Research section Research projects and references

Research: INFLUENZA Folding Project #12404

Project #12404 overview

Project Summary AI Beta

This project explores how miniproteins, tiny engineered proteins, bind to viral targets like the H1 influenza A virus. Scientists are using computer simulations to understand how changes in miniprotein design affect their ability to block the virus's grip on cells. This research could lead to better miniprotein-based drugs for treating 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 12404

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

Data as of Sunday, 02 August 2026 22:09:56

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 7950X 16-CORE 32 40,418 1,293,376 AMD
2 APPLE M2 ULTRA 24 42,624 1,022,976 Apple
3 RYZEN THREADRIPPER 3960X 24-CORE 48 15,545 746,160 AMD
4 13TH GEN CORE I9-13900K 32 23,109 739,488 Intel
5 RYZEN 9 7900 12-CORE 24 28,376 681,024 AMD
6 RYZEN 9 7900X 12-CORE 24 27,699 664,776 AMD
7 RYZEN 7 7800X3D 8-CORE 16 39,013 624,208 AMD
8 RYZEN 9 7950X3D 16-CORE 32 17,611 563,552 AMD
9 CORE I5-14600K 20 25,885 517,700 Intel
10 RYZEN 7 7700X 8-CORE 16 31,906 510,496 AMD
11 RYZEN 9 5950X 16-CORE 32 15,334 490,688 AMD
12 CORE I9-14900K 32 14,884 476,288 Intel
13 XEON CPU E5-2696 V4 @ 2.20GHZ 44 10,721 471,724 Intel
14 RYZEN 7 5800X3D 8-CORE 16 29,160 466,560 AMD
15 RYZEN 5 7600 6-CORE 12 37,898 454,776 AMD
16 RYZEN 7 5700X 8-CORE 16 25,978 415,648 AMD
17 RYZEN 7 5800X 8-CORE 16 21,950 351,200 AMD
18 RYZEN 5 5600 6-CORE 12 24,806 297,672 AMD
19 13TH GEN CORE I5-13600K 14 21,165 296,310 Intel
20 RYZEN 7 5700G 16 18,349 293,584 AMD
21 12TH GEN CORE I7-12700F 20 14,367 287,340 Intel
22 RYZEN 9 3900X 12-CORE 24 11,372 272,928 AMD
23 RYZEN 5 5600X 6-CORE 12 21,934 263,208 AMD
24 RYZEN 9 3950X 16-CORE 32 7,875 252,000 AMD
25 RYZEN 9 5900X 12-CORE 24 10,424 250,176 AMD
26 CORE I7-10700K CPU @ 3.80GHZ 16 14,641 234,256 Intel
27 13TH GEN CORE I7-13700 24 9,596 230,304 Intel
28 XEON GOLD 5120 CPU @ 2.20GHZ 28 8,136 227,808 Intel
29 12TH GEN CORE I5-12400 12 17,714 212,568 Intel
30 RYZEN 9 5900 12-CORE 24 8,779 210,696 AMD
31 RYZEN 7 3800X 8-CORE 16 12,532 200,512 AMD
32 RYZEN 9 3900XT 12-CORE 24 8,178 196,272 AMD
33 CORE I5-10600KF CPU @ 4.10GHZ 12 14,843 178,116 Intel
34 13TH GEN CORE I7-13700K 24 6,982 167,568 Intel
35 RYZEN 7 8700G W/ RADEON 780M GRAPHICS 16 10,298 164,768 AMD
36 CORE I9-9900K CPU @ 3.60GHZ 16 10,267 164,272 Intel
37 RYZEN 7 3700X 8-CORE 16 9,972 159,552 AMD
38 CORE I7-5930K CPU @ 3.50GHZ 12 12,907 154,884 Intel
39 RYZEN 5 3600 6-CORE 12 12,346 148,152 AMD
40 CORE I7-5820K CPU @ 3.30GHZ 12 11,937 143,244 Intel
41 11TH GEN CORE I9-11900F @ 2.50GHZ 16 8,174 130,784 Intel
42 CORE I7-8700 CPU @ 3.20GHZ 12 10,394 124,728 Intel
43 11TH GEN CORE I7-11700F @ 2.50GHZ 16 7,594 121,504 Intel
44 CORE I7-8700K CPU @ 3.70GHZ 12 9,263 111,156 Intel
45 XEON CPU E5-2660 V3 @ 2.60GHZ 20 5,312 106,240 Intel
46 CORE I9-8950HK CPU @ 2.90GHZ 12 8,805 105,660 Intel
47 XEON SILVER 4114 CPU @ 2.20GHZ 40 2,618 104,720 Intel
48 RYZEN 9 5900HS 16 6,127 98,032 AMD
49 12TH GEN CORE I7-12700H 20 4,552 91,040 Intel
50 CORE I7-10700T CPU @ 2.00GHZ 16 5,615 89,840 Intel
51 RYZEN 5 5600H 12 6,566 78,792 AMD
52 RYZEN 5 5600G 12 6,370 76,440 AMD
53 11TH GEN CORE I7-11700 @ 2.50GHZ 16 4,745 75,920 Intel
54 CORE I7-9750H CPU @ 2.60GHZ 12 6,056 72,672 Intel
55 RYZEN 7 4800U 16 4,039 64,624 AMD
56 CORE I5-10400F CPU @ 2.90GHZ 12 5,217 62,604 Intel
57 CORE I7-7820X CPU @ 3.60GHZ 16 3,797 60,752 Intel
58 XEON CPU E5-2697 V2 @ 2.70GHZ 24 2,044 49,056 Intel
59 12TH GEN CORE I5-12600K 16 2,466 39,456 Intel
60 12TH GEN CORE I5-12600KF 16 2,449 39,184 Intel