RESEARCH: CANCER
FOLDING PROJECT #16989 PROFILE

PROJECT TEAM

Manager(s): Prof. Vincent Voelz
Institution: Temple University

WORK UNIT INFO

Atoms: 23,400
Core: 0xa8
Status: Public

TLDR; PROJECT SUMMARY AI BETA

This project relates to understanding how proteins fold into specific shapes. By changing the protein's building blocks and adding special links, scientists want to see how this affects its stability and ability to bind to other molecules. This knowledge could help design better cancer treatments that target specific cells.

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

Note: Glossary items are a high level summary and may not be 100% accurate.

alpha-helical hairpins

Structural motif in proteins with alpha helix coils.

Scientific: Biotechnology
Biotechnology / Protein Structure

Alpha-helical hairpins are a common structural feature in proteins. They consist of two alpha helices connected by a short loop. These structures play important roles in protein folding and function.


disulfide cross-linkers

Covalent bonds between cysteine amino acids in proteins.

Scientific: Biotechnology
Biotechnology / Protein Structure

Disulfide cross-linkers are covalent bonds that form between two sulfur atoms in cysteine amino acids. These bonds stabilize protein structures and can influence protein folding and function.


sequence variants

Variations in the DNA sequence of a gene.

Scientific: Biotechnology
Biotechnology / Genetic Engineering

Sequence variants are alterations in the DNA sequence of a gene. These variations can lead to changes in protein structure and function, and can be associated with disease or altered traits.


folding thermodynamics

Study of energy changes in protein folding.

Scientific: Biotechnology
Biotechnology / Protein Structure

Folding thermodynamics investigates the energy changes associated with the process of protein folding. This field helps understand how proteins adopt their stable three-dimensional structures and the factors that influence this process.


kinetics

Rate of chemical reactions.

Scientific: Biotechnology
Biotechnology / Protein Structure

Kinetics studies the rates and mechanisms of chemical reactions. In the context of protein folding, kinetics investigates how fast proteins fold and the steps involved in this process.


molecular simulation methods

Computer-based techniques to model molecular behavior.

Scientific: Biotechnology
Biotechnology / Computational Biology

Molecular simulation methods use computer algorithms to simulate the behavior of molecules and their interactions. These techniques are widely used in biotechnology to study protein folding, drug design, and other biological processes.


affibody

Affibody is a proprietary engineered protein scaffold

Technical: Pharmaceuticals
Biotechnology / Drug Development

An affibody is a small, engineered protein scaffold that binds specifically to target molecules. These scaffolds are often used in drug development as they can be designed to deliver drugs to specific cells or tissues.


cancer therapeutics

Medicines used to treat cancer.

Technical: Pharmaceuticals
Biotechnology / Drug Development

Cancer therapeutics are medications used to treat various types of cancer. These treatments aim to kill cancer cells or slow their growth.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 00:42:08
Rank
Project
Model Name
Folding@Home Identifier
Make
Brand
GPU
Model
PPD
Average
Points WU
Average
WUs Day
Average
WU Time
Average

