PROJECT #18478 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: 93,435

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 7 7700X 8-CORE 16 41,707 667,312 AMD
2 CORE I9-9900K CPU @ 3.60GHZ 16 38,448 615,168 Intel
3 RYZEN 7 5700X 8-CORE 16 27,376 438,016 AMD
4 RYZEN 9 5950X 16-CORE 32 13,226 423,232 AMD
5 RYZEN THREADRIPPER 2950X 16-CORE 32 10,640 340,480 AMD
6 12TH GEN CORE I5-12600K 16 19,946 319,136 Intel
7 CORE I7-10700K CPU @ 3.80GHZ 16 19,794 316,704 Intel
8 RYZEN 7 5700G 16 18,332 293,312 AMD
9 12TH GEN CORE I7-12700 20 14,193 283,860 Intel
10 XEON PLATINUM 8370C CPU @ 2.80GHZ 16 17,525 280,400 Intel
11 CORE I9-7940X CPU @ 3.10GHZ 28 6,878 192,584 Intel
12 CORE I7-10700T CPU @ 2.00GHZ 16 5,578 89,248 Intel