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, 23 May 2022 03:34:39

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Folding@Home Identifier
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PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Monday, 23 May 2022 03:34:39

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 12TH GEN CORE I9-12900K 24 39,517 948,408 Intel
2 RYZEN 9 3900X 12-CORE 24 38,970 935,280 AMD
3 12TH GEN CORE I7-12700K 20 38,219 764,380 Intel
4 RYZEN 9 5950X 16-CORE 32 17,847 571,104 AMD
5 RYZEN 9 3900 12-CORE 24 17,352 416,448 AMD
6 RYZEN 9 3950X 16-CORE 32 12,644 404,608 AMD
7 RYZEN THREADRIPPER 2950X 16-CORE 32 12,382 396,224 AMD
8 RYZEN 7 5800X 8-CORE 16 24,405 390,480 AMD
9 RYZEN 9 5900X 12-CORE 24 15,892 381,408 AMD
10 12TH GEN CORE I7-12700 20 17,883 357,660 Intel
11 CORE I9-7920X CPU @ 2.90GHZ 24 14,768 354,432 Intel
12 11TH GEN CORE I7-11700K @ 3.60GHZ 16 21,793 348,688 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 CORE I9-10850K CPU @ 3.60GHZ 20 15,180 303,600 Intel
17 GENUINE CPU 0000 @ 2.10GHZ 44 6,376 280,544 Intel
18 11TH GEN CORE I9-11900K @ 3.50GHZ 16 17,060 272,960 Intel
19 CORE I9-10900X CPU @ 3.70GHZ 20 13,003 260,060 Intel
20 RYZEN 7 3800X 8-CORE 16 15,536 248,576 AMD
21 XEON CPU E5-2650 V2 @ 2.60GHZ 32 7,462 238,784 Intel
22 CORE I9-9900K CPU @ 3.60GHZ 16 13,575 217,200 Intel
23 XEON CPU E5-2680 V3 @ 2.50GHZ 24 9,030 216,720 Intel
24 RYZEN 5 3600 6-CORE 12 13,800 165,600 AMD
25 RYZEN 7 PRO 4750G 16 9,678 154,848 AMD
26 CORE I7-10700 CPU @ 2.90GHZ 16 8,051 128,816 Intel
27 RYZEN 7 3700X 8-CORE 16 7,875 126,000 AMD
28 CORE I5-10400 CPU @ 2.90GHZ 12 10,229 122,748 Intel
29 CORE I7-10700T CPU @ 2.00GHZ 16 6,723 107,568 Intel
30 XEON CPU E5-2680 0 @ 2.70GHZ 16 5,330 85,280 Intel