PROJECT #18412 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 21,103 675,296 AMD
2 RYZEN 7 5800X3D 8-CORE 16 37,904 606,464 AMD
3 12TH GEN CORE I9-12900K 24 24,333 583,992 Intel
4 12TH GEN CORE I7-12700K 20 25,489 509,780 Intel
5 RYZEN 9 3950X 16-CORE 32 15,438 494,016 AMD
6 RYZEN 7 5700G 16 30,655 490,480 AMD
7 RYZEN 7 5800X 8-CORE 16 28,899 462,384 AMD
8 RYZEN 9 5900X 12-CORE 24 18,737 449,688 AMD
9 12TH GEN CORE I7-12700 20 21,936 438,720 Intel
10 RYZEN 9 3900 12-CORE 24 17,283 414,792 AMD
11 RYZEN 9 3900XT 12-CORE 24 14,067 337,608 AMD
12 RYZEN 7 3800X 8-CORE 16 20,012 320,192 AMD
13 CORE I9-10850K CPU @ 3.60GHZ 20 15,447 308,940 Intel
14 XEON CPU E5-2650 V2 @ 2.60GHZ 32 8,122 259,904 Intel
15 RYZEN 9 3900X 12-CORE 24 10,470 251,280 AMD
16 CORE I9-9900K CPU @ 3.60GHZ 16 15,015 240,240 Intel
17 XEON CPU E5-2680 V3 @ 2.50GHZ 24 9,107 218,568 Intel
18 11TH GEN CORE I7-11700K @ 3.60GHZ 16 13,564 217,024 Intel
19 CORE I9-7920X CPU @ 2.90GHZ 24 8,822 211,728 Intel
20 XEON CPU E5-2660 V3 @ 2.60GHZ 20 10,566 211,320 Intel
21 RYZEN 7 3700X 8-CORE 16 13,008 208,128 AMD
22 CORE I9-10900X CPU @ 3.70GHZ 20 10,271 205,420 Intel
23 CORE I9-9900 CPU @ 3.10GHZ 16 12,228 195,648 Intel
24 CORE I7-10700K CPU @ 3.80GHZ 16 11,947 191,152 Intel
25 RYZEN 7 2700X EIGHT-CORE 16 11,862 189,792 AMD
26 11TH GEN CORE I7-11850H @ 2.50GHZ 16 11,476 183,616 Intel
27 RYZEN 7 PRO 4750G 16 10,608 169,728 AMD
28 RYZEN 9 5900HX 16 9,998 159,968 AMD
29 CORE I7-10700 CPU @ 2.90GHZ 16 7,632 122,112 Intel
30 XEON CPU E5-2690 V2 @ 3.00GHZ 20 6,017 120,340 Intel
31 CORE I7-10700T CPU @ 2.00GHZ 16 7,381 118,096 Intel
32 XEON CPU E5-2680 0 @ 2.70GHZ 16 5,273 84,368 Intel
33 XEON CPU E5-2450 0 @ 2.10GHZ 10 6,157 61,570 Intel
34 XEON CPU E5-2697 V2 @ 2.70GHZ 24 1,966 47,184 Intel