RESEARCH: INFLUENZA
FOLDING PROJECT #12423 PROFILE

PROJECT TEAM

Manager(s): Dylan Novack
Institution: Temple University
Project URL: View Project Website

WORK UNIT INFO

Atoms: 93,432
Core: 0xa8
Status: Public

TLDR; PROJECT SUMMARY AI BETA

Miniproteins are small drugs being designed to fight viruses. This project uses computer simulations to understand how changes to miniproteins affect their ability to bind to a virus protein called hemagglutinin, helping scientists design better antiviral 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

Note: Glossary items are a high level summary and may not be 100% accurate.

miniproteins

Small engineered proteins with therapeutic potential.

scientific: pharmaceuticals
biotechnology / drug design

Miniproteins are lab-made proteins smaller than traditional antibodies. They're being explored as new drugs because they can be designed to target specific molecules in the body.


biomolecules

Molecules essential to life processes.

scientific: healthcare
biology / pharmacology

Biomolecules are the building blocks of living organisms. They include proteins, carbohydrates, lipids, and nucleic acids.


monoclonal antibodies

Laboratory-produced antibodies that target specific antigens.

scientific: pharmaceuticals
biotechnology / immunotherapy

Monoclonal antibodies are a type of drug that uses the body's own immune system to fight diseases. They are designed to recognize and bind to specific molecules on cells or viruses.


hemagglutinin

A viral protein that allows influenza A to bind to cells.

scientific: healthcare
virology / influenza

Hemagglutinin is a protein found on the surface of the influenza virus. It helps the virus attach to and enter human cells.


affinity maturation

The process of improving antibody binding strength.

scientific: biotechnology
immunology / antibody engineering

Affinity maturation is a natural process by which the immune system improves the ability of antibodies to bind to their targets.


molecular simulation

A computer-based method for studying molecular interactions.

