RESEARCH: CANCER
FOLDING PROJECT #18408 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 64,500
Core: 0xa8
Status: Public

TLDR; PROJECT SUMMARY AI BETA

This project uses computer simulations to predict how changes to a mini-protein's design affect its ability to bind to a bacterial protein. The goal is to find better ways to block this bacterial protein, which helps bacteria form harmful biofilms. This could lead to new and more effective antibiotics.

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

OFFICAL PROJECT DESCRIPTION

Can molecular simulation be used for virtual affinity-maturation of de novo designed protein binders? That’s the question this project aims to address.

The Bahl Lab at the Institute for Protein Innovation has had some amazing success using computational design to develop high-affinity mini-proteins that can inhibit protein targets by tightly binding to them.

In practice, the current approach requires the experimental screening of thousands of computational designs to discover a few tight binders, and similarly expensive experimental screens to optimize their binding (i.e.

“affinity maturation”).

If we can make more accurate predictions of how sequence mutations affect binding affinity, we may be able to offload this expensive task to computers, boosting the efficiency of these efforts considerably. In this project, we use relative free energy calculations to predict how single-point mutations of a computationally designed mini-protein alter the binding affinity to the periplasmic protease LapG, an important regulator of bacterial biofilm formation.

These predictions will be compared to high-throughput experimental measurements of binding affinity provided by the Bahl lab.

An important end goal of this work is to develop new classes of inhibitors to make antibiotic therapies more successful.

.

RELATED TERMS GLOSSARY AI BETA

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

molecular simulation

Using computer models to simulate molecular interactions.

Technical: Pharmaceutical
Biotechnology / Drug Discovery

Molecular simulation is a technique used in biotechnology and drug discovery to understand how molecules interact at the atomic level. It involves creating computer models of molecules and simulating their behavior over time. This can be used to predict how drugs might bind to target proteins or how different chemical compounds might interact with each other.


affinity-maturation

The process of improving the binding affinity of a protein to its target.

Technical: Pharmaceutical
Biotechnology / Protein Engineering

Affinity maturation is a crucial step in developing effective drugs. It involves modifying the structure of a protein (often an antibody) to increase its ability to bind tightly to a specific target molecule. This enhanced binding makes the drug more potent and effective.


de novo designed

Created from scratch using computational design.

Technical: Pharmaceutical
Biotechnology / Protein Engineering

De novo design refers to the creation of new proteins or molecules from the ground up, rather than modifying existing ones. This process often involves computer algorithms that predict the structure and function of potential designs.


mini-proteins

Small proteins with specific functions.

Technical: Pharmaceutical
Biotechnology / Protein Engineering

Mini-proteins are compact versions of traditional proteins that retain their biological activity. These smaller proteins can be easier to produce and manipulate, making them attractive for various applications, such as drug development.


periplasmic protease

An enzyme found in the periplasm of bacteria.

Scientific: Pharmaceutical
Biotechnology / Microbiology

Periplasmic proteases are enzymes located within the periplasm, a space between the inner and outer membranes of bacteria. They play various roles, including breaking down proteins and regulating cellular processes.


LapG

A specific type of periplasmic protease.

Technical: Pharmaceutical
Biotechnology / Microbiology

LapG is a bacterial protein that functions as a protease in the periplasm. It plays a role in regulating biofilm formation, which is essential for bacterial survival and pathogenesis.


biofilm

A community of bacteria encased in a protective matrix.

Scientific: Pharmaceutical
Biotechnology / Microbiology

Biofilms are complex communities of microorganisms that adhere to surfaces and enclose themselves within a self-produced extracellular matrix. This matrix provides protection from environmental stresses and antibiotics, making biofilms difficult to eradicate.


antibiotic therapies

Medical treatments using antibiotics to fight bacterial infections.

