RESEARCH: CANCER
FOLDING PROJECT #18420 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 aims to use computer simulations to predict how changes to a mini-protein's design will affect its ability to bind to a bacterial enzyme. By making these predictions, we hope to speed up the process of finding new 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

Using computer models to simulate molecular behavior.

Technical: Pharmaceutical
Biotechnology / Computational Drug Design

Molecular simulation involves using computer programs to mimic the movements and interactions of atoms and molecules. This technique is widely used in biotechnology and pharmacology to study chemical reactions, protein folding, and drug-target binding.


affinity maturation

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

Scientific: Pharmaceutical
Biotechnology / Drug Discovery

Affinity maturation is a crucial step in drug development. It involves making small changes to a molecule's structure to enhance its ability to bind to its intended target (e.g., a protein). This increased binding affinity leads to more effective drugs.


mini-proteins

Small proteins with specific functions.

Technical: Pharmaceutical
Biotechnology / Protein Engineering

Mini-proteins are smaller versions of traditional proteins that often retain their functional capabilities. They have gained attention in biotechnology due to their potential applications in drug discovery, diagnostics, and therapeutics.


LapG

Leptospira interrogans protease G

Scientific: Pharmaceutical
Biotechnology / Bacterial Physiology

LapG is a bacterial enzyme involved in the formation of biofilms. Biofilms are communities of bacteria that adhere to surfaces and can cause infections.


periplasmic protease

A protease found in the periplasm of bacteria.

Scientific: Pharmaceutical
Biotechnology / Microbiology

Proteases are enzymes that break down proteins. Periplasmic proteases are located in the periplasm, a space between the cell membrane and the outer membrane of bacteria. They play various roles in bacterial metabolism and defense.


antibiotic therapies

Medical treatments using antibiotics to fight bacterial infections.

Scientific: Pharmaceutical
Biotechnology / Antimicrobial Drug Development

Antibiotic therapies are essential for treating bacterial infections. These therapies involve the use of medications that kill or inhibit the growth of bacteria.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 03:29:32
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 03:29:32
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 34,120 1,091,840 Intel
2 RYZEN 9 7950X 16-CORE 32 27,620 883,840 AMD
3 12TH GEN CORE I9-12900K 24 23,180 556,320 Intel
4 RYZEN 9 5950X 16-CORE 32 16,443 526,176 AMD
5 12TH GEN CORE I7-12700K 20 25,834 516,680 Intel
6 RYZEN 7 5800X3D 8-CORE 16 30,536 488,576 AMD
7 RYZEN 7 5700X 8-CORE 16 29,436 470,976 AMD
8 RYZEN 9 3950X 16-CORE 32 14,692 470,144 AMD
9 RYZEN 7 5800X 8-CORE 16 29,148 466,368 AMD
10 12TH GEN CORE I7-12700 20 21,351 427,020 Intel
11 RYZEN 7 7700X 8-CORE 16 26,010 416,160 AMD
12 11TH GEN CORE I7-11700K @ 3.60GHZ 16 24,792 396,672 Intel
13 RYZEN 5 7600X 6-CORE 12 32,878 394,536 AMD
14 RYZEN 9 3900XT 12-CORE 24 15,249 365,976 AMD
15 RYZEN 9 5900X 12-CORE 24 13,694 328,656 AMD
16 XEON CPU E5-2690 V4 @ 2.60GHZ 28 11,699 327,572 Intel
17 RYZEN 5 5600 6-CORE 12 26,193 314,316 AMD
18 RYZEN 7 5700G 16 18,901 302,416 AMD
19 RYZEN 9 3900X 12-CORE 24 11,764 282,336 AMD
20 RYZEN 5 PRO 5650G 12 22,406 268,872 AMD
21 RYZEN THREADRIPPER 2950X 16-CORE 32 7,751 248,032 AMD
22 CORE I9-9900K CPU @ 3.60GHZ 16 14,395 230,320 Intel
23 RYZEN 5 5600X 6-CORE 12 18,973 227,676 AMD
24 XEON CPU E5-2680 V3 @ 2.50GHZ 24 9,486 227,664 Intel
25 RYZEN 7 3800X 8-CORE 16 13,554 216,864 AMD
26 CORE I7-8700 CPU @ 3.20GHZ 12 18,066 216,792 Intel
27 RYZEN 7 PRO 4750G 16 12,433 198,928 AMD
28 RYZEN 5 5600G 12 16,533 198,396 AMD
29 RYZEN 7 2700 EIGHT-CORE 16 11,728 187,648 AMD
30 CORE I7-6800K CPU @ 3.40GHZ 12 15,043 180,516 Intel
31 RYZEN 7 3700X 8-CORE 16 11,198 179,168 AMD
32 12TH GEN CORE I9-12900H 20 8,845 176,900 Intel
33 RYZEN 5 3600X 6-CORE 12 14,648 175,776 AMD
34 XEON CPU E5-2698 V4 @ 2.20GHZ 16 10,825 173,200 Intel
35 RYZEN 5 3600 6-CORE 12 14,046 168,552 AMD
36 XEON CPU E5-2650 V2 @ 2.60GHZ 32 5,263 168,416 Intel
37 CORE I7-6950X CPU @ 3.00GHZ 20 8,154 163,080 Intel
38 XEON CPU E5-2670 0 @ 2.60GHZ 32 4,939 158,048 Intel
39 EPYC 7262 8-CORE 16 9,710 155,360 AMD
40 CORE I7-5930K CPU @ 3.50GHZ 12 12,702 152,424 Intel
41 CORE I9-9900KF CPU @ 3.60GHZ 16 9,493 151,888 Intel
42 CORE I7-10700 CPU @ 2.90GHZ 16 7,735 123,760 Intel
43 CORE I7-9750H CPU @ 2.60GHZ 12 10,058 120,696 Intel
44 XEON CPU E5-2650L V4 @ 1.70GHZ 28 4,305 120,540 Intel
45 12TH GEN CORE I5-12600K 16 7,100 113,600 Intel
46 11TH GEN CORE I5-11400 @ 2.60GHZ 12 9,364 112,368 Intel
47 CORE I7-10700T CPU @ 2.00GHZ 16 6,695 107,120 Intel
48 11TH GEN CORE I5-11400F @ 2.60GHZ 12 8,628 103,536 Intel
49 RYZEN 5 1600 SIX-CORE 12 8,499 101,988 AMD
50 RYZEN 5 2600 SIX-CORE 12 8,427 101,124 AMD
51 RYZEN 7 1700 EIGHT-CORE 16 6,175 98,800 AMD
52 XEON CPU X5660 @ 2.80GHZ 24 4,045 97,080 Intel
53 CORE I9-8950HK CPU @ 2.90GHZ 12 7,780 93,360 Intel
54 XEON GOLD 6128 CPU @ 3.40GHZ 12 7,475 89,700 Intel
55 RYZEN 7 2700X EIGHT-CORE 16 5,470 87,520 AMD
56 CORE I5-10400 CPU @ 2.90GHZ 12 6,901 82,812 Intel
57 XEON CPU E5-2697 V2 @ 2.70GHZ 24 3,083 73,992 Intel
58 XEON CPU E5-2680 0 @ 2.70GHZ 16 4,475 71,600 Intel
59 XEON CPU E5-2620 0 @ 2.00GHZ 12 2,692 32,304 Intel