RESEARCH: CANCER
FOLDING PROJECT #18425 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 changing the design of mini-proteins will affect their ability to bind to a bacterial enzyme. The goal is to create better antibiotics by finding mini-proteins that block this 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

Use of computer models to simulate molecular behavior.

Technical: Pharmaceuticals
Biotechnology / Drug Discovery

Molecular simulation uses computer programs to mimic how atoms and molecules interact. This helps researchers understand chemical reactions, design new drugs, and predict material properties.


affinity-maturation

Process of improving the binding affinity of a molecule to its target.

Technical: Pharmaceuticals
Biotechnology / Drug Discovery

Affinity maturation is like fine-tuning a drug's ability to bind to its target. By making small changes to the drug's structure, scientists can increase its effectiveness and reduce side effects.


de novo

From scratch; created anew.

Technical: Pharmaceuticals
Biotechnology / Protein Engineering

De novo design means creating something entirely new, like designing a protein molecule from the ground up without relying on existing structures.


protein binders

Molecules that bind to other proteins.

Technical: Pharmaceuticals
Biotechnology / Drug Discovery

Protein binders are molecules that attach to specific proteins. This can be useful for developing drugs that block harmful protein activity or for creating diagnostic tools.


mini-proteins

Small proteins with specific functions.

Technical: Pharmaceuticals
Biotechnology / Protein Engineering

Mini-proteins are compact versions of larger proteins that can still perform important tasks. They are often easier to design and produce than full-sized proteins.


inhibitors

Substances that block the activity of other molecules.

Technical: Pharmaceuticals
Biotechnology / Drug Discovery

Inhibitors are compounds that prevent or slow down chemical reactions. They are widely used in medicine to treat diseases by blocking the action of harmful proteins.


LapG

Periplasmic protease involved in bacterial biofilm formation.

Technical: Pharmaceuticals
Biotechnology / Pathology

LapG is an enzyme produced by bacteria that helps them form biofilms. Biofilms are complex communities of bacteria that can be difficult to treat with antibiotics.


antibiotic therapies

Medical treatments that use antibiotics to kill bacteria.

