What is Neoantigen?

Neoantigens are newly formed antigens that have not been previously recognized by the immune system. Neoantigens can arise from altered tumor proteins formed as a result of tumor mutations or from viral proteins. It could be specifically recognized by neoantigen-specific T cell receptors (TCRs) in the context of major histocompatibility complexes (MHCs) molecules. Neoantigen is an ideal immunotherapy target because they are distinguished from germline and could be recognized as non-self by the host immune system.

The current model of AI new drug research and development is to find a certain protein produced by gene mutations. However, we may find that each patient has a different mutation site of tumor gene, resulting in displaying different neoantigen on the tumor cell surface.

It is time-consuming and laborious for researchers to verify the gene mutation site and the corresponding neoantigen. Currently, the development of gene mutation technology makes it possible to quickly and effectively screen neoantigens of each patient individually, which lays the foundation for the clinical application of neoantigen vaccines.

Figure 1 Flowchart for tumor neoantigen prediction and detection of T cell–recognized neoantigens (Jiang, T., Shi, T., Zhang, H. et al. 2019)

Our Neoantigen Prediction Service

Our self-developed AI macromolecular drug research and development platform has been applied to three sub-fields of drug design:

• High-throughput screening of neoantigen-specific TCR;
• Antibody design;
• Rational design of PROTACs.

Application Scenarios

• Neoantigen cancer vaccine (therapeutic cancer vaccine)
• Neoantigen-specific TCR-T cell therapy

Advantages

AI can be used to guide high-throughput experiments and more conducive to the development of new drug.

• Big data, especially development data of biotechnology;
• High-throughput experimental platform (High-throughput sequencing enhanced phage display, high-throughput screening of antibodies)
• Biophysics foundations

Our Team

Judging from the current talent composition of the company, more team members come from the Department of Biology, Chemistry, Computational Biology, etc.

Challenges

• Insufficient Data
  In our Neoantigen-TCR platform, TCR sequence data is relatively small, and protein structure data is relatively small. It's necessary to develop multiple dimensions to evaluate data, such as physics and chemistry, and then achieve a more accurate prediction through AI.
• Complex Design
  There are many types of antibodies (such as nanobodies and bifunctional antibodies, etc.), which require certain design and calculation, and then build diverse antibody libraries for various targets. For difficult targets such as GPCR, it is difficult to establish a database.

Our Goals

• Build a good AI platform for drug design and get more experimental verification;
• Cooperate with more biomedical companies and incorporate the biomedical industry chain to bring new technologies and ideas to the traditional medicine industry;
• After consolidating the platform and accumulated experience in drug research and development, gradually carry out independent drug pipelines.