PROJECT #17245 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: 352,448

Core: GRO_A8

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Monday, 27 September 2021 17:59:05

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 Monday, 27 September 2021 17:59:05

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 RYZEN THREADRIPPER 3960X 24-CORE 48 15,642 750,816 AMD
2 XEON CPU E5-2680 V3 @ 2.50GHZ 24 15,812 379,488 Intel
3 XEON W-10855M CPU @ 2.80GHZ 12 7,418 89,016 Intel
4 CORE I5-2520M CPU @ 2.50GHZ 4 5,181 20,724 Intel
5 CORE I5-4460 CPU @ 3.20GHZ 4 5,170 20,680 Intel