PROJECT #18463 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: 14,125

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 54,846 877,536 AMD
2 RYZEN 9 7900 12-CORE 24 24,214 581,136 AMD
3 RYZEN 7 5700X 8-CORE 16 29,421 470,736 AMD
4 RYZEN 7 5800X 8-CORE 16 26,779 428,464 AMD
5 RYZEN 9 5950X 16-CORE 32 12,583 402,656 AMD
6 11TH GEN CORE I7-11700K @ 3.60GHZ 16 21,545 344,720 Intel
7 RYZEN 7 3800X 8-CORE 16 21,246 339,936 AMD
8 12TH GEN CORE I5-12600K 16 19,415 310,640 Intel
9 12TH GEN CORE I7-12700 20 12,604 252,080 Intel
10 CORE I7-9700K CPU @ 3.60GHZ 8 27,537 220,296 Intel
11 CORE I9-9900K CPU @ 3.60GHZ 16 13,045 208,720 Intel
12 RYZEN 5 3500 6-CORE 6 32,339 194,034 AMD
13 RYZEN 5 3600 6-CORE 12 14,560 174,720 AMD
14 CORE I7-7700K CPU @ 4.20GHZ 8 16,474 131,792 Intel
15 CORE I7-5930K CPU @ 3.50GHZ 12 10,661 127,932 Intel
16 CORE I7-5820K CPU @ 3.30GHZ 12 9,632 115,584 Intel
17 CORE I7-6950X CPU @ 3.00GHZ 20 5,393 107,860 Intel
18 12TH GEN CORE I7-12700H 20 5,160 103,200 Intel
19 CORE I9-8950HK CPU @ 2.90GHZ 12 8,253 99,036 Intel
20 CORE I7-8705G CPU @ 3.10GHZ 8 11,793 94,344 Intel
21 CORE I7-6700K CPU @ 4.00GHZ 8 10,202 81,616 Intel
22 APPLE M1 8 8,991 71,928 Apple
23 CORE I7-3770K CPU @ 3.50GHZ 8 7,620 60,960 Intel
24 APPLE M1 PRO 10 4,888 48,880 Apple