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Research: INFLUENZA Folding Project #18469

Project #18469 overview

Project Summary AI Beta

This project explores how miniproteins, tiny proteins designed to fight diseases, bind to a target called hemagglutinin found in the flu virus. Scientists are using computer simulations to understand how changes in miniprotein structure affect their ability to bind hemagglutinin and 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 18469

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

Data as of Sunday, 02 August 2026 21:51:09

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 19,293 1,234,752 AMD
2 RYZEN 9 7900X 12-CORE 24 48,078 1,153,872 AMD
3 RYZEN 9 5950X 16-CORE 32 32,953 1,054,496 AMD
4 RYZEN 9 7950X 16-CORE 32 32,788 1,049,216 AMD
5 RYZEN 9 7900 12-CORE 24 27,756 666,144 AMD
6 RYZEN 7 7700X 8-CORE 16 37,189 595,024 AMD
7 RYZEN 7 5800X 8-CORE 16 31,338 501,408 AMD
8 CORE I9-10900K CPU @ 3.70GHZ 20 22,700 454,000 Intel
9 RYZEN 7 5700X 8-CORE 16 28,278 452,448 AMD
10 13TH GEN CORE I5-13500 20 17,220 344,400 Intel
11 12TH GEN CORE I7-12700 20 14,059 281,180 Intel
12 CORE I7-10700K CPU @ 3.80GHZ 16 15,479 247,664 Intel
13 RYZEN 7 5700G 16 14,701 235,216 AMD
14 XEON PLATINUM 8370C CPU @ 2.80GHZ 16 10,476 167,616 Intel
15 EPYC 7262 8-CORE 16 8,713 139,408 AMD
16 12TH GEN CORE I7-12700H 20 6,885 137,700 Intel
17 CORE I7-10700T CPU @ 2.00GHZ 16 5,608 89,728 Intel
18 XEON CPU E5-2697 V2 @ 2.70GHZ 24 2,915 69,960 Intel
19 CORE I5-6400 CPU @ 2.70GHZ 4 16,834 67,336 Intel
20 RYZEN THREADRIPPER 2950X 16-CORE 32 1,812 57,984 AMD