20240029821. PEPTIDE BASED VACCINE GENERATION SYSTEM WITH DUAL PROJECTION GENERATIVE ADVERSARIAL NETWORKS simplified abstract (NEC Laboratories America, Inc.)
Contents
PEPTIDE BASED VACCINE GENERATION SYSTEM WITH DUAL PROJECTION GENERATIVE ADVERSARIAL NETWORKS
Organization Name
NEC Laboratories America, Inc.
Inventor(s)
Renqiang Min of Princeton NJ (US)
Hans Peter Graf of South Amboy NJ (US)
PEPTIDE BASED VACCINE GENERATION SYSTEM WITH DUAL PROJECTION GENERATIVE ADVERSARIAL NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240029821 titled 'PEPTIDE BASED VACCINE GENERATION SYSTEM WITH DUAL PROJECTION GENERATIVE ADVERSARIAL NETWORKS
Simplified Explanation
The abstract describes a computer-implemented method for generating new binding peptides to major histocompatibility complex (MHC) proteins. The method involves training a generative adversarial network (GAN) on a set of binding peptide sequences and non-binding peptide sequences. The GAN consists of a generator and a discriminator, which are iteratively updated to distinguish between real and fake peptide sequences. The training also includes optimizing the GAN training objective while learning two projection vectors for a binding class with two cross-entropy losses.
- The method involves training a generative adversarial network (GAN) on binding and non-binding peptide sequences.
- The GAN consists of a generator and a discriminator, which are updated iteratively.
- The discriminator is trained to distinguish between real and fake peptide sequences.
- The generator is trained to fool the discriminator.
- The training includes optimizing the GAN training objective and learning projection vectors for a binding class.
- Two cross-entropy losses are used to discriminate between binding and non-binding peptide sequences.
Potential applications of this technology:
- Discovery of new binding peptides for major histocompatibility complex (MHC) proteins.
- Design of peptides for therapeutic purposes, such as vaccines or immunotherapies.
- Development of personalized medicine based on individual MHC profiles.
Problems solved by this technology:
- Traditional methods for generating binding peptides may be time-consuming and costly.
- Limited availability of binding peptide sequences for MHC proteins.
- Difficulty in predicting binding peptides accurately.
Benefits of this technology:
- Accelerates the discovery of new binding peptides.
- Enables the design of more effective therapeutic peptides.
- Facilitates personalized medicine approaches.
- Reduces the time and cost associated with peptide discovery and development.
Original Abstract Submitted
a computer-implemented method is provided for generating new binding peptides to major histocompatibility complex (mhc) proteins. the method includes training, by a processor device, a generative adversarial network gan having a generator and a discriminator only on a set of binding peptide sequences given training data comprising the set of binding peptide sequences and a set of non-binding peptide sequences. a gan training objective includes the discriminator being iteratively updated to distinguish generated peptide sequences from sampled binding peptide sequences as fake or real and the generator being iteratively updated to fool the discriminator. the training includes optimizing the gan training objective while learning two projection vectors for a binding class with two cross-entropy losses. a first loss discriminating binding peptide sequences in the training data from non-binding peptide sequences in the training data. a second loss discriminating generated binding peptide sequences from non-binding peptide sequences in the training data.
