PROJECT #17244 RESEARCH FOR UNSPECIFIED
FOLDING PERFORMANCE PROFILE

PROJECT SUMMARY

Proteins come in all shapes and sizes and contain different chemical properties. This variety allows them to have an extraordinary diversity of functions from helping your muscles contract to breaking down the food you eat to helping with the copying of your genetic code. Given the versatility of proteins in nature, people have become interested in designing new proteins to carry out new functions. In particular, people have been interested in designing new immunoglobulin proteins, as these proteins are good at identifying and binding to specific target proteins, which can be used to modulate the function of an existing system in your body. In this project, we are simulating a large variety of single-domain immunoglobulin proteins then using the resulting data to train an AI algorithm to design new immunoglobulin proteins!

PROJECT INFO

Manager(s): Neha Vithani

Institution: Washington University in St. Louis

PROJECT WORK UNIT SUMMARY

Atoms: 211,693

Core: GRO_A8

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Tuesday, 28 September 2021 06:02:47

Rank
Project
Model Name
Folding@Home Identifier
Make
Brand
GPU
Model
PPD
Average
Points WU
Average
WUs Day
Average
WU Time
Average

PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Tuesday, 28 September 2021 06:02:47

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 CORE I9-10850K CPU @ 3.60GHZ 20 18,150 363,000 Intel
2 CORE I7-8700 CPU @ 3.20GHZ 12 17,881 214,572 Intel
3 RYZEN 5 2600X SIX-CORE 12 14,879 178,548 AMD
4 RYZEN 5 3600 6-CORE 12 9,611 115,332 AMD
5 RYZEN 5 2600 SIX-CORE 12 4,917 59,004 AMD
6 CORE I5-7600 CPU @ 3.50GHZ 4 8,936 35,744 Intel