RESEARCH: PARASITIC WEED
FOLDING PROJECT #18900 PROFILE
PROJECT TEAM
Manager(s): Jiming ChenInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 43,328Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
Parasitic weeds like Striga cause billions in crop damage each year. They use a special signal from host plants to sprout, and their sensors are super sensitive! This project wants to understand how this works so we can make chemicals to stop these weeds.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
Hormone signaling in parasitic weeds Parasitic weeds such as Striga destroy ~$10 billion worth of food crops worldwide every year.
Their seeds can lie dormant in the soil for years and germinate when it detects a chemical signal from host plants called strigolactone.
Additionally, the receptor proteins that perceive strigolactone are several orders of magnitude more sensitive than the ones found in host plants, meaning they can induce signaling at much lower levels of strigolactone.
This series of project aims to provide molecular insight into the strigolactone signaling process to facilitate the design of agrochemicals that can be used to combat parasitic weeds.
RELATED TERMS GLOSSARY AI BETA
Hormone
A chemical messenger produced by glands.
Hormones are chemicals that plants produce to regulate growth, development, and responses to the environment. They act like messengers, traveling through the plant to trigger specific actions in different tissues.
Signaling
The transmission of information within or between cells.
Signaling is the process by which plants receive and respond to signals from their environment. This can involve chemical messengers, electrical impulses, or mechanical forces.
Parasitic weeds
Plants that obtain nutrients from other plants.
Parasitic weeds are plants that grow on or in other plants (host plants) to steal their water and nutrients. This can cause significant damage to crops and natural ecosystems.
Striga
A genus of parasitic weeds.
Striga is a group of parasitic weeds that are particularly damaging to cereal crops in Africa. They can cause severe yield losses and threaten food security.
Strigolactone
A class of plant hormones that signal to parasitic weeds.
Strigolactones are chemical signals produced by plants that attract and stimulate the germination of parasitic weed seeds. They play a crucial role in the interaction between host plants and their parasitic invaders.
Receptor proteins
Proteins that bind to specific molecules and trigger a cellular response.
Receptor proteins are essential for cells to sense and respond to their environment. They act like antennae, receiving signals from outside the cell and relaying them to the appropriate internal machinery.
Agrochemicals
Chemicals used in agriculture to control pests.
Agrochemicals are substances used to protect crops from pests, diseases, and weeds. This can include insecticides, herbicides, fungicides, and other specialized compounds.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 03:27:10|
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 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 4,784,368 | 71,242 | 67.16 | 0 hrs 21 mins |
| 2 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 4,358,202 | 69,166 | 63.01 | 0 hrs 23 mins |
| 3 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 3,671,843 | 65,389 | 56.15 | 0 hrs 26 mins |
| 4 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 3,596,371 | 66,599 | 54.00 | 0 hrs 27 mins |
| 5 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 3,533,921 | 65,224 | 54.18 | 0 hrs 27 mins |
| 6 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 3,371,499 | 64,651 | 52.15 | 0 hrs 28 mins |
| 7 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,336,372 | 64,274 | 51.91 | 0 hrs 28 mins |
| 8 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 3,249,294 | 63,933 | 50.82 | 0 hrs 28 mins |
| 9 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 3,239,323 | 63,737 | 50.82 | 0 hrs 28 mins |
| 10 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,000,826 | 62,517 | 48.00 | 0 hrs 30 mins |
| 11 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 2,759,759 | 60,689 | 45.47 | 0 hrs 32 mins |
| 12 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,335,382 | 57,281 | 40.77 | 0 hrs 35 mins |
| 13 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 2,294,925 | 57,063 | 40.22 | 0 hrs 36 mins |
| 14 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,478,115 | 49,612 | 29.79 | 0 hrs 48 mins |
| 15 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,333,881 | 47,521 | 28.07 | 0 hrs 51 mins |
| 16 | GeForce RTX 2060 Mobile / Max-Q TU106M [GeForce RTX 2060 Mobile / Max-Q] |
Nvidia | TU106M | 1,116,623 | 44,792 | 24.93 | 0 hrs 58 mins |
| 17 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,073,199 | 44,085 | 24.34 | 0 hrs 59 mins |
| 18 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,064,797 | 44,201 | 24.09 | 0 hrs 60 mins |
| 19 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 953,145 | 41,883 | 22.76 | 1 hrs 3 mins |
|
|
|||||||
| 20 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 895,605 | 41,432 | 21.62 | 1 hrs 7 mins |
| 21 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 811,673 | 39,256 | 20.68 | 1 hrs 10 mins |
| 22 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 481,182 | 33,784 | 14.24 | 1 hrs 41 mins |
| 23 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 474,425 | 33,732 | 14.06 | 1 hrs 42 mins |
| 24 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 449,226 | 33,447 | 13.43 | 1 hrs 47 mins |
| 25 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 365,755 | 30,408 | 12.03 | 1 hrs 60 mins |
| 26 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 288,356 | 28,407 | 10.15 | 2 hrs 22 mins |
| 27 | GeForce GTX 770 GK104 [GeForce GTX 770] 3213 |
Nvidia | GK104 | 99,306 | 20,037 | 4.96 | 4 hrs 51 mins |
| 28 | GeForce GTX 660 Ti GK104 [GeForce GTX 660 Ti] 2634 |
Nvidia | GK104 | 72,775 | 18,337 | 3.97 | 6 hrs 3 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 03:27:10|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|