Abstract: Controlling antibody affinity and expression are key to clinical applications. High affinity antibodies correlate with higher specificity and can be used at lower doses. Presently, antibody maturation is tackled with directed evolution methods. In this case, an initial library of mutated binders is seeded into a process and affinity is improved through multiple rounds of mutation and selection. However, the present disclosure employs a machine learning approach to computationally mature antibody sequences using a process having parallels to directed evolution. These antibody sequences can be manufactured into physical antibodies after their computation and verification. Additionally, the present method has the potential to outperform directed evolution when targeting a specific affinity, and is applicable to general protein-protein interactions.

Abstract: Accurate function estimations and well-calibrated uncertainties are important for Bayesian optimization (BO). Most theoretical guarantees for BO are established for methods that model the objective function with a surrogate drawn from a Gaussian process (GP) prior. GP priors are poorly-suited for discrete, high-dimensional, combinatorial spaces, such as biopolymer sequences. Using a neural network (NN) as the surrogate function can obtain more accurate function estimates. Using a NN can allow arbitrarily complex models, removing the GP prior assumption, and enable easy pretraining, which is beneficial in the low-data BO regime. However, a fully-Bayesian treatment of uncertainty in NNs remains intractable, and existing approximate methods, like Monte Carlo dropout and variational inference, can highly miscalibrate uncertainty estimates.

Abstract: Systems, apparatuses, software, and methods for engineering amino acid sequences configured to have specific protein functions or properties. Machine learning is implemented by methods to process an input seed sequence and generate as output an optimized sequence having the desired function or property.

Abstract: Systems, apparatuses, software, and methods for identifying associations between amino acid sequences and protein functions or properties. The application of machine learning is used to generate models that identify such associations based on input data such as amino acid sequence information. Various techniques including transfer learning can be utilized to enhance the accuracy of the associations.