PROJECT #18483 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: 93,432

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
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Points WU
Average
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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 38,664 618,624 AMD
2 RYZEN 9 5950X 16-CORE 32 15,896 508,672 AMD
3 12TH GEN CORE I7-12700K 20 21,329 426,580 Intel
4 XEON PLATINUM 8370C CPU @ 2.80GHZ 16 18,860 301,760 Intel
5 RYZEN 7 5700G 16 18,685 298,960 AMD
6 RYZEN 9 3900X 12-CORE 24 11,919 286,056 AMD
7 12TH GEN CORE I7-12700 20 13,907 278,140 Intel
8 RYZEN 9 5900 12-CORE 24 11,230 269,520 AMD
9 CORE I7-10700K CPU @ 3.80GHZ 16 15,513 248,208 Intel
10 CORE I7-10700T CPU @ 2.00GHZ 16 5,844 93,504 Intel