PROJECT SUMMARY/ABSTRACT
As a molecular detection platform, antibodies have growing importance in modern medical technology, ranging
from diagnostic tests, to imaging, to therapeutics. The current market size for antibodies and their related
products is estimated to be around $200 billion USD. The growing need for antibodies with customized specificity
provides a rich environment for engineering efforts. Computational protein design has seen rapid progress in
recent years. Many methods have been developed to address antibody engineering needs. Researchers have
hoped that, through modeling and design, the cost for antibody development and improvements can be reduced
and the pace for creating new targeting molecules can be expedited. In recent years, the experimental pipeline
has been streamlined, but even so, extensive libraries and screen campaigns are usually required to get an initial
binding signal. A major advancement would be to directly design a binder from scratch, providing a signal for
potential optimization by artificial evolution. Current computational methods, however, have not taken a leading
role due to a number of shortcomings with the current modeling approach. We have extensive expertise in protein
design and have pioneered the use of generative neural network models for protein structures in recent years.
We have observed several key advantages in neural network approaches over existing methods: namely, their
ability to make inferences, interpolate, incorporate topological information, and accelerate sampling. These
advantages can be developed independently or used in conjunction with existing methods, and they can
significantly boost the performance of protein design. This project aims at leveraging several new advances we
have developed to date to inspire new strategies in response to the challenges in antibody engineering, or AI-
based protein design in general. We will develop new tools and design pipelines for expanding the specificities
for multispecific antibodies and customizing epitope-specific antibodies (using snake venoms and CXCR4 as
targets). This project will deliver both computational methods and constructs that can be deployed in clinical
settings. The results from this research will be highly impactful.