RESEARCH: CANCER
FOLDING PROJECT #18410 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 the design of mini-proteins affects their ability to bind to a bacterial enzyme. This could lead to the development of new antibiotics that are more effective against bacteria.

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

Simulation of molecular interactions using computational models.

Technical: Pharmaceutical
Biotechnology / Drug Discovery

Molecular simulation uses computer programs to mimic how molecules interact with each other. This is helpful in drug discovery as it allows scientists to test different molecule combinations virtually before conducting expensive and time-consuming lab experiments.


affinity maturation

A process of improving the binding affinity (strength) of a protein or antibody.

Technical: Pharmaceutical
Biotechnology / Drug Discovery

Affinity maturation is like fine-tuning a lock and key. Scientists start with a molecule that binds to its target (the lock), but they want it to bind even stronger. Through repeated rounds of testing and modifications, they 'mature' the molecule until it fits perfectly.


mini-proteins

Small proteins with specific functions.

Technical: Pharmaceutical
Biotechnology / Protein Engineering

Mini-proteins are like tiny versions of regular proteins. They're smaller and simpler, but they can still do important jobs, such as binding to other molecules or catalyzing reactions. Scientists use them in various applications, including drug development.


periplasmic protease

A type of enzyme found in the periplasm (space between the cell membrane and cell wall) of bacteria.

Scientific: Pharmaceutical
Biotechnology / Bacterial Physiology

Periplasmic proteases are enzymes that break down proteins. They're important for various bacterial processes, such as breaking down nutrients or defending against invading molecules.


LapG

Protease LapG

Acronym: Pharmaceutical
Biotechnology / Bacterial Physiology

LapG is a type of protease found in bacteria that plays a role in biofilm formation. Biofilms are communities of bacteria that attach to surfaces and can be difficult to treat with antibiotics.


biofilm

A community of microorganisms that adhere to a surface and are encased in a self-produced matrix.

Scientific: Pharmaceutical
Biotechnology / Bacterial Physiology

Biofilms are like cities for bacteria. They form when bacteria attach to surfaces and create a protective layer around themselves. This makes them resistant to antibiotics and other treatments, making them a major problem in healthcare.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 03:29:47
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PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 03:29:47
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 26,361 843,552 AMD
2 13TH GEN CORE I9-13900KS 32 25,996 831,872 Intel
3 CORE I9-14900KF 24 32,067 769,608 Intel
4 RYZEN THREADRIPPER PRO 5965WX 24-CORES 48 12,927 620,496 AMD
5 RYZEN 7 7700X 8-CORE 16 38,774 620,384 AMD
6 12TH GEN CORE I9-12900K 24 24,560 589,440 Intel
7 RYZEN 9 3950X 16-CORE 32 17,126 548,032 AMD
8 RYZEN 9 5950X 16-CORE 32 16,739 535,648 AMD
9 12TH GEN CORE I7-12700K 20 24,476 489,520 Intel
10 RYZEN THREADRIPPER 1950X 16-CORE 32 14,363 459,616 AMD
11 RYZEN 7 5800X 8-CORE 16 28,429 454,864 AMD
12 RYZEN 7 5800X3D 8-CORE 16 26,990 431,840 AMD
13 RYZEN THREADRIPPER 2950X 16-CORE 32 13,322 426,304 AMD
14 RYZEN 9 5900X 12-CORE 24 16,573 397,752 AMD
15 RYZEN 7 5700X 8-CORE 16 24,182 386,912 AMD
16 12TH GEN CORE I7-12700 20 19,159 383,180 Intel
17 RYZEN 9 3900 12-CORE 24 15,963 383,112 AMD
18 11TH GEN CORE I7-11700K @ 3.60GHZ 16 23,203 371,248 Intel
19 GENUINE CPU 0000 @ 2.10GHZ 44 7,803 343,332 Intel
20 12TH GEN CORE I5-12600K 16 20,457 327,312 Intel
21 XEON CPU E5-2690 V4 @ 2.60GHZ 28 11,667 326,676 Intel
22 CORE I9-7940X CPU @ 3.10GHZ 28 11,232 314,496 Intel
23 RYZEN 7 3800X 8-CORE 16 19,211 307,376 AMD
24 XEON CPU E5-2680 V4 @ 2.40GHZ 28 10,616 297,248 Intel
25 RYZEN 9 3900X 12-CORE 24 12,326 295,824 AMD
26 CORE I9-10850K CPU @ 3.60GHZ 20 14,448 288,960 Intel
27 RYZEN 7 5700G 16 17,624 281,984 AMD
28 RYZEN 9 3900XT 12-CORE 24 11,674 280,176 AMD
29 CORE I9-10900X CPU @ 3.70GHZ 20 13,590 271,800 Intel
30 CORE I9-7920X CPU @ 2.90GHZ 24 10,683 256,392 Intel
31 RYZEN 5 5600G 12 21,201 254,412 AMD
32 12TH GEN CORE I9-12900H 20 12,381 247,620 Intel
33 XEON CPU E5-2680 V3 @ 2.50GHZ 24 10,237 245,688 Intel
34 RYZEN 7 5800H 16 14,419 230,704 AMD
35 CORE I9-9900K CPU @ 3.60GHZ 16 14,172 226,752 Intel
36 RYZEN 5 3600 6-CORE 12 17,050 204,600 AMD
37 XEON CPU E5-2650 V2 @ 2.60GHZ 32 5,980 191,360 Intel
38 CORE I7-10870H CPU @ 2.20GHZ 16 11,927 190,832 Intel
39 RYZEN 7 3700X 8-CORE 16 11,446 183,136 AMD
40 RYZEN 9 5900 12-CORE 24 6,905 165,720 AMD
41 RYZEN 5 5600X 6-CORE 12 13,787 165,444 AMD
42 CORE I7-10700 CPU @ 2.90GHZ 16 10,193 163,088 Intel
43 RYZEN 7 PRO 4750G 16 9,594 153,504 AMD
44 EPYC 7262 8-CORE 16 9,080 145,280 AMD
45 11TH GEN CORE I9-11900F @ 2.50GHZ 16 8,739 139,824 Intel
46 XEON CPU E5-2670 0 @ 2.60GHZ 32 3,894 124,608 Intel
47 CORE I7-10700T CPU @ 2.00GHZ 16 6,710 107,360 Intel
48 RYZEN 7 1700 EIGHT-CORE 16 6,331 101,296 AMD
49 XEON CPU E5-2680 0 @ 2.70GHZ 16 5,763 92,208 Intel
50 XEON CPU E5-2697 V2 @ 2.70GHZ 24 3,681 88,344 Intel
51 XEON CPU E5-2698 V4 @ 2.20GHZ 16 2,935 46,960 Intel