Technical: Pharmaceuticals
Medicine / Infectious Diseases

Antibiotic therapies are used to treat bacterial infections. Antibiotics work by interfering with the growth and reproduction of bacteria.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 03:29:24
Rank
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Model Name
Folding@Home Identifier
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Brand
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Model
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PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 03:29:24
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 27,396 876,672 AMD
2 RYZEN 9 5950X 16-CORE 32 21,887 700,384 AMD
3 RYZEN 7 5800X3D 8-CORE 16 39,324 629,184 AMD
4 RYZEN 7 7700X 8-CORE 16 37,391 598,256 AMD
5 12TH GEN CORE I9-12900K 24 21,374 512,976 Intel
6 RYZEN 7 5700X 8-CORE 16 29,381 470,096 AMD
7 12TH GEN CORE I7-12700 20 22,762 455,240 Intel
8 RYZEN 9 3900 12-CORE 24 18,659 447,816 AMD
9 RYZEN THREADRIPPER 2970WX 24-CORE 48 9,083 435,984 AMD
10 12TH GEN CORE I7-12700K 20 21,558 431,160 Intel
11 RYZEN 9 3950X 16-CORE 32 13,402 428,864 AMD
12 11TH GEN CORE I7-11700K @ 3.60GHZ 16 26,581 425,296 Intel
13 CORE I9-10850K CPU @ 3.60GHZ 20 20,938 418,760 Intel
14 CORE I9-10920X CPU @ 3.50GHZ 24 16,838 404,112 Intel
15 CORE I9-10900K CPU @ 3.70GHZ 20 18,666 373,320 Intel
16 RYZEN 9 3900XT 12-CORE 24 15,220 365,280 AMD
17 RYZEN 7 5800X 8-CORE 16 22,410 358,560 AMD
18 RYZEN 9 3900X 12-CORE 24 13,990 335,760 AMD
19 XEON CPU E5-2690 V4 @ 2.60GHZ 28 11,989 335,692 Intel
20 11TH GEN CORE I9-11900K @ 3.50GHZ 16 20,895 334,320 Intel
21 12TH GEN CORE I5-12400 12 27,561 330,732 Intel
22 12TH GEN CORE I5-12600K 16 19,718 315,488 Intel
23 RYZEN 9 5900X 12-CORE 24 12,914 309,936 AMD
24 XEON CPU E5-2680 V2 @ 2.80GHZ 40 7,594 303,760 Intel
25 CORE I9-9900K CPU @ 3.60GHZ 16 18,972 303,552 Intel
26 RYZEN THREADRIPPER 2950X 16-CORE 32 9,007 288,224 AMD
27 RYZEN 7 3800X 8-CORE 16 16,464 263,424 AMD
28 RYZEN 5 5600X 6-CORE 12 20,993 251,916 AMD
29 RYZEN 5 PRO 5650G 12 20,345 244,140 AMD
30 CORE I9-10900X CPU @ 3.70GHZ 20 11,942 238,840 Intel
31 RYZEN 7 5800H 16 14,556 232,896 AMD
32 12TH GEN CORE I9-12900H 20 11,416 228,320 Intel
33 XEON CPU E5-2680 V3 @ 2.50GHZ 24 9,485 227,640 Intel
34 RYZEN 5 5600G 12 18,637 223,644 AMD
35 CORE I7-8700 CPU @ 3.20GHZ 12 18,559 222,708 Intel
36 XEON CPU E5-2660 V3 @ 2.60GHZ 20 10,744 214,880 Intel
37 RYZEN 7 5700G 16 13,129 210,064 AMD
38 RYZEN 5 3600 6-CORE 12 15,081 180,972 AMD
39 CORE I7-10700K CPU @ 3.80GHZ 16 11,286 180,576 Intel
40 XEON CPU X5660 @ 2.80GHZ 24 7,351 176,424 Intel
41 XEON CPU E5-2670 0 @ 2.60GHZ 32 5,500 176,000 Intel
42 RYZEN 7 PRO 4750G 16 10,841 173,456 AMD
43 CORE I5-10400 CPU @ 2.90GHZ 12 13,207 158,484 Intel
44 CORE I7-5820K CPU @ 3.30GHZ 12 13,157 157,884 Intel
45 XEON CPU E5-2698 V4 @ 2.20GHZ 16 9,851 157,616 Intel
46 CORE I7-10700 CPU @ 2.90GHZ 16 8,931 142,896 Intel
47 RYZEN 7 3700X 8-CORE 16 8,376 134,016 AMD
48 11TH GEN CORE I5-11400 @ 2.60GHZ 12 10,176 122,112 Intel
49 RYZEN 7 1700 EIGHT-CORE 16 7,157 114,512 AMD
50 CORE I7-10700T CPU @ 2.00GHZ 16 6,835 109,360 Intel
51 CORE I7-10750H CPU @ 2.60GHZ 12 8,762 105,144 Intel
52 RYZEN 7 2700X EIGHT-CORE 16 6,022 96,352 AMD
53 CORE I9-8950HK CPU @ 2.90GHZ 12 8,018 96,216 Intel
54 RYZEN 5 5500U 12 6,945 83,340 AMD
55 APPLE M1 PRO 10 7,497 74,970 Apple
56 XEON CPU E5-2680 0 @ 2.70GHZ 16 3,915 62,640 Intel
57 XEON CPU E5-2450 0 @ 2.10GHZ 10 6,100 61,000 Intel
58 CORE I7-8750H CPU @ 2.20GHZ 12 3,183 38,196 Intel
59 XEON CPU E5-2620 0 @ 2.00GHZ 12 3,069 36,828 Intel
60 12TH GEN CORE I5-12600KF 16 Intel