RESEARCH: INFLUENZA
FOLDING PROJECT #12414 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 93,429
Core: 0xa8
Status: Public

TLDR; PROJECT SUMMARY AI BETA

Miniproteins are tiny proteins that can be designed to fight viruses. Scientists are using computer simulations to understand how these miniproteins work and how to make them even better at blocking viral infections 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 engineered for therapeutic use.

Scientific: Pharmaceuticals
Biotechnology / Drug Discovery

Miniproteins are a new class of drugs that are smaller than traditional antibodies. They can be designed to bind to specific targets in the body, such as viruses or bacteria, and block their activity. This makes them promising candidates for treating a variety of diseases.


therapeutics

Substances used for treating diseases or medical conditions.

Scientific: Pharmaceuticals
Biotechnology / Drug Development

Therapeutics are substances that are used to treat or prevent disease. They can be in the form of drugs, vaccines, or other treatments. The goal of therapeutics is to improve health and quality of life.


hemagglutinin

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

Scientific: Pharmaceuticals, Biotechnology
Virology / Infectious Diseases

Hemagglutinin is a protein found on the surface of many viruses. It helps the virus attach to and enter host cells. Hemagglutinin is a major target for antiviral drugs.


affinity maturation

The process of improving the binding affinity of an antibody.

Scientific: Biotechnology, Pharmaceuticals
Immunology / Antibody Engineering

Affinity maturation is a process that makes antibodies stronger at recognizing and binding to their target. This process happens naturally in our immune system when we are exposed to a new pathogen.


molecular simulation

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

Scientific: Biotechnology, Pharmaceuticals
Biophysics / Computational Biology

Molecular simulation is a powerful tool that allows scientists to study how molecules interact with each other. This information can be used to design new drugs, materials, and understand biological processes.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:34:43
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PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Tuesday, 14 April 2026 06:34:43
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 23,688 1,516,032 AMD
2 APPLE M2 ULTRA 24 40,210 965,040 Apple
3 RYZEN 9 7950X 16-CORE 32 27,924 893,568 AMD
4 RYZEN 9 7900X 12-CORE 24 28,623 686,952 AMD
5 RYZEN 7 7800X3D 8-CORE 16 41,314 661,024 AMD
6 RYZEN 9 7900 12-CORE 24 26,390 633,360 AMD
7 RYZEN THREADRIPPER 3960X 24-CORE 48 11,987 575,376 AMD
8 RYZEN 7 7700 8-CORE 16 35,536 568,576 AMD
9 RYZEN 7 7700X 8-CORE 16 35,271 564,336 AMD
10 CORE I5-14600K 20 25,912 518,240 Intel
11 RYZEN 9 5950X 16-CORE 32 15,910 509,120 AMD
12 RYZEN 5 7600 6-CORE 12 40,370 484,440 AMD
13 XEON CPU E5-2696 V4 @ 2.20GHZ 44 10,854 477,576 Intel
14 RYZEN 9 7950X3D 16-CORE 32 14,762 472,384 AMD
15 12TH GEN CORE I5-12400F 12 37,364 448,368 Intel
16 RYZEN 7 5800X3D 8-CORE 16 26,595 425,520 AMD
17 13TH GEN CORE I5-13500 20 20,500 410,000 Intel
18 12TH GEN CORE I7-12700F 20 19,251 385,020 Intel
19 RYZEN 7 5800X 8-CORE 16 24,022 384,352 AMD
20 RYZEN 5 7600X 6-CORE 12 31,059 372,708 AMD
21 RYZEN 7 5700X 8-CORE 16 21,556 344,896 AMD
22 13TH GEN CORE I5-13600K 14 23,007 322,098 Intel
23 11TH GEN CORE I7-11700K @ 3.60GHZ 16 20,117 321,872 Intel
24 RYZEN 9 5900X 12-CORE 24 12,366 296,784 AMD
25 RYZEN 5 5600 6-CORE 12 23,636 283,632 AMD
26 RYZEN 9 3900X 12-CORE 24 10,765 258,360 AMD
27 EPYC 7B12 64-CORE 64 3,997 255,808 AMD
28 13TH GEN CORE I7-13700 24 10,266 246,384 Intel
29 CORE I9-10900K CPU @ 3.70GHZ 20 12,272 245,440 Intel
30 CORE I7-10700K CPU @ 3.80GHZ 16 14,650 234,400 Intel
31 13TH GEN CORE I9-13900K 32 7,228 231,296 Intel
32 CORE I9-7920X CPU @ 2.90GHZ 24 9,126 219,024 Intel
33 RYZEN 7 5700G 16 13,260 212,160 AMD
34 CORE I9-14900K 32 6,478 207,296 Intel
35 RYZEN 5 5600X 6-CORE 12 16,886 202,632 AMD
36 RYZEN 9 3950X 16-CORE 32 6,263 200,416 AMD
37 RYZEN 5 5600G 12 14,983 179,796 AMD
38 RYZEN 7 3800X 8-CORE 16 11,094 177,504 AMD
39 RYZEN 7 3700X 8-CORE 16 10,302 164,832 AMD
40 CORE I7-5930K CPU @ 3.50GHZ 12 13,618 163,416 Intel
41 11TH GEN CORE I7-11800H @ 2.30GHZ 16 10,060 160,960 Intel
42 CORE I5-10600KF CPU @ 4.10GHZ 12 12,754 153,048 Intel
43 11TH GEN CORE I7-11700F @ 2.50GHZ 16 7,898 126,368 Intel
44 RYZEN 5 3600 6-CORE 12 10,475 125,700 AMD
45 11TH GEN CORE I9-11900F @ 2.50GHZ 16 7,807 124,912 Intel
46 RYZEN 5 7535HS 12 10,024 120,288 AMD
47 12TH GEN CORE I7-12700H 20 5,984 119,680 Intel
48 CORE I7-5820K CPU @ 3.30GHZ 12 9,908 118,896 Intel
49 RYZEN 5 2600 SIX-CORE 12 9,756 117,072 AMD
50 RYZEN 7 2700 EIGHT-CORE 16 7,285 116,560 AMD
51 XEON CPU E5-2660 V3 @ 2.60GHZ 20 5,035 100,700 Intel
52 CORE I7-8700K CPU @ 3.70GHZ 12 8,108 97,296 Intel
53 CORE I9-8950HK CPU @ 2.90GHZ 12 7,919 95,028 Intel
54 XEON CPU E5-2697 V2 @ 2.70GHZ 24 3,906 93,744 Intel
55 CORE I5-10400 CPU @ 2.90GHZ 12 7,178 86,136 Intel
56 APPLE M2 PRO 10 8,608 86,080 Apple
57 CORE I7-8700T CPU @ 2.40GHZ 12 7,066 84,792 Intel
58 CORE I7-10700T CPU @ 2.00GHZ 16 5,272 84,352 Intel
59 APPLE M1 PRO 10 6,895 68,950 Apple
60 XEON CPU E5-2680 0 @ 2.70GHZ 16 3,800 60,800 Intel
61 CORE I7-7820X CPU @ 3.60GHZ 16 3,648 58,368 Intel
62 RYZEN 7 4800U 16 3,120 49,920 AMD
63 12TH GEN CORE I7-12700 20 1,964 39,280 Intel
64 12TH GEN CORE I5-12600KF 16 2,315 37,040 Intel
65 13TH GEN CORE I5-13400F 16 1,383 22,128 Intel
66 XEON CPU X5650 @ 2.67GHZ 12 Intel