RESEARCH: CANCER
FOLDING PROJECT #18422 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 80,500
Core: 0xa8
Status: Public

TLDR; PROJECT SUMMARY AI BETA

This project uses computer simulations to predict how changes in a mini-protein's design affect its ability to bind to a bacterial enzyme. The goal is to develop new, more effective antibiotics by designing mini-proteins that block the enzyme and prevent bacteria from forming biofilms.

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

Using computer models to simulate molecular interactions.

Technical: Pharmaceutical
Biotechnology / Drug Discovery

Molecular simulation uses computer programs to imitate how atoms and molecules interact. This helps scientists understand chemical reactions, design new drugs, and predict the properties of materials.


affinity maturation

A process of improving the binding affinity of a molecule, such as an antibody.

Scientific: Pharmaceutical
Biotechnology / Protein Engineering

Affinity maturation is like fine-tuning a lock and key. Scientists use this process to make molecules bind more strongly to their targets. This is important for developing better drugs and therapies.


mini-proteins

Small proteins with specific functions.

Scientific: Pharmaceutical
Biotechnology / Protein Engineering

Mini-proteins are like compact versions of regular proteins. They can have important jobs in the body and are being studied for their potential use in medicine.


LapG

A periplasmic protease.

Technical: Pharmaceutical
Biotechnology / Microbiology

LapG is a bacterial enzyme that breaks down proteins. It plays a role in the formation of biofilms, which are communities of bacteria that can be difficult to treat with antibiotics.


periplasmic

Located in the periplasm, the space between the cell membrane and the outer membrane of bacteria.

Scientific: Pharmaceutical
Biotechnology / Microbiology

The periplasm is a region within bacteria. It's like a small pocket between two layers of the bacterial cell wall. Periplasmic proteins have important jobs in processes like nutrient uptake and waste removal.


biofilm

A community of bacteria that adhere to a surface and are encased in a matrix.

Scientific: Pharmaceutical
Biotechnology / Microbiology

Biofilms are like cities for bacteria. They are groups of bacteria that stick together and create a protective layer around themselves. This makes them harder to treat with antibiotics.

PROJECT FOLDING PPD AVERAGES BY GPU

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

Data as of Sunday, 26 April 2026 03:29:29
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 13TH GEN CORE I9-13900KS 32 34,386 1,100,352 Intel
2 RYZEN 9 7900X 12-CORE 24 34,482 827,568 AMD
3 RYZEN 9 5950X 16-CORE 32 21,092 674,944 AMD
4 RYZEN 7 7700X 8-CORE 16 38,725 619,600 AMD
5 12TH GEN CORE I7-12700K 20 30,831 616,620 Intel
6 RYZEN 7 5800X3D 8-CORE 16 36,099 577,584 AMD
7 12TH GEN CORE I9-12900K 24 23,920 574,080 Intel
8 RYZEN 7 5700X 8-CORE 16 29,646 474,336 AMD
9 RYZEN 7 5800X 8-CORE 16 28,507 456,112 AMD
10 RYZEN 9 3950X 16-CORE 32 13,682 437,824 AMD
11 RYZEN 9 3900 12-CORE 24 17,924 430,176 AMD
12 RYZEN 9 5900X 12-CORE 24 17,654 423,696 AMD
13 12TH GEN CORE I7-12700 20 20,245 404,900 Intel
14 11TH GEN CORE I7-11700K @ 3.60GHZ 16 24,335 389,360 Intel
15 CORE I9-7920X CPU @ 2.90GHZ 24 16,024 384,576 Intel
16 RYZEN 9 3900XT 12-CORE 24 15,344 368,256 AMD
17 CORE I9-7940X CPU @ 3.10GHZ 28 12,129 339,612 Intel
18 12TH GEN CORE I5-12400 12 27,333 327,996 Intel
19 XEON CPU E5-2690 V4 @ 2.60GHZ 28 11,248 314,944 Intel
20 RYZEN 7 3800X 8-CORE 16 19,152 306,432 AMD
21 RYZEN 9 3900X 12-CORE 24 12,629 303,096 AMD
22 RYZEN 7 5700G 16 18,139 290,224 AMD
23 XEON CPU E5-2680 V2 @ 2.80GHZ 40 7,032 281,280 Intel
24 XEON CPU E5-2680 V3 @ 2.50GHZ 24 10,279 246,696 Intel
25 RYZEN 5 5600X 6-CORE 12 19,774 237,288 AMD
26 CORE I7-8700 CPU @ 3.20GHZ 12 18,955 227,460 Intel
27 CORE I9-10900X CPU @ 3.70GHZ 20 11,326 226,520 Intel
28 CORE I9-9900 CPU @ 3.10GHZ 16 13,906 222,496 Intel
29 CORE I9-9900K CPU @ 3.60GHZ 16 13,525 216,400 Intel
30 RYZEN 5 5600G 12 17,668 212,016 AMD
31 13TH GEN CORE I5-13500 20 10,562 211,240 Intel
32 RYZEN 5 3600 6-CORE 12 17,067 204,804 AMD
33 XEON CPU E5-2665 0 @ 2.40GHZ 32 6,377 204,064 Intel
34 CORE I7-10700K CPU @ 3.80GHZ 16 12,690 203,040 Intel
35 XEON CPU E5-2698 V4 @ 2.20GHZ 16 12,116 193,856 Intel
36 RYZEN 9 6900HS CREATOR EDITION 16 11,869 189,904 AMD
37 RYZEN 7 PRO 4750G 16 11,590 185,440 AMD
38 RYZEN 7 3700X 8-CORE 16 10,986 175,776 AMD
39 CORE I7-5930K CPU @ 3.50GHZ 12 13,495 161,940 Intel
40 11TH GEN CORE I9-11900F @ 2.50GHZ 16 9,741 155,856 Intel
41 EPYC 7401P 24-CORE 48 3,188 153,024 AMD
42 XEON CPU E5-2650 V2 @ 2.60GHZ 32 4,380 140,160 Intel
43 XEON CPU E5-2650L V4 @ 1.70GHZ 28 4,976 139,328 Intel
44 11TH GEN CORE I5-11400 @ 2.60GHZ 12 11,392 136,704 Intel
45 CORE I7-10700 CPU @ 2.90GHZ 16 8,425 134,800 Intel
46 CORE I5-10400 CPU @ 2.90GHZ 12 10,341 124,092 Intel
47 CORE I7-9750H CPU @ 2.60GHZ 12 10,076 120,912 Intel
48 RYZEN 7 2700X EIGHT-CORE 16 7,398 118,368 AMD
49 CORE I7-5820K CPU @ 3.30GHZ 12 9,719 116,628 Intel
50 CORE I7-10700T CPU @ 2.00GHZ 16 6,996 111,936 Intel
51 RYZEN 5 1600 SIX-CORE 12 9,105 109,260 AMD
52 11TH GEN CORE I7-11700F @ 2.50GHZ 16 6,406 102,496 Intel
53 CORE I9-8950HK CPU @ 2.90GHZ 12 7,329 87,948 Intel
54 RYZEN 7 1700 EIGHT-CORE 16 5,437 86,992 AMD
55 CORE I7-5600U CPU @ 2.60GHZ 4 19,557 78,228 Intel
56 APPLE M1 PRO 10 7,772 77,720 Apple
57 XEON CPU E5-2680 0 @ 2.70GHZ 16 4,595 73,520 Intel
58 XEON CPU E5-2697 V2 @ 2.70GHZ 24 2,236 53,664 Intel