PROJECT #18405 RESEARCH FOR CANCER
FOLDING PERFORMANCE PROFILE

PROJECT SUMMARY

Can molecular simulation be used for virtual affinity-maturation of de novo designed protein binders? That’s the question this project aims to address.

The Bahl Lab at the Institute for Protein Innovation has had some amazing success using computational design to develop high-affinity mini-proteins that can inhibit protein targets by tightly binding to them.

In practice, the current approach requires the experimental screening of thousands of computational designs to discover a few tight binders, and similarly expensive experimental screens to optimize their binding (i.e.

“affinity maturation”).

If we can make more accurate predictions of how sequence mutations affect binding affinity, we may be able to offload this expensive task to computers, boosting the efficiency of these efforts considerably. In this project, we use relative free energy calculations to predict how single-point mutations of a computationally designed mini-protein alter the binding affinity to the periplasmic protease LapG, an important regulator of bacterial biofilm formation.

These predictions will be compared to high-throughput experimental measurements of binding affinity provided by the Bahl lab.

An important end goal of this work is to develop new classes of inhibitors to make antibiotic therapies more successful.

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PROJECT INFO

Manager(s): Prof. Vincent Voelz

Institution: Temple University

PROJECT WORK UNIT SUMMARY

Atoms: 24,700

Core: GRO_A8

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Sunday, 26 June 2022 09:56:59

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Folding@Home Identifier
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GPU
Model
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PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Sunday, 26 June 2022 09:56:59

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 CORE I9-8950HK CPU @ 2.90GHZ 12 220,357 2,644,284 Intel
2 RYZEN 9 3950X 16-CORE 32 34,263 1,096,416 AMD
3 RYZEN 9 5950X 16-CORE 32 24,009 768,288 AMD
4 RYZEN 7 5800X 8-CORE 16 45,709 731,344 AMD
5 RYZEN 9 5900X 12-CORE 24 27,734 665,616 AMD
6 12TH GEN CORE I7-12700K 20 27,411 548,220 Intel
7 12TH GEN CORE I9-12900K 24 21,375 513,000 Intel
8 RYZEN 7 5700X 8-CORE 16 31,121 497,936 AMD
9 11TH GEN CORE I9-11900K @ 3.50GHZ 16 25,883 414,128 Intel
10 11TH GEN CORE I7-11700K @ 3.60GHZ 16 24,616 393,856 Intel
11 RYZEN 9 3900 12-CORE 24 15,811 379,464 AMD
12 RYZEN 9 3900XT 12-CORE 24 14,869 356,856 AMD
13 RYZEN 7 3800X 8-CORE 16 21,246 339,936 AMD
14 CORE I9-10850K CPU @ 3.60GHZ 20 16,758 335,160 Intel
15 CORE I9-9900K CPU @ 3.60GHZ 16 18,239 291,824 Intel
16 CORE I7-10700K CPU @ 3.80GHZ 16 16,385 262,160 Intel
17 RYZEN 5 5600X 6-CORE 12 21,250 255,000 AMD
18 RYZEN 9 3900X 12-CORE 24 10,281 246,744 AMD
19 RYZEN 9 5900HS 16 14,982 239,712 AMD
20 RYZEN THREADRIPPER 3960X 24-CORE 48 4,849 232,752 AMD
21 XEON CPU E5-2680 V3 @ 2.50GHZ 24 9,218 221,232 Intel
22 RYZEN 5 2600X SIX-CORE 12 17,811 213,732 AMD
23 CORE I7-8700 CPU @ 3.20GHZ 12 17,210 206,520 Intel
24 RYZEN 5 3600 6-CORE 12 15,204 182,448 AMD
25 RYZEN 7 PRO 4750G 16 10,275 164,400 AMD
26 CORE I7-9700K CPU @ 3.60GHZ 8 19,366 154,928 Intel
27 CORE I5-10400 CPU @ 2.90GHZ 12 12,608 151,296 Intel
28 11TH GEN CORE I5-11400 @ 2.60GHZ 12 9,375 112,500 Intel
29 CORE I7-3770 CPU @ 3.40GHZ 8 13,986 111,888 Intel
30 RYZEN 5 3400G 8 13,440 107,520 AMD
31 CORE I7-4790K CPU @ 4.00GHZ 8 11,649 93,192 Intel
32 CORE I7-6700K CPU @ 4.00GHZ 8 11,545 92,360 Intel
33 RYZEN 5 1600 SIX-CORE 12 7,258 87,096 AMD
34 RYZEN 5 PRO 2400G 8 10,698 85,584 AMD
35 11TH GEN CORE I9-11900F @ 2.50GHZ 16 4,900 78,400 Intel
36 XEON CPU E5-1630 V3 @ 3.70GHZ 8 8,904 71,232 Intel
37 XEON W-10855M CPU @ 2.80GHZ 12 4,690 56,280 Intel
38 CORE I7-4770HQ CPU @ 2.20GHZ 8 6,687 53,496 Intel
39 11TH GEN CORE I5-1145G7 @ 2.60GHZ 8 6,102 48,816 Intel
40 XEON CPU E3-1245 V2 @ 3.40GHZ 8 5,261 42,088 Intel
41 XEON CPU E31245 @ 3.30GHZ 8 4,774 38,192 Intel
42 11TH GEN CORE I7-1165G7 @ 2.80GHZ 8 1,169 9,352 Intel