PROJECT #18419 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.

PROJECT INFO

Manager(s): Prof. Vincent Voelz

Institution: Temple University

PROJECT WORK UNIT SUMMARY

Atoms: 35,650

Core: GRO_A8

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Sunday, 16 January 2022 11:42:01

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Folding@Home Identifier
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PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Sunday, 16 January 2022 11:42:01

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 32,836 1,050,752 AMD
2 RYZEN 7 5800X 8-CORE 16 44,089 705,424 AMD
3 RYZEN 7 2700X EIGHT-CORE 16 41,567 665,072 AMD
4 RYZEN 9 3950X 16-CORE 32 20,605 659,360 AMD
5 RYZEN 7 5700G 16 27,399 438,384 AMD
6 RYZEN 9 5900X 12-CORE 24 18,002 432,048 AMD
7 11TH GEN CORE I7-11700K @ 3.60GHZ 16 21,361 341,776 Intel
8 11TH GEN CORE I9-11900K @ 3.50GHZ 16 21,264 340,224 Intel
9 RYZEN 9 3900XT 12-CORE 24 13,236 317,664 AMD
10 RYZEN 9 3900X 12-CORE 24 12,133 291,192 AMD
11 RYZEN 7 3800X 8-CORE 16 17,845 285,520 AMD
12 CORE I9-10900X CPU @ 3.70GHZ 20 12,847 256,940 Intel
13 RYZEN 5 5600X 6-CORE 12 18,255 219,060 AMD
14 CORE I7-5960X CPU @ 3.00GHZ 16 13,272 212,352 Intel
15 RYZEN 7 3700X 8-CORE 16 13,201 211,216 AMD
16 CORE I7-8700 CPU @ 3.20GHZ 12 16,474 197,688 Intel
17 RYZEN 5 3600X 6-CORE 12 16,277 195,324 AMD
18 11TH GEN CORE I9-11900F @ 2.50GHZ 16 10,704 171,264 Intel
19 11TH GEN CORE I7-1185G7 @ 3.00GHZ 8 20,661 165,288 Intel
20 XEON CPU E5-2680 V3 @ 2.50GHZ 24 6,585 158,040 Intel
21 RYZEN 7 1700 EIGHT-CORE 16 9,831 157,296 AMD
22 RYZEN 5 3600 6-CORE 12 12,276 147,312 AMD
23 CORE I7-10700 CPU @ 2.90GHZ 16 8,024 128,384 Intel
24 CORE I7-9700K CPU @ 3.60GHZ 8 13,806 110,448 Intel
25 CORE I5-10400 CPU @ 2.90GHZ 12 8,688 104,256 Intel
26 11TH GEN CORE I5-11400 @ 2.60GHZ 12 8,453 101,436 Intel
27 11TH GEN CORE I7-11850H @ 2.50GHZ 16 6,234 99,744 Intel
28 CORE I9-8950HK CPU @ 2.90GHZ 12 7,426 89,112 Intel
29 RYZEN 5 1600 SIX-CORE 12 6,868 82,416 AMD
30 CORE I5-8350U CPU @ 1.70GHZ 8 9,703 77,624 Intel
31 CORE I7-8705G CPU @ 3.10GHZ 8 9,278 74,224 Intel
32 CORE I7-4770K CPU @ 3.50GHZ 8 8,558 68,464 Intel
33 11TH GEN CORE I5-1135G7 @ 2.40GHZ 8 7,911 63,288 Intel
34 CORE I7-4770HQ CPU @ 2.20GHZ 8 6,151 49,208 Intel
35 CORE I7-10610U CPU @ 1.80GHZ 8 5,604 44,832 Intel
36 CORE I7-3770 CPU @ 3.40GHZ 8 5,213 41,704 Intel
37 XEON CPU E3-1245 V2 @ 3.40GHZ 8 4,930 39,440 Intel
38 CORE I7-4765T CPU @ 2.00GHZ 8 4,732 37,856 Intel
39 XEON CPU E31245 @ 3.30GHZ 8 4,627 37,016 Intel
40 CORE I5-10210U CPU @ 1.60GHZ 7 5,184 36,288 Intel
41 CORE I5-7200U CPU @ 2.50GHZ 4 5,960 23,840 Intel
42 FX-6100 SIX-CORE 6 1,643 9,858 AMD