PROJECT #18462 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: 14,121

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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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 34,728 555,648 AMD
2 RYZEN 7 5800X3D 8-CORE 16 32,599 521,584 AMD
3 RYZEN 7 5700X 8-CORE 16 27,644 442,304 AMD
4 12TH GEN CORE I7-12700K 20 21,865 437,300 Intel
5 RYZEN 9 5950X 16-CORE 32 13,347 427,104 AMD
6 11TH GEN CORE I7-11700K @ 3.60GHZ 16 21,844 349,504 Intel
7 RYZEN 7 3800X 8-CORE 16 20,422 326,752 AMD
8 CORE I7-9700K CPU @ 3.60GHZ 8 33,922 271,376 Intel
9 RYZEN 9 5900X 12-CORE 24 11,244 269,856 AMD
10 RYZEN 9 3900X 12-CORE 24 10,867 260,808 AMD
11 12TH GEN CORE I7-12700 20 13,000 260,000 Intel
12 11TH GEN CORE I9-11900K @ 3.50GHZ 16 12,598 201,568 Intel
13 RYZEN 5 3500 6-CORE 6 32,111 192,666 AMD
14 RYZEN 7 3700X 8-CORE 16 10,252 164,032 AMD
15 RYZEN 5 3600 6-CORE 12 13,299 159,588 AMD
16 CORE I7-5930K CPU @ 3.50GHZ 12 10,818 129,816 Intel
17 CORE I7-7700K CPU @ 4.20GHZ 8 16,210 129,680 Intel
18 CORE I9-8950HK CPU @ 2.90GHZ 12 7,969 95,628 Intel
19 CORE I7-8705G CPU @ 3.10GHZ 8 11,144 89,152 Intel
20 CORE I7-6700K CPU @ 4.00GHZ 8 10,283 82,264 Intel
21 CORE I7-4770HQ CPU @ 2.20GHZ 8 7,977 63,816 Intel
22 CORE I7-3770K CPU @ 3.50GHZ 8 7,675 61,400 Intel