Design of Nuclear-Targeting Peptides for Macromolecule Delivery via Machine Learning
The effective design of functional peptide sequences remains a fundamental challenge in biomedicine. For example, cell-penetrating peptides (CPPs) are capable of delivering macromolecular cargo to intracellular targets that are otherwise inaccessible. However, design of novel CPPs with high activity...
Main Author: | Schissel, Carly Katherine |
---|---|
Other Authors: | Pentelute, Bradley L |
Format: | Thesis |
Published: |
Massachusetts Institute of Technology
2022
|
Online Access: | https://hdl.handle.net/1721.1/143244 https://orcid.org/ 0000-0003-0773-5168 |
Similar Items
-
Deep Learning Enables Discovery of a Short Nuclear Targeting Peptide for Efficient Delivery of Antisense Oligomers
by: López-Vidal, Eva M, et al.
Published: (2022) -
Deep Learning Enables Discovery of a Short Nuclear Targeting Peptide for Efficient Delivery of Antisense Oligomers
by: López-Vidal, Eva M., et al.
Published: (2022) -
Designing Macromolecules using Machine Learning and Simulations
by: Mohapatra, Somesh
Published: (2024) -
Deep learning to design nuclear-targeting abiotic miniproteins
by: Schissel, Carly K, et al.
Published: (2022) -
Deep learning to design nuclear-targeting abiotic miniproteins
by: Schissel, Carly K., et al.
Published: (2022)