RESEARCH: INFLUENZA
FOLDING PROJECT #12412 PROFILE

PROJECT TEAM

Manager(s): Dylan Novack
Institution: Temple University
Project URL: View Project Website

WORK UNIT INFO

Atoms: 93,437
Core: 0xa8
Status: Public

TLDR; PROJECT SUMMARY AI BETA

This project explores how tiny proteins called miniproteins work to fight viruses. Scientists are using computer simulations to figure out how changes in these miniproteins can make them better at blocking viruses like the flu.

Note: This TLDR is a simplication and may not be 100% accurate.

OFFICAL 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.

RELATED TERMS GLOSSARY AI BETA

Note: Glossary items are a high level summary and may not be 100% accurate.

miniproteins

Small proteins designed for therapeutic use.

scientific: pharmaceuticals
biotechnology / drug discovery

Miniproteins are engineered proteins with sizes between small molecules and antibodies. They are being researched as potential treatments for various diseases because they can be designed to bind specific targets in the body.


hemagglutinin

A viral protein that allows influenza viruses to attach to and infect host cells.

scientific: pharmaceuticals
medicine / infectious disease

Hemagglutinin is a key protein found on the surface of influenza viruses. It helps the virus bind to sialic acid receptors on cells, enabling it to enter and infect the host.


affinity maturation

The process of increasing the binding affinity of antibodies or other proteins.

scientific: pharmaceuticals
biotechnology / immunology

Affinity maturation is a process where the strength of binding between an antibody and its target antigen is improved. This is often achieved through repeated rounds of mutation and selection in laboratory settings.


molecular simulation

Computer-based modeling of molecular behavior.

scientific: research
biotechnology / computational biology

Molecular simulation uses computer algorithms to simulate the movement and interactions of atoms and molecules. This allows scientists to study the behavior of biological systems at a detailed atomic level.


expanded ensemble simulations

A type of molecular simulation that uses multiple independent simulations to explore a wider range of possible states.

