RESEARCH: CANCER
FOLDING PROJECT #16921 PROFILE
PROJECT TEAM
Manager(s): Prof. Vincent VoelzInstitution: Temple University
WORK UNIT INFO
Atoms: 21,000Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project relates to studying how small protein structures fold. Scientists are looking at how chemical bonds and slight changes in the protein's code affect how it folds, hoping to design better proteins that can target and fight cancer.
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
simulations
The process of creating computer models to mimic real-world phenomena.
Simulations are used in biotechnology to study complex biological systems, such as protein folding, by creating virtual models that can be manipulated and observed. This allows researchers to explore how different factors, like chemical bonds and temperature, affect the behavior of proteins.
folding
The process by which a protein assumes its three-dimensional structure.
Folding is essential for proteins to function properly. Proteins are long chains of amino acids that must fold into specific shapes to perform their roles in the body. The shape of a protein determines its function, and misfolded proteins can lead to diseases.
alpha-helical hairpins
A type of protein structure consisting of two alpha-helices connected by a short loop.
Alpha-helical hairpins are a common structural motif in proteins. They are characterized by two alpha-helices that are coiled into a hairpin shape. These structures are important for protein function and interactions.
disulfide cross-linkers
Covalent bonds between cysteine amino acids that stabilize protein structure.
Disulfide cross-linkers are chemical bonds formed between sulfur atoms in cysteine amino acids. They play a crucial role in stabilizing the three-dimensional shape of proteins and can influence their function.
sequence variants
Changes in the DNA sequence that can alter protein structure and function.
Sequence variants are alterations in the genetic code that can lead to changes in protein sequences. These variations can affect protein folding, stability, and activity, potentially leading to different biological outcomes.
thermodynamics
The study of energy changes in chemical and physical processes.
Thermodynamics explores how energy is transferred and transformed during biological processes. In protein folding, thermodynamics helps understand the factors influencing the stability and preferred conformation of proteins.
kinetics
The study of the rates and mechanisms of chemical reactions.
Kinetics focuses on how fast chemical reactions occur and the steps involved. In protein folding, kinetics examines the time required for proteins to adopt their final structure and the influence of factors like temperature and concentration.
protein binder scaffolds
Structural frameworks designed to bind specifically to target proteins.
Protein binder scaffolds are engineered structures that can selectively attach to specific proteins. These scaffolds serve as platforms for developing new drugs and therapies by targeting and modulating protein activity.
affibody
Affibody
An engineered protein domain that binds with high affinity and specificity to target molecules. Affibodys are often used in drug delivery systems due to their small size and ability to penetrate tissues.
cancer therapeutics
Drugs or treatments designed to fight cancer.
Cancer therapeutics encompass a wide range of medications and therapies aimed at treating various types of cancer. These treatments can target specific cancer cells, inhibit their growth, and reduce tumor size.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:43:19|
Rank Project |
Model Name Folding@Home Identifier |
Make Brand |
GPU Model |
PPD Average |
