PROJECT #18453 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: 37,048

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 1,780,874 109,129 16.32 1 hrs 28 mins
2 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A]
Nvidia TU106 1,732,051 109,916 15.76 2 hrs 31 mins
3 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 1,566,590 103,524 15.13 2 hrs 35 mins
4 GeForce RTX 2070
TU106 [GeForce RTX 2070]
Nvidia TU106 1,457,340 100,312 14.53 2 hrs 39 mins
5 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 1,364,829 93,316 14.63 2 hrs 38 mins
6 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,320,041 98,427 13.41 2 hrs 47 mins
7 GeForce GTX 1660 Ti
TU116 [GeForce GTX 1660 Ti]
Nvidia TU116 1,278,060 97,566 13.10 2 hrs 50 mins
8 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,272,803 96,413 13.20 2 hrs 49 mins
9 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 1,238,402 95,763 12.93 2 hrs 51 mins
10 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,090,302 91,550 11.91 2 hrs 1 mins
11 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,062,415 91,697 11.59 2 hrs 4 mins
12 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 975,101 88,952 10.96 2 hrs 11 mins
13 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 966,951 88,280 10.95 2 hrs 11 mins
14 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 898,657 85,625 10.50 2 hrs 17 mins
15 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 812,940 83,747 9.71 2 hrs 28 mins
16 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 718,400 80,314 8.94 3 hrs 41 mins
17 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 560,492 74,164 7.56 3 hrs 11 mins
18 Quadro P2200
GP106GL [Quadro P2200]
Nvidia GP106GL 465,676 69,670 6.68 4 hrs 35 mins
19 GeForce GTX 1060 Mobile
GP106M [GeForce GTX 1060 Mobile]
Nvidia GP106M 374,351 64,321 5.82 4 hrs 7 mins
20 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 337,003 62,454 5.40 4 hrs 27 mins
21 P106-090
GP106 [P106-090]
Nvidia GP106 328,501 61,639 5.33 5 hrs 30 mins
22 RX 470/480/570/580/590
Ellesmere XT [RX 470/480/570/580/590]
Ellesmere XT 227,270 54,758 4.15 6 hrs 47 mins
23 GeForce GTX 1050 LP
GP107 [GeForce GTX 1050 LP] 1862
Nvidia GP107 225,185 54,181 4.16 6 hrs 46 mins
24 GeForce GTX 950
GM206 [GeForce GTX 950] 1572
Nvidia GM206 224,172 54,607 4.11 6 hrs 51 mins
25 Quadro P1000
GP107GL [Quadro P1000]
Nvidia GP107GL 164,290 49,037 3.35 7 hrs 10 mins
26 RX Vega M GL
Polaris 22 XL [RX Vega M GL]
Polaris 22 XL 127,149 45,099 2.82 9 hrs 31 mins
27 GeForce GT 1030
GP108 [GeForce GT 1030]
Nvidia GP108 123,338 44,449 2.77 9 hrs 39 mins
28 Quadro K1200
GM107GL [Quadro K1200]
Nvidia GM107GL 64,877 36,169 1.79 13 hrs 23 mins
29 GeForce MX450
TU117M [GeForce MX450]
Nvidia TU117M 50,859 35,319 1.44 17 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