Research section Research projects and references

Research: INFLUENZA Folding Project #18473

Project #18473 overview

Project Summary AI Beta

Miniproteins are small proteins that can be designed to fight viruses like the flu. Scientists want to use computer simulations to understand how changes to miniprotein design affect their ability to bind to the flu virus and block its spread. This could lead to better flu treatments 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 18473

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

Data as of Sunday, 02 August 2026 21:51:05

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 40,894 654,304 AMD
2 RYZEN 9 7900 12-CORE 24 25,652 615,648 AMD
3 RYZEN 7 5800X3D 8-CORE 16 27,635 442,160 AMD
4 11TH GEN CORE I7-11700K @ 3.60GHZ 16 26,524 424,384 Intel
5 RYZEN 7 5700X 8-CORE 16 25,449 407,184 AMD
6 RYZEN 7 5800X 8-CORE 16 20,910 334,560 AMD
7 RYZEN 5 5600 6-CORE 12 25,385 304,620 AMD
8 RYZEN 5 5600X 6-CORE 12 19,985 239,820 AMD
9 RYZEN 9 5950X 16-CORE 32 7,299 233,568 AMD
10 CORE I7-9700K CPU @ 3.60GHZ 8 27,198 217,584 Intel
11 RYZEN 5 3500 6-CORE 6 31,311 187,866 AMD
12 RYZEN 5 3600 6-CORE 12 14,114 169,368 AMD
13 CORE I7-5930K CPU @ 3.50GHZ 12 11,320 135,840 Intel
14 CORE I7-7700K CPU @ 4.20GHZ 8 16,268 130,144 Intel
15 CORE I9-9900K CPU @ 3.60GHZ 16 7,781 124,496 Intel
16 CORE I7-5820K CPU @ 3.30GHZ 12 9,952 119,424 Intel
17 CORE I7-8700 CPU @ 3.20GHZ 12 8,343 100,116 Intel
18 CORE I9-8950HK CPU @ 2.90GHZ 12 8,084 97,008 Intel
19 CORE I7-8705G CPU @ 3.10GHZ 8 11,715 93,720 Intel
20 CORE I7-6700K CPU @ 4.00GHZ 8 9,780 78,240 Intel
21 CORE I7-10700T CPU @ 2.00GHZ 16 4,544 72,704 Intel
22 CORE I7-4770HQ CPU @ 2.20GHZ 8 7,875 63,000 Intel
23 CORE I7-3770K CPU @ 3.50GHZ 8 7,678 61,424 Intel
24 XEON CPU E3-1245 V3 @ 3.40GHZ 8 7,674 61,392 Intel
25 APPLE M1 8 7,171 57,368 Apple
26 RYZEN 5 5500U 12 2,318 27,816 AMD