Points WU Average |
WUs Day Average |
WU Time Average |
|---|---|---|---|---|---|---|---|
| 1 | Tesla V100 SXM2 16GB GV100GL [Tesla V100 SXM2 16GB] M 14899 |
Nvidia | GV100GL | 2,680,144 | 58,938 | 45.47 | 0 hrs 32 mins |
| 2 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 2,658,252 | 59,117 | 44.97 | 0 hrs 32 mins |
| 3 | GeForce RTX 3080 10GB / 20GB GA102 [GeForce RTX 3080 10GB / 20GB] |
Nvidia | GA102 | 2,526,143 | 57,823 | 43.69 | 0 hrs 33 mins |
| 4 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 2,328,774 | 55,854 | 41.69 | 0 hrs 35 mins |
| 5 | TITAN RTX TU102 [TITAN RTX] 16310 |
Nvidia | TU102 | 2,268,878 | 56,055 | 40.48 | 0 hrs 36 mins |
| 6 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 2,226,438 | 55,691 | 39.98 | 0 hrs 36 mins |
| 7 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 1,859,840 | 52,213 | 35.62 | 0 hrs 40 mins |
| 8 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] |
Nvidia | TU104 | 1,804,356 | 52,209 | 34.56 | 0 hrs 42 mins |
| 9 | GeForce RTX 2080 TU104 [GeForce RTX 2080] |
Nvidia | TU104 | 1,747,593 | 50,790 | 34.41 | 0 hrs 42 mins |
| 10 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 1,592,797 | 49,954 | 31.89 | 0 hrs 45 mins |
| 11 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 1,544,871 | 48,876 | 31.61 | 0 hrs 46 mins |
| 12 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] M 7465 |
Nvidia | TU106 | 1,539,124 | 49,559 | 31.06 | 0 hrs 46 mins |
| 13 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,389,925 | 47,147 | 29.48 | 0 hrs 49 mins |
| 14 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 1,327,821 | 46,549 | 28.53 | 0 hrs 50 mins |
| 15 | GeForce RTX 2080 SUPER Mobile / Max-Q TU104M [GeForce RTX 2080 SUPER Mobile / Max-Q] |
Nvidia | TU104M | 1,318,967 | 46,479 | 28.38 | 0 hrs 51 mins |
| 16 | GeForce RTX 2080 SUPER Mobile / Max-Q TU104BM [GeForce RTX 2080 SUPER Mobile / Max-Q] |
Nvidia | TU104BM | 1,231,586 | 45,614 | 27.00 | 0 hrs 53 mins |
| 17 | GeForce GTX 1080 Mobile GP104M [GeForce GTX 1080 Mobile] |
Nvidia | GP104M | 1,124,771 | 44,262 | 25.41 | 0 hrs 57 mins |
| 18 | Tesla T4 TU104GL [Tesla T4] 8141 |
Nvidia | TU104GL | 1,117,157 | 43,304 | 25.80 | 0 hrs 56 mins |
| 19 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,114,223 | 43,863 | 25.40 | 0 hrs 57 mins |
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| 20 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,104,975 | 43,540 | 25.38 | 0 hrs 57 mins |
| 21 | GeForce RTX 2070 TU106 [GeForce RTX 2070] M 6497 |
Nvidia | TU106 | 1,100,991 | 43,293 | 25.43 | 0 hrs 57 mins |
| 22 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,078,215 | 43,227 | 24.94 | 0 hrs 58 mins |
| 23 | GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti] |
Nvidia | TU116 | 1,020,877 | 42,923 | 23.78 | 1 hrs 1 mins |
| 24 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,020,439 | 42,347 | 24.10 | 0 hrs 60 mins |
| 25 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 918,195 | 41,412 | 22.17 | 1 hrs 5 mins |
| 26 | GeForce GTX 1070 Mobile GP104M [GeForce GTX 1070 Mobile] |
Nvidia | GP104M | 901,484 | 41,201 | 21.88 | 1 hrs 6 mins |
| 27 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 866,652 | 40,208 | 21.55 | 1 hrs 7 mins |
| 28 | GeForce GTX Titan X GM200 [GeForce GTX Titan X] 6144 |
Nvidia | GM200 | 836,968 | 40,291 | 20.77 | 1 hrs 9 mins |
| 29 | Quadro RTX 6000/8000 TU102GL [Quadro RTX 6000/8000] |
Nvidia | TU102GL | 823,247 | 39,494 | 20.84 | 1 hrs 9 mins |
| 30 | Radeon RX 5600/5600 XT - 5700/5700 XT Navi 10 [Radeon RX 5600 OEM/5600 XT / 5700/5700 XT] |
AMD | Navi 10 | 680,027 | 36,889 | 18.43 | 1 hrs 18 mins |
| 31 | GeForce GTX 760 GK104 [GeForce GTX 760] 2258 |
Nvidia | GK104 | 669,425 | 32,360 | 20.69 | 1 hrs 10 mins |
