RESEARCH: CANCER
FOLDING PROJECT #16920 PROFILE
PROJECT TEAM
Manager(s): Prof. Vincent VoelzInstitution: Temple University
WORK UNIT INFO
Atoms: 21,050Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project relates to understanding how tiny proteins fold into shapes. By changing their building blocks and adding special links, scientists want to learn how to design these proteins as cancer-fighting tools.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
These simulations are designed to test our understanding the folding mechanism of alpha-helical hairpins.
We are trying to study how disulfide cross-linkers and sequence variants affect the folding thermodynamics and kinetics of these proteins, to learn how we might better use molecular simulation methods to design effective protein binder scaffolds, for use as "affibody" cancer therapeutics, for example.
RELATED TERMS GLOSSARY AI BETA
alpha-helical hairpins
A type of protein structure characterized by alpha-helix formations.
Alpha-helical hairpins are common structural motifs in proteins. They consist of short stretches of alpha-helices connected by loops, forming a hairpin shape. Understanding their stability and folding is crucial for designing effective therapeutics and understanding biological processes.
disulfide cross-linkers
Covalent bonds formed between cysteine amino acids in proteins.
Disulfide cross-linkers are important for protein stability and folding. They form strong covalent bonds between cysteine residues, often contributing to the overall structure of a protein. Disrupting these cross-links can alter protein function.
sequence variants
Variations in the amino acid sequence of a protein.
Sequence variants arise from changes in the DNA code that dictates protein structure. These variations can alter protein function, stability, and interactions. Understanding sequence variants is crucial for developing new therapeutics and understanding disease mechanisms.
folding thermodynamics
The energy changes associated with protein folding.
Folding thermodynamics describes the stability and energetics of protein structures. Understanding these principles is essential for designing proteins with desired properties and predicting how environmental factors affect protein folding.
folding kinetics
The rate of protein folding.
Folding kinetics refers to the speed at which proteins adopt their functional structures. Factors influencing folding rates include temperature, pH, and the presence of chaperone molecules.
protein binder scaffolds
Structural frameworks for designing proteins that bind to specific targets.
Protein binder scaffolds are used as templates for creating therapeutic proteins. By modifying the scaffold's structure and amino acid sequence, researchers can engineer proteins with high affinity and specificity for desired targets, such as cancer cells or disease-causing molecules.
affibody
Affibody is a small, engineered protein scaffold.
Affibody refers to a type of engineered protein based on the Z domain of staphylococcal protein A. These proteins are known for their high affinity and specificity towards various targets. They have potential applications as diagnostic tools and therapeutic agents in cancer and other diseases.
cancer therapeutics
Treatments aimed at preventing or curing cancer.
Cancer therapeutics encompass a wide range of approaches designed to combat cancer. These include chemotherapy, radiation therapy, immunotherapy, and targeted therapies.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:43:20|
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 | 1,698,991 | 52,612 | 32.29 | 0 hrs 45 mins |
| 2 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 1,601,277 | 49,732 | 32.20 | 0 hrs 45 mins |
| 3 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 1,585,823 | 49,486 | 32.05 | 0 hrs 45 mins |
| 4 | GeForce RTX 2080 TU104 [GeForce RTX 2080] |
Nvidia | TU104 | 1,581,565 | 49,563 | 31.91 | 0 hrs 45 mins |
| 5 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 1,499,180 | 48,697 | 30.79 | 0 hrs 47 mins |
| 6 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 1,120,395 | 44,047 | 25.44 | 0 hrs 57 mins |
| 7 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,008,985 | 42,498 | 23.74 | 1 hrs 1 mins |
| 8 | GeForce RTX 2070 TU106 [GeForce RTX 2070] M 6497 |
Nvidia | TU106 | 997,873 | 42,092 | 23.71 | 1 hrs 1 mins |
| 9 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 983,071 | 42,148 | 23.32 | 1 hrs 2 mins |
| 10 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 951,608 | 41,301 | 23.04 | 1 hrs 2 mins |
| 11 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 947,042 | 41,673 | 22.73 | 1 hrs 3 mins |
| 12 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 896,299 | 41,142 | 21.79 | 1 hrs 6 mins |
| 13 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 828,898 | 39,147 | 21.17 | 1 hrs 8 mins |
| 14 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 705,132 | 38,414 | 18.36 | 1 hrs 18 mins |
| 15 | Radeon RX 5600/5600 XT - 5700/5700 XT Navi 10 [Radeon RX 5600 OEM/5600 XT / 5700/5700 XT] |
AMD | Navi 10 | 651,212 | 36,684 | 17.75 | 1 hrs 21 mins |
| 16 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 498,716 | 34,056 | 14.64 | 1 hrs 38 mins |
| 17 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 439,147 | 32,485 | 13.52 | 1 hrs 47 mins |
| 18 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 435,058 | 31,713 | 13.72 | 1 hrs 45 mins |
| 19 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 417,896 | 29,836 | 14.01 | 1 hrs 43 mins |
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| 20 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 255,357 | 27,012 | 9.45 | 2 hrs 32 mins |
| 21 | Radeon R9 Fury X Fiji XT [Radeon R9 Fury X] |
AMD | Fiji XT | 246,576 | 26,537 | 9.29 | 2 hrs 35 mins |
| 22 | Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590] |
AMD | Ellesmere XT | 246,310 | 26,798 | 9.19 | 2 hrs 37 mins |
| 23 | Quadro T1000 Mobile TU117GLM [Quadro T1000 Mobile] |
Nvidia | TU117GLM | 240,207 | 26,425 | 9.09 | 2 hrs 38 mins |
| 24 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 217,059 | 23,521 | 9.23 | 2 hrs 36 mins |
| 25 | GeForce GTX 680 GK104 [GeForce GTX 680] 3250 |
Nvidia | GK104 | 122,227 | 21,141 | 5.78 | 4 hrs 9 mins |
| 26 | GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389 |
Nvidia | GM107 | 102,073 | 19,954 | 5.12 | 4 hrs 42 mins |
| 27 | Radeon HD 7800 Pitcairn [Radeon HD 7800] |
AMD | Pitcairn | 89,829 | 19,205 | 4.68 | 5 hrs 8 mins |
| 28 | GeForce GTX 770 GK104 [GeForce GTX 770] 3213 |
Nvidia | GK104 | 88,941 | 19,058 | 4.67 | 5 hrs 9 mins |
| 29 | Radeon R9 200/300 Series Hawaii [Radeon R9 200/300 Series] |
AMD | Hawaii | 74,809 | 18,096 | 4.13 | 5 hrs 48 mins |
| 30 | GeForce GTX 760 GK104 [GeForce GTX 760] 2258 |
Nvidia | GK104 | 65,599 | 17,321 | 3.79 | 6 hrs 20 mins |
| 31 | GeForce GT 1030 GP108 [GeForce GT 1030] 1127 |
Nvidia | GP108 | 44,844 | 11,938 | 3.76 | 6 hrs 23 mins |
| 32 | GeForce GTX 460 v2 GF114 [GeForce GTX 460 v2] 1045.6 |
Nvidia | GF114 | 23,294 | 12,248 | 1.90 | 12 hrs 37 mins |
| 33 | GeForce GTX 550 Ti GF116 [GeForce GTX 550 Ti] 691 |
Nvidia | GF116 | 16,747 | 10,952 | 1.53 | 15 hrs 42 mins |
| 34 | GeForce GT 640 GK107 [GeForce GT 640] 693 |
Nvidia | GK107 | 11,027 | 9,452 | 1.17 | 20 hrs 34 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:43:20|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|