RESEARCH: INFLUENZA
FOLDING PROJECT #18475 PROFILE
PROJECT TEAM
Manager(s): Dylan NovackInstitution: Temple University
Project URL: View Project Website
WORK UNIT INFO
Atoms: 93,437Core: 0xa8
Status: Public
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TLDR; PROJECT SUMMARY AI BETA
This project studies miniproteins – tiny proteins that can be designed to fight diseases. Scientists are using computer simulations to understand how changes in these miniproteins affect their ability to bind to viruses like influenza. The goal is to design even better 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 engineered for therapeutic purposes.
Miniproteins are a new type of drug that are smaller than antibodies but larger than traditional small-molecule drugs. They can be designed to bind to specific proteins in the body, making them useful for treating a variety of diseases.
therapeutics
Agents used for treating diseases or medical conditions.
Therapeutics are medications and treatments used to diagnose, prevent, or treat diseases. They can range from simple painkillers to complex biologics.
hemagglutinin
A viral protein that binds to host cells.
Hemagglutinin is a protein found on the surface of influenza viruses. It helps the virus attach to and enter human cells.
affinity maturation
The process of improving the binding affinity of an antibody.
Affinity maturation is a natural process by which antibodies become more effective at binding to their target antigens. It involves random mutations in the genes that encode antibodies, followed by selection for those with improved binding.
molecular simulation
A computational method for modeling the behavior of molecules.
Molecular simulation is a technique used to study the behavior of molecules using computers. By simulating the interactions between atoms and molecules, researchers can gain insights into chemical reactions, protein folding, and other biological processes.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 03:28:27|
Rank Project |
Model Name Folding@Home Identifier |
Make Brand |
GPU Model |
PPD Average |
Points WU Average |
WUs Day Average |
WU Time Average |
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PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 03:28:27|
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 | 17,795 | 1,138,880 | AMD |
| 2 | RYZEN 9 7950X 16-CORE | 32 | 34,578 | 1,106,496 | AMD |
| 3 | RYZEN 9 7900X 12-CORE | 24 | 32,535 | 780,840 | AMD |
| 4 | RYZEN 7 7700X 8-CORE | 16 | 42,988 | 687,808 | AMD |
| 5 | RYZEN 9 5950X 16-CORE | 32 | 16,960 | 542,720 | AMD |
| 6 | RYZEN 9 5900X 12-CORE | 24 | 21,636 | 519,264 | AMD |
| 7 | RYZEN 7 5700X 8-CORE | 16 | 21,969 | 351,504 | AMD |
| 8 | RYZEN 7 5800X 8-CORE | 16 | 21,274 | 340,384 | AMD |
| 9 | RYZEN 7 5800X3D 8-CORE | 16 | 17,229 | 275,664 | AMD |
| 10 | RYZEN 7 5700G | 16 | 16,359 | 261,744 | AMD |
| 11 | 12TH GEN CORE I7-12700 | 20 | 12,111 | 242,220 | Intel |
| 12 | 11TH GEN CORE I9-11900K @ 3.50GHZ | 16 | 13,407 | 214,512 | Intel |
| 13 | XEON PLATINUM 8370C CPU @ 2.80GHZ | 16 | 9,961 | 159,376 | Intel |
| 14 | CORE I9-7940X CPU @ 3.10GHZ | 28 | 5,528 | 154,784 | Intel |
| 15 | 12TH GEN CORE I7-12700H | 20 | 6,390 | 127,800 | Intel |
| 16 | RYZEN 7 3700X 8-CORE | 16 | 7,734 | 123,744 | AMD |
| 17 | CORE I7-10700T CPU @ 2.00GHZ | 16 | 5,014 | 80,224 | Intel |
| 18 | XEON CPU E5-2697 V2 @ 2.70GHZ | 24 | 2,609 | 62,616 | Intel |