RESEARCH: INFLUENZA
FOLDING PROJECT #12405 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 93,427
Core: 0xa8
Status: Public

TLDR; PROJECT SUMMARY AI BETA

Miniproteins are small proteins that can be designed to fight viruses. This project uses computer simulations to study how changes to miniproteins affect their ability to bind to a virus protein called hemagglutinin. The goal is to better understand how miniproteins work and improve the design of new antiviral drugs.

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 / therapeutic development

Miniproteins are a novel class of therapeutics. They are smaller than traditional antibodies but larger than small molecule drugs. Their size allows them to bind specific protein targets with high affinity, making them promising candidates for treating various diseases.


hemagglutinin

Viral protein responsible for binding to host cells.

scientific: biopharmaceuticals
virology / influenza A virus

Hemagglutinin is a surface protein found on influenza A viruses. It plays a crucial role in the virus's ability to infect host cells by binding to sialic acid receptors on cell surfaces.


affinity maturation

Process of enhancing antibody binding affinity.

scientific: biotechnology
immunology / antibody engineering

Affinity maturation is a process used to improve the binding strength of antibodies. This involves introducing mutations into the antibody gene sequence, which can lead to increased binding affinity for the target antigen.


molecular simulation

Computer-based model of molecular interactions.

scientific: biotechnology
computational biology / drug discovery

Molecular simulations use computer algorithms to simulate the behavior of molecules. This technique is widely used in drug discovery and development to predict how molecules interact with each other and with biological targets.


expanded ensemble simulation

Type of molecular simulation that uses multiple sets of parameters.

scientific: biotechnology
computational biology / molecular dynamics

Expanded ensemble simulations are a specialized type of molecular dynamics simulation that use multiple sets of simulation parameters. This allows for more accurate and comprehensive exploration of complex systems.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:34:48
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Folding@Home Identifier
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PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Tuesday, 14 April 2026 06:34:48
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 RYZEN THREADRIPPER 3990X 64-CORE 64 22,654 1,449,856 AMD
2 RYZEN 9 7950X 16-CORE 32 40,613 1,299,616 AMD
3 RYZEN 7 7800X3D 8-CORE 16 43,917 702,672 AMD
4 RYZEN THREADRIPPER 3960X 24-CORE 48 14,304 686,592 AMD
5 RYZEN 9 7900X 12-CORE 24 28,254 678,096 AMD
6 RYZEN 9 7900 12-CORE 24 25,919 622,056 AMD
7 RYZEN 9 5950X 16-CORE 32 17,307 553,824 AMD
8 12TH GEN CORE I7-12700K 20 25,852 517,040 Intel
9 RYZEN 7 7840HS W/ RADEON 780M GRAPHICS 16 32,228 515,648 AMD
10 CORE I9-14900KF 24 20,341 488,184 Intel
11 CORE I5-14600K 20 24,280 485,600 Intel
12 RYZEN 5 7600 6-CORE 12 38,826 465,912 AMD
13 RYZEN 9 7950X3D 16-CORE 32 13,278 424,896 AMD
14 RYZEN 7 5800X3D 8-CORE 16 24,629 394,064 AMD
15 RYZEN 7 5700X 8-CORE 16 24,375 390,000 AMD
16 CORE I9-14900K 32 12,050 385,600 Intel
17 RYZEN 7 5800X 8-CORE 16 24,059 384,944 AMD
18 RYZEN 7 7700X 8-CORE 16 23,325 373,200 AMD
19 12TH GEN CORE I7-12700F 20 18,642 372,840 Intel
20 13TH GEN CORE I5-13600K 14 26,054 364,756 Intel
21 11TH GEN CORE I7-11700K @ 3.60GHZ 16 20,905 334,480 Intel
22 RYZEN 9 3950X 16-CORE 32 8,758 280,256 AMD
23 RYZEN THREADRIPPER 1950X 16-CORE 32 8,640 276,480 AMD
24 RYZEN 9 3900X 12-CORE 24 11,345 272,280 AMD
25 RYZEN 9 5900X 12-CORE 24 11,256 270,144 AMD
26 RYZEN 5 5600 6-CORE 12 22,199 266,388 AMD
27 RYZEN 5 5600X 6-CORE 12 21,405 256,860 AMD
28 RYZEN 7 5700G 16 15,564 249,024 AMD
29 13TH GEN CORE I7-13700 24 10,240 245,760 Intel
30 CORE I7-10700K CPU @ 3.80GHZ 16 14,824 237,184 Intel
31 CORE I9-10900K CPU @ 3.70GHZ 20 11,774 235,480 Intel
32 XEON CPU E5-2680 V2 @ 2.80GHZ 40 5,849 233,960 Intel
33 EPYC 7B12 64-CORE 64 3,621 231,744 AMD
34 XEON GOLD 5120 CPU @ 2.20GHZ 28 8,271 231,588 Intel
35 12TH GEN CORE I5-12400 12 19,263 231,156 Intel
36 CORE I9-7920X CPU @ 2.90GHZ 24 7,868 188,832 Intel
37 RYZEN 5 3600 6-CORE 12 15,636 187,632 AMD
38 CORE I9-9900K CPU @ 3.60GHZ 16 10,342 165,472 Intel
39 RYZEN 7 3800X 8-CORE 16 10,086 161,376 AMD
40 12TH GEN CORE I5-12600K 16 10,059 160,944 Intel
41 CORE I7-5930K CPU @ 3.50GHZ 12 13,381 160,572 Intel
42 RYZEN 7 3700X 8-CORE 16 8,920 142,720 AMD
43 11TH GEN CORE I7-11800H @ 2.30GHZ 16 8,539 136,624 Intel
44 11TH GEN CORE I7-11700F @ 2.50GHZ 16 8,326 133,216 Intel
45 RYZEN 5 5600G 12 10,864 130,368 AMD
46 XEON CPU E5-2697 V2 @ 2.70GHZ 24 5,318 127,632 Intel
47 13TH GEN CORE I5-13500 20 5,989 119,780 Intel
48 11TH GEN CORE I9-11900F @ 2.50GHZ 16 6,984 111,744 Intel
49 12TH GEN CORE I7-12700H 20 5,125 102,500 Intel
50 RYZEN 5 5600H 12 8,072 96,864 AMD
51 CORE I9-8950HK CPU @ 2.90GHZ 12 7,941 95,292 Intel
52 CORE I7-10700T CPU @ 2.00GHZ 16 5,768 92,288 Intel
53 RYZEN 5 2600X SIX-CORE 12 6,501 78,012 AMD
54 APPLE M1 PRO 10 7,277 72,770 Apple
55 APPLE M2 MAX 12 5,545 66,540 Apple
56 CORE I7-9750H CPU @ 2.60GHZ 12 5,130 61,560 Intel
57 CORE I7-14700K 28 2,180 61,040 Intel
58 CORE I7-8700K CPU @ 3.70GHZ 12 5,043 60,516 Intel
59 13TH GEN CORE I7-1355U 12 4,103 49,236 Intel
60 XEON CPU X5560 @ 2.80GHZ 16 3,012 48,192 Intel
61 12TH GEN CORE I7-12700 20 2,192 43,840 Intel
62 RYZEN 7 4800U 16 2,679 42,864 AMD
63 XEON CPU L5640 @ 2.27GHZ 24 1,708 40,992 Intel
64 12TH GEN CORE I5-12600KF 16 2,333 37,328 Intel
65 13TH GEN CORE I9-13900K 32 Intel