RESEARCH: INFLUENZA
FOLDING PROJECT #12415 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 93,435
Core: 0xa8
Status: Public

TLDR; PROJECT SUMMARY AI BETA

Miniproteins are small, engineered proteins that can fight diseases. Scientists want to understand how miniproteins bind to viruses, like the flu. They're using computer simulations to study how changes in miniproteins affect their binding strength and aim to improve these tiny 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 / Drug Design

Miniproteins are engineered proteins that are smaller than traditional antibodies. They can be designed to bind specific targets in the body, making them useful for treating diseases. Researchers are exploring their potential in areas like infectious disease treatment.


Hemagglutinin

A viral protein that binds to cell surfaces, enabling the virus to infect cells.

Scientific: Biotechnology
Virology / Influenza

Hemagglutinin is a crucial protein found on the surface of influenza viruses. It allows the virus to attach to and enter human cells, leading to infection. Researchers are studying hemagglutinin to develop new antiviral drugs and vaccines.


Affinity Maturation

The process of enhancing the binding strength of antibodies or proteins to their targets.

Technical: Pharmaceuticals
Biotechnology / Immunology

Affinity maturation is a technique used to improve the effectiveness of antibodies or proteins that bind to specific targets. It involves making small changes to the protein's structure, which can increase its ability to attach and interact with its target.


Molecular Simulation

Computer-based modeling of molecular interactions and processes.

Scientific: Biotechnology
Computational Biology / Drug Discovery

Molecular simulation uses computer algorithms to simulate the behavior of molecules at an atomic level. This allows researchers to study how molecules interact, which is essential for understanding biological processes and designing new drugs.


Expanded Ensemble Simulations

A type of molecular simulation that samples multiple energy states simultaneously.

Scientific: Biotechnology
Computational Biology / Biophysics

Expanded ensemble simulations are a powerful technique used in computational biology to study complex systems. By sampling multiple energy states at once, they can provide a more complete and accurate picture of the system's behavior.

