PROJECT #18452 RESEARCH FOR CANCER
FOLDING PERFORMANCE PROFILE
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..
Manager(s): Dylan Novack
Institution: Temple University
Project URL: http://voelzlab.org
PROJECT WORK UNIT SUMMARY
PROJECT FOLDING PPD AVERAGES BY GPU
PPDDB data as of Saturday, 28 January 2023 00:14:36
|1||GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
|Nvidia||TU106||1,920,697||154,518||12.43||2 hrs 56 mins|
|2||GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
|Nvidia||GP102||1,775,129||148,723||11.94||2 hrs 1 mins|
|3||GeForce RTX 2070
TU106 [GeForce RTX 2070]
|Nvidia||TU106||1,652,370||146,685||11.26||2 hrs 8 mins|
|4||GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
|Nvidia||GA106M||1,209,729||127,861||9.46||3 hrs 32 mins|
|5||GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
|Nvidia||GP104||1,204,678||131,951||9.13||3 hrs 38 mins|
|6||GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
|Nvidia||GM200||1,140,216||127,327||8.96||3 hrs 41 mins|
|7||GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
|Nvidia||GP104||1,039,746||122,941||8.46||3 hrs 50 mins|
|8||GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
|Nvidia||TU116||1,039,100||124,876||8.32||3 hrs 53 mins|
GM200GL [Tesla M40] 6844
|Nvidia||GM200GL||929,424||120,817||7.69||3 hrs 7 mins|
|10||GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
|Nvidia||GP104||838,206||114,307||7.33||3 hrs 16 mins|
GP104GL [Tesla P4] 5704
|Nvidia||GP104GL||806,781||115,739||6.97||3 hrs 27 mins|
|12||GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
|Nvidia||GP106||730,451||111,646||6.54||4 hrs 40 mins|
|13||GeForce GTX 980
GM204 [GeForce GTX 980] 4612
|Nvidia||GM204||716,794||109,348||6.56||4 hrs 40 mins|
|14||GeForce RTX 2060
TU104 [GeForce RTX 2060]
|Nvidia||TU104||603,503||102,828||5.87||4 hrs 5 mins|
|15||GeForce GTX 1660
TU116 [GeForce GTX 1660]
|Nvidia||TU116||504,203||96,452||5.23||5 hrs 35 mins|
|16||GeForce GTX 1060 Mobile
GP106M [GeForce GTX 1060 Mobile]
|Nvidia||GP106M||381,048||89,702||4.25||6 hrs 39 mins|
|Nvidia||GP106||313,543||84,692||3.70||6 hrs 29 mins|
|18||GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
|Nvidia||GP107||297,773||83,466||3.57||7 hrs 44 mins|
Ellesmere XT [RX 470/480/570/580/590]
|Ellesmere XT||236,801||72,842||3.25||7 hrs 23 mins|
|20||GeForce GTX 1050 LP
GP107 [GeForce GTX 1050 LP] 1862
|Nvidia||GP107||226,174||75,941||2.98||8 hrs 3 mins|
|21||GeForce GTX 950
GM206 [GeForce GTX 950] 1572
|Nvidia||GM206||208,794||73,430||2.84||8 hrs 26 mins|
GP107GL [Quadro P1000]
|Nvidia||GP107GL||170,625||68,764||2.48||10 hrs 40 mins|
|23||RX Vega M GL
Polaris 22 XL [RX Vega M GL]
|Polaris 22 XL||121,377||61,533||1.97||12 hrs 10 mins|
|24||GeForce GT 1030
GP108 [GeForce GT 1030]
|Nvidia||GP108||109,334||59,460||1.84||13 hrs 3 mins|
GM107GL [Quadro K2200]
|Nvidia||GM107GL||82,529||59,151||1.40||17 hrs 12 mins|
GM107GL [Quadro K1200]
|Nvidia||GM107GL||57,899||47,975||1.21||20 hrs 53 mins|
PROJECT FOLDING PPD AVERAGES BY CPU BETA
PPDDB data as of Saturday, 28 January 2023 00:14:36
AVG PPD per 1 LP