PROJECT #18421 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 RYZEN 5 2600 SIX-CORE 12 357,157 4,285,884 AMD
2 12TH GEN CORE I7-12700K 20 30,355 607,100 Intel
3 RYZEN 9 5950X 16-CORE 32 18,813 602,016 AMD
4 RYZEN 9 3900 12-CORE 24 19,438 466,512 AMD
5 12TH GEN CORE I7-12700 20 22,465 449,300 Intel
6 RYZEN 9 3950X 16-CORE 32 13,706 438,592 AMD
7 11TH GEN CORE I7-11700K @ 3.60GHZ 16 26,219 419,504 Intel
8 RYZEN 7 5800X 8-CORE 16 24,891 398,256 AMD
9 CORE I9-10900K CPU @ 3.70GHZ 20 19,355 387,100 Intel
10 RYZEN 9 3900XT