| 32 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 653,942 | 37,040 | 17.66 | 1 hrs 22 mins |
| 33 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 620,533 | 35,983 | 17.25 | 1 hrs 24 mins |
| 34 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 601,933 | 34,126 | 17.64 | 1 hrs 22 mins |
| 35 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 555,948 | 35,194 | 15.80 | 1 hrs 31 mins |
| 36 | Radeon VII Vega 20 [Radeon VII] 13,284 |
AMD | Vega 20 | 541,551 | 34,964 | 15.49 | 1 hrs 33 mins |
| 37 | GeForce GTX 1650 TU116 [GeForce GTX 1650] 2984 |
Nvidia | TU116 | 505,672 | 33,966 | 14.89 | 1 hrs 37 mins |
| 38 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 501,459 | 34,106 | 14.70 | 1 hrs 38 mins |
| 39 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 482,091 | 33,057 | 14.58 | 1 hrs 39 mins |
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| 40 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 472,335 | 33,188 | 14.23 | 1 hrs 41 mins |
| 41 | GeForce GTX 780 Ti GK110 [GeForce GTX 780 Ti] 5046 |
Nvidia | GK110 | 378,783 | 30,888 | 12.26 | 1 hrs 57 mins |
| 42 | Radeon RX 5500/5500M / Pro 5500M Navi 14 [Radeon RX 5500/5500M / Pro 5500M] |
AMD | Navi 14 | 376,268 | 30,773 | 12.23 | 1 hrs 58 mins |
| 43 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 320,057 | 29,155 | 10.98 | 2 hrs 11 mins |
| 44 | GeForce GTX 960 GM206 [GeForce GTX 960] 2308 |
Nvidia | GM206 | 307,422 | 28,828 | 10.66 | 2 hrs 15 mins |
| 45 | Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590] |
AMD | Ellesmere XT | 296,198 | 28,258 | 10.48 | 2 hrs 17 mins |
| 46 | Quadro T1000 Mobile TU117GLM [Quadro T1000 Mobile] |
Nvidia | TU117GLM | 267,753 | 27,284 | 9.81 | 2 hrs 27 mins |
| 47 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 264,828 | 27,313 | 9.70 | 2 hrs 29 mins |
| 48 | Radeon R9 Fury X Fiji XT [Radeon R9 Fury X] |
AMD | Fiji XT | 244,278 | 26,283 | 9.29 | 2 hrs 35 mins |
| 49 | Quadro M2200 GM206 [Quadro M2200] |
Nvidia | GM206 | 229,899 | 26,074 | 8.82 | 2 hrs 43 mins |
| 50 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 211,543 | 25,519 | 8.29 | 2 hrs 54 mins |
| 51 | P106-100 GP106 [P106-100] |
Nvidia | GP106 | 179,573 | 24,118 | 7.45 | 3 hrs 13 mins |
| 52 | Radeon R9 200/300 Series Hawaii [Radeon R9 200/300 Series] |
AMD | Hawaii | 166,024 | 23,122 | 7.18 | 3 hrs 21 mins |
| 53 | GeForce GTX 660 Ti GK104 [GeForce GTX 660 Ti] 2634 |
Nvidia | GK104 | 161,570 | 22,236 | 7.27 | 3 hrs 18 mins |
| 54 | Radeon R9 200/300X Series Hawaii [Radeon R9 200/300X Series] |
AMD | Hawaii | 158,531 | 22,385 | 7.08 | 3 hrs 23 mins |
| 55 | GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389 |
Nvidia | GM107 | 134,124 | 21,960 | 6.11 | 3 hrs 56 mins |
| 56 | GeForce GTX 680 GK104 [GeForce GTX 680] 3250 |
Nvidia | GK104 | 104,199 | 20,078 | 5.19 | 4 hrs 37 mins |
| 57 | GeForce GT 1030 GP108 [GeForce GT 1030] 1127 |
Nvidia | GP108 | 103,466 | 20,065 | 5.16 | 4 hrs 39 mins |
| 58 | GeForce GTX 770 GK104 [GeForce GTX 770] 3213 |
Nvidia | GK104 | 100,044 | 19,935 | 5.02 | 4 hrs 47 mins |
| 59 | GeForce GTX 670MX GK106 [GeForce GTX 670MX] |
Nvidia | GK106 | 90,459 | 17,927 | 5.05 | 4 hrs 45 mins |
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| 60 | Radeon HD 7800 Pitcairn [Radeon HD 7800] |
AMD | Pitcairn | 89,498 | 19,141 | 4.68 | 5 hrs 8 mins |
| 61 | Ryzen 4900HS mobile Renoir [Ryzen 4900HS mobile] |
AMD | Renoir | 32,037 | 12,009 | 2.67 | 8 hrs 60 mins |
| 62 | Quadro K2000 GK107 [Quadro K2000] |
Nvidia | GK107 | 27,581 | 13,030 | 2.12 | 11 hrs 20 mins |
| 63 | GeForce GTS 450 GF106 [GeForce GTS 450] |
Nvidia | GF106 | 13,022 | 10,034 | 1.30 | 18 hrs 30 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:43:19|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|