scientific: pharmaceuticals
computational biology / drug discovery

Molecular simulation is a powerful tool that allows scientists to study how molecules interact with each other. This information can be used to design new drugs and materials.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:34:38
Rank
Project
Model Name
Folding@Home Identifier
Make
Brand
GPU
Model
PPD
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Points WU
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PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Tuesday, 14 April 2026 06:34:38
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 RYZEN THREADRIPPER 7980X 64-CORES 64 29,407 1,882,048 AMD
2 RYZEN THREADRIPPER 3990X 64-CORE 64 22,791 1,458,624 AMD
3 12TH GEN CORE I9-12900K 24 50,971 1,223,304 Intel
4 RYZEN 9 9900X 12-CORE 24 49,391 1,185,384 AMD
5 RYZEN 9 9950X 16-CORE 32 34,368 1,099,776 AMD
6 RYZEN 9 7950X 16-CORE 32 30,270 968,640 AMD
7 CORE I9-14900K 32 26,813 858,016 Intel
8 CORE ULTRA 7 265K 20 41,712 834,240 Intel
9 RYZEN 7 7800X3D 8-CORE 16 50,958 815,328 AMD
10 XEON W-3245 CPU @ 3.20GHZ 32 25,288 809,216 Intel
11 RYZEN 9 7900X 12-CORE 24 31,419 754,056 AMD
12 RYZEN 9 5900X 12-CORE 24 30,293 727,032 AMD
13 RYZEN 9 7950X3D 16-CORE 32 21,359 683,488 AMD
14 RYZEN THREADRIPPER 3970X 32-CORE 64 9,799 627,136 AMD
15 RYZEN 9 7900 12-CORE 24 25,889 621,336 AMD
16 RYZEN 7 7700 8-CORE 16 37,320 597,120 AMD
17 RYZEN 7 5800X3D 8-CORE 16 36,941 591,056 AMD
18 12TH GEN CORE I7-12700 20 27,830 556,600 Intel
19 RYZEN 7 7700X 8-CORE 16 32,488 519,808 AMD
20 XEON CPU E5-2696 V4 @ 2.20GHZ 44 11,535 507,540 Intel
21 RYZEN 7 5700X3D 8-CORE 16 29,508 472,128 AMD
22 RYZEN THREADRIPPER 3960X 24-CORE 48 9,573 459,504 AMD
23 RYZEN 9 5950X 16-CORE 32 12,519 400,608 AMD
24 12TH GEN CORE I7-12700F 20 19,330 386,600 Intel
25 RYZEN 9 3950X 16-CORE 32 11,874 379,968 AMD
26 RYZEN 7 5800X 8-CORE 16 23,597 377,552 AMD
27 CORE I9-10900K CPU @ 3.70GHZ 20 17,278 345,560 Intel
28 12TH GEN CORE I7-12700K 20 16,953 339,060 Intel
29 13TH GEN CORE I5-13600K 14 23,737 332,318 Intel
30 RYZEN 7 5700X 8-CORE 16 20,738 331,808 AMD
31 XEON CPU E5-2680 V2 @ 2.80GHZ 40 7,158 286,320 Intel
32 RYZEN 5 5600 6-CORE 12 23,305 279,660 AMD
33 RYZEN 7 5700G 16 17,053 272,848 AMD
34 XEON CPU E5-2650 V4 @ 2.20GHZ 24 10,669 256,056 Intel
35 RYZEN 5 5600X 6-CORE 12 20,862 250,344 AMD
36 13TH GEN CORE I7-13700 24 9,957 238,968 Intel
37 13TH GEN CORE I5-13500 20 11,893 237,860 Intel
38 RYZEN 9 3900X 12-CORE 24 9,889 237,336 AMD
39 CORE I7-10700K CPU @ 3.80GHZ 16 14,182 226,912 Intel
40 CORE I7-6900K CPU @ 3.20GHZ 16 13,666 218,656 Intel
41 RYZEN 7 3800X 8-CORE 16 12,371 197,936 AMD
42 RYZEN 5 7640HS W/ RADEON 760M GRAPHICS 12 16,273 195,276 AMD
43 11TH GEN CORE I7-11700KF @ 3.60GHZ 16 10,894 174,304 Intel
44 RYZEN 7 5800H 16 9,962 159,392 AMD
45 CORE I7-6950X CPU @ 3.00GHZ 20 7,748 154,960 Intel
46 RYZEN 9 3900XT 12-CORE 24 6,390 153,360 AMD
47 11TH GEN CORE I7-11800H @ 2.30GHZ 16 9,223 147,568 Intel
48 RYZEN 5 3600 6-CORE 12 11,843 142,116 AMD
49 CORE I7-5930K CPU @ 3.50GHZ 12 11,585 139,020 Intel
50 CORE I7-5820K CPU @ 3.30GHZ 12 11,468 137,616 Intel
51 RYZEN 7 7735HS 16 8,196 131,136 AMD
52 XEON CPU E5-2697 V2 @ 2.70GHZ 24 5,459 131,016 Intel
53 RYZEN 7 2700X EIGHT-CORE 16 8,004 128,064 AMD
54 CORE I5-14500 20 6,329 126,580 Intel
55 12TH GEN CORE I7-12700H 20 6,238 124,760 Intel
56 RYZEN 7 3700X 8-CORE 16 7,123 113,968 AMD
57 11TH GEN CORE I9-11900F @ 2.50GHZ 16 7,018 112,288 Intel
58 11TH GEN CORE I7-11700F @ 2.50GHZ 16 6,400 102,400 Intel
59 XEON SILVER 4114 CPU @ 2.20GHZ 40 2,489 99,560 Intel
60 CORE I9-8950HK CPU @ 2.90GHZ 12 7,998 95,976 Intel
61 XEON CPU E5-2660 V3 @ 2.60GHZ 20 4,776 95,520 Intel
62 11TH GEN CORE I7-1165G7 @ 2.80GHZ 8 11,658 93,264 Intel
63 CORE I7-10700T CPU @ 2.00GHZ 16 5,713 91,408 Intel
64 RYZEN 5 2600X SIX-CORE 12 7,271 87,252 AMD
65 RYZEN 5 5600H 12 7,188 86,256 AMD
66 APPLE M3 8 10,021 80,168 Apple
67 RYZEN 7 4800U 16 4,329 69,264 AMD
68 APPLE M1 PRO 10 6,144 61,440 Apple
69 12TH GEN CORE I5-12450H 12 3,851 46,212 Intel
70 12TH GEN CORE I5-12600K 16 2,858 45,728 Intel
71 12TH GEN CORE I5-12600KF 16 2,335 37,360 Intel
72 XEON CPU E5540 @ 2.53GHZ 16 2,239 35,824 Intel
73 CORE I7-14650HX 24 1,222 29,328 Intel