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Research: INFLUENZA Folding Project #18468

Project #18468 overview

Project Summary AI Beta

Miniproteins are small proteins being developed as new drugs to fight diseases like the flu. Scientists are using powerful computer simulations to understand how miniproteins bind to viruses and how changes in their design can improve their effectiveness.
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 18468

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

Data as of Sunday, 02 August 2026 21:51:10

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 18,410 1,178,240 AMD
2 RYZEN 9 7950X 16-CORE 32 30,358 971,456 AMD
3 RYZEN 7 7700X 8-CORE 16 42,685 682,960 AMD
4 RYZEN 9 5950X 16-CORE 32 16,227 519,264 AMD
5 RYZEN 7 5800X3D 8-CORE 16 31,490 503,840 AMD
6 12TH GEN CORE I7-12700K 20 23,675 473,500 Intel
7 RYZEN 7 5700X 8-CORE 16 29,491 471,856 AMD
8 RYZEN 9 5900X 12-CORE 24 16,556 397,344 AMD
9 RYZEN 9 3900X 12-CORE 24 13,530 324,720 AMD
10 RYZEN 7 5700G 16 18,569 297,104 AMD
11 RYZEN 7 3800X 8-CORE 16 16,962 271,392 AMD
12 12TH GEN CORE I7-12700 20 13,451 269,020 Intel
13 XEON PLATINUM 8370C CPU @ 2.80GHZ 16 15,658 250,528 Intel
14 CORE I7-10700K CPU @ 3.80GHZ 16 15,022 240,352 Intel
15 RYZEN 7 5800X 8-CORE 16 13,210 211,360 AMD
16 11TH GEN CORE I9-11900K @ 3.50GHZ 16 9,723 155,568 Intel
17 RYZEN 7 3700X 8-CORE 16 9,647 154,352 AMD
18 XEON CPU E5-2697 V2 @ 2.70GHZ 24 2,641 63,384 Intel