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 00:42:08
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 RYZEN THREADRIPPER 7980X 64-CORES 64 26,121 1,671,744 AMD
2 RYZEN 9 7950X 16-CORE 32 50,037 1,601,184 AMD
3 12TH GEN CORE I9-12900K 24 39,598 950,352 Intel
4 13TH GEN CORE I9-13900KS 32 27,524 880,768 Intel
5 RYZEN 7 5800X3D 8-CORE 16 50,234 803,744 AMD
6 RYZEN 7 7700X 8-CORE 16 37,770 604,320 AMD
7 RYZEN 7 5800X 8-CORE 16 37,655 602,480 AMD
8 RYZEN 9 3900 12-CORE 24 22,819 547,656 AMD
9 RYZEN 9 5950X 16-CORE 32 17,029 544,928 AMD
10 RYZEN 9 5900X 12-CORE 24 19,855 476,520 AMD
11 12TH GEN CORE I7-12700K 20 22,277 445,540 Intel
12 RYZEN 7 5700X 8-CORE 16 27,137 434,192 AMD
13 CORE I7-7820X CPU @ 3.60GHZ 16 25,858 413,728 Intel
14 11TH GEN CORE I7-11700K @ 3.60GHZ 16 24,941 399,056 Intel
15 RYZEN 9 3950X 16-CORE 32 12,347 395,104 AMD
16 RYZEN 7 3800X 8-CORE 16 20,871 333,936 AMD
17 CORE I9-14900K 32 10,376 332,032 Intel
18 CORE I9-10850K CPU @ 3.60GHZ 20 16,442 328,840 Intel
19 RYZEN THREADRIPPER 1950X 16-CORE 32 10,034 321,088 AMD
20 RYZEN 9 3900XT 12-CORE 24 11,479 275,496 AMD
21 RYZEN 5 5600 6-CORE 12 22,921 275,052 AMD
22 CORE I9-10900X CPU @ 3.70GHZ 20 13,691 273,820 Intel
23 XEON CPU E5-2680 V3 @ 2.50GHZ 24 10,846 260,304 Intel
24 CORE I9-9900K CPU @ 3.60GHZ 16 15,509 248,144 Intel
25 RYZEN 5 5600X 6-CORE 12 19,955 239,460 AMD
26 CORE I7-8700 CPU @ 3.20GHZ 12 19,422 233,064 Intel
27 12TH GEN CORE I7-12700 20 11,536 230,720 Intel
28 RYZEN 9 3900X 12-CORE 24 9,384 225,216 AMD
29 RYZEN 9 5900HS 16 14,017 224,272 AMD
30 RYZEN 5 3600 6-CORE 12 17,642 211,704 AMD
31 XEON CPU E5-2690 V4 @ 2.60GHZ 28 6,872 192,416 Intel
32 RYZEN 5 5600G 12 15,218 182,616 AMD
33 CORE I7-9700 CPU @ 3.00GHZ 8 21,261 170,088 Intel
34 XEON CPU E5-2660 V3 @ 2.60GHZ 20 8,293 165,860 Intel
35 RYZEN 7 PRO 4750G 16 10,279 164,464 AMD
36 RYZEN 7 3700X 8-CORE 16 10,181 162,896 AMD
37 CORE I5-10400 CPU @ 2.90GHZ 12 13,207 158,484 Intel
38 CORE I7-5820K CPU @ 3.30GHZ 12 10,644 127,728 Intel
39 11TH GEN CORE I5-11400 @ 2.60GHZ 12 10,470 125,640 Intel
40 APPLE M1 MAX 10 10,960 109,600 Apple
41 CORE I7-10700T CPU @ 2.00GHZ 16 6,646 106,336 Intel
42 RYZEN 5 2600X SIX-CORE 12 8,857 106,284 AMD
43 CORE I7-8705G CPU @ 3.10GHZ 8 11,603 92,824 Intel
44 CORE I7-4790K CPU @ 4.00GHZ 8 10,948 87,584 Intel
45 RYZEN 5 1600 SIX-CORE 12 7,122 85,464 AMD
46 XEON CPU E3-1270 V5 @ 3.60GHZ 8 10,575 84,600 Intel
47 CORE I3-10100 CPU @ 3.60GHZ 8 9,919 79,352 Intel
48 CORE I7-6700K CPU @ 4.00GHZ 8 9,676 77,408 Intel
49 CORE I9-8950HK CPU @ 2.90GHZ 12 6,349 76,188 Intel
50 EPYC 7251 8-CORE 16 4,685 74,960 AMD
51 11TH GEN CORE I7-1185G7 @ 3.00GHZ 8 9,196 73,568 Intel
52 APPLE M1 8 8,684 69,472 Apple
53 CORE I7-4770 CPU @ 3.40GHZ 8 8,610 68,880 Intel
54 XEON W-10855M CPU @ 2.80GHZ 12 5,452 65,424 Intel
55 CORE I7-4770HQ CPU @ 2.20GHZ 8 7,642 61,136 Intel
56 CORE I7-3770 CPU @ 3.40GHZ 8 6,649 53,192 Intel
57 CORE I7-4770K CPU @ 3.50GHZ 8 6,545 52,360 Intel
58 CORE I7-4790T CPU @ 2.70GHZ 8 6,136 49,088 Intel
59 XEON CPU E31245 @ 3.30GHZ 8 4,827 38,616 Intel
60 CORE I7 CPU 975 @ 3.33GHZ 8 3,782 30,256 Intel
61 CORE I7-6700 CPU @ 3.40GHZ 8 2,429 19,432 Intel
62 12TH GEN CORE I5-12600K 16 Intel