RESEARCH: CANCER
FOLDING PROJECT #18424 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 small changes in a protein's design can improve its ability to block bacterial growth. The goal is to create new antibiotics by making existing designs more effective.

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: Pharmaceuticals
Biotechnology / Drug Discovery

Molecular simulation uses computers to mimic how molecules behave and interact. This is helpful in drug discovery to understand how drugs might bind to target proteins.


affinity-maturation

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

Scientific: Pharmaceuticals
Biotechnology / Protein Engineering

Affinity maturation is like refining a drug's ability to stick to its target. Scientists make small changes to a drug's structure to increase how strongly it binds, leading to better effectiveness.


de novo

Designed from scratch, not derived from existing molecules.

Scientific: Pharmaceuticals
Biotechnology / Protein Design

De novo design means creating something completely new. In protein design, it refers to building a protein molecule from the ground up, rather than modifying an existing one.


mini-proteins

Small proteins with specific functions.

Technical: Pharmaceuticals
Biotechnology / Protein Engineering

Mini-proteins are like compact versions of regular proteins. They have a simpler structure and can often target specific biological processes efficiently.


binding affinity

The strength of the attraction between a molecule and its target.

Scientific: Pharmaceuticals
Biotechnology / Drug Discovery

Binding affinity describes how strongly a molecule (like a drug) sticks to its target (like a protein). Higher affinity means a stronger bond.


periplasmic protease

An enzyme found in the periplasm of bacteria.

Scientific: Biotechnology
Microbiology / Bacterial Physiology

Periplasmic proteases are enzymes that break down proteins in a specific region called the periplasm, which surrounds the cell membrane of bacteria. They play a role in bacterial growth and survival.


LapG

A periplasmic protease.

Acronym: Biotechnology
Microbiology / Bacterial Physiology

LapG is a specific type of bacterial enzyme that breaks down proteins in the periplasm. It's important for regulating bacterial biofilm formation.


biofilm

A community of bacteria encased in a protective matrix.

