Research section Research projects and references

Research: INFLUENZA Folding Project #18475

Project #18475 overview

Project Summary AI Beta

This project studies miniproteins – tiny proteins that can be designed to fight diseases. Scientists are using computer simulations to understand how changes in these miniproteins affect their ability to bind to viruses like influenza. The goal is to design even better miniprotein drugs.
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 18475

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

Data as of Sunday, 02 August 2026 21:51:03

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 17,795 1,138,880 AMD
2 RYZEN 9 7950X 16-CORE 32 34,578 1,106,496 AMD
3 RYZEN 9 7900X 12-CORE 24 32,535 780,840 AMD
4 RYZEN 7 7700X 8-CORE 16 42,988 687,808 AMD
5 RYZEN 9 5950X 16-CORE 32 16,960 542,720 AMD
6 RYZEN 9 5900X 12-CORE 24 21,636 519,264 AMD
7 RYZEN 7 5700X 8-CORE 16 21,969 351,504 AMD
8 RYZEN 7 5800X 8-CORE 16 21,274 340,384 AMD
9 RYZEN 7 5800X3D 8-CORE 16 17,229 275,664 AMD
10 RYZEN 7 5700G 16 16,359 261,744 AMD
11 12TH GEN CORE I7-12700 20 12,111 242,220 Intel
12 11TH GEN CORE I9-11900K @ 3.50GHZ 16 13,407 214,512 Intel
13 XEON PLATINUM 8370C CPU @ 2.80GHZ 16 9,961 159,376 Intel
14 CORE I9-7940X CPU @ 3.10GHZ 28 5,528 154,784 Intel
15 12TH GEN CORE I7-12700H 20 6,390 127,800 Intel
16 RYZEN 7 3700X 8-CORE 16 7,734 123,744 AMD
17 CORE I7-10700T CPU @ 2.00GHZ 16 5,014 80,224 Intel
18 XEON CPU E5-2697 V2 @ 2.70GHZ 24 2,609 62,616 Intel