PROJECT #18451 RESEARCH FOR CANCER
FOLDING PERFORMANCE PROFILE

PROJECT SUMMARY

The function of a protein depends on its native shape, or fold.

Deciphering the sequence-dependent process by which proteins reach their folded state has been the goal of much experimental and computational work, and the inspiration for the e Folding@home project.

Small “fast folding” mini-proteins, such as the protein G B1 domain (GB1) and hpin1 WW domain, have been studied extensively both experimentally and through simulation, producing a wealth of data on wild-type protein function, and much insight on how mutations affect the folding reaction.

In 2022, the Voelz lab published a study on the folding reaction of a transcription factor, FOXO1, in which a simple hydrophobic transfer model was used to accurately predict mutational effects from models derived from massively parallel Folding@home simulations (Novack et al.).

We found that this model made more accurate predictions than other state of the art methods of mutational analysis.

We hypothesize that this is because our simulations have more accurate descriptions of the unfolded ensemble of a protein than other methods, highlighting the importance of adequately describing the unfolded state of a protein in determining mutational effects on folding reaction.

In these projects, we aim to validate this hypothesis on the well studied miniproteins GB1 and hpin1 WW domain, as well as the MeCP2 protein.

MeCP2 is a protein that binds methylated DNA and is found in high levels in nerve cells.

the main disease associated with mutations of MeCP2 is Rett syndrome, a neurological disorder that severely impairs people’s ability to speak, walk, eat, and breathe.

The gene lives on X chromosomes, thus for people with an XY chromosomal configuration (lacking a complimentary natively functioning gene), this disease can be fatal within the first 2 years due to encephalopathy.

There are at least 4668 mutational variants of MeCP2 identified, with 1171 being in the only structured region of the protein, the MBD.

Stability effects of mutation in MeCP2 have been investigated both experimentally and computationally, which makes it a promising test case for both validation of hydrophobic transfer as a model of mutation effects, as well as an opportunity to gain atomic insight that can elucidate new avenues for therapeutics targeting MeCP2..

PROJECT INFO

Manager(s): Dylan Novack

Institution: Temple University

Project URL: http://voelzlab.org

PROJECT WORK UNIT SUMMARY

Atoms: 40,908

Core: 0x22

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Saturday, 28 January 2023 00:14:36

Rank
Project
Model Name
Folding@Home Identifier
Make
Brand
GPU
Model
PPD
Average
Points WU
Average
WUs Day
Average
WU Time
Average
1 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,826,182 122,475 14.91 2 hrs 37 mins
2 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 1,696,010 118,772 14.28 2 hrs 41 mins
3 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A]
Nvidia TU106 1,539,604 97,886 15.73 2 hrs 32 mins
4 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 1,306,780 105,431 12.39 2 hrs 56 mins
5 GeForce RTX 2070
TU106 [GeForce RTX 2070]
Nvidia TU106 1,261,915 97,942 12.88 2 hrs 52 mins
6 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,206,956 105,946 11.39 2 hrs 6 mins
7 GeForce GTX 1660 Ti
TU116 [GeForce GTX 1660 Ti]
Nvidia TU116 1,206,404 106,409 11.34 2 hrs 7 mins
8 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,097,286 103,734 10.58 2 hrs 16 mins
9 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,075,156 101,982 10.54 2 hrs 17 mins
10 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 1,018,418 100,598 10.12 2 hrs 22 mins
11 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 989,191 100,296 9.86 2 hrs 26 mins
12 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 944,063 98,252 9.61 2 hrs 30 mins
13 Tesla P4
GP104GL [Tesla P4] 5704
Nvidia GP104GL 922,743 97,332 9.48 3 hrs 32 mins
14 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 881,600 96,056 9.18 3 hrs 37 mins
15 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 762,480 91,738 8.31 3 hrs 53 mins
16 Tesla P100 16GB
GP100GL [Tesla P100 16GB] 9340
Nvidia GP100GL 686,150 78,405 8.75 3 hrs 45 mins
17 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 672,498 87,567 7.68 3 hrs 8 mins
18 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 541,453 81,123 6.67 4 hrs 36 mins
19 GeForce GTX 1650
TU117 [GeForce GTX 1650] 3091
Nvidia TU117 517,744 82,034 6.31 4 hrs 48 mins
20 GeForce GTX 1060 Mobile
GP106M [GeForce GTX 1060 Mobile]
Nvidia GP106M 362,721 71,004 5.11 5 hrs 42 mins
21 P106-090
GP106 [P106-090]
Nvidia GP106 293,966 66,796 4.40 5 hrs 27 mins
22 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 288,384 67,435 4.28 6 hrs 37 mins
23 RX 470/480/570/580/590
Ellesmere XT [RX 470/480/570/580/590]
Ellesmere XT 172,160 51,396 3.35 7 hrs 10 mins
24 Quadro P1000
GP107GL [Quadro P1000]
Nvidia GP107GL 171,285 55,333 3.10 8 hrs 45 mins
25 RX Vega M GL
Polaris 22 XL [RX Vega M GL]
Polaris 22 XL 124,044 49,935 2.48 10 hrs 40 mins
26 Quadro K1200
GM107GL [Quadro K1200]
Nvidia GM107GL 60,175 39,296 1.53 16 hrs 40 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Saturday, 28 January 2023 00:14:36

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make