PROJECT #18423 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: 80,500

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 CORE I9-8950HK CPU @ 2.90GHZ 12 169,292 2,031,504 Intel
2 12TH GEN CORE I7-12700K 20 29,870 597,400 Intel
3 CORE I7-8700 CPU @ 3.20GHZ 12 43,279 519,348 Intel
4 RYZEN 9 3950X 16-CORE 32 16,197 518,304 AMD
5 RYZEN 9 3900 12-CORE 24 18,000 432,000 AMD
6 RYZEN 7 5800X 8-CORE 16 25,920 414,720 AMD
7 CORE I7-10870H CPU @ 2.20GHZ 16 23,738 379,808 Intel
8 RYZEN 7 5700G 16 23,260 372,160 AMD
9 12TH GEN CORE I7-12700 20 17,776 355,520 Intel
10 RYZEN 7 5800X3D 8-CORE 16 22,044 352,704 AMD
11 CORE I9-10850K CPU @ 3.60GHZ 20 17,618 352,360 Intel
12 RYZEN 9 5900X 12-CORE 24 14,440 346,560 AMD
13 RYZEN 9 5950X 16-CORE 32 10,795 345,440 AMD
14 CORE I9-7920X CPU @ 2.90GHZ 24 14,222 341,328 Intel
15 EPYC 7401P 24-CORE 48 7,102 340,896 AMD
16 XEON CPU E5-2690 V4 @ 2.60GHZ 28 11,999 335,972 Intel
17 11TH GEN CORE I5-11600K @ 3.90GHZ 12 27,458 329,496 Intel
18 RYZEN 9 3900X 12-CORE 24 13,242 317,808 AMD
19 RYZEN THREADRIPPER 2970WX 24-CORE 48 6,536 313,728 AMD
20 RYZEN 7 3800X 8-CORE 16 19,427 310,832 AMD
21 CORE I9-10900X CPU @ 3.70GHZ 20 12,499 249,980 Intel
22 RYZEN 5 3600 6-CORE 12 20,244 242,928 AMD
23 XEON CPU E5-2680 V3 @ 2.50GHZ 24 10,116 242,784 Intel
24 RYZEN 5 5600X 6-CORE 12 18,867 226,404 AMD
25 CORE I9-9900X CPU @ 3.50GHZ 20 11,234 224,680 Intel
26 CORE I5-10600 CPU @ 3.30GHZ 12 17,294 207,528 Intel
27 RYZEN 7 2700X EIGHT-CORE 16 12,187 194,992 AMD
28 CORE I5-10400 CPU @ 2.90GHZ 12 14,628 175,536 Intel
29 11TH GEN CORE I9-11900F @ 2.50GHZ 16 10,179 162,864 Intel
30 CORE I7-9750H CPU @ 2.60GHZ 12 13,172 158,064 Intel
31 RYZEN 7 PRO 4750G 16 9,861 157,776 AMD
32 RYZEN 7 3700X 8-CORE 16 9,795 156,720 AMD
33 RYZEN 5 2600 SIX-CORE 12 11,248 134,976 AMD
34 CORE I7-10700 CPU @ 2.90GHZ 16 8,189 131,024 Intel
35 XEON CPU E5-2698 V4 @ 2.20GHZ 16 7,401 118,416 Intel
36 XEON CPU E5-2680 0 @ 2.70GHZ 16 7,256 116,096 Intel
37 CORE I7-10700T CPU @ 2.00GHZ 16 7,085 113,360 Intel
38 RYZEN 5 1600 SIX-CORE 12 9,298 111,576 AMD
39 11TH GEN CORE I5-11400 @ 2.60GHZ 12 8,966 107,592 Intel
40 CORE I7-8850H CPU @ 2.60GHZ 12 4,159 49,908 Intel
41 XEON CPU E5-2620 0 @ 2.00GHZ 12 3,815 45,780 Intel
42 XEON CPU E5-2697 V2 @ 2.70GHZ 24 1,608 38,592 Intel