Abstract: Presented herein are systems and methods for predicting which amino acid sites of a target proteins of interest will be binding sites—for example, locations and/or identifications of particular amino acid sites—that are amenable or likely to participate in binding interactions with other ligands, such as other proteins. These binding site predictions may, for example, be generated for target proteins that are implicated in disease and, accordingly, be targets for potential new biologic drugs. Binding site prediction technologies described herein may thus be used to guide design and/or testing of new and/or custom biologic drugs, either experimentally or in-silico. In this manner, binding site prediction technologies of the present disclosure can facilitate design and/or testing of new biologic drugs, leading to new and improved candidates and/or improving, among other things, developmental efficiency, success rates of clinical trials, and time to market.

Abstract: Described herein are systems and methods for designing and testing custom biologic molecules in silico which are useful, for example, for the treatment, prevention, and diagnosis of disease. In particular, in certain embodiments, the biomolecule engineering technologies described herein employ artificial intelligence (AI) software modules to accurately predict performance of candidate biomolecules and/or portions thereof with respect to particular design criteria. In certain embodiments, the AI-powered modules described herein determine performance scores with respect to design criteria such as binding to a particular target. AI-computed performance scores may, for example, be used as objective functions for computer implemented optimization routines that efficiently search a landscape of potential protein backbone orientations and binding interface amino-acid sequences.

Abstract: Presented herein are systems and methods for prediction of protein interfaces for binding to target molecules. In certain embodiments, technologies described herein utilize graph-based neural networks to predict portions of protein/peptide structures that are located at an interface of custom biologic (e.g., a protein and/or peptide) that is being designed for binding to a target molecule, such as another protein or peptide. In certain embodiments, graph-based neural network models described herein may receive, as input, a representation (e.g., a graph representation) of a complex comprising a target and a partially-defined custom biologic. Portions of the partially-defined custom biologic may be known, while other portions, such an amino acid sequence and/or particular amino acid types at certain locations of an interface, are unknown and/or to be customized for binding to a particular target.

Abstract: Described herein are systems and methods for designing and testing custom biologic molecules in silico which are useful, for example, for the treatment, prevention, and diagnosis of disease. In particular, in certain embodiments, the biomolecule engineering technologies described herein employ artificial intelligence (AI) software modules to accurately predict performance of candidate biomolecules and/or portions thereof with respect to particular design criteria. In certain embodiments, the AI-powered modules described herein determine performance scores with respect to design criteria such as binding to a particular target. AI-computed performance scores may, for example, be used as objective functions for computer implemented optimization routines that efficiently search a landscape of potential protein backbone orientations and binding interface amino-acid sequences.