PROJECT #18484 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: 93,430

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 12TH GEN CORE I9-12900K 24 30,384 729,216 Intel
2 RYZEN 7 7700X 8-CORE 16 40,283 644,528 AMD
3 RYZEN 9 5900X 12-CORE 24 20,714 497,136 AMD
4 RYZEN 9 5950X 16-CORE 32 13,533 433,056 AMD
5 12TH GEN CORE I7-12700K 20 21,492 429,840 Intel
6 RYZEN 7 5700X 8-CORE 16 24,804 396,864 AMD
7 RYZEN 7 5800X 8-CORE 16 23,366 373,856 AMD
8 XEON PLATINUM 8370C CPU @ 2.80GHZ 16 18,024 288,384 Intel
9 12TH GEN CORE I7-12700 20 14,012 280,240 Intel
10 CORE I7-10700K CPU @ 3.80GHZ 16 15,998 255,968 Intel
11 11TH GEN CORE I9-11900K @ 3.50GHZ 16 11,969 191,504 Intel
12 RYZEN 7 3700X 8-CORE 16 9,820 157,120 AMD
13 11TH GEN CORE I7-11700F @ 2.50GHZ 16 8,034 128,544 Intel
14 CORE I7-10700T CPU @ 2.00GHZ 16 5,412 86,592 Intel