PROJECT #18482 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: 14,088

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 9 5950X 16-CORE 32 32,291 1,033,312 AMD
2 RYZEN 7 5700G 16 50,819 813,104 AMD
3 RYZEN 7 7700X 8-CORE 16 44,277 708,432 AMD
4 11TH GEN CORE I7-11700K @ 3.60GHZ 16 24,827 397,232 Intel
5 RYZEN 7 5800X 8-CORE 16 24,501 392,016 AMD
6 RYZEN 7 5700X 8-CORE 16 22,516 360,256 AMD
7 12TH GEN CORE I5-12600K 16 17,593 281,488 Intel
8 CORE I7-10700K CPU @ 3.80GHZ 16 16,714 267,424 Intel
9 RYZEN 9 3900X 12-CORE 24 10,552 253,248 AMD
10 RYZEN 5 5600X 6-CORE 12 18,022 216,264 AMD
11 RYZEN 5 3500 6-CORE 6 35,573 213,438 AMD
12 11TH GEN CORE I9-11900K @ 3.50GHZ 16 11,662 186,592 Intel
13 RYZEN 5 3600 6-CORE 12 13,953 167,436 AMD
14 RYZEN 7 3700X 8-CORE 16 10,149 162,384 AMD
15 CORE I7-5930K CPU @ 3.50GHZ 12 10,906 130,872 Intel
16 CORE I7-9750H CPU @ 2.60GHZ 12 10,727 128,724 Intel
17 CORE I7-5820K CPU @ 3.30GHZ 12 9,850 118,200 Intel
18 CORE I7-10700T CPU @ 2.00GHZ 16 6,323 101,168 Intel
19 CORE I9-8950HK CPU @ 2.90GHZ 12 7,920 95,040 Intel
20 CORE I7-8705G CPU @ 3.10GHZ 8 10,790 86,320 Intel
21 CORE I7-3770K CPU @ 3.50GHZ 8 7,538 60,304 Intel
22 APPLE M1 8 7,192 57,536 Apple
23 XEON CPU E5-1620 V2 @ 3.70GHZ 8 6,836 54,688 Intel
24 APPLE M1 PRO 10 3,470 34,700 Apple
25 RYZEN 5 5500U 12 2,803 33,636 AMD