PROJECT #18471 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 06:14:51

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 06:14:51

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 RYZEN 7 5700G 16 50,135 802,160 AMD
2 RYZEN 9 7900X 12-CORE 24 27,041 648,984 AMD
3 RYZEN 7 7700X 8-CORE 16 37,045 592,720 AMD
4 RYZEN 7 5800X3D 8-CORE 16 29,546 472,736 AMD
5 RYZEN 7 5700X 8-CORE 16 28,121 449,936 AMD
6 RYZEN 9 5950X 16-CORE 32 12,681 405,792 AMD
7 11TH GEN CORE I7-11700K @ 3.60GHZ 16 20,683 330,928 Intel
8 RYZEN 7 5800X 8-CORE 16 19,557 312,912 AMD
9 CORE I7-10700K CPU @ 3.80GHZ 16 17,026 272,416 Intel
10 RYZEN 7 3700X 8-CORE 16 15,979 255,664 AMD
11 RYZEN 5 5600X 6-CORE 12 18,948 227,376 AMD
12 RYZEN 5 3500 6-CORE 6 33,948 203,688 AMD
13 11TH GEN CORE I9-11900K @ 3.50GHZ 16 12,351 197,616 Intel
14 RYZEN 5 3600 6-CORE 12 13,551 162,612 AMD
15 12TH GEN CORE I7-12700 20 7,572 151,440 Intel
16 CORE I7-7700K CPU @ 4.20GHZ 8 17,098 136,784 Intel
17 CORE I7-5820K CPU @ 3.30GHZ 12 10,179 122,148 Intel
18 CORE I7-5930K CPU @ 3.50GHZ 12 9,765 117,180 Intel
19 CORE I9-8950HK CPU @ 2.90GHZ 12 8,239 98,868 Intel
20 CORE I7-6700T CPU @ 2.80GHZ 8 11,887 95,096 Intel
21 CORE I7-6700K CPU @ 4.00GHZ 8 9,872 78,976 Intel
22 CORE I7-4770HQ CPU @ 2.20GHZ 8 7,915 63,320 Intel
23 CORE I7-3770K CPU @ 3.50GHZ 8 7,808 62,464 Intel
24 APPLE M1 8 6,938 55,504 Apple
25 XEON CPU E5-2697 V2 @ 2.70GHZ 24 1,066 25,584 Intel