We will develop and apply a new high-throughput methodology for rapidly designing and testing antibodies for a myriad of purposes, including cancer and infectious disease immunotherapeutics. We will improve upon current approaches for antibody design by providing time, cost, and humane benefits over immunized animal methods and greatly improving the power of present synthetic methods that use randomized designs. To accomplish this, we will display millions of computationally designed antibody sequences using recently available technology, test the displayed antibodies in a high-throughput format at low cost, and use the resulting test data to train molecular dynamics and machine learning methods to generate new sequences for testing. Based on our test data our computational method will identify sequences that have ideal properties for target binding and therapeutic efficacy. We will accomplish these goals with three specific aims. We will develop a new approach to integrated molecular dynamics and machine learning using control targets and known receptor sequences to refine our methods for receptor generalization and model updating from observed data (Aim 1). We will design an iterative framework intended to enable identification of highly effective antibodies within a minimal number of experiments, in which our methods automatically propose promising antibody sequences to profile in subsequent assays (Aim 2). We will employ rounds of automated synthetic design, affinity test, and model improvement to produce highly target-specific antibodies.
(Aim 3). !

Public Health Relevance

We will develop new computational methods that learn from millions of examples to design antibodies that can be used to help cure a wide variety of human diseases such as cancer and viral infection. Previous antibody design approaches used a trial and error approach to find antibodies that worked well. In contrast our mathematical methods will directly produce new antibody designs by learning from large-scale experiments that test antibodies for function against disease targets. !

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
1R01CA218094-01A1
Application #
9520706
Study Section
Modeling and Analysis of Biological Systems Study Section (MABS)
Program Officer
Ossandon, Miguel
Project Start
2018-03-09
Project End
2023-02-28
Budget Start
2018-03-09
Budget End
2019-02-28
Support Year
1
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Massachusetts Institute of Technology
Department
Engineering (All Types)
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
001425594
City
Cambridge
State
MA
Country
United States
Zip Code
02142