PROJECT #18413 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: 64,500

Core: GRO_A8

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Monday, 03 October 2022 12:16:28

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Folding@Home Identifier
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PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Monday, 03 October 2022 12:16:28

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 19,243 615,776 AMD
2 12TH GEN CORE I7-12700K 20 29,313 586,260 Intel
3 12TH GEN CORE I9-12900K 24 23,914 573,936 Intel
4 RYZEN 7 5700X 8-CORE 16 35,031 560,496 AMD
5 RYZEN 5 1600 SIX-CORE 12 38,680 464,160 AMD
6 RYZEN 9 3900 12-CORE 24 17,352 416,448 AMD
7 RYZEN 9 5900X 12-CORE 24 17,133 411,192 AMD
8 RYZEN 9 3950X 16-CORE 32 12,644 404,608 AMD
9 11TH GEN CORE I7-11700K @ 3.60GHZ 16 24,921 398,736 Intel
10 RYZEN THREADRIPPER 2950X 16-CORE 32 12,382 396,224 AMD
11 RYZEN 7 5800X 8-CORE 16 24,405 390,480 AMD
12 12TH GEN CORE I7-12700 20 17,883 357,660 Intel
13 CORE I7-10700F CPU @ 2.90GHZ 16 21,738 347,808 Intel
14 RYZEN 9 3900XT 12-CORE 24 14,397 345,528 AMD
15 XEON CPU E5-2690 V4 @ 2.60GHZ 28 11,691 327,348 Intel
16 RYZEN 7 5800X3D 8-CORE 16 20,408 326,528 AMD
17 CORE I9-7920X CPU @ 2.90GHZ 24 12,975 311,400 Intel
18 CORE I9-10850K CPU @ 3.60GHZ 20 15,077 301,540 Intel
19 RYZEN 9 3900X 12-CORE 24 12,450 298,800 AMD
20 GENUINE CPU 0000 @ 2.10GHZ 44 6,376 280,544 Intel
21 11TH GEN CORE I9-11900K @ 3.50GHZ 16 17,060 272,960 Intel
22 RYZEN 7 3800X 8-CORE 16 16,343 261,488 AMD
23 CORE I9-10900X CPU @ 3.70GHZ 20 13,003 260,060 Intel
24 XEON CPU E5-2650 V2 @ 2.60GHZ 32 7,766 248,512 Intel
25 CORE I9-9900K CPU @ 3.60GHZ 16 13,575 217,200 Intel
26 XEON CPU E5-2680 V3 @ 2.50GHZ 24 9,030 216,720 Intel
27 RYZEN 5 5600G 12 16,435 197,220 AMD
28 RYZEN 5 3600 6-CORE 12 13,800 165,600 AMD
29 CORE I7-10700K CPU @ 3.80GHZ 16 10,179 162,864 Intel
30 RYZEN 7 3700X 8-CORE 16 9,585 153,360 AMD
31 RYZEN 7 PRO 4750G 16 9,574 153,184 AMD
32 CORE I7-10700 CPU @ 2.90GHZ 16 8,051 128,816 Intel
33 CORE I5-10400 CPU @ 2.90GHZ 12 10,229 122,748 Intel
34 CORE I7-10700T CPU @ 2.00GHZ 16 6,723 107,568 Intel
35 XEON CPU E5-2680 0 @ 2.70GHZ 16 5,289 84,624 Intel