Project description:We applied the high-throughput interaction assay SORTCERY to measure thousands of protein-peptide binding affinities and used the data to parameterize models of the peptide-binding landscape for three members of the Bcl-2 family of proteins. We applied the models to design peptides that bound with high affinity and specificity to just one of Bcl-xL, Mcl-1, or Bfl-1. We designed additional peptides that bound selectively to two out of three of these proteins. The raw data provided are the multiplexed fastq files that serve as inputs to our analysis pipeline. Additional detail is available in our corresponding publication and at the following github repository. https://github.com/KeatingLab/sortcery_design

2018-10-01 | GSE118147 | GEO

Deep learning for non-parameterized MEMS structural design.

Project description: Not available

| S-EPMC9424241 | biostudies-literature

Dynamically adjusted cell fate decisions and resilience to mutant invasion during steady-state hematopoiesis revealed by an experimentally parameterized mathematical model.

Project description: Not available

| S-EPMC11420203 | biostudies-literature

Mathematical models used to inform study design or surveillance systems in infectious diseases: a systematic review.

Project description: Not available

| S-EPMC5735541 | biostudies-literature

HIV-1 CD4-binding site germline antibody-Env structures inform vaccine design.

Project description: Not available