PROJECT #18411 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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Model
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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 3950X 16-CORE 32 18,195 582,240 AMD
2 RYZEN 9 5950X 16-CORE 32 17,901 572,832 AMD
3 12TH GEN CORE I9-12900K 24 23,330 559,920 Intel
4 12TH GEN CORE I7-12700K 20 24,670 493,400 Intel
5 RYZEN 7 5800X 8-CORE 16 28,394 454,304 AMD
6 RYZEN 7 5700X 8-CORE 16 27,811 444,976 AMD
7 RYZEN 9 3900X 12-CORE 24 16,487 395,688 AMD
8 CORE I9-10850K CPU @ 3.60GHZ 20 19,441 388,820 Intel
9 12TH GEN CORE I7-12700 20 19,110 382,200 Intel
10 11TH GEN CORE I9-11900K @ 3.50GHZ 16 23,790 380,640 Intel
11 11TH GEN CORE I7-11700K @ 3.60GHZ 16 23,767 380,272 Intel
12 RYZEN 9 5900X 12-CORE 24 15,556 373,344 AMD
13 RYZEN 9 3900 12-CORE 24 15,473 371,352 AMD
14 RYZEN 9 3900XT 12-CORE 24 14,372 344,928 AMD
15 XEON CPU E5-2690 V4 @ 2.60GHZ 28 11,702 327,656 Intel
16 XEON CPU E5-2680 V4 @ 2.40GHZ 28 11,471 321,188 Intel
17 CORE I9-9900K CPU @ 3.60GHZ 16 16,358 261,728 Intel
18 RYZEN 7 3800X 8-CORE 16 16,278 260,448 AMD
19 XEON CPU E5-2680 V3 @ 2.50GHZ 24 9,133 219,192 Intel
20 RYZEN THREADRIPPER 2950X 16-CORE 32 6,047 193,504 AMD
21 XEON CPU E5-2698 V4 @ 2.20GHZ 16 11,097 177,552 Intel
22 RYZEN 7 PRO 4750G 16 10,111 161,776 AMD
23 RYZEN 7 3700X 8-CORE 16 8,934 142,944 AMD
24 XEON CPU E5-2650L V4 @ 1.70GHZ 28 4,578 128,184 Intel
25 CORE I7-10700T CPU @ 2.00GHZ 16 6,738 107,808 Intel
26 EPYC 7251 8-CORE 16 6,596 105,536 AMD
27 CORE I9-9880H CPU @ 2.30GHZ 16 6,523 104,368 Intel
28 XEON CPU E5-2680 0 @ 2.70GHZ 16 5,247 83,952 Intel
29 RYZEN 7 2700X EIGHT-CORE 16 4,570 73,120 AMD
30 RYZEN 9 5900HX 16 4,254 68,064 AMD
31 XEON CPU E5-2697 V2 @ 2.70GHZ 24 2,710 65,040 Intel