RESEARCH: CANCER
FOLDING PROJECT #18116 PROFILE
PROJECT TEAM
Manager(s): Rafal WiewioraInstitution: Roivant Sciences (Silicon Therapeutics)
Project URL: View Project Website
WORK UNIT INFO
Atoms: 52,212Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
Scientists are using computer simulations to study how a protein called KRas binds to small molecules. This project aims to understand how KRas works and potentially design new drugs that target it. The data from this research is being made publicly available.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
18110/5: simulation for the complex of KRas(G12D)-GDP-KRpep-2d, with the initial structure of peptide ligand in the known binding position, exploring the binding dynamics of the complex and binding poses. 18111/6: binding simulation for the complex of KRas(G12D)-GDP-KRpep-2d, with the initial structure of peptide ligand 35A away from the protein center, exploring if the correct binding pose will be observed through the simulation. 18112-18114: binding simulation for the complex of KRas(G12D)-GDP-peptide, the binding pocket and binding pose is unknown.
We start with 100 randomly sampled initial structures, in each structure, the ligand center is 40A away from the protein center, with random direction and rotation.
The simulation is aimed to identify potential binding pocket and binding poses of the peptide.
The interactions between KRas protein and the peptide would provide insights in designing small molecules that bind with KRas. This is a project run by Roivant Sciences (formerly Silicon Therapeutics) as was officially announced in this press release: https://foldingathome.org/2021/04/20/maximizing-the-impact-of-foldinghome-by-engaging-industry-collaborators/ All data is being made publicly available in real time at https://console.cloud.google.com/storage/browser/stxfah-bucket.
RELATED TERMS GLOSSARY AI BETA
KRas
Kirsten rat sarcoma viral oncogene homolog
KRas is a protein that plays a crucial role in cell growth and division. Mutations in the KRas gene can lead to uncontrolled cell growth, contributing to the development of various cancers.
G12D
Glycine to Aspartic Acid at codon 12
G12D is a specific mutation in the KRas gene where the amino acid glycine is replaced by aspartic acid at position 12. This mutation is commonly found in various cancers and contributes to their uncontrolled growth.
GDP
Guanosine diphosphate
GDP is a molecule that plays a vital role in cellular processes, including signal transduction and protein synthesis. It acts as a precursor to GTP (guanosine triphosphate), which is involved in many energy-dependent reactions.
KRpep
KRas peptide inhibitor
KRpep is a synthetic peptide designed to inhibit the activity of KRas protein. Peptides are short chains of amino acids, and they can be used as therapeutic agents by targeting specific proteins involved in disease processes.
Binding Pose
The specific 3D arrangement of a molecule (ligand) when bound to another molecule (protein).
Binding pose describes how a molecule, such as a drug or peptide, fits into the active site of a protein target. Understanding binding poses is crucial for designing effective drugs as it allows scientists to optimize the interaction between the drug and the target.
Simulation
A computer model that imitates the behavior of a system over time.
Simulations are used in various fields, including drug discovery, to study complex biological processes. By running simulations, researchers can explore different scenarios and gain insights into how molecules interact and influence each other.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:32:06|
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 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 5,047,197 | 321,432 | 15.70 | 1 hrs 32 mins |
| 2 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 4,980,115 | 317,464 | 15.69 | 1 hrs 32 mins |
| 3 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 4,700,042 | 310,227 | 15.15 | 1 hrs 35 mins |
| 4 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 3,709,021 | 290,616 | 12.76 | 1 hrs 53 mins |
| 5 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 3,144,300 | 274,993 | 11.43 | 2 hrs 6 mins |
| 6 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 3,133,127 | 275,699 | 11.36 | 2 hrs 7 mins |
| 7 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,089,726 | 273,759 | 11.29 | 2 hrs 8 mins |
| 8 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 3,042,037 | 272,475 | 11.16 | 2 hrs 9 mins |
| 9 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 3,012,135 | 259,899 | 11.59 | 2 hrs 4 mins |
| 10 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 2,600,914 | 258,403 | 10.07 | 2 hrs 23 mins |
| 11 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 2,600,206 | 258,808 | 10.05 | 2 hrs 23 mins |
| 12 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,479,947 | 253,960 | 9.77 | 2 hrs 27 mins |
| 13 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 2,460,698 | 253,741 | 9.70 | 2 hrs 28 mins |
| 14 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,269,940 | 246,655 | 9.20 | 2 hrs 36 mins |
| 15 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,226,231 | 242,577 | 9.18 | 2 hrs 37 mins |
| 16 | GeForce RTX 2080 TU104 [GeForce RTX 2080] |
Nvidia | TU104 | 2,195,800 | 244,761 | 8.97 | 2 hrs 41 mins |
| 17 | GeForce RTX 2070 TU106 [GeForce RTX 2070] M 6497 |
Nvidia | TU106 | 1,779,373 | 227,565 | 7.82 | 3 hrs 4 mins |
| 18 | GeForce RTX 3060 GA106 [GeForce RTX 3060] |
Nvidia | GA106 | 1,730,618 | 225,974 | 7.66 | 3 hrs 8 mins |
| 19 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,668,200 | 222,750 | 7.49 | 3 hrs 12 mins |
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| 20 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,354,111 | 207,770 | 6.52 | 3 hrs 41 mins |
| 21 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,157,210 | 196,596 | 5.89 | 4 hrs 5 mins |
| 22 | GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti] |
Nvidia | TU116 | 1,083,945 | 193,512 | 5.60 | 4 hrs 17 mins |
| 23 | RTX A2000 Mobile GA107GLM [RTX A2000 Mobile] |
Nvidia | GA107GLM | 807,042 | 175,696 | 4.59 | 5 hrs 13 mins |
| 24 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 744,821 | 171,173 | 4.35 | 5 hrs 31 mins |
| 25 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 708,407 | 169,026 | 4.19 | 5 hrs 44 mins |
| 26 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 620,313 | 160,611 | 3.86 | 6 hrs 13 mins |
| 27 | GeForce GTX 1650 Mobile / Max-Q TU117M [GeForce GTX 1650 Mobile / Max-Q] |
Nvidia | TU117M | 380,764 | 133,342 | 2.86 | 8 hrs 24 mins |
| 28 | GeForce GTX 680 GK104 [GeForce GTX 680] 3250 |
Nvidia | GK104 | 228,030 | 115,063 | 1.98 | 12 hrs 7 mins |
| 29 | GeForce GT 1030 GP108 [GeForce GT 1030] 1127 |
Nvidia | GP108 | 122,525 | 93,528 | 1.31 | 18 hrs 19 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:32:06|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|