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Research: INFLUENZA Folding Project #12422

Project #12422 overview

Project Summary AI Beta

This project researches miniproteins, tiny drug-like molecules that can block viruses like the flu. Scientists are using computer simulations to see how changing miniprotein designs affects their ability to bind to viral proteins and stop infection. This could lead to better treatments for infectious diseases.
Automated summary; simplified and may not be fully accurate.
Source material

Official 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.

Performance data

Hardware Performance for Project 12422

Compare community-sampled Folding@Home output for the GPUs and CPUs processing this project.

Data as of Sunday, 02 August 2026 22:09:38

CPU PPD Averages Beta

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 RYZEN 9 9900X 12-CORE 24 50,940 1,222,560 AMD
2 RYZEN 9 9950X 16-CORE 32 35,781 1,144,992 AMD
3 RYZEN 9 7950X 16-CORE 32 33,518 1,072,576 AMD
4 CORE I9-14900K 32 32,057 1,025,824 Intel
5 APPLE M2 ULTRA 24 37,884 909,216 Apple
6 RYZEN 9 7950X3D 16-CORE 32 26,847 859,104 AMD
7 RYZEN 9 7900X 12-CORE 24 35,782 858,768 AMD
8 XEON W-3245 CPU @ 3.20GHZ 32 26,679 853,728 Intel
9 RYZEN 7 9800X3D 8-CORE 16 48,665 778,640 AMD
10 RYZEN 7 7800X3D 8-CORE 16 44,563 713,008 AMD
11 RYZEN 7 5800X3D 8-CORE 16 39,350 629,600 AMD
12 RYZEN 9 7900 12-CORE 24 24,397 585,528 AMD
13 13TH GEN CORE I9-13900K 32 17,875 572,000 Intel
14 12TH GEN CORE I7-12700 20 27,884 557,680 Intel
15 RYZEN 5 7600 6-CORE 12 44,156 529,872 AMD
16 RYZEN 7 7700 8-CORE 16 32,159 514,544 AMD
17 RYZEN 9 5900X 12-CORE 24 21,299 511,176 AMD
18 CORE I5-14600K 20 25,027 500,540 Intel
19 RYZEN 7 7700X 8-CORE 16 29,908 478,528 AMD
20 CORE I9-14900KF 24 19,934 478,416 Intel
21 13TH GEN CORE I5-13600 20 22,592 451,840 Intel
22 XEON CPU E5-2696 V4 @ 2.20GHZ 44 10,202 448,888 Intel
23 13TH GEN CORE I5-13600KF 20 21,365 427,300 Intel
24 XEON GOLD 6140 CPU @ 2.30GHZ 36 10,953 394,308 Intel
25 RYZEN 7 5800X 8-CORE 16 24,517 392,272 AMD
26 RYZEN 9 5950X 16-CORE 32 12,159 389,088 AMD
27 RYZEN 9 7900X3D 12-CORE 24 15,799 379,176 AMD
28 12TH GEN CORE I7-12700F 20 18,638 372,760 Intel
29 RYZEN 7 5700X 8-CORE 16 21,820 349,120 AMD
30 12TH GEN CORE I7-12700K 20 16,817 336,340 Intel
31 13TH GEN CORE I5-13600K 14 23,167 324,338 Intel
32 RYZEN 7 5700X3D 8-CORE 16 19,083 305,328 AMD
33 RYZEN 5 5600 6-CORE 12 22,858 274,296 AMD
34 RYZEN 7 5700G 16 15,643 250,288 AMD
35 13TH GEN CORE I7-13700 24 10,247 245,928 Intel
36 XEON CPU E5-2680 V2 @ 2.80GHZ 40 6,022 240,880 Intel
37 RYZEN 9 3900X 12-CORE 24 9,972 239,328 AMD
38 CORE I7-10700K CPU @ 3.80GHZ 16 14,482 231,712 Intel
39 CORE I7-6900K CPU @ 3.20GHZ 16 14,338 229,408 Intel
40 RYZEN 7 3800X 8-CORE 16 14,276 228,416 AMD
41 CORE I9-9900 CPU @ 3.10GHZ 16 13,542 216,672 Intel
42 CORE I5-14500 20 10,442 208,840 Intel
43 CORE I7-10700 CPU @ 2.90GHZ 16 12,907 206,512 Intel
44 RYZEN 9 3900XT 12-CORE 24 8,109 194,616 AMD
45 EPYC 7K62 48-CORE 96 1,872 179,712 AMD
46 RYZEN 5 5600X 6-CORE 12 14,827 177,924 AMD
47 RYZEN THREADRIPPER 3960X 24-CORE 48 3,620 173,760 AMD
48 RYZEN 5 3600 6-CORE 12 13,835 166,020 AMD
49 RYZEN 7 3700X 8-CORE 16 9,904 158,464 AMD
50 RYZEN 7 5800H 16 9,444 151,104 AMD
51 CORE I7-5930K CPU @ 3.50GHZ 12 12,450 149,400 Intel
52 CORE I7-5820K CPU @ 3.30GHZ 12 11,178 134,136 Intel
53 13TH GEN CORE I5-13500 20 6,612 132,240 Intel
54 11TH GEN CORE I7-11700F @ 2.50GHZ 16 7,432 118,912 Intel
55 APPLE M3 8 14,812 118,496 Apple
56 CORE I7-8700 CPU @ 3.20GHZ 12 9,686 116,232 Intel
57 CORE I9-8950HK CPU @ 2.90GHZ 12 7,994 95,928 Intel
58 12TH GEN CORE I7-12700H 20 4,686 93,720 Intel
59 CORE I7-10700T CPU @ 2.00GHZ 16 5,772 92,352 Intel
60 CORE I7-9750H CPU @ 2.60GHZ 12 6,960 83,520 Intel
61 11TH GEN CORE I7-11700 @ 2.50GHZ 16 4,863 77,808 Intel
62 XEON CPU E5-2697 V2 @ 2.70GHZ 24 3,224 77,376 Intel
63 CORE I7-8700K CPU @ 3.70GHZ 12 6,151 73,812 Intel
64 XEON CPU X5680 @ 3.33GHZ 12 5,337 64,044 Intel
65 CORE I5-7500 CPU @ 3.40GHZ 4 15,244 60,976 Intel
66 XEON CPU E5-2680 0 @ 2.70GHZ 16 3,445 55,120 Intel
67 RYZEN 5 5500U 12 3,634 43,608 AMD
68 RYZEN 7 4800U 16 2,653 42,448 AMD
69 12TH GEN CORE I5-12600K 16 2,528 40,448 Intel
70 12TH GEN CORE I5-12600KF 16 2,367 37,872 Intel