Research section Research projects and references

Research: INFLUENZA Folding Project #18479

Project #18479 overview

Project Summary AI Beta

Researchers are using computer simulations to understand how miniproteins, small engineered proteins, bind to a viral protein called hemagglutinin. They want to see how changing the miniprotein's structure affects its binding strength. This project could lead to better design of miniprotein drugs to fight viruses like influenza.
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 18479

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

Data as of Sunday, 02 August 2026 21:50:59

CPU PPD Averages Beta

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 12TH GEN CORE I9-12900K 24 51,871 1,244,904 Intel
2 EPYC 7B12 64-CORE 64 14,297 915,008 AMD
3 RYZEN 7 5700G 16 50,731 811,696 AMD
4 RYZEN 7 7700X 8-CORE 16 37,749 603,984 AMD
5 RYZEN 7 5800X3D 8-CORE 16 33,633 538,128 AMD
6 RYZEN 9 5950X 16-CORE 32 14,485 463,520 AMD
7 12TH GEN CORE I7-12700K 20 22,067 441,340 Intel
8 RYZEN 7 5700X 8-CORE 16 25,126 402,016 AMD
9 11TH GEN CORE I7-11700K @ 3.60GHZ 16 19,280 308,480 Intel
10 RYZEN 5 5600 6-CORE 12 24,851 298,212 AMD
11 RYZEN 5 5600X 6-CORE 12 21,839 262,068 AMD
12 RYZEN 5 3500 6-CORE 6 34,001 204,006 AMD
13 RYZEN 9 5900X 12-CORE 24 7,663 183,912 AMD
14 RYZEN 5 3600 6-CORE 12 15,037 180,444 AMD
15 CORE I7-7700K CPU @ 4.20GHZ 8 16,163 129,304 Intel
16 CORE I7-5930K CPU @ 3.50GHZ 12 10,572 126,864 Intel
17 CORE I7-5820K CPU @ 3.30GHZ 12 10,254 123,048 Intel
18 CORE I7-8700 CPU @ 3.20GHZ 12 9,899 118,788 Intel
19 CORE I7-10700T CPU @ 2.00GHZ 16 6,616 105,856 Intel
20 11TH GEN CORE I7-11800H @ 2.30GHZ 16 6,427 102,832 Intel
21 12TH GEN CORE I7-12700H 20 5,094 101,880 Intel
22 CORE I9-8950HK CPU @ 2.90GHZ 12 7,858 94,296 Intel
23 CORE I7-8705G CPU @ 3.10GHZ 8 11,043 88,344 Intel
24 CORE I7-6700K CPU @ 4.00GHZ 8 10,136 81,088 Intel
25 CORE I7-4770HQ CPU @ 2.20GHZ 8 7,672 61,376 Intel
26 CORE I7-3770K CPU @ 3.50GHZ 8 7,403 59,224 Intel
27 APPLE M1 8 7,174 57,392 Apple
28 XEON CPU E5-2697 V2 @ 2.70GHZ 24 2,123 50,952 Intel
29 APPLE M1 PRO 10 3,517 35,170 Apple