Ramin Ansari

20230040576, Feb 9, 2023

Jul 22, 2022

17/871425

- Culver City CA, US
Julien Jorda - Los Angeles CA, US
Matthias Maria Alessandro Malago - Santa Monica CA, US
Thibault Marie Duplay - Los Angeles CA, US
Lisa Juliette Madeleine Barel - Los Angeles CA, US
Ramin Ansari - Los Angeles CA, US

G16B 15/30
G16B 40/00
G16B 45/00

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. A graph-based neural network model as described herein may then, based on the received input, generate predictions of likely acid sequences and/or types of particular amino acids at the unknown portions. These predictions can then be used to determine (e.g., fill in) amino acid sequences and/or structures to complete the custom biologic.