PROJECT #18460 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
Points WU
Average
WUs Day
Average
WU Time
Average

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 49,334 789,344 AMD
2 13TH GEN CORE I9-13900KS 32 23,266 744,512 Intel
3 RYZEN 7 7700X 8-CORE 16 41,712 667,392 AMD
4 RYZEN 9 7900 12-CORE 24 21,073 505,752 AMD
5 12TH GEN CORE I7-12700K 20 23,132 462,640 Intel
6 RYZEN 7 5700X 8-CORE 16 24,903 398,448 AMD
7 11TH GEN CORE I7-11700K @ 3.60GHZ 16 23,531 376,496 Intel
8 RYZEN 9 5900X 12-CORE 24 14,934 358,416 AMD
9 RYZEN 7 5800X3D 8-CORE 16 21,286 340,576 AMD
10 RYZEN 9 5950X 16-CORE 32 10,469 335,008 AMD
11 RYZEN 7 3800X 8-CORE 16 20,904 334,464 AMD
12 CORE I7-10700K CPU @ 3.80GHZ 16 17,957 287,312 Intel
13 RYZEN 9 3900X 12-CORE 24 10,478 251,472 AMD
14 RYZEN 7 5800X 8-CORE 16 15,421 246,736 AMD
15 RYZEN 5 5600X 6-CORE 12 18,296 219,552 AMD
16 CORE I7-9700K CPU @ 3.60GHZ 8 26,555 212,440 Intel
17 12TH GEN CORE I3-12100F 8 25,794 206,352 Intel
18 RYZEN 5 3500 6-CORE 6 33,847 203,082 AMD
19 RYZEN 5 3600 6-CORE 12 14,147 169,764 AMD
20 RYZEN 7 PRO 4750G 16 10,578 169,248 AMD
21 RYZEN 7 3700X 8-CORE 16 10,492 167,872 AMD
22 CORE I7-6950X CPU @ 3.00GHZ 20 8,259 165,180 Intel
23 CORE I9-9900K CPU @ 3.60GHZ 16 10,276 164,416 Intel
24 XEON CPU E5-2697 V2 @ 2.70GHZ 24 6,035 144,840 Intel
25 CORE I7-7700K CPU @ 4.20GHZ 8 16,271 130,168 Intel
26 CORE I9-8950HK CPU @ 2.90GHZ 12 8,143 97,716 Intel
27 CORE I7-8705G CPU @ 3.10GHZ 8 12,022 96,176 Intel
28 CORE I7-4770HQ CPU @ 2.20GHZ 8 8,072 64,576 Intel
29 CORE I7-3770K CPU @ 3.50GHZ 8 7,605 60,840 Intel
30 APPLE M1 8 7,378 59,024 Apple
31 XEON CPU L5640 @ 2.27GHZ 24 2,397 57,528 Intel
32 RYZEN 5 2400G 8 5,578 44,624 AMD
33 APPLE M1 PRO 10 4,150 41,500 Apple
34 11TH GEN CORE I5-1135G7 @ 2.40GHZ 8 4,767 38,136 Intel
35 XEON CPU E5-1620 V2 @ 3.70GHZ 8 2,352 18,816 Intel