RESEARCH: CYCLIC-TETRAPEPTIDE-SYNTHESIS
FOLDING PROJECT #12461 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 7,600
Core: 0xa8
Status: Public

Related Projects

TLDR; PROJECT SUMMARY AI BETA

Scientists are studying tiny ring-shaped molecules called cyclic tetrapeptides (CTPs) because they could be useful for making new drugs. The project relates to figuring out how to make these CTPs efficiently. Researchers found that the shape of the building blocks used matters a lot for whether the rings form properly. They're using powerful computers to learn more about how these molecules move and behave, which could help them design better ways to make new CTPs in the future.

Note: This TLDR is a simplication and may not be 100% accurate.

OFFICAL PROJECT DESCRIPTION

Cyclic tetrapeptides (CTPs) are a relatively unexplored class of small, ring-shaped molecules built from amino acids.

Their unique structure makes them promising candidates for drug discovery, but there's still much we don't know about how to make them efficiently.

The main challenge in synthesizing CTPs is getting the ends of their linear precursors to “close” and make the ring, a step that’s limited by their small size and rigidity. Recent findings from the O’Reilly Lab at Villanova University suggest that the success of this cyclization process depends heavily on the specific L/D stereochemistry of the amino acids involved.

In collaboration with the O'Reilly Lab, the Voelz Lab at Temple University is using distributed computing on Folding@Home to gather kinetic data on all L/D variants of a key precursor.

This large dataset will help us understand how stereochemistry affects ease of synthesis, tell us more about the motion and behavior of these molecules, and may inform future efforts to synthesize novel CTPs.

RELATED TERMS GLOSSARY AI BETA

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

Cyclic tetrapeptides

Small, ring-shaped molecules built from amino acids.

Scientific: Pharmaceutical
Drug Discovery / Biotechnology

Cyclic tetrapeptides (CTPs) are a class of small molecules formed by linking four amino acids in a ring. They have potential as drugs due to their unique structure and ability to interact with biological targets. Researchers are studying how to efficiently synthesize CTPs, focusing on the stereochemistry of the amino acids involved.


Amino acids

Building blocks of proteins.

Scientific: Pharmaceutical
Biochemistry / Drug Discovery

Amino acids are the fundamental units that make up proteins. They have various chemical properties and play essential roles in biological processes such as enzyme activity, structural support, and cell signaling.


Drug discovery

The process of identifying and developing new medications.

Technical: Pharmaceutical
Pharmaceutical Research / Biotechnology

Drug discovery is a complex process that involves identifying promising drug candidates, testing their effectiveness and safety, and ultimately bringing them to market. It relies on scientific research, advanced technologies, and collaboration between researchers, clinicians, and industry partners.


Stereochemistry

The arrangement of atoms in space.

Scientific: Pharmaceutical
Chemistry / Drug Discovery

Stereochemistry is the study of how atoms are arranged in molecules and how this arrangement affects their properties. In drug discovery, understanding stereochemistry is crucial because even small differences in the spatial arrangement of atoms can significantly impact a molecule's biological activity.


Cyclization

The formation of a ring structure.

Scientific: Pharmaceutical
Chemistry / Drug Discovery

Cyclization is a chemical reaction that involves joining two or more molecules to form a cyclic (ring-shaped) structure. In drug discovery, cyclization is often used to create novel compounds with specific properties.


Folding@Home

A distributed computing project for simulating protein folding.