Scientific: Biotechnology
Microbiology / Bacterial Physiology

Biofilms are like bacterial cities. They're formed when bacteria stick together and create a slimy coating around themselves. This makes them more resistant to antibiotics and other treatments.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 03:29:25
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 03:29:25
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 33,818 1,082,176 Intel
2 RYZEN 9 7950X 16-CORE 32 30,608 979,456 AMD
3 RYZEN 7 7700X 8-CORE 16 40,285 644,560 AMD
4 12TH GEN CORE I9-12900K 24 23,839 572,136 Intel
5 RYZEN 9 5950X 16-CORE 32 17,495 559,840 AMD
6 EPYC 7V12 64-CORE 64 8,681 555,584 AMD
7 RYZEN 7 5800X3D 8-CORE 16 31,833 509,328 AMD
8 RYZEN 9 5900X 12-CORE 24 20,926 502,224 AMD
9 RYZEN 7 5700X 8-CORE 16 28,948 463,168 AMD
10 RYZEN 9 3900XT 12-CORE 24 19,067 457,608 AMD
11 RYZEN 7 5800X 8-CORE 16 28,116 449,856 AMD
12 RYZEN 9 3950X 16-CORE 32 13,972 447,104 AMD
13 RYZEN 9 3900 12-CORE 24 17,468 419,232 AMD
14 11TH GEN CORE I7-11700K @ 3.60GHZ 16 26,136 418,176 Intel
15 12TH GEN CORE I7-12700 20 19,849 396,980 Intel
16 11TH GEN CORE I9-11900K @ 3.50GHZ 16 20,775 332,400 Intel
17 XEON CPU E5-2690 V4 @ 2.60GHZ 28 11,841 331,548 Intel
18 XEON W-1290P CPU @ 3.70GHZ 20 16,426 328,520 Intel
19 12TH GEN CORE I5-12400 12 26,502 318,024 Intel
20 RYZEN 9 3900X 12-CORE 24 12,059 289,416 AMD
21 11TH GEN CORE I5-11400F @ 2.60GHZ 12 23,426 281,112 Intel
22 CORE I7-10700K CPU @ 3.80GHZ 16 17,448 279,168 Intel
23 RYZEN 9 5900 12-CORE 24 10,829 259,896 AMD
24 RYZEN 5 PRO 5650G 12 21,534 258,408 AMD
25 CORE I7-5960X CPU @ 3.00GHZ 16 14,868 237,888 Intel
26 RYZEN 7 5800H 16 14,848 237,568 AMD
27 CORE I9-10900X CPU @ 3.70GHZ 20 11,866 237,320 Intel
28 RYZEN 7 3800X 8-CORE 16 14,549 232,784 AMD
29 XEON CPU E5-2680 V3 @ 2.50GHZ 24 9,195 220,680 Intel
30 CORE I7-8700 CPU @ 3.20GHZ 12 18,176 218,112 Intel
31 RYZEN 5 5600X 6-CORE 12 17,865 214,380 AMD
32 CORE I9-9900K CPU @ 3.60GHZ 16 13,234 211,744 Intel
33 RYZEN 5 5600G 12 17,619 211,428 AMD
34 RYZEN 5 5600 6-CORE 12 17,533 210,396 AMD
35 RYZEN 7 5700G 16 12,981 207,696 AMD
36 12TH GEN CORE I7-12700K 20 10,366 207,320 Intel
37 XEON CPU E5-2650 V2 @ 2.60GHZ 32 6,321 202,272 Intel
38 RYZEN 5 3600 6-CORE 12 16,423 197,076 AMD
39 RYZEN 7 3700X 8-CORE 16 12,097 193,552 AMD
40 XEON CPU X5660 @ 2.80GHZ 24 7,866 188,784 Intel
41 RYZEN 9 6900HS CREATOR EDITION 16 11,303 180,848 AMD
42 XEON GOLD 6128 CPU @ 3.40GHZ 12 14,830 177,960 Intel
43 RYZEN 7 PRO 4750G 16 10,749 171,984 AMD
44 CORE I5-10400 CPU @ 2.90GHZ 12 13,101 157,212 Intel
45 CORE I7-10700 CPU @ 2.90GHZ 16 9,740 155,840 Intel
46 CORE I7-5930K CPU @ 3.50GHZ 12 12,910 154,920 Intel
47 XEON CPU E5-2670 0 @ 2.60GHZ 32 4,675 149,600 Intel
48 CORE I7-5820K CPU @ 3.30GHZ 12 12,181 146,172 Intel
49 XEON CPU E5-2698 V4 @ 2.20GHZ 16 8,962 143,392 Intel
50 11TH GEN CORE I9-11900F @ 2.50GHZ 16 8,389 134,224 Intel
51 CORE I7-8700K CPU @ 3.70GHZ 12 10,835 130,020 Intel
52 RYZEN 5 1600 SIX-CORE 12 9,308 111,696 AMD
53 RYZEN 9 5900HX 16 6,749 107,984 AMD
54 RYZEN 5 2600 SIX-CORE 12 8,786 105,432 AMD
55 CORE I7-10700T CPU @ 2.00GHZ 16 6,480 103,680 Intel
56 11TH GEN CORE I5-11400 @ 2.60GHZ 12 8,245 98,940 Intel
57 XEON CPU E5-2680 0 @ 2.70GHZ 16 5,851 93,616 Intel
58 CORE I9-8950HK CPU @ 2.90GHZ 12 7,558 90,696 Intel
59 APPLE M1 PRO 10 7,831 78,310 Apple
60 XEON CPU E5-2697 V2 @ 2.70GHZ 24 2,831 67,944 Intel
61 XEON CPU E5-2450 0 @ 2.10GHZ 10 5,823 58,230 Intel
62 RYZEN 5 5500U 12 3,254 39,048 AMD
63 12TH GEN CORE I5-12600KF 16 2,164 34,624 Intel
64 13TH GEN CORE I7-13700 24 Intel
65 CORE I5-9600K CPU @ 3.70GHZ 6 Intel