Research section Research projects and references

Research: INFLUENZA Folding Project #18480

Project #18480 overview

Project Summary AI Beta

This project studies miniproteins - tiny proteins designed to fight diseases like the flu. Researchers use computer simulations to see how changes in these miniproteins affect their ability to bind to the flu virus, aiming to create 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 18480

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

Data as of Sunday, 02 August 2026 21:50:58

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 5950X 16-CORE 32 34,608 1,107,456 AMD
2 RYZEN 7 5700G 16 50,822 813,152 AMD
3 RYZEN 7 7700X 8-CORE 16 43,102 689,632 AMD
4 RYZEN 7 5700X 8-CORE 16 25,947 415,152 AMD
5 11TH GEN CORE I7-11700K @ 3.60GHZ 16 22,257 356,112 Intel
6 RYZEN 9 3900X 12-CORE 24 10,552 253,248 AMD
7 RYZEN 5 5600 6-CORE 12 20,936 251,232 AMD
8 CORE I7-9700K CPU @ 3.60GHZ 8 30,442 243,536 Intel
9 RYZEN 5 5600X 6-CORE 12 20,089 241,068 AMD
10 RYZEN 5 3500 6-CORE 6 34,256 205,536 AMD
11 XEON W-2245 CPU @ 3.90GHZ 16 11,395 182,320 Intel
12 RYZEN 5 3600 6-CORE 12 14,530 174,360 AMD
13 CORE I7-5930K CPU @ 3.50GHZ 12 11,199 134,388 Intel
14 CORE I7-7700K CPU @ 4.20GHZ 8 16,373 130,984 Intel
15 CORE I9-9900K CPU @ 3.60GHZ 16 7,551 120,816 Intel
16 CORE I7-5820K CPU @ 3.30GHZ 12 9,997 119,964 Intel
17 CORE I7-8700 CPU @ 3.20GHZ 12 8,934 107,208 Intel
18 CORE I7-8705G CPU @ 3.10GHZ 8 11,178 89,424 Intel
19 CORE I7-4770HQ CPU @ 2.20GHZ 8 8,092 64,736 Intel
20 CORE I7-6700K CPU @ 4.00GHZ 8 7,820 62,560 Intel
21 APPLE M1 8 7,808 62,464 Apple
22 CORE I7-3770K CPU @ 3.50GHZ 8 7,742 61,936 Intel
23 XEON CPU L5640 @ 2.27GHZ 24 2,412 57,888 Intel
24 XEON CPU E3-1245 V3 @ 3.40GHZ 8 7,227 57,816 Intel
25 XEON CPU E5-1620 V2 @ 3.70GHZ 8 7,109 56,872 Intel
26 XEON CPU E5-2697 V2 @ 2.70GHZ 24 1,804 43,296 Intel
27 APPLE M1 PRO 10 4,247 42,470 Apple
28 CORE I5-10210U CPU @ 1.60GHZ 7 5,280 36,960 Intel