PROJECT #18473 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
Average
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 54,518 872,288 AMD
2 RYZEN 9 7900 12-CORE 24 25,652 615,648 AMD
3 RYZEN 7 5700X 8-CORE 16 29,647 474,352 AMD
4 RYZEN 7 5800X3D 8-CORE 16 27,635 442,160 AMD
5 RYZEN 7 5800X 8-CORE 16 20,910 334,560 AMD
6 RYZEN 9 5950X 16-CORE 32 8,435 269,920 AMD
7 RYZEN 5 5600X 6-CORE 12 20,251 243,012 AMD
8 CORE I7-9700K CPU @ 3.60GHZ 8 27,198 217,584 Intel
9 RYZEN 5 3500 6-CORE 6 31,311 187,866 AMD
10 RYZEN 5 3600 6-CORE 12 14,191 170,292 AMD
11 CORE I7-7700K CPU @ 4.20GHZ 8 16,268 130,144 Intel
12 CORE I9-9900K CPU @ 3.60GHZ 16 7,781 124,496 Intel
13 CORE I7-8700 CPU @ 3.20GHZ 12 8,657 103,884 Intel
14 CORE I9-8950HK CPU @ 2.90GHZ 12 8,214 98,568 Intel
15 CORE I7-6700K CPU @ 4.00GHZ 8 9,780 78,240 Intel
16 APPLE M1 8 7,171 57,368 Apple
17 RYZEN 5 5500U 12 2,318 27,816 AMD