Research section Research projects and references

Research: CANCER Folding Project #12436

Project #12436 overview

Project Summary AI Beta

This project investigates how small proteins called peptides can block autophagy, a cellular recycling process. By tweaking the design of these peptides, scientists hope to develop new cancer treatments that make tumors more vulnerable to chemotherapy. They're using computer simulations to understand how different peptide structures interact with target proteins and enhance their effectiveness.
Automated summary; simplified and may not be fully accurate.

Project team

Manager(s)
Prof. Vincent Voelz
Institution
Temple University

Work unit

Atoms
21,000
Core
0xa8
Status
Public
Source material

Official Project Description

GABARAP (gamma-aminobutyric acid receptor-associated protein) plays an important role in autophagy, the process by which cytosolic material is transported to cellular compartments called lysosomes for degradation.

It is also a target for cancer therapy: inhibiting the function of GABARAP can help sensitize cancer cells to chemotherapy.

The Kritzer lab at Tufts University has developed stapled peptide inhibitors of LC3 and GABARAP proteins (Brown et al.

2022).

We are using molecular simulation and free energy approaches to understand how peptide sequence and the staple linker chemistry control the affinity and selectivity of these peptide binders, both through their interactions at the protein surface, but also through the extent of peptide preorganization in solution.

Our long-term goal is to use these methods to improve the affinity and bioavailability of conformationally constrained peptides through N-methylation and other non-natural modifications. Reference Brown, Hawley, Mia Chung, Alina Üffing, Nefeli Batistatou, Tiffany Tsang, Samantha Doskocil, Weiqun Mao, et al.

“Structure-Based Design of Stapled Peptides That Bind GABARAP and Inhibit Autophagy.” Journal of the American Chemical Society 144, no.

32 (August 17, 2022): 14687–97.

https://doi.org/10.1021/jacs.2c04699.


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Performance data

Hardware Performance for Project 12436

Compare community-sampled Folding@Home output for the GPUs and CPUs processing this project.

Data as of Sunday, 02 August 2026 22:09:23

CPU PPD Averages Beta

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 12TH GEN CORE I9-12900K 24 39,327 943,848 Intel
2 RYZEN 7 5800X 8-CORE 16 34,145 546,320 AMD
3 RYZEN 5 7600 6-CORE 12 41,213 494,556 AMD
4 RYZEN 5 5500 12 37,847 454,164 AMD
5 RYZEN 7 5800X3D 8-CORE 16 23,429 374,864 AMD
6 12TH GEN CORE I7-12700F 20 18,164 363,280 Intel
7 12TH GEN CORE I5-12400F 12 28,800 345,600 Intel
8 RYZEN 9 3900X 12-CORE 24 13,540 324,960 AMD
9 RYZEN 9 5950X 16-CORE 32 10,072 322,304 AMD
10 CORE I9-14900K 32 9,407 301,024 Intel
11 RYZEN 5 5600X 6-CORE 12 24,931 299,172 AMD
12 RYZEN 5 5600 6-CORE 12 24,749 296,988 AMD
13 13TH GEN CORE I5-13600K 14 20,761 290,654 Intel
14 CORE I7-10700K CPU @ 3.80GHZ 16 17,038 272,608 Intel
15 13TH GEN CORE I7-13700 24 8,846 212,304 Intel
16 CORE I7-9700 CPU @ 3.00GHZ 8 22,383 179,064 Intel
17 APPLE M2 8 21,600 172,800 Apple
18 CORE I7-5930K CPU @ 3.50GHZ 12 12,202 146,424 Intel
19 CORE I7-8700K CPU @ 3.70GHZ 12 10,620 127,440 Intel
20 CORE I5-5675R CPU @ 3.10GHZ 4 21,643 86,572 Intel
21 CORE I7-6700 CPU @ 3.40GHZ 8 10,449 83,592 Intel
22 CORE I3-8100 CPU @ 3.60GHZ 4 15,469 61,876 Intel
23 CORE I5-4590 CPU @ 3.30GHZ 4 12,423 49,692 Intel
24 CORE I7-8665U CPU @ 1.90GHZ 8 4,196 33,568 Intel
25 CORE I3-4150 CPU @ 3.50GHZ 4 7,380 29,520 Intel
26 CORE I7-7600U CPU @ 2.80GHZ 4 7,129 28,516 Intel
27 CORE I3-4160T CPU @ 3.10GHZ 4 4,968 19,872 Intel
28 ATOM(TM) CPU Z3770 @ 1.46GHZ 4 653 2,612 Intel