PROJECT #16958 RESEARCH FOR CANCER
FOLDING PERFORMANCE PROFILE

PROJECT SUMMARY

These projects aim to simulate the dynamics of a large number of designed mini-proteins  (< 50 residues), to establish the ability of molecular simulations to accurately predict thermodynamic and kinetic effects of designed mutations.  We aim to develop a library of experimental and simulation data so we can compare the performance of different techniques for computational protein design, such as adaptive sampling.   Prediction and design of mini-protein structure and dynamics is highly relevant to developing new biotherapeutics for a wide range of human diseases.

 

Project System

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16935 chignolin

 

PROJECT INFO

Manager(s): Prof. Vincent Voelz

Institution: Temple University

PROJECT WORK UNIT SUMMARY

Atoms: 12,120

Core: GRO_A8

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Friday, 23 July 2021 15:14:10

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 Friday, 23 July 2021 15:14:10

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 RYZEN 5 5600X 6-CORE 12 26,524 318,288 AMD
2 RYZEN 7 5800X 8-CORE 16 17,900 286,400 AMD
3 RYZEN 9 5950X 16-CORE 32 8,073 258,336 AMD
4 RYZEN 7 3700X 8-CORE 16 11,496 183,936 AMD
5 CORE I3-9100F CPU @ 3.60GHZ 4 28,281 113,124 Intel
6 RYZEN 5 2600 SIX-CORE 12 5,822 69,864 AMD
7 RYZEN THREADRIPPER 3960X 24-CORE 48 1,312 62,976 AMD
8 CORE M3-7Y30 CPU @ 1.00GHZ 4 3,865 15,460 Intel