scientific: research
biotechnology / computational biology

Expanded ensemble simulations are a powerful technique used in computational biology to study systems with complex energy landscapes. By running multiple simulations with different starting conditions, researchers can explore a broader range of possible configurations and obtain more accurate results.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:34:44
Rank
Project
Model Name
Folding@Home Identifier
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Model
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PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Tuesday, 14 April 2026 06:34:44
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 RYZEN 9 7950X 16-CORE 32 33,245 1,063,840 AMD
2 APPLE M2 ULTRA 24 32,869 788,856 Apple
3 RYZEN 9 7900 12-CORE 24 29,796 715,104 AMD
4 RYZEN 9 7950X3D 16-CORE 32 21,939 702,048 AMD
5 RYZEN 7 7800X3D 8-CORE 16 43,690 699,040 AMD
6 CORE I9-14900K 32 21,044 673,408 Intel
7 RYZEN THREADRIPPER 3960X 24-CORE 48 13,727 658,896 AMD
8 CORE I5-14600K 20 27,675 553,500 Intel
9 RYZEN 9 5950X 16-CORE 32 15,651 500,832 AMD
10 XEON CPU E5-2696 V4 @ 2.20GHZ 44 11,259 495,396 Intel
11 RYZEN 5 7600 6-CORE 12 39,216 470,592 AMD
12 RYZEN 7 5800X3D 8-CORE 16 26,496 423,936 AMD
13 12TH GEN CORE I7-12700K 20 21,002 420,040 Intel
14 12TH GEN CORE I5-12400F 12 34,898 418,776 Intel
15 RYZEN 7 7840HS W/ RADEON 780M GRAPHICS 16 25,711 411,376 AMD
16 RYZEN 5 7600X 6-CORE 12 33,724 404,688 AMD
17 RYZEN 7 5800X 8-CORE 16 22,534 360,544 AMD
18 RYZEN 7 5700X 8-CORE 16 22,306 356,896 AMD
19 RYZEN 9 5900X 12-CORE 24 14,785 354,840 AMD
20 RYZEN 5 5600X 6-CORE 12 25,235 302,820 AMD
21 12TH GEN CORE I7-12700 20 14,948 298,960 Intel
22 RYZEN 5 5600 6-CORE 12 23,904 286,848 AMD
23 13TH GEN CORE I5-13600K 14 20,248 283,472 Intel
24 RYZEN 9 3900X 12-CORE 24 11,765 282,360 AMD
25 12TH GEN CORE I7-12700F 20 13,684 273,680 Intel
26 13TH GEN CORE I7-13700 24 11,385 273,240 Intel
27 RYZEN THREADRIPPER 2990WX 32-CORE 64 4,071 260,544 AMD
28 RYZEN 7 5700G 16 15,104 241,664 AMD
29 XEON GOLD 5120 CPU @ 2.20GHZ 28 8,432 236,096 Intel
30 CORE I7-10700K CPU @ 3.80GHZ 16 14,264 228,224 Intel
31 XEON CPU E5-2683 V4 @ 2.10GHZ 32 6,913 221,216 Intel
32 CORE I9-14900KF 24 8,918 214,032 Intel
33 13TH GEN CORE I9-13900K 32 6,684 213,888 Intel
34 CORE I9-10900K CPU @ 3.70GHZ 20 10,680 213,600 Intel
35 CORE I7-10700 CPU @ 2.90GHZ 16 12,695 203,120 Intel
36 13TH GEN CORE I5-13500 20 9,500 190,000 Intel
37 RYZEN 7 3800X 8-CORE 16 11,872 189,952 AMD
38 RYZEN 5 3600 6-CORE 12 15,607 187,284 AMD
39 RYZEN 9 3900XT 12-CORE 24 7,640 183,360 AMD
40 CORE I7-5930K CPU @ 3.50GHZ 12 12,999 155,988 Intel
41 11TH GEN CORE I7-11800H @ 2.30GHZ 16 9,088 145,408 Intel
42 EPYC 7262 8-CORE 16 8,915 142,640 AMD
43 RYZEN 5 2600 SIX-CORE 12 11,342 136,104 AMD
44 11TH GEN CORE I9-11900F @ 2.50GHZ 16 8,217 131,472 Intel
45 XEON CPU E5-2697 V2 @ 2.70GHZ 24 5,068 121,632 Intel
46 11TH GEN CORE I7-11700F @ 2.50GHZ 16 7,586 121,376 Intel
47 RYZEN 7 3700X 8-CORE 16 7,542 120,672 AMD
48 RYZEN 5 5600G 12 9,888 118,656 AMD
49 12TH GEN CORE I9-12900H 20 5,772 115,440 Intel
50 11TH GEN CORE I7-11700K @ 3.60GHZ 16 6,560 104,960 Intel
51 11TH GEN CORE I7-11700 @ 2.50GHZ 16 6,220 99,520 Intel
52 12TH GEN CORE I7-12700H 20 4,961 99,220 Intel
53 CORE I9-8950HK CPU @ 2.90GHZ 12 8,105 97,260 Intel
54 CORE I7-4930K CPU @ 3.40GHZ 12 7,921 95,052 Intel
55 CORE I7-10700T CPU @ 2.00GHZ 16 5,878 94,048 Intel
56 APPLE M1 PRO 10 9,039 90,390 Apple
57 CORE I7-9750H CPU @ 2.60GHZ 12 7,258 87,096 Intel
58 RYZEN 5 2600X SIX-CORE 12 6,669 80,028 AMD
59 CORE I7-7820X CPU @ 3.60GHZ 16 4,605 73,680 Intel
60 APPLE M2 MAX 12 5,962 71,544 Apple
61 11TH GEN CORE I5-11400 @ 2.60GHZ 12 5,415 64,980 Intel
62 13TH GEN CORE I7-1355U 12 4,892 58,704 Intel
63 CORE I7-8700K CPU @ 3.70GHZ 12 4,579 54,948 Intel
64 RYZEN 5 5500U 12 3,915 46,980 AMD
65 XEON CPU E5520 @ 2.27GHZ 16 2,871 45,936 Intel
66 12TH GEN CORE I5-12600KF 16 2,338 37,408 Intel
67 13TH GEN CORE I5-13500T 20 1,745 34,900 Intel
68 12TH GEN CORE I5-1235U 12 1,892 22,704 Intel
69 13TH GEN CORE I5-13400F 16 1,337 21,392 Intel
70 13TH GEN CORE I7-13700K 24 Intel
71 XEON CPU E5-2640 0 @ 2.50GHZ 24 Intel