PROJECT #18480 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: 14,088

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
Average
WU Time
Average

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 9 5950X 16-CORE 32 34,608 1,107,456 AMD
2 RYZEN 7 7700X 8-CORE 16 43,102 689,632 AMD
3 RYZEN 7 5700X 8-CORE 16 26,233 419,728 AMD
4 11TH GEN CORE I7-11700K @ 3.60GHZ 16 24,108 385,728 Intel
5 RYZEN 5 5600X 6-CORE 12 22,109 265,308 AMD
6 RYZEN 9 3900X 12-CORE 24 10,552 253,248 AMD
7 CORE I7-9700K CPU @ 3.60GHZ 8 30,442 243,536 Intel
8 RYZEN 5 3500 6-CORE 6 33,376 200,256 AMD
9 XEON W-2245 CPU @ 3.90GHZ 16 11,395 182,320 Intel
10 RYZEN 5 3600 6-CORE 12 15,126 181,512 AMD
11 CORE I7-7700K CPU @ 4.20GHZ 8 17,476 139,808 Intel
12 CORE I7-5930K CPU @ 3.50GHZ 12 11,040 132,480 Intel
13 CORE I9-9900K CPU @ 3.60GHZ 16 7,551 120,816 Intel
14 CORE I7-5820K CPU @ 3.30GHZ 12 9,857 118,284 Intel
15 CORE I7-8705G CPU @ 3.10GHZ 8 11,178 89,424 Intel
16 CORE I7-4770HQ CPU @ 2.20GHZ 8 8,092 64,736 Intel
17 CORE I7-6700K CPU @ 4.00GHZ 8 7,820 62,560 Intel
18 CORE I7-3770K CPU @ 3.50GHZ 8 7,744 61,952 Intel
19 APPLE M1 8 7,372 58,976 Apple
20 XEON CPU L5640 @ 2.27GHZ 24 2,412 57,888 Intel
21 XEON CPU E5-1620 V2 @ 3.70GHZ 8 7,109 56,872 Intel
22 XEON CPU E5-2697 V2 @ 2.70GHZ 24 1,804 43,296 Intel
23 APPLE M1 PRO 10 4,247 42,470 Apple
24 CORE I5-10210U CPU @ 1.60GHZ 7 5,280 36,960 Intel