RESEARCH: CANCER
FOLDING PROJECT #18411 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 changing tiny parts of a protein designed to block bacterial growth will affect its ability to bind to bacteria. By comparing these predictions to real experiments, researchers hope to develop 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.

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RELATED TERMS GLOSSARY AI BETA

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

molecular simulation

Simulating molecular interactions using computers.

Scientific: Medicine & Pharmaceuticals
Biotechnology / Protein Design

Molecular simulation uses computer models to mimic how atoms and molecules interact. This helps scientists understand chemical reactions, design new materials, and study biological processes like protein folding.


affinity-maturation

The process of improving the binding affinity of a protein or drug molecule.

Scientific: Medicine & Pharmaceuticals
Biotechnology / Drug Discovery

Affinity maturation is like fine-tuning a molecular lock and key. Scientists modify a protein or drug to bind more strongly to its target, making it more effective.


de novo designed

Created from scratch using computational methods.

Scientific: Medicine & Pharmaceuticals
Biotechnology / Protein Engineering

De novo designed proteins are built from the ground up using computer algorithms. This allows scientists to create novel proteins with specific functions.


protein binders

Proteins that bind specifically to other molecules.

Scientific: Medicine & Pharmaceuticals
Biotechnology / Drug Discovery

Protein binders are like molecular magnets, attaching to specific targets. They have important roles in drug development and biological processes.


mini-proteins

Small proteins with specific functions.

Scientific: Medicine & Pharmaceuticals
Biotechnology / Drug Discovery

Mini-proteins are compact versions of regular proteins. They offer advantages in drug development due to their size and stability.


periplasmic protease

An enzyme found in the periplasm of bacteria.

Scientific: Medicine & Pharmaceuticals
Biotechnology / Microbiology

Periplasmic proteases are enzymes located in the space between the inner and outer membranes of bacteria. They play roles in various cellular processes, including protein degradation.


LapG

Protease LapG.

Scientific: Medicine & Pharmaceuticals
Biotechnology / Microbiology

LapG is a protease enzyme involved in regulating bacterial biofilm formation.


antibiotic therapies

Medical treatments using antibiotics to fight bacterial infections.

Scientific: Pharmaceuticals
Medicine / Infectious Diseases

Antibiotic therapies are essential for treating bacterial infections. They work by killing or inhibiting the growth of bacteria.


biofilm formation

The process by which bacteria attach to surfaces and form communities.

Scientific: Medicine & Pharmaceuticals
Microbiology / Infectious Diseases

Biofilm formation allows bacteria to create protective layers that resist antibiotics and host immune systems. This makes infections harder to treat.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 03:29:46
Rank
Project
Model Name
Folding@Home Identifier
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Brand
GPU
Model
PPD
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PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 03:29:46
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 EPYC 7B12 64-CORE 64 17,080 1,093,120 AMD
2 RYZEN THREADRIPPER 3960X 24-CORE 48 17,829 855,792 AMD
3 RYZEN 7 7700X 8-CORE 16 38,492 615,872 AMD
4 RYZEN 9 7900X 12-CORE 24 25,505 612,120 AMD
5 RYZEN 9 3950X 16-CORE 32 18,195 582,240 AMD
6 12TH GEN CORE I9-12900K 24 23,330 559,920 Intel
7 RYZEN 9 5950X 16-CORE 32 16,199 518,368 AMD
8 RYZEN 7 5800X 8-CORE 16 28,344 453,504 AMD
9 RYZEN 7 5700X 8-CORE 16 27,999 447,984 AMD
10 12TH GEN CORE I7-12700K 20 21,330 426,600 Intel
11 RYZEN 7 5800X3D 8-CORE 16 25,220 403,520 AMD
12 CORE I9-10920X CPU @ 3.50GHZ 24 16,210 389,040 Intel
13 CORE I9-10850K CPU @ 3.60GHZ 20 19,441 388,820 Intel
14 12TH GEN CORE I7-12700 20 19,110 382,200 Intel
15 11TH GEN CORE I7-11700K @ 3.60GHZ 16 23,767 380,272 Intel
16 RYZEN 9 5900X 12-CORE 24 15,624 374,976 AMD
17 RYZEN 9 3900 12-CORE 24 15,158 363,792 AMD
18 RYZEN 9 3900XT 12-CORE 24 14,372 344,928 AMD
19 XEON CPU E5-2690 V4 @ 2.60GHZ 28 11,702 327,656 Intel
20 12TH GEN CORE I5-12400 12 26,779 321,348 Intel
21 XEON CPU E5-2680 V4 @ 2.40GHZ 28 11,471 321,188 Intel
22 11TH GEN CORE I9-11900K @ 3.50GHZ 16 18,960 303,360 Intel
23 RYZEN 9 3900X 12-CORE 24 11,859 284,616 AMD
24 XEON CPU E5-2680 V2 @ 2.80GHZ 40 7,090 283,600 Intel
25 CORE I9-9900K CPU @ 3.60GHZ 16 16,358 261,728 Intel
26 12TH GEN CORE I9-12900H 20 12,684 253,680 Intel
27 RYZEN 7 3800X 8-CORE 16 15,606 249,696 AMD
28 GENUINE 0000 @ 1.80GHZ 16 14,423 230,768 Intel
29 XEON CPU E5-2680 V3 @ 2.50GHZ 24 9,133 219,192 Intel
30 CORE I7-10700K CPU @ 3.80GHZ 16 13,151 210,416 Intel
31 RYZEN THREADRIPPER 2950X 16-CORE 32 6,047 193,504 AMD
32 XEON CPU E5-2698 V4 @ 2.20GHZ 16 11,097 177,552 Intel
33 RYZEN 7 PRO 4750G 16 10,544 168,704 AMD
34 XEON CPU E5-2665 0 @ 2.40GHZ 32 5,169 165,408 Intel
35 RYZEN THREADRIPPER 1920X 12-CORE 24 6,690 160,560 AMD
36 RYZEN 7 3700X 8-CORE 16 9,619 153,904 AMD
37 XEON CPU E5-2650L V4 @ 1.70GHZ 28 4,578 128,184 Intel
38 11TH GEN CORE I7-11700F @ 2.50GHZ 16 7,057 112,912 Intel
39 CORE I9-9880H CPU @ 2.30GHZ 16 6,523 104,368 Intel
40 EPYC 7251 8-CORE 16 6,322 101,152 AMD
41 CORE I7-10700T CPU @ 2.00GHZ 16 6,150 98,400 Intel
42 XEON CPU E5-2697 V2 @ 2.70GHZ 24 3,149 75,576 Intel
43 XEON CPU E5-2680 0 @ 2.70GHZ 16 4,687 74,992 Intel
44 RYZEN 7 2700X EIGHT-CORE 16 4,570 73,120 AMD
45 RYZEN 9 5900HX 16 4,254 68,064 AMD
46 11TH GEN CORE I7-11800H @ 2.30GHZ 16 3,547 56,752 Intel