RESEARCH: CANCER
FOLDING PROJECT #18412 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 small changes in a protein's design can make it bind better to a bacterial target. By doing this, scientists hope to create new antibiotics that are more effective at fighting infections.

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 computer programs to mimic how molecules interact with each other. This can help researchers understand complex biological processes and design new drugs.


affinity-maturation

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

Scientific: Pharmaceuticals
Biotechnology / Protein Engineering

Affinity maturation is a technique used to enhance how strongly a protein binds to its intended target. It's often applied in drug development to create more effective medications.


de novo designed

Proteins created from scratch using computational design.

Technical: Pharmaceuticals
Biotechnology / Protein Engineering

De novo designed proteins are not found in nature. Scientists create them using computer algorithms to specify their structure and function.


mini-proteins

Small proteins with specific binding capabilities.

Scientific: Pharmaceuticals
Biotechnology / Protein Engineering

Mini-proteins are compact versions of proteins that retain their ability to bind to specific targets. They're often used in drug development due to their small size and ease of production.


binding affinity

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

Scientific: Pharmaceuticals, Biotechnology
Biochemistry / Protein-Ligand Interactions

Binding affinity describes how strongly a molecule, like a drug, attaches to its target, such as a protein. A higher binding affinity means a stronger interaction.


periplasmic protease

An enzyme found in the periplasm of bacteria that degrades proteins.

Scientific: Biotechnology, Pharmaceuticals
Microbiology / Bacterial Physiology

Periplasmic proteases are enzymes located in the periplasm, a region between the cell membrane and cell wall of bacteria. They play a role in protein degradation and other cellular processes.


LapG

L-Asparagine Peptidase G

Acronym: Biotechnology, Pharmaceuticals
Microbiology / Bacterial Physiology

LapG is a periplasmic protease involved in bacterial biofilm formation.


biofilm

A community of bacteria encased in a self-produced matrix.

Scientific: Biotechnology, Pharmaceuticals
Microbiology / Bacterial Physiology

Biofilms are communities of bacteria that adhere to surfaces and form a protective layer around themselves. They can be found in various environments and pose challenges for human health and industry.


antibiotic therapies

Treatments using antibiotics to combat bacterial infections.

Medical: Pharmaceuticals
Medicine / Infectious Disease

Antibiotic therapies involve using drugs to kill or inhibit the growth of bacteria. They are essential for treating various bacterial infections.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 03:29:44
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PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 03:29:44
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 RYZEN 7 7700X 8-CORE 16 39,395 630,320 AMD
2 RYZEN 9 5950X 16-CORE 32 19,624 627,968 AMD
3 RYZEN 7 5800X3D 8-CORE 16 38,003 608,048 AMD
4 12TH GEN CORE I9-12900K 24 24,333 583,992 Intel
5 12TH GEN CORE I7-12700K 20 23,823 476,460 Intel
6 RYZEN 9 3950X 16-CORE 32 14,292 457,344 AMD
7 RYZEN 9 5900X 12-CORE 24 18,463 443,112 AMD
8 RYZEN 7 5800X 8-CORE 16 27,644 442,304 AMD
9 12TH GEN CORE I7-12700 20 21,936 438,720 Intel
10 RYZEN 7 5700X 8-CORE 16 26,615 425,840 AMD
11 RYZEN 9 3900 12-CORE 24 17,283 414,792 AMD
12 RYZEN 9 3900XT 12-CORE 24 14,067 337,608 AMD
13 RYZEN 7 5700G 16 20,651 330,416 AMD
14 12TH GEN CORE I5-12600K 16 19,759 316,144 Intel
15 CORE I9-10850K CPU @ 3.60GHZ 20 15,447 308,940 Intel
16 RYZEN 7 3800X 8-CORE 16 18,804 300,864 AMD
17 RYZEN 9 5900 12-CORE 24 12,322 295,728 AMD
18 XEON CPU E5-2680 V2 @ 2.80GHZ 40 7,097 283,880 Intel
19 RYZEN 9 3900X 12-CORE 24 9,214 221,136 AMD
20 XEON CPU E5-2680 V3 @ 2.50GHZ 24 9,107 218,568 Intel
21 CORE I9-7920X CPU @ 2.90GHZ 24 9,058 217,392 Intel
22 11TH GEN CORE I7-11700K @ 3.60GHZ 16 13,564 217,024 Intel
23 XEON CPU E5-2660 V3 @ 2.60GHZ 20 10,566 211,320 Intel
24 CORE I9-10900X CPU @ 3.70GHZ 20 10,271 205,420 Intel
25 RYZEN 7 3700X 8-CORE 16 12,733 203,728 AMD
26 CORE I7-10700K CPU @ 3.80GHZ 16 12,717 203,472 Intel
27 CORE I9-9900K CPU @ 3.60GHZ 16 12,631 202,096 Intel
28 XEON CPU E5-2650 V2 @ 2.60GHZ 32 6,313 202,016 Intel
29 RYZEN 7 PRO 4750G 16 12,608 201,728 AMD
30 CORE I9-9900 CPU @ 3.10GHZ 16 12,228 195,648 Intel
31 11TH GEN CORE I7-11850H @ 2.50GHZ 16 11,476 183,616 Intel
32 12TH GEN CORE I9-12900H 20 8,446 168,920 Intel
33 RYZEN 7 2700X EIGHT-CORE 16 9,879 158,064 AMD
34 CORE I7-10700 CPU @ 2.90GHZ 16 7,632 122,112 Intel
35 XEON CPU E5-2690 V2 @ 3.00GHZ 20 6,017 120,340 Intel
36 RYZEN 7 1800X EIGHT-CORE 16 7,040 112,640 AMD
37 CORE I7-10700T CPU @ 2.00GHZ 16 6,979 111,664 Intel
38 XEON CPU E5-2670 0 @ 2.60GHZ 32 3,106 99,392 Intel
39 RYZEN 9 5900HX 16 5,341 85,456 AMD
40 XEON CPU E5-2680 0 @ 2.70GHZ 16 5,273 84,368 Intel
41 XEON CPU E5-2697 V2 @ 2.70GHZ 24 3,263 78,312 Intel
42 XEON CPU E5-2450 0 @ 2.10GHZ 10 6,157 61,570 Intel
43 XEON CPU E5-2620 V3 @ 2.40GHZ 12 4,252 51,024 Intel