevant for antibody development, including databases, computational predictors of antibody properties and structure, and computational antibody design methods with an emphasis on machine learning (ML) models, and the design of complementarity-determining region (CDR) loops, antibody structural components critical for binding.

Brain Organoids: A New, Transformative Investigational Tool for Neuroscience Research.

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.