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 9 7950X 16-CORE 32 34,543 1,105,376 AMD
2 CORE I9-14900K 32 22,411 717,152 Intel
3 RYZEN THREADRIPPER 3960X 24-CORE 48 14,543 698,064 AMD
4 RYZEN 7 7800X3D 8-CORE 16 42,236 675,776 AMD
5 RYZEN 9 7900X 12-CORE 24 25,789 618,936 AMD
6 RYZEN 7 PRO 7840U W/ RADEON 780M GRAPHICS 16 36,536 584,576 AMD
7 RYZEN 7 7700X 8-CORE 16 35,440 567,040 AMD
8 RYZEN 5 7600X 6-CORE 12 46,271 555,252 AMD
9 RYZEN 9 7900 12-CORE 24 22,807 547,368 AMD
10 RYZEN 9 7950X3D 16-CORE 32 16,664 533,248 AMD
11 CORE I5-14600K 20 26,054 521,080 Intel
12 RYZEN 5 7600 6-CORE 12 42,077 504,924 AMD
13 RYZEN 9 5950X 16-CORE 32 15,484 495,488 AMD
14 RYZEN 7 7840HS W/ RADEON 780M GRAPHICS 16 29,675 474,800 AMD
15 RYZEN 7 5800X3D 8-CORE 16 28,922 462,752 AMD
16 12TH GEN CORE I7-12700K 20 22,145 442,900 Intel
17 12TH GEN CORE I5-12400F 12 35,723 428,676 Intel
18 RYZEN 7 5700X 8-CORE 16 25,522 408,352 AMD
19 RYZEN 7 5800X 8-CORE 16 24,047 384,752 AMD
20 12TH GEN CORE I7-12700F 20 18,603 372,060 Intel
21 RYZEN 9 5900 12-CORE 24 13,278 318,672 AMD
22 13TH GEN CORE I5-13600K 14 22,347 312,858 Intel
23 RYZEN 5 5600 6-CORE 12 22,958 275,496 AMD
24 RYZEN 9 5900X 12-CORE 24 11,218 269,232 AMD
25 RYZEN 9 3900X 12-CORE 24 11,120 266,880 AMD
26 11TH GEN CORE I7-11700KF @ 3.60GHZ 16 16,054 256,864 Intel
27 XEON GOLD 5120 CPU @ 2.20GHZ 28 8,767 245,476 Intel
28 13TH GEN CORE I7-13700 24 10,199 244,776 Intel
29 RYZEN 7 5700G 16 14,821 237,136 AMD
30 CORE I7-10700K CPU @ 3.80GHZ 16 14,710 235,360 Intel
31 RYZEN 5 5600X 6-CORE 12 18,235 218,820 AMD
32 RYZEN 9 3950X 16-CORE 32 6,688 214,016 AMD
33 EPYC 7713 64-CORE 64 3,310 211,840 AMD
34 13TH GEN CORE I7-13700K 24 8,503 204,072 Intel
35 RYZEN 9 3900XT 12-CORE 24 8,115 194,760 AMD
36 CORE I9-9900K CPU @ 3.60GHZ 16 11,882 190,112 Intel
37 APPLE M1 MAX 10 18,900 189,000 Apple
38 XEON CPU E5-2697 V2 @ 2.70GHZ 24 7,182 172,368 Intel
39 RYZEN 5 3600 6-CORE 12 14,009 168,108 AMD
40 CORE I7-5930K CPU @ 3.50GHZ 12 13,625 163,500 Intel
41 CORE I7-5820K CPU @ 3.30GHZ 12 11,928 143,136 Intel
42 CORE I5-10600KF CPU @ 4.10GHZ 12 11,074 132,888 Intel
43 CORE I7-9850H CPU @ 2.60GHZ 12 10,614 127,368 Intel
44 RYZEN 7 3700X 8-CORE 16 7,932 126,912 AMD
45 RYZEN 9 5900HS 16 7,465 119,440 AMD
46 11TH GEN CORE I7-11700F @ 2.50GHZ 16 7,172 114,752 Intel
47 12TH GEN CORE I7-12700H 20 5,671 113,420 Intel
48 CORE I7-8700K CPU @ 3.70GHZ 12 9,227 110,724 Intel
49 RYZEN 7 5800H 16 6,725 107,600 AMD
50 RYZEN 9 5900HX 16 6,143 98,288 AMD
51 CORE I9-8950HK CPU @ 2.90GHZ 12 8,053 96,636 Intel
52 RYZEN 5 5600H 12 7,483 89,796 AMD
53 CORE I7-10700T CPU @ 2.00GHZ 16 5,493 87,888 Intel
54 CORE I7-4930K CPU @ 3.40GHZ 12 7,150 85,800 Intel
55 CORE I7-7820X CPU @ 3.60GHZ 16 4,838 77,408 Intel
56 APPLE M2 PRO 10 7,687 76,870 Apple
57 CORE I7-9750H CPU @ 2.60GHZ 12 6,390 76,680 Intel
58 11TH GEN CORE I9-11900F @ 2.50GHZ 16 4,659 74,544 Intel
59 APPLE M1 PRO 10 7,411 74,110 Apple
60 RYZEN 5 2600X SIX-CORE 12 5,802 69,624 AMD
61 XEON CPU E5-2680 0 @ 2.70GHZ 16 4,271 68,336 Intel
62 13TH GEN CORE I5-13500 20 2,700 54,000 Intel
63 XEON CPU E5-2620 V3 @ 2.40GHZ 12 4,007 48,084 Intel
64 RYZEN 7 4800U 16 2,398 38,368 AMD
65 12TH GEN CORE I5-12600KF 16 2,349 37,584 Intel
66 XEON CPU X5650 @ 2.67GHZ 12 2,925 35,100 Intel
67 XEON CPU E5540 @ 2.53GHZ 16 1,808 28,928 Intel
68 CORE I7-14700K 28 Intel