Technical: Pharmaceutical
Bioinformatics / Drug Discovery

Folding@Home is a volunteer-based computational platform that harnesses the power of personal computers to simulate protein folding. This simulation helps researchers understand how proteins fold into their complex shapes, which is crucial for drug discovery and other biological research.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:34:21
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 Tuesday, 14 April 2026 06:34:21
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 RYZEN 9 9950X 16-CORE 32 48,264 1,544,448 AMD
2 RYZEN THREADRIPPER 7980X 64-CORES 64 23,895 1,529,280 AMD
3 XEON W-3245 CPU @ 3.20GHZ 32 39,086 1,250,752 Intel
4 APPLE M4 PRO 14 70,469 986,566 Apple
5 RYZEN 9 7900X 12-CORE 24 40,737 977,688 AMD
6 RYZEN 7 7700X 8-CORE 16 58,223 931,568 AMD
7 CORE I9-14900K 32 28,462 910,784 Intel
8 RYZEN 7 7800X3D 8-CORE 16 42,016 672,256 AMD
9 RYZEN 7 5700G 16 41,153 658,448 AMD
10 RYZEN 5 7600 6-CORE 12 48,196 578,352 AMD
11 RYZEN 7 5800X 8-CORE 16 33,787 540,592 AMD
12 CORE I9-14900KF 24 18,358 440,592 Intel
13 CORE I7-10700K CPU @ 3.80GHZ 16 23,472 375,552 Intel
14 APPLE M1 MAX 10 34,226 342,260 Apple
15 RYZEN 5 5600G 12 23,999 287,988 AMD
16 RYZEN 5 5600 6-CORE 12 23,526 282,312 AMD
17 CORE I7-9700 CPU @ 3.00GHZ 8 34,628 277,024 Intel
18 RYZEN 5 5500 12 22,613 271,356 AMD
19 RYZEN 5 5600X 6-CORE 12 21,265 255,180 AMD
20 RYZEN 7 5700X 8-CORE 16 15,578 249,248 AMD
21 RYZEN 9 5950X 16-CORE 32 7,159 229,088 AMD
22 CORE I7-4930K CPU @ 3.40GHZ 12 17,623 211,476 Intel
23 RYZEN 5 3600 6-CORE 12 17,603 211,236 AMD
24 CORE I5-9400 CPU @ 2.90GHZ 6 34,574 207,444 Intel
25 CORE I9-8950HK CPU @ 2.90GHZ 12 16,556 198,672 Intel
26 CORE I7-8705G CPU @ 3.10GHZ 8 19,867 158,936 Intel
27 CORE I7-5820K CPU @ 3.30GHZ 12 13,150 157,800 Intel
28 CORE I5-10600KF CPU @ 4.10GHZ 12 12,657 151,884 Intel
29 CORE I7-6700K CPU @ 4.00GHZ 8 16,122 128,976 Intel
30 RYZEN 5 1600 SIX-CORE 12 10,038 120,456 AMD
31 XEON CPU E3-1270 V5 @ 3.60GHZ 8 13,595 108,760 Intel
32 RYZEN 7 5800H 16 6,079 97,264 AMD
33 CORE I5-6500 CPU @ 3.20GHZ 4 23,695 94,780 Intel
34 RYZEN 5 2400G 8 10,134 81,072 AMD
35 CORE I5-9300H CPU @ 2.40GHZ 8 8,901 71,208 Intel
36 CORE I7-4770S CPU @ 3.10GHZ 8 8,041 64,328 Intel
37 CORE I5-6500T CPU @ 2.50GHZ 4 15,438 61,752 Intel
38 CORE I5-4460 CPU @ 3.20GHZ 4 14,594 58,376 Intel
39 CORE I5-3350P CPU @ 3.10GHZ 4 13,704 54,816 Intel
40 CORE I5-4590 CPU @ 3.30GHZ 4 12,767 51,068 Intel
41 RYZEN 5 4500U 6 7,864 47,184 AMD
42 CORE I5-2500 CPU @ 3.30GHZ 4 9,597 38,388 Intel
43 CORE I7-7600U CPU @ 2.80GHZ 4 8,776 35,104 Intel
44 XEON CPU E5-2609 0 @ 2.40GHZ 4 7,132 28,528 Intel
45 CORE I3-4160T CPU @ 3.10GHZ 4 6,531 26,124 Intel
46 CORE I5-3330 CPU @ 3.00GHZ 4 6,273 25,092 Intel
47 PHENOM II X4 970 4 5,268 21,072 AMD
48 CORE I7 CPU 975 @ 3.33GHZ 8 1,356 10,848 Intel
49 CORE I3-2330M CPU @ 2.20GHZ 4 2,143 8,572 Intel
50 CORE2 QUAD CPU Q6600 @ 2.40GHZ 4 2,130 8,520 Intel
51 APPLE M1 8 Apple
52 APPLE M2 PRO 10 Apple
53 APPLE M4 10 Apple