RESEARCH: CANCER
FOLDING PROJECT #18401 PROFILE
PROJECT TEAM
Manager(s): Prof. Vincent VoelzInstitution: Temple University
WORK UNIT INFO
Atoms: 64,500Core: GRO_A8
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project aims to use computer simulations to design better mini-proteins that can block harmful bacteria. By predicting how small changes in the mini-protein's structure affect its ability to bind to bacteria, scientists hope to speed up the process of finding effective 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
molecular simulation
Using computer models to simulate molecular interactions.
Molecular simulation uses computer programs to imitate how atoms and molecules behave. This helps scientists understand chemical reactions, design new materials, and develop drugs.
affinity maturation
The process of improving the binding affinity of a molecule to its target.
Affinity maturation is like fine-tuning a lock and key. Scientists use it to make proteins bind more strongly to their targets (like bacteria or viruses), making them better at fighting diseases.
mini-proteins
Small proteins with specific functions.
Mini-proteins are like tiny versions of regular proteins. They're designed to have very specific jobs, such as blocking harmful molecules or triggering helpful responses in the body.
binding affinity
The strength of the attraction between a molecule and its target.
Binding affinity is like how strongly two puzzle pieces fit together. The stronger the attraction, the better the molecule can do its job.
periplasmic protease
An enzyme found in the periplasm of bacteria.
Periplasmic proteases are like tiny scissors inside bacteria. They break down proteins that enter the bacterial cell.
LapG
A specific periplasmic protease.
LapG is a type of enzyme found in bacteria. It plays an important role in helping bacteria form biofilms, which are communities of bacteria that can be difficult to treat.
biofilm formation
The process by which bacteria attach to surfaces and form communities.
Biofilm formation is like bacteria building their own cities. They stick together in slimy layers that protect them from antibiotics and the immune system.
antibiotic therapies
Treatments that use antibiotics to fight bacterial infections.
Antibiotic therapies are like weapons against bacteria. They help our bodies fight off infections caused by these tiny organisms.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 03:30:01|
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 03:30:01|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|---|---|---|---|---|
| 1 | RYZEN 9 3950X 16-CORE | 32 | 31,071 | 994,272 | AMD |
| 2 | RYZEN 7 5800X 8-CORE | 16 | 32,311 | 516,976 | AMD |
| 3 | RYZEN 9 5950X 16-CORE | 32 | 14,204 | 454,528 | AMD |
| 4 | XEON CPU E5-2680 V3 @ 2.50GHZ | 24 | 15,203 | 364,872 | Intel |
| 5 | CORE I9-10850K CPU @ 3.60GHZ | 20 | 14,916 | 298,320 | Intel |
| 6 | CORE I9-10900X CPU @ 3.70GHZ | 20 | 13,958 | 279,160 | Intel |
| 7 | XEON CPU E5-2690 V4 @ 2.60GHZ | 28 | 9,062 | 253,736 | Intel |
| 8 | RYZEN 7 3700X 8-CORE | 16 | 14,952 | 239,232 | AMD |
| 9 | RYZEN 9 5900X 12-CORE | 24 | 9,475 | 227,400 | AMD |