PROJECT #18479 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: 14,089

Core: 0xa8

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Monday, 20 March 2023 12:15:00

Rank
Project
Model Name
Folding@Home Identifier
Make
Brand
GPU
Model
PPD
Average
Points WU
Average
WUs Day
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PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Monday, 20 March 2023 12:15:00

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 37,749 603,984 AMD
2 RYZEN 7 5800X3D 8-CORE 16 33,633 538,128 AMD
3 RYZEN 9 5950X 16-CORE 32 14,204 454,528 AMD
4 12TH GEN CORE I7-12700K 20 22,067 441,340 Intel
5 RYZEN 7 5700X 8-CORE 16 27,057 432,912 AMD
6 11TH GEN CORE I7-11700K @ 3.60GHZ 16 21,955 351,280 Intel
7 RYZEN 5 5600X 6-CORE 12 24,366 292,392 AMD
8 RYZEN 5 3500 6-CORE 6 34,001 204,006 AMD
9 RYZEN 5 3600 6-CORE 12 15,431 185,172 AMD
10 RYZEN 9 5900X 12-CORE 24 7,663 183,912 AMD
11 CORE I7-7700K CPU @ 4.20GHZ 8 15,913 127,304 Intel
12 CORE I7-8700 CPU @ 3.20GHZ 12 9,899 118,788 Intel
13 11TH GEN CORE I7-11800H @ 2.30GHZ 16 6,427 102,832 Intel
14 CORE I9-8950HK CPU @ 2.90GHZ 12 7,773 93,276 Intel
15 CORE I7-8705G CPU @ 3.10GHZ 8 11,043 88,344 Intel
16 CORE I7-4770HQ CPU @ 2.20GHZ 8 7,679 61,432 Intel
17 CORE I7-3770K CPU @ 3.50GHZ 8 7,416 59,328 Intel
18 APPLE M1 8 7,174 57,392 Apple
19 XEON CPU E5-2697 V2 @ 2.70GHZ 24 2,123 50,952 Intel
20 APPLE M1 PRO 10 3,517 35,170 Apple