RESEARCH: INFLUENZA
FOLDING PROJECT #18483 PROFILE
PROJECT TEAM
Manager(s): Dylan NovackInstitution: Temple University
Project URL: View Project Website
WORK UNIT INFO
Atoms: 93,432Core: 0xa8
Status: Public
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TLDR; PROJECT SUMMARY AI BETA
Miniproteins are tiny proteins that can be designed to fight diseases. Researchers want to use computer simulations to understand how changes in miniproteins affect their ability to bind to viruses, like the flu. This could help design better treatments.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
Designed miniproteins are a class of biomolecules with intermediate sizes—larger than small-molecule drugs, but smaller than monoclonal antibodies.
Miniproteins can be computationally designed to tightly bind protein targets for use as potential therapeutics, a promising new avenue for treating infectious disease. Hemagglutinin is a viral fusion protein that allows H1 influenza A (HA) to bind sialic acid on cell surfaces, as well as being involved in the post-endocytosis mechanism of cellular infection.
The Baker lab at University of Washington has developed de novo designed miniproteins that bind hemagglutinin, and improved their binding through affinity maturation (Chevalier et al.
2017).
Many of the mutations seen in affinity-matured sequences are not found in the binding interface, and it remains an open question how these changes lead to higher affinity.
Furthermore, many of the computational predictions of how single-point mutations affect binding deviate significantly from the experimentally determined values. Could all-atom molecular simulation approaches achieve more accurate predictions? In this set of simulations, we aim to use massively parallel expanded ensemble simulations to predict mutational effects on affinities to hemagglutinin.
By pairing these simulations with other simulations aimed at modeling the binding reactions of these miniproteins to hemagglutinin, we aim to have a relatively complete picture of a miniprotein-target binding reaction and how mutations affect it.
These studies are a large-scale investigation on how miniprotein binding reactions work in atomic detail, towards a better understanding of computational design and modulation of miniprotein therapeutics.
RELATED TERMS GLOSSARY AI BETA
Miniproteins
Small proteins designed for therapeutic use.
Miniproteins are small, engineered proteins designed to treat diseases. They are larger than small-molecule drugs but smaller than antibodies, making them easier to produce and potentially more effective.
Monoclonal Antibodies
Laboratory-produced antibodies that target specific antigens.
Monoclonal antibodies are lab-made versions of our body's natural defense system. They recognize and attack specific targets (antigens) on cells or molecules, often used to treat diseases like cancer.
Hemagglutinin
Viral protein that binds to sialic acid on cell surfaces.
Hemagglutinin is a protein found on the surface of influenza viruses. It helps the virus attach to and enter human cells by binding to sugar molecules called sialic acid.
Affinity Maturation
Process of improving the binding affinity of a protein.
Affinity maturation is like fine-tuning a protein's ability to stick to its target. Scientists use this process to create proteins that bind more strongly and effectively.
Molecular Simulation
Computer-based modeling of molecular interactions.
Molecular simulations use computer programs to mimic how molecules behave and interact. This helps scientists understand complex biological processes and design new drugs.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 03:28:14|
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Model Name Folding@Home Identifier |
Make Brand |
GPU Model |
PPD Average |
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PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 03:28:14|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|---|---|---|---|---|
| 1 | EPYC 7B12 64-CORE | 64 | 19,021 | 1,217,344 | AMD |
| 2 | RYZEN 9 7950X 16-CORE | 32 | 30,786 | 985,152 | AMD |
| 3 | RYZEN 7 7700X 8-CORE | 16 | 38,545 | 616,720 | AMD |
| 4 | RYZEN 9 5950X 16-CORE | 32 | 15,896 | 508,672 | AMD |
| 5 | 12TH GEN CORE I7-12700K | 20 | 21,329 | 426,580 | Intel |
| 6 | RYZEN 7 5700X 8-CORE | 16 | 26,655 | 426,480 | AMD |
| 7 | XEON PLATINUM 8370C CPU @ 2.80GHZ | 16 | 18,860 | 301,760 | Intel |
| 8 | RYZEN 7 5700G | 16 | 17,966 | 287,456 | AMD |
| 9 | RYZEN 9 3900X 12-CORE | 24 | 11,919 | 286,056 | AMD |
| 10 | RYZEN 9 5900 12-CORE | 24 | 11,230 | 269,520 | AMD |
| 11 | 12TH GEN CORE I7-12700 | 20 | 13,234 | 264,680 | Intel |
| 12 | CORE I7-10700K CPU @ 3.80GHZ | 16 | 15,651 | 250,416 | Intel |
| 13 | 11TH GEN CORE I9-11900K @ 3.50GHZ | 16 | 9,501 | 152,016 | Intel |
| 14 | CORE I7-10700T CPU @ 2.00GHZ | 16 | 5,844 | 93,504 | Intel |