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Research: INFLUENZA Folding Project #18461

Project #18461 overview

Project Summary AI Beta

Miniproteins are tiny proteins that can fight diseases. Scientists are using computer simulations to understand how miniproteins bind to viruses, like the flu. They want to see if these simulations can predict how changes to miniproteins affect their ability to fight viruses. This will help create better miniprotein drugs in the future.
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 18461

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

Data as of Sunday, 02 August 2026 21:51:16

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 7 5700G 16 52,681 842,896 AMD
2 RYZEN 9 5950X 16-CORE 32 20,855 667,360 AMD
3 RYZEN 7 7700X 8-CORE 16 40,545 648,720 AMD
4 RYZEN 9 7900 12-CORE 24 25,840 620,160 AMD
5 RYZEN 7 5700X 8-CORE 16 25,303 404,848 AMD
6 11TH GEN CORE I7-11700K @ 3.60GHZ 16 23,780 380,480 Intel
7 RYZEN 5 5600 6-CORE 12 26,970 323,640 AMD
8 APPLE M1 MAX 10 26,853 268,530 Apple
9 RYZEN 7 5800X3D 8-CORE 16 15,898 254,368 AMD
10 CORE I7-10700K CPU @ 3.80GHZ 16 15,763 252,208 Intel
11 RYZEN 9 3900X 12-CORE 24 10,419 250,056 AMD
12 RYZEN 5 5600X 6-CORE 12 18,584 223,008 AMD
13 RYZEN 5 3500 6-CORE 6 30,582 183,492 AMD
14 RYZEN 5 3600 6-CORE 12 12,666 151,992 AMD
15 RYZEN 7 2700X EIGHT-CORE 16 8,297 132,752 AMD
16 CORE I7-5930K CPU @ 3.50GHZ 12 10,944 131,328 Intel
17 CORE I7-5820K CPU @ 3.30GHZ 12 10,194 122,328 Intel
18 CORE I7-7700K CPU @ 4.20GHZ 8 15,284 122,272 Intel
19 CORE I7-8700 CPU @ 3.20GHZ 12 9,413 112,956 Intel
20 CORE I9-8950HK CPU @ 2.90GHZ 12 8,255 99,060 Intel
21 CORE I7-10700T CPU @ 2.00GHZ 16 5,290 84,640 Intel
22 CORE I7-8705G CPU @ 3.10GHZ 8 10,507 84,056 Intel
23 RYZEN 7 3700X 8-CORE 16 4,385 70,160 AMD
24 CORE I7-6700K CPU @ 4.00GHZ 8 8,363 66,904 Intel
25 CORE I7-3770K CPU @ 3.50GHZ 8 7,514 60,112 Intel
26 APPLE M1 8 7,346 58,768 Apple
27 CORE I7-3770 CPU @ 3.40GHZ 8 6,764 54,112 Intel
28 XEON CPU E5-1620 V2 @ 3.70GHZ 8 5,980 47,840 Intel
29 APPLE M1 PRO 10 2,977 29,770 Apple
30 11TH GEN CORE I5-1135G7 @ 2.40GHZ 8 1,040 8,320 Intel