RESEARCH: INFLUENZA
FOLDING PROJECT #18477 PROFILE
PROJECT TEAM
Manager(s): Dylan NovackInstitution: Temple University
Project URL: View Project Website
WORK UNIT INFO
Atoms: 93,429Core: 0xa8
Status: Public
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TLDR; PROJECT SUMMARY AI BETA
Miniproteins are tiny proteins being designed to fight diseases like the flu. Scientists want to understand how miniproteins bind to viruses and how to make them even better at fighting infection. They're using computer simulations to study this process in detail, hoping to create more effective miniprotein drugs.
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 engineered proteins smaller than antibodies but larger than small molecules. They can be designed to bind specific targets in the body, making them promising candidates for treating diseases.
biomolecules
Molecules essential to life processes.
Biomolecules are the building blocks of living organisms. This includes large molecules like proteins, carbohydrates, and nucleic acids, as well as smaller molecules like lipids.
monoclonal antibodies
Laboratory-produced antibodies that target specific antigens.
Monoclonal antibodies are a type of immunotherapy treatment. They are engineered to bind to specific proteins on the surface of cancer cells or other harmful targets.
hemagglutinin
A viral protein that allows influenza to attach to host cells.
Hemagglutinin is a key protein on the surface of the influenza virus. It helps the virus attach to and enter host cells.
H1 influenza A
A subtype of the influenza A virus.
H1N1 is a common type of influenza virus. It's characterized by its hemagglutinin (HA) protein subtype.
affinity maturation
The process of improving the binding affinity of an antibody.
Affinity maturation is a key process in developing effective antibodies. It involves making small changes to the antibody's structure to increase its ability to bind to its target.
molecular simulation
A computer-based method for modeling molecular interactions.
Molecular simulation uses computer algorithms to mimic the behavior of molecules. This helps researchers understand how molecules interact and can be used in drug discovery.
expanded ensemble simulations
A type of molecular simulation that explores a wider range of possible energy states.
Expanded ensemble simulations help overcome the limitations of traditional simulations by allowing for exploration of more complex systems and protein dynamics.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 03:28:24|
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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 03:28:24|
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 | 18,174 | 1,163,136 | AMD |
| 2 | RYZEN 9 7950X 16-CORE | 32 | 29,662 | 949,184 | AMD |
| 3 | RYZEN 9 7900 12-CORE | 24 | 31,658 | 759,792 | AMD |
| 4 | RYZEN 7 7700X 8-CORE | 16 | 37,407 | 598,512 | AMD |
| 5 | 11TH GEN CORE I7-11700K @ 3.60GHZ | 16 | 26,591 | 425,456 | Intel |
| 6 | RYZEN 9 5950X 16-CORE | 32 | 12,470 | 399,040 | AMD |
| 7 | RYZEN 7 5800X3D 8-CORE | 16 | 23,767 | 380,272 | AMD |
| 8 | RYZEN 7 5700X 8-CORE | 16 | 22,891 | 366,256 | AMD |
| 9 | RYZEN 7 5800X 8-CORE | 16 | 20,388 | 326,208 | AMD |
| 10 | RYZEN 9 5900X 12-CORE | 24 | 13,069 | 313,656 | AMD |
| 11 | XEON PLATINUM 8370C CPU @ 2.80GHZ | 16 | 18,202 | 291,232 | Intel |
| 12 | RYZEN 9 3900X 12-CORE | 24 | 11,844 | 284,256 | AMD |
| 13 | 12TH GEN CORE I7-12700F | 20 | 13,796 | 275,920 | Intel |
| 14 | 12TH GEN CORE I7-12700 | 20 | 13,025 | 260,500 | Intel |
| 15 | RYZEN 7 3800X 8-CORE | 16 | 16,182 | 258,912 | AMD |
| 16 | CORE I7-10700K CPU @ 3.80GHZ | 16 | 15,088 | 241,408 | Intel |
| 17 | RYZEN 7 3700X 8-CORE | 16 | 9,486 | 151,776 | AMD |
| 18 | EPYC 7262 8-CORE | 16 | 8,787 | 140,592 | AMD |
| 19 | 12TH GEN CORE I7-12700H | 20 | 5,514 | 110,280 | Intel |
| 20 | CORE I7-10700T CPU @ 2.00GHZ | 16 | 5,589 | 89,424 | Intel |
| 21 | XEON CPU L5640 @ 2.27GHZ | 24 | 2,439 | 58,536 | Intel |
| 22 | XEON CPU E5-2697 V2 @ 2.70GHZ | 24 | 1,894 | 45,456 | Intel |