Knowledge Vault 5 /95 - CVPR 2024
Design of New Protein Functions Using Deep Learning
David Baker
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Concept Graph & Resume using Claude 3 Opus | Chat GPT4o | Llama 3:

Design of New
Protein Functions Using
Deep Learning
Protein Design
AI Models
Applications
Protein Properties
Design Techniques
Creating novel proteins
computationally using deep learning 1
Designing proteins with
multiple conformational states 22
New catalysts for
various applications 17
Proteins template growth
of inorganic materials 12
Proteins functionalize carbon nanotubes 13
Designing RNA with
specific structures, functions 16
Deep learning model
predicts protein structures 2
Diffusion model generates
protein structures, sequences 3
Neural network designs
amino acid sequences 4
Diffusion models create
small molecule binders 15
Exploring latent space
for protein diffusion 29
Designed proteins target
various medical conditions 5
Proteins neutralize snake
venom toxins 6
Synthetic antibodies mimic
immune system components 7
Cylindrical proteins function
as molecular channels 8
Proteins bind specific
DNA sequences 9
Self-assembling proteins for
drug delivery 10
De novo proteins
exhibit high stability 20
Improved stability reduces
cold storage need 23
Flexible building blocks
create larger assemblies 11
Technique specifies catalytic
site geometry 18
Proteins mimic photosystems
for light harvesting 19
Applying computer vision
to protein design 24
Considering sequences and
3D coordinates simultaneously 27
Applying diffusion models
to 3D structures 28
Applying reinforcement learning
to protein design 30
Protein networks compute
within cells 14
Protein-based vaccines protect
multiple virus strains 21
Computationally generated data
augments structure databases 25
Interacting proteins perform
cellular computations 26

Resume:

1.- De novo protein design: Creating brand new proteins from scratch using computational methods, particularly deep learning techniques.

2.- RosettaFold: A deep learning model developed to predict protein structures from amino acid sequences.

3.- RF diffusion: A diffusion model for generating new protein structures and sequences.

4.- Protein MPNN: A message-passing neural network for designing amino acid sequences that fold into specific structures.

5.- Medical applications: Using designed proteins to target diseases like autoimmune disorders, cancer, and neurodegenerative conditions.

6.- Snake venom antidote: Designing proteins that bind to and neutralize snake venom toxins.

7.- Antibody design: Creating synthetic antibodies for therapeutic use, mimicking natural immune system components.

8.- Protein channels: Designing cylindrical proteins that can function as ion channels or molecular sensors.

9.- DNA-binding proteins: Creating proteins that recognize and bind to specific DNA sequences for potential gene editing applications.

10.- Nanoparticle design: Developing self-assembling protein structures for vaccine platforms and drug delivery.

11.- Quasi-symmetry: A method to create larger protein assemblies by introducing flexibility in building blocks.

12.- Mineralization: Designing proteins that can template the growth of inorganic materials like zinc oxide.

13.- Carbon nanotube binding: Creating proteins that can wrap around and functionalize carbon nanotubes.

14.- Protein-based computing: Designing protein networks that can perform simple computations within cells.

15.- Small molecule binding: Using diffusion models to create proteins that bind specific small molecules.

16.- RNA design: Applying diffusion models to design RNA molecules with specific structures and functions.

17.- Enzyme design: Creating new catalysts for various applications, including plastic degradation and green chemistry.

18.- Guideposting: A technique to specify catalytic site geometry during the protein design process.

19.- Artificial photosynthesis: Designing proteins that mimic natural photosystems for light harvesting and electron transfer.

20.- Protein stability: De novo designed proteins often exhibit high thermal stability compared to natural proteins.

21.- Universal vaccines: Developing protein-based vaccines that could protect against multiple strains or families of viruses.

22.- Protein dynamics: Designing proteins with multiple conformational states or continuous motions for specific functions.

23.- Shelf stability: De novo designed proteins often have improved stability, potentially reducing the need for cold chain storage.

24.- Computer vision in biology: Applying techniques from computer vision and image processing to protein design and analysis.

25.- Synthetic training data: Exploring the use of computationally generated data to augment experimental protein structure databases.

26.- Protein programming: Creating networks of interacting proteins to perform computations or make decisions within cells.

27.- Multimodal protein representation: Considering both amino acid sequences and 3D coordinates in protein diffusion models.

28.- Structure diffusion: Applying diffusion models directly to 3D protein structures rather than sequences.

29.- Latent diffusion: Exploring latent space representations for simultaneous diffusion of protein sequence and structure.

30.- Reinforcement learning: Applying techniques like DPO (Direct Preference Optimization) to protein design tasks.

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