PROJECT #18481 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: 14,092

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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Brand
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 RYZEN 7 7700X 8-CORE 16 45,305 724,880 AMD
2 RYZEN 9 5950X 16-CORE 32 17,444 558,208 AMD
3 RYZEN 7 5700X 8-CORE 16 27,772 444,352 AMD
4 RYZEN 7 3800X 8-CORE 16 19,799 316,784 AMD
5 RYZEN 7 5800X 8-CORE 16 18,599 297,584 AMD
6 CORE I7-10700K CPU @ 3.80GHZ 16 16,780 268,480 Intel
7 RYZEN 9 3900X 12-CORE 24 9,550 229,200 AMD
8 RYZEN 5 5600X 6-CORE 12 18,053 216,636 AMD
9 CORE I7-9700K CPU @ 3.60GHZ 8 25,365 202,920 Intel
10 RYZEN 9 5900 12-CORE 24 8,436 202,464 AMD
11 RYZEN 5 3500 6-CORE 6 31,735 190,410 AMD
12 RYZEN 5 3600 6-CORE 12 12,119 145,428 AMD
13 CORE I7-7700K CPU @ 4.20GHZ 8 16,067 128,536 Intel
14 CORE I9-9900K CPU @ 3.60GHZ 16 6,854 109,664 Intel
15 CORE I9-8950HK CPU @ 2.90GHZ 12 8,232 98,784 Intel
16 CORE I7-6700T CPU @ 2.80GHZ 8 11,812 94,496 Intel
17 CORE I7-8705G CPU @ 3.10GHZ 8 11,797 94,376 Intel
18 CORE I7-4770HQ CPU @ 2.20GHZ 8 7,855 62,840 Intel
19 RYZEN 5 2400G 8 7,713 61,704 AMD
20 CORE I7-3770K CPU @ 3.50GHZ 8 7,443 59,544 Intel
21 APPLE M1 8 7,155 57,240 Apple
22 XEON CPU E5-1620 V2 @ 3.70GHZ 8 4,540 36,320 Intel