Research section Research projects and references

Research: INFLUENZA Folding Project #18476

Project #18476 overview

Project Summary AI Beta

Researchers are using computer simulations to study how tiny proteins called miniproteins bind to a viral protein called hemagglutinin. This could help them design better drugs to fight the flu and other viruses. The project relates to understanding how changes in miniprotein structure affect their ability to bind, and ultimately improve the effectiveness of these potential new 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 18476

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

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

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 7 7700X 8-CORE 16 35,979 575,664 AMD
2 RYZEN 9 5950X 16-CORE 32 15,772 504,704 AMD
3 12TH GEN CORE I7-12700K 20 21,807 436,140 Intel
4 RYZEN 7 5800X 8-CORE 16 22,315 357,040 AMD
5 RYZEN 7 5700X 8-CORE 16 21,501 344,016 AMD
6 XEON PLATINUM 8370C CPU @ 2.80GHZ 16 19,138 306,208 Intel
7 RYZEN 7 5700G 16 18,042 288,672 AMD
8 RYZEN 7 3800X 8-CORE 16 16,167 258,672 AMD
9 CORE I7-10700K CPU @ 3.80GHZ 16 14,333 229,328 Intel
10 12TH GEN CORE I7-12700 20 11,353 227,060 Intel
11 RYZEN 7 3700X 8-CORE 16 13,643 218,288 AMD
12 11TH GEN CORE I9-11900K @ 3.50GHZ 16 9,035 144,560 Intel
13 12TH GEN CORE I7-12700H 20 5,468 109,360 Intel
14 CORE I7-10700T CPU @ 2.00GHZ 16 5,687 90,992 Intel
15 XEON CPU E5-2697 V2 @ 2.70GHZ 24 1,473 35,352 Intel