Research section Research projects and references

Research: INFLUENZA Folding Project #12406

Project #12406 overview

Project Summary AI Beta

Scientists are studying miniproteins, tiny proteins that can block viruses. They want to understand how changes in these miniproteins affect their ability to bind to the flu virus hemagglutinin protein. Using computer simulations, they hope to learn how to design even better miniprotein drugs for fighting infections.
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 12406

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

Data as of Sunday, 02 August 2026 22:09:54

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 THREADRIPPER 3990X 64-CORE 64 22,713 1,453,632 AMD
2 RYZEN 9 7950X 16-CORE 32 36,456 1,166,592 AMD
3 APPLE M2 ULTRA 24 40,877 981,048 Apple
4 RYZEN THREADRIPPER 3960X 24-CORE 48 17,763 852,624 AMD
5 RYZEN 7 7800X3D 8-CORE 16 38,467 615,472 AMD
6 RYZEN 9 7900X 12-CORE 24 24,825 595,800 AMD
7 RYZEN 9 7900 12-CORE 24 22,470 539,280 AMD
8 RYZEN 9 5950X 16-CORE 32 16,477 527,264 AMD
9 RYZEN 9 5900X 12-CORE 24 21,065 505,560 AMD
10 CORE I9-14900K 32 15,739 503,648 Intel
11 RYZEN 5 7600 6-CORE 12 41,069 492,828 AMD
12 13TH GEN CORE I9-13900K 32 14,619 467,808 Intel
13 RYZEN 5 7600X 6-CORE 12 37,401 448,812 AMD
14 RYZEN 7 7700X 8-CORE 16 27,525 440,400 AMD
15 RYZEN 7 5800X3D 8-CORE 16 25,991 415,856 AMD
16 CORE I9-14900KF 24 16,790 402,960 Intel
17 RYZEN 7 5800X 8-CORE 16 24,465 391,440 AMD
18 CORE I5-14600K 20 19,502 390,040 Intel
19 RYZEN 7 5700X 8-CORE 16 24,357 389,712 AMD
20 RYZEN 7 5700X3D 8-CORE 16 21,649 346,384 AMD
21 RYZEN THREADRIPPER 2990WX 32-CORE 64 5,253 336,192 AMD
22 RYZEN 5 5600X 6-CORE 12 26,824 321,888 AMD
23 EPYC 7B12 64-CORE 64 4,826 308,864 AMD
24 RYZEN 9 5900 12-CORE 24 12,055 289,320 AMD
25 RYZEN 9 3900X 12-CORE 24 11,881 285,144 AMD
26 12TH GEN CORE I7-12700F 20 13,749 274,980 Intel
27 RYZEN 5 5600G 12 22,876 274,512 AMD
28 RYZEN 5 5600 6-CORE 12 22,849 274,188 AMD
29 RYZEN 7 5700G 16 16,733 267,728 AMD
30 13TH GEN CORE I5-13600K 14 17,941 251,174 Intel
31 13TH GEN CORE I7-13700 24 10,297 247,128 Intel
32 CORE I7-10700K CPU @ 3.80GHZ 16 14,289 228,624 Intel
33 XEON CPU E5-2683 V4 @ 2.10GHZ 32 6,775 216,800 Intel
34 CORE I9-9900K CPU @ 3.60GHZ 16 11,943 191,088 Intel
35 RYZEN 9 3900XT 12-CORE 24 7,928 190,272 AMD
36 XEON GOLD 5120 CPU @ 2.20GHZ 28 6,280 175,840 Intel
37 12TH GEN CORE I5-12400 12 13,972 167,664 Intel
38 RYZEN 7 3800X 8-CORE 16 10,475 167,600 AMD
39 RYZEN 5 3600 6-CORE 12 13,509 162,108 AMD
40 RYZEN 7 3700X 8-CORE 16 10,033 160,528 AMD
41 CORE I7-5820K CPU @ 3.30GHZ 12 12,211 146,532 Intel
42 CORE I7-5930K CPU @ 3.50GHZ 12 11,943 143,316 Intel
43 11TH GEN CORE I7-11850H @ 2.50GHZ 16 8,491 135,856 Intel
44 11TH GEN CORE I7-11700F @ 2.50GHZ 16 7,660 122,560 Intel
45 XEON CPU E5-2697 V2 @ 2.70GHZ 24 4,743 113,832 Intel
46 XEON CPU E5-1650 V4 @ 3.60GHZ 12 9,298 111,576 Intel
47 12TH GEN CORE I7-12700H 20 5,562 111,240 Intel
48 APPLE M1 MAX 10 10,441 104,410 Apple
49 11TH GEN CORE I9-11900F @ 2.50GHZ 16 6,491 103,856 Intel
50 XEON W-2145 CPU @ 3.70GHZ 16 6,171 98,736 Intel
51 CORE I7-7820X CPU @ 3.60GHZ 16 6,070 97,120 Intel
52 APPLE M2 PRO 10 9,441 94,410 Apple
53 CORE I9-8950HK CPU @ 2.90GHZ 12 7,861 94,332 Intel
54 CORE I7-10700T CPU @ 2.00GHZ 16 5,740 91,840 Intel
55 12TH GEN CORE I9-12900K 24 3,807 91,368 Intel
56 CORE I7-9750H CPU @ 2.60GHZ 12 7,102 85,224 Intel
57 RYZEN 5 2600X SIX-CORE 12 6,649 79,788 AMD
58 XEON CPU E5-2680 0 @ 2.70GHZ 16 4,550 72,800 Intel
59 CORE I7-8700K CPU @ 3.70GHZ 12 5,986 71,832 Intel
60 APPLE M1 PRO 10 6,713 67,130 Apple
61 RYZEN 5 5500U 12 4,365 52,380 AMD
62 11TH GEN CORE I5-11400 @ 2.60GHZ 12 4,299 51,588 Intel
63 RYZEN 7 4800U 16 2,713 43,408 AMD
64 12TH GEN CORE I5-12600KF 16 2,340 37,440 Intel
65 CORE I7-14650HX 24 1,078 25,872 Intel