RESEARCH: INFLUENZA
FOLDING PROJECT #12420 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 93,432
Core: 0xa8
Status: Public

TLDR; PROJECT SUMMARY AI BETA

Miniproteins are small drug-like molecules being designed to fight infections. This project uses computer simulations to understand how miniprotein changes affect their ability to bind to a viral protein called hemagglutinin, which helps the flu virus infect cells. These simulations will help scientists design even better miniprotein 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 with therapeutic potential.

Technical: Pharmaceutical
Biotechnology / Drug Design

Miniproteins are a class of biomolecules engineered for drug development. They are smaller than traditional antibodies but larger than small-molecule drugs. Due to their size, they can effectively target specific proteins involved in diseases.


therapeutics

Medicinal substances used to treat or prevent diseases.

Scientific: Pharmaceutical
Biotechnology / Drug Development

Therapeutics encompass a wide range of medicinal agents, including drugs, biologics, and vaccines. Their primary purpose is to diagnose, treat, cure, or prevent diseases.


hemagglutinin

A viral protein that binds to sialic acid on cell surfaces.

Scientific: Biomedical Research
Virology / Influenza

Hemagglutinin is a key viral protein found on the surface of influenza viruses. It plays a crucial role in enabling the virus to attach to and infect host cells by binding to sialic acid receptors.


affinity maturation

The process of increasing the binding affinity of an antibody.

Scientific: Biopharmaceutical Research
Immunology / Antibody Engineering

Affinity maturation is a critical step in developing highly effective antibodies. It involves introducing mutations into the antibody gene to enhance its ability to bind to its target antigen with greater strength.


molecular simulation

A computer-based method for modeling the behavior of molecules.

Scientific: Biotechnology Research
Biochemistry / Computational Biology

Molecular simulations use computational algorithms to mimic the movements and interactions of atoms and molecules. This technique allows researchers to study complex biological processes at an atomic level.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:34:40
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PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Tuesday, 14 April 2026 06:34:40
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 RYZEN THREADRIPPER 7980X 64-CORES 64 34,497 2,207,808 AMD
2 RYZEN 9 9900X 12-CORE 24 52,136 1,251,264 AMD
3 12TH GEN CORE I9-12900K 24 51,427 1,234,248 Intel
4 RYZEN 9 9950X 16-CORE 32 33,338 1,066,816 AMD
5 RYZEN 9 7950X 16-CORE 32 29,818 954,176 AMD
6 XEON W-3245 CPU @ 3.20GHZ 32 29,497 943,904 Intel
7 RYZEN 7 9800X3D 8-CORE 16 58,568 937,088 AMD
8 CORE ULTRA 9 285K 24 38,556 925,344 Intel
9 CORE I9-14900K 32 26,813 858,016 Intel
10 RYZEN 9 7950X3D 16-CORE 32 24,982 799,424 AMD
11 RYZEN 7 7700X 8-CORE 16 46,530 744,480 AMD
12 RYZEN 9 7900X 12-CORE 24 29,549 709,176 AMD
13 RYZEN 7 7800X3D 8-CORE 16 43,898 702,368 AMD
14 RYZEN 9 7900 12-CORE 24 27,524 660,576 AMD
15 RYZEN 7 5800X3D 8-CORE 16 39,486 631,776 AMD
16 RYZEN 7 7700 8-CORE 16 36,327 581,232 AMD
17 12TH GEN CORE I7-12700 20 28,091 561,820 Intel
18 RYZEN 9 5950X 16-CORE 32 13,000 416,000 AMD
19 13TH GEN CORE I5-13500 20 20,530 410,600 Intel
20 XEON GOLD 6140 CPU @ 2.30GHZ 36 11,297 406,692 Intel
21 RYZEN 7 5800X 8-CORE 16 25,187 402,992 AMD
22 12TH GEN CORE I7-12700K 20 19,195 383,900 Intel
23 12TH GEN CORE I7-12700F 20 18,430 368,600 Intel
24 13TH GEN CORE I5-13600K 14 25,715 360,010 Intel
25 RYZEN 7 5700X3D 8-CORE 16 21,576 345,216 AMD
26 RYZEN 9 5900X 12-CORE 24 14,146 339,504 AMD
27 RYZEN 7 5700X 8-CORE 16 20,444 327,104 AMD
28 CORE I9-10900K CPU @ 3.70GHZ 20 14,756 295,120 Intel
29 RYZEN 9 3900X 12-CORE 24 12,235 293,640 AMD
30 EPYC 7K62 48-CORE 96 3,005 288,480 AMD
31 RYZEN 5 5600X 6-CORE 12 23,788 285,456 AMD
32 RYZEN THREADRIPPER 1950X 16-CORE 32 8,640 276,480 AMD
33 RYZEN 5 5600 6-CORE 12 22,219 266,628 AMD
34 XEON CPU E5-2680 V2 @ 2.80GHZ 40 6,556 262,240 Intel
35 RYZEN THREADRIPPER 3960X 24-CORE 48 5,128 246,144 AMD
36 13TH GEN CORE I7-13700 24 10,228 245,472 Intel
37 CORE I7-6900K CPU @ 3.20GHZ 16 15,314 245,024 Intel
38 CORE I7-10700K CPU @ 3.80GHZ 16 14,597 233,552 Intel
39 RYZEN 7 5700G 16 12,423 198,768 AMD
40 RYZEN 5 5600G 12 14,711 176,532 AMD
41 RYZEN 9 3900XT 12-CORE 24 7,158 171,792 AMD
42 RYZEN 5 3600 6-CORE 12 13,800 165,600 AMD
43 CORE I7-7700K CPU @ 4.20GHZ 8 20,165 161,320 Intel
44 11TH GEN CORE I7-11800H @ 2.30GHZ 16 9,675 154,800 Intel
45 CORE I5-10600KF CPU @ 4.10GHZ 12 12,637 151,644 Intel
46 CORE I7-5930K CPU @ 3.50GHZ 12 12,293 147,516 Intel
47 RYZEN 7 5800H 16 9,166 146,656 AMD
48 RYZEN 7 3700X 8-CORE 16 8,529 136,464 AMD
49 CORE I7-5820K CPU @ 3.30GHZ 12 11,301 135,612 Intel
50 11TH GEN CORE I7-11700 @ 2.50GHZ 16 8,459 135,344 Intel
51 CORE I7-9800X CPU @ 3.80GHZ 16 7,227 115,632 Intel
52 12TH GEN CORE I7-12700H 20 5,020 100,400 Intel
53 CORE I9-8950HK CPU @ 2.90GHZ 12 8,119 97,428 Intel
54 RYZEN 7 3800X 8-CORE 16 5,858 93,728 AMD
55 CORE I7-8700K CPU @ 3.70GHZ 12 7,602 91,224 Intel
56 CORE I7-10700T CPU @ 2.00GHZ 16 5,363 85,808 Intel
57 RYZEN 5 2600X SIX-CORE 12 6,866 82,392 AMD
58 XEON CPU E5-2697 V2 @ 2.70GHZ 24 3,248 77,952 Intel
59 APPLE M1 MAX 10 7,212 72,120 Apple
60 APPLE M3 8 8,460 67,680 Apple
61 13TH GEN CORE I7-13700H 20 2,769 55,380 Intel
62 12TH GEN CORE I5-12600KF 16 2,243 35,888 Intel
63 RYZEN 7 4800U 16 1,961 31,376 AMD
64 CORE I7-14650HX 24 1,088 26,112 Intel