Research section Research projects and references

Research: INFLUENZA Folding Project #18478

Project #18478 overview

Project Summary AI Beta

This project studies miniproteins – tiny proteins that can be designed to block viruses. They're looking at how changes in the miniprotein affect its ability to bind to a flu virus protein, using powerful computer simulations. This could help us design better antiviral drugs.
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 18478

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

Data as of Sunday, 02 August 2026 21:51:00

CPU PPD Averages Beta

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 EPYC 7B12 64-CORE 64 18,171 1,162,944 AMD
2 RYZEN 9 7950X 16-CORE 32 30,760 984,320 AMD
3 RYZEN 7 7700X 8-CORE 16 41,707 667,312 AMD
4 RYZEN 9 7900X 12-CORE 24 25,744 617,856 AMD
5 RYZEN 7 5800X3D 8-CORE 16 30,542 488,672 AMD
6 RYZEN 9 5950X 16-CORE 32 14,588 466,816 AMD
7 RYZEN 7 5700X 8-CORE 16 23,645 378,320 AMD
8 RYZEN 7 5800X 8-CORE 16 22,472 359,552 AMD
9 12TH GEN CORE I5-12600K 16 19,946 319,136 Intel
10 XEON PLATINUM 8370C CPU @ 2.80GHZ 16 17,506 280,096 Intel
11 XEON CPU E5-2680 V2 @ 2.80GHZ 40 6,674 266,960 Intel
12 RYZEN 7 5700G 16 16,445 263,120 AMD
13 12TH GEN CORE I7-12700 20 12,062 241,240 Intel
14 CORE I7-10700K CPU @ 3.80GHZ 16 14,494 231,904 Intel
15 RYZEN 7 3700X 8-CORE 16 12,816 205,056 AMD
16 12TH GEN CORE I7-12700F 20 10,149 202,980 Intel
17 CORE I9-9900K CPU @ 3.60GHZ 16 10,460 167,360 Intel
18 CORE I9-7940X CPU @ 3.10GHZ 28 5,578 156,184 Intel
19 11TH GEN CORE I9-11900K @ 3.50GHZ 16 9,574 153,184 Intel
20 EPYC 7262 8-CORE 16 8,753 140,048 AMD
21 RYZEN THREADRIPPER 2950X 16-CORE 32 4,120 131,840 AMD
22 12TH GEN CORE I7-12700H 20 5,697 113,940 Intel
23 CORE I7-10700T CPU @ 2.00GHZ 16 5,578 89,248 Intel
24 XEON CPU E5-2697 V2 @ 2.70GHZ 24 2,839 68,136 Intel
25 12TH GEN CORE I7-1270P 16 3,019 48,304 Intel