PROJECT #18474 RESEARCH FOR INFLUENZA
FOLDING PERFORMANCE PROFILE

PROJECT SUMMARY

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.

PROJECT INFO

Manager(s): Dylan Novack

Institution: Temple University

Project URL: http://voelzlab.org

PROJECT WORK UNIT SUMMARY

Atoms: 14,112

Core: 0xa8

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Monday, 20 March 2023 12:15:00

Rank
Project
Model Name
Folding@Home Identifier
Make
Brand
GPU
Model
PPD
Average
Points WU
Average
WUs Day
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WU Time
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PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Monday, 20 March 2023 12:15:00

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 RYZEN 7 7700X 8-CORE 16 40,662 650,592 AMD
2 RYZEN 7 5700X 8-CORE 16 28,466 455,456 AMD
3 11TH GEN CORE I7-11700K @ 3.60GHZ 16 24,656 394,496 Intel
4 RYZEN 7 3800X 8-CORE 16 20,137 322,192 AMD
5 CORE I9-7940X CPU @ 3.10GHZ 28 10,625 297,500 Intel
6 CORE I7-10700K CPU @ 3.80GHZ 16 16,974 271,584 Intel
7 RYZEN 9 5900X 12-CORE 24 10,211 245,064 AMD
8 RYZEN 5 5600X 6-CORE 12 19,774 237,288 AMD
9 11TH GEN CORE I9-11900K @ 3.50GHZ 16 12,309 196,944 Intel
10 RYZEN 5 3500 6-CORE 6 30,598 183,588 AMD
11 CORE I7-9700K CPU @ 3.60GHZ 8 22,040 176,320 Intel
12 RYZEN 5 3600 6-CORE 12 14,372 172,464 AMD
13 CORE I7-5930K CPU @ 3.50GHZ 12 10,572 126,864 Intel
14 CORE I9-9900K CPU @ 3.60GHZ 16 7,846 125,536 Intel
15 CORE I7-7700K CPU @ 4.20GHZ 8 15,366 122,928 Intel
16 CORE I5-9600K CPU @ 3.70GHZ 6 16,829 100,974 Intel
17 CORE I9-8950HK CPU @ 2.90GHZ 12 7,882 94,584 Intel
18 CORE I7-8700 CPU @ 3.20GHZ 12 6,640 79,680 Intel
19 CORE I7-4770HQ CPU @ 2.20GHZ 8 8,111 64,888 Intel
20 CORE I7-3770K CPU @ 3.50GHZ 8 7,680 61,440 Intel
21 CORE I7-6700K CPU @ 4.00GHZ 8 7,432 59,456 Intel
22 APPLE M1 8 7,306 58,448 Apple
23 RYZEN 5 5500U 12 2,314 27,768 AMD