PROJECT #18467 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: 93,427

Core: 0xa8

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Monday, 20 March 2023 06:14:51

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Project
Model Name
Folding@Home Identifier
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GPU
Model
PPD
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PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Monday, 20 March 2023 06:14:51

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 XEON CPU L5640 @ 2.27GHZ 24 80,234 1,925,616 Intel
2 RYZEN 7 7700X 8-CORE 16 42,128 674,048 AMD
3 RYZEN 9 5950X 16-CORE 32 18,422 589,504 AMD
4 11TH GEN CORE I7-11700K @ 3.60GHZ 16 36,174 578,784 Intel
5 RYZEN 7 5800X3D 8-CORE 16 29,203 467,248 AMD
6 RYZEN 7 5700X 8-CORE 16 27,991 447,856 AMD
7 12TH GEN CORE I7-12700K 20 21,622 432,440 Intel
8 RYZEN THREADRIPPER 2950X 16-CORE 32 13,291 425,312 AMD
9 RYZEN 9 5900X 12-CORE 24 16,104 386,496 AMD
10 12TH GEN CORE I5-12600K 16 20,039 320,624 Intel
11 RYZEN 7 5700G 16 18,194 291,104 AMD
12 RYZEN 9 3900X 12-CORE 24 11,873 284,952 AMD
13 RYZEN 7 5800X 8-CORE 16 16,215 259,440 AMD
14 11TH GEN CORE I9-11900K @ 3.50GHZ 16 13,881 222,096 Intel
15 RYZEN 7 3700X 8-CORE 16 13,195 211,120 AMD
16 CORE I9-9900K CPU @ 3.60GHZ 16 12,246 195,936 Intel
17 CORE I9-7940X CPU @ 3.10GHZ 28 6,919 193,732 Intel
18 12TH GEN CORE I7-12700H 20 5,289 105,780 Intel