PROJECT #18408 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 CORE I7-10700K CPU @ 3.80GHZ 16 55,899 894,384 Intel
2 RYZEN 9 5950X 16-CORE 32 19,593 626,976 AMD
3 RYZEN 7 5800X3D 8-CORE 16 39,075 625,200 AMD
4 RYZEN 9 3950X 16-CORE 32 18,980 607,360 AMD
5 12TH GEN CORE I9-12900K 24 23,599 566,376 Intel
6 RYZEN 7 5800X 8-CORE 16 29,961 479,376 AMD
7 RYZEN 9 5900X 12-CORE 24 19,478 467,472 AMD
8 RYZEN 7 5700G 16 29,144 466,304 AMD
9 RYZEN 9 3900 12-CORE 24 18,770 450,480 AMD
10 XEON CPU E5-2680 V4 @ 2.40GHZ 28 15,008 420,224 Intel
11 12TH GEN CORE I7-12700K 20 20,314 406,280 Intel
12 11TH GEN CORE I7-11700K @ 3.60GHZ 16 25,295 404,720 Intel
13 12TH GEN CORE I7-12700 20 20,079 401,580 Intel
14 RYZEN 9 3900XT 12-CORE 24 14,449 346,776 AMD
15 EPYC 7V12 64-CORE 64 5,149 329,536 AMD
16 CORE I9-10850K CPU @ 3.60GHZ 20 16,159 323,180 Intel
17 XEON CPU E5-2690 V4 @ 2.60GHZ 28 11,315 316,820 Intel
18 RYZEN 7 5800H 16 18,108 289,728 AMD
19 RYZEN 9 3900X 12-CORE 24 11,933 286,392 AMD
20 RYZEN 7 3800X 8-CORE 16 17,433 278,928 AMD
21 RYZEN 5 3600 6-CORE 12 23,082 276,984 AMD
22 RYZEN THREADRIPPER 2950X 16-CORE 32 8,622 275,904 AMD
23 XEON CPU E5-2680 V3 @ 2.50GHZ 24 10,820 259,680 Intel
24 CORE I9-9900K CPU @ 3.60GHZ 16 14,810 236,960 Intel
25 XEON CPU E5-2650 V2 @ 2.60GHZ 32 7,321 234,272 Intel
26 11TH GEN CORE I9-11900K @ 3.50GHZ 16 12,624 201,984 Intel
27 11TH GEN CORE I5-11400 @ 2.60GHZ 12 16,206 194,472 Intel
28 RYZEN 7 4800H 16 11,430 182,880 AMD
29 RYZEN 7 PRO 4750G 16 11,284 180,544 AMD
30 XEON CPU E5-2698 V4 @ 2.20GHZ 16 8,765 140,240 Intel
31 RYZEN 7 3700X 8-CORE 16 8,654 138,464 AMD
32 CORE I7-10700T CPU @ 2.00GHZ 16 7,967 127,472 Intel
33 RYZEN 7 2700X EIGHT-CORE 16 7,860 125,760 AMD
34 XEON CPU E5-2680 0 @ 2.70GHZ 16 5,507 88,112 Intel
35 XEON CPU E5-2697 V2 @ 2.70GHZ 24 2,071 49,704 Intel
36 OPTERON(TM) 6380 64 330 21,120 AMD