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Research: INFLUENZA Folding Project #18481

Project #18481 overview

Project Summary AI Beta

Miniproteins are small proteins designed to fight diseases. Scientists are using computer simulations to understand how these miniproteins bind to a virus protein called hemagglutinin. They want to learn how changes to the miniprotein's design affect its ability to bind and potentially develop better treatments.
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 18481

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

Data as of Sunday, 02 August 2026 21:50:57

CPU PPD Averages Beta

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 12TH GEN CORE I9-12900K 24 49,289 1,182,936 Intel
2 EPYC 7B12 64-CORE 64 13,953 892,992 AMD
3 RYZEN 7 7700X 8-CORE 16 45,305 724,880 AMD
4 RYZEN 9 5950X 16-CORE 32 17,054 545,728 AMD
5 RYZEN 7 5700G 16 28,576 457,216 AMD
6 RYZEN 7 5700X 8-CORE 16 27,621 441,936 AMD
7 11TH GEN CORE I7-11700K @ 3.60GHZ 16 20,758 332,128 Intel
8 RYZEN 7 3800X 8-CORE 16 19,799 316,784 AMD
9 RYZEN 7 5800X 8-CORE 16 18,599 297,584 AMD
10 CORE I7-10700K CPU @ 3.80GHZ 16 16,780 268,480 Intel
11 RYZEN 5 5600X 6-CORE 12 19,433 233,196 AMD
12 RYZEN 9 3900X 12-CORE 24 9,550 229,200 AMD
13 CORE I7-9700K CPU @ 3.60GHZ 8 25,365 202,920 Intel
14 RYZEN 9 5900 12-CORE 24 8,436 202,464 AMD
15 RYZEN 5 3500 6-CORE 6 31,735 190,410 AMD
16 RYZEN 5 3600 6-CORE 12 13,053 156,636 AMD
17 CORE I7-5930K CPU @ 3.50GHZ 12 10,959 131,508 Intel
18 CORE I7-7700K CPU @ 4.20GHZ 8 16,129 129,032 Intel
19 CORE I9-9900K CPU @ 3.60GHZ 16 6,854 109,664 Intel
20 CORE I9-8950HK CPU @ 2.90GHZ 12 8,232 98,784 Intel
21 CORE I7-6700T CPU @ 2.80GHZ 8 11,812 94,496 Intel
22 CORE I7-4790K CPU @ 4.00GHZ 8 11,244 89,952 Intel
23 CORE I7-8705G CPU @ 3.10GHZ 8 11,108 88,864 Intel
24 CORE I7-4770HQ CPU @ 2.20GHZ 8 7,714 61,712 Intel
25 RYZEN 5 2400G 8 7,713 61,704 AMD
26 XEON CPU E3-1245 V3 @ 3.40GHZ 8 7,557 60,456 Intel
27 CORE I7-3770K CPU @ 3.50GHZ 8 7,533 60,264 Intel
28 APPLE M1 8 7,155 57,240 Apple
29 XEON CPU E5-1630 V3 @ 3.70GHZ 8 5,604 44,832 Intel
30 11TH GEN CORE I7-1165G7 @ 2.80GHZ 8 3,661 29,288 Intel
31 XEON CPU E5-1620 V2 @ 3.70GHZ 8 2,410 19,280 Intel