Clinical: Pharmaceutical
Medicine / Infectious Diseases

Antibiotic therapies are essential for treating bacterial infections. Antibiotics work by targeting specific processes within bacteria, such as cell wall synthesis or protein production, ultimately killing the bacteria.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 03:29:50
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 03:29:50
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 RYZEN 9 7950X 16-CORE 32 27,921 893,472 AMD
2 RYZEN 7 7700X 8-CORE 16 39,156 626,496 AMD
3 RYZEN 9 3950X 16-CORE 32 18,980 607,360 AMD
4 RYZEN 9 5950X 16-CORE 32 17,701 566,432 AMD
5 12TH GEN CORE I9-12900K 24 23,599 566,376 Intel
6 RYZEN 7 5800X3D 8-CORE 16 34,470 551,520 AMD
7 RYZEN 9 5900X 12-CORE 24 20,034 480,816 AMD
8 RYZEN 7 5800X 8-CORE 16 28,852 461,632 AMD
9 RYZEN THREADRIPPER 1950X 16-CORE 32 13,520 432,640 AMD
10 CORE I9-10920X CPU @ 3.50GHZ 24 17,085 410,040 Intel
11 RYZEN 9 3900 12-CORE 24 16,950 406,800 AMD
12 RYZEN 7 5700X 8-CORE 16 25,320 405,120 AMD
13 11TH GEN CORE I7-11700K @ 3.60GHZ 16 25,295 404,720 Intel
14 12TH GEN CORE I7-12700 20 19,443 388,860 Intel
15 12TH GEN CORE I7-12700K 20 19,331 386,620 Intel
16 13TH GEN CORE I5-13500 20 18,165 363,300 Intel
17 RYZEN 9 3900XT 12-CORE 24 14,449 346,776 AMD
18 EPYC 7V12 64-CORE 64 5,149 329,536 AMD
19 12TH GEN CORE I5-12600K 16 20,593 329,488 Intel
20 CORE I9-10850K CPU @ 3.60GHZ 20 15,881 317,620 Intel
21 XEON CPU E5-2690 V4 @ 2.60GHZ 28 11,315 316,820 Intel
22 RYZEN 9 5900 12-CORE 24 11,929 286,296 AMD
23 RYZEN 7 5800H 16 17,338 277,408 AMD
24 RYZEN THREADRIPPER 2950X 16-CORE 32 8,622 275,904 AMD
25 RYZEN 9 3900X 12-CORE 24 11,464 275,136 AMD
26 XEON CPU E5-2680 V2 @ 2.80GHZ 40 6,687 267,480 Intel
27 XEON CPU E5-2680 V3 @ 2.50GHZ 24 10,820 259,680 Intel
28 RYZEN 7 3800X 8-CORE 16 16,058 256,928 AMD
29 11TH GEN CORE I9-11900K @ 3.50GHZ 16 15,936 254,976 Intel
30 CORE I7-10700K CPU @ 3.80GHZ 16 14,956 239,296 Intel
31 RYZEN 7 5700G 16 14,746 235,936 AMD
32 RYZEN 5 3600 6-CORE 12 18,776 225,312 AMD
33 XEON CPU E5-2680 V4 @ 2.40GHZ 28 8,044 225,232 Intel
34 CORE I9-9980XE CPU @ 3.00GHZ 36 6,158 221,688 Intel
35 CORE I9-9900K CPU @ 3.60GHZ 16 13,604 217,664 Intel
36 11TH GEN CORE I5-11400 @ 2.60GHZ 12 16,206 194,472 Intel
37 RYZEN 7 PRO 4750G 16 11,475 183,600 AMD
38 RYZEN 7 4800H 16 11,430 182,880 AMD
39 XEON CPU E5-2650 V2 @ 2.60GHZ 32 5,454 174,528 Intel
40 RYZEN 7 3700X 8-CORE 16 10,058 160,928 AMD
41 CORE I9-7940X CPU @ 3.10GHZ 28 5,513 154,364 Intel
42 12TH GEN CORE I7-12700H 20 6,637 132,740 Intel
43 RYZEN 7 2700X EIGHT-CORE 16 7,860 125,760 AMD
44 XEON CPU E5-2698 V4 @ 2.20GHZ 16 7,753 124,048 Intel
45 CORE I7-7820X CPU @ 3.60GHZ 16 6,505 104,080 Intel
46 CORE I7-10700T CPU @ 2.00GHZ 16 6,197 99,152 Intel
47 XEON CPU E5-2680 0 @ 2.70GHZ 16 5,212 83,392 Intel
48 11TH GEN CORE I7-11700 @ 2.50GHZ 16 4,488 71,808 Intel
49 XEON CPU E5-2697 V2 @ 2.70GHZ 24 2,071 49,704 Intel
50 12TH GEN CORE I7-12700KF 20 2,206 44,120 Intel
51 OPTERON(TM) 6380 64 330 21,120 AMD