PROJECT #18461 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: 14,124

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
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Average
WUs Day
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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 9 5950X 16-CORE 32 20,855 667,360 AMD
2 RYZEN 7 7700X 8-CORE 16 40,545 648,720 AMD
3 RYZEN 9 7900 12-CORE 24 25,840 620,160 AMD
4 RYZEN 7 5700X 8-CORE 16 27,395 438,320 AMD
5 11TH GEN CORE I7-11700K @ 3.60GHZ 16 24,716 395,456 Intel
6 RYZEN 7 5800X3D 8-CORE 16 15,898 254,368 AMD
7 RYZEN 9 3900X 12-CORE 24 10,419 250,056 AMD
8 RYZEN 5 5600X 6-CORE 12 18,584 223,008 AMD
9 RYZEN 5 3500 6-CORE 6 30,582 183,492 AMD
10 RYZEN 5 3600 6-CORE 12 11,804 141,648 AMD
11 CORE I7-5930K CPU @ 3.50GHZ 12 10,944 131,328 Intel
12 CORE I7-7700K CPU @ 4.20GHZ 8 15,754 126,032 Intel
13 CORE I7-5820K CPU @ 3.30GHZ 12 10,108 121,296 Intel
14 CORE I7-8700 CPU @ 3.20GHZ 12 9,413 112,956 Intel
15 CORE I9-8950HK CPU @ 2.90GHZ 12 8,166 97,992 Intel
16 CORE I7-10700T CPU @ 2.00GHZ 16 5,290 84,640 Intel
17 CORE I7-8705G CPU @ 3.10GHZ 8 10,507 84,056 Intel
18 RYZEN 7 3700X 8-CORE 16 4,385 70,160 AMD
19 CORE I7-6700K CPU @ 4.00GHZ 8 8,363 66,904 Intel
20 APPLE M1 8 7,346 58,768 Apple
21 CORE I7-3770 CPU @ 3.40GHZ 8 6,764 54,112 Intel
22 XEON CPU E5-1620 V2 @ 3.70GHZ 8 5,787 46,296 Intel
23 APPLE M1 PRO 10 2,977 29,770 Apple
24 11TH GEN CORE I5-1135G7 @ 2.40GHZ 8 1,040 8,320 Intel