PROJECT #19010 RESEARCH FOR CANNABINOID
FOLDING PERFORMANCE PROFILE

PROJECT SUMMARY

Cannabinoid Receptors Cannabinoid receptors (CBs) are part of the endocannabinoid signaling system, which help to maintain homeostasis in neuron signaling to control pain, obesity, and other neurological disorders.

Therefore, synthetic cannabinoids (SCs) were designed and tested to target CBs as potential therapeutical selective drugs.

Initially, SCs were designed by modulating the scaffolds of known phytocannabinoids.

However, chemically diverse synthetic cannabinoids (Novel Psychoactive Substance (NPS)) were
discovered rapidly, which have a high affinity towards CBs and significantly modulate the receptor activities.

These molecules were started to get sold in the market as abusive drugs under different brand names (e.g., K2, spice) and caused thousands of hospitalizations of patients across the US due to more adverse effects, including impairment of fine motor skills and increased blood pressure, tachycardia.

It is hypothesized that β-arrestin biased downstream signaling of these NPSs causes more adversarial effects compared to classical cannabinoids.

However, how these classical and non-classical cannabinoids affect receptor conformational dynamics distinctly,
has not been mechanistically studied.

In this project, we compare the unbinding mechanism and kinetics of a non-classical cannabinoid, MDMB-Fubinaca, and a classical cannabinoid, HU-210, using biased and unbiased simulation..

PROJECT INFO

Manager(s): Soumajit Dutta

Institution: University of Illinois Urbana-Champaign

Project URL: shuklagroup.org/

PROJECT WORK UNIT SUMMARY

Atoms: 89,957

Core: OPENMM_22

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Sunday, 04 December 2022 18:13:18

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 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 7,121,506 268,182 26.55 0 hrs 54 mins
2 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 6,362,892 257,689 24.69 0 hrs 58 mins
3 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 5,827,272 247,306 23.56 1 hrs 1 mins
4 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 4,722,427 234,324 20.15 1 hrs 11 mins
5 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 4,338,238 228,045 19.02 1 hrs 16 mins
6 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,228,778 224,292 18.85 1 hrs 16 mins
7 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,853,706 218,240 17.66 1 hrs 22 mins
8 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 3,349,226 185,718 18.03 1 hrs 20 mins
9 GeForce RTX 3070 Mobile / Max-Q
GA104M [GeForce RTX 3070 Mobile / Max-Q]
Nvidia GA104M 3,113,631 205,042 15.19 2 hrs 35 mins
10 GeForce RTX 3080 Mobile / Max-Q 8GB/16GB
GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB]
Nvidia GA104M 3,017,374 194,789 15.49 2 hrs 33 mins
11 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,902,717 199,194 14.57 2 hrs 39 mins
12 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 2,788,340 197,053 14.15 2 hrs 42 mins
13 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 2,358,880 179,781 13.12 2 hrs 50 mins
14 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,291,895 183,654 12.48 2 hrs 55 mins
15 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 2,065,730 178,408 11.58 2 hrs 4 mins
16 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 1,916,313 174,091 11.01 2 hrs 11 mins
17 GeForce RTX 3060
GA104 [GeForce RTX 3060]
Nvidia GA104 1,707,689 167,416 10.20 2 hrs 21 mins
18 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,692,851 160,578 10.54 2 hrs 17 mins
19 Quadro RTX 4000
TU104GL [Quadro RTX 4000]
Nvidia TU104GL 1,633,081 164,724 9.91 2 hrs 25 mins
20 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,320,085 153,402 8.61 3 hrs 47 mins
21 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,179,880 146,920 8.03 3 hrs 59 mins
22 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,145,811 145,698 7.86 3 hrs 3 mins
23 Geforce RTX 3050
GA106 [Geforce RTX 3050]
Nvidia GA106 1,117,506 142,953 7.82 3 hrs 4 mins
24 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,038,128 139,438 7.45 3 hrs 13 mins
25 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 984,230 115,672 8.51 3 hrs 49 mins
26 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 864,160 133,592 6.47 4 hrs 43 mins
27 P104-100
GP104 [P104-100]
Nvidia GP104 785,032 129,855 6.05 4 hrs 58 mins
28 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 726,319 126,078 5.76 4 hrs 10 mins
29 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 584,095 116,943 4.99 5 hrs 48 mins
30 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 543,659 114,190 4.76 5 hrs 2 mins
31 GeForce GTX 1650 Mobile / Max-Q
TU117M [GeForce GTX 1650 Mobile / Max-Q]
Nvidia TU117M 534,893 111,303 4.81 5 hrs 60 mins
32 Quadro P2200
GP106GL [Quadro P2200]
Nvidia GP106GL 287,779 91,373 3.15 8 hrs 37 mins
33 P106-090
GP106 [P106-090]
Nvidia GP106 260,262 89,740 2.90 8 hrs 17 mins
34 GeForce GTX 1050 LP
GP107 [GeForce GTX 1050 LP] 1862
Nvidia GP107 185,948 85,944 2.16 11 hrs 6 mins
35 GeForce GTX 750 Ti
GM107 [GeForce GTX 750 Ti] 1389
Nvidia GM107 113,492 68,027 1.67 14 hrs 23 mins
36 Quadro M2000
GM206GL [Quadro M2000]
Nvidia GM206GL 81,187 63,691 1.27 19 hrs 50 mins
37 GeForce GT 1030
GP108 [GeForce GT 1030]
Nvidia GP108 80,926 60,420 1.34 18 hrs 55 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Sunday, 04 December 2022 18:13:18

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