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Research: INFLUENZA Folding Project #18470

Project #18470 overview

Project Summary AI Beta

Miniproteins are tiny drugs that can fight infections. Scientists are using computer simulations to understand how miniproteins bind to viruses like the flu. They hope this will help them design even better miniprotein drugs 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 18470

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

Data as of Sunday, 02 August 2026 21:51:08

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 9 7950X 16-CORE 32 30,612 979,584 AMD
2 RYZEN 7 7700X 8-CORE 16 33,795 540,720 AMD
3 RYZEN 9 5900X 12-CORE 24 22,297 535,128 AMD
4 RYZEN 7 5800X3D 8-CORE 16 29,953 479,248 AMD
5 RYZEN 9 5950X 16-CORE 32 11,849 379,168 AMD
6 RYZEN 7 5700X 8-CORE 16 20,157 322,512 AMD
7 12TH GEN CORE I5-12600K 16 19,455 311,280 Intel
8 RYZEN 7 5700G 16 16,986 271,776 AMD
9 RYZEN 7 5800X 8-CORE 16 15,859 253,744 AMD
10 12TH GEN CORE I7-12700 20 11,691 233,820 Intel
11 11TH GEN CORE I9-11900K @ 3.50GHZ 16 14,375 230,000 Intel
12 CORE I9-7940X CPU @ 3.10GHZ 28 7,091 198,548 Intel
13 RYZEN 7 3700X 8-CORE 16 6,809 108,944 AMD
14 CORE I7-10700T CPU @ 2.00GHZ 16 5,273 84,368 Intel
15 12TH GEN CORE I7-1270P 16 3,121 49,936 Intel