Research section Research projects and references

Research: INFLUENZA Folding Project #18483

Project #18483 overview

Project Summary AI Beta

Miniproteins are tiny proteins that can be designed to fight diseases. Researchers want to use computer simulations to understand how changes in miniproteins affect their ability to bind to viruses, like the flu. This could help design 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 18483

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

Data as of Sunday, 02 August 2026 21:50:55

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 19,021 1,217,344 AMD
2 RYZEN 9 7950X 16-CORE 32 30,786 985,152 AMD
3 RYZEN 7 7700X 8-CORE 16 38,545 616,720 AMD
4 RYZEN 9 5950X 16-CORE 32 15,896 508,672 AMD
5 12TH GEN CORE I7-12700K 20 21,329 426,580 Intel
6 RYZEN 7 5700X 8-CORE 16 26,655 426,480 AMD
7 XEON PLATINUM 8370C CPU @ 2.80GHZ 16 18,860 301,760 Intel
8 RYZEN 7 5700G 16 17,966 287,456 AMD
9 RYZEN 9 3900X 12-CORE 24 11,919 286,056 AMD
10 RYZEN 9 5900 12-CORE 24 11,230 269,520 AMD
11 12TH GEN CORE I7-12700 20 13,234 264,680 Intel
12 CORE I7-10700K CPU @ 3.80GHZ 16 15,651 250,416 Intel
13 11TH GEN CORE I9-11900K @ 3.50GHZ 16 9,501 152,016 Intel
14 CORE I7-10700T CPU @ 2.00GHZ 16 5,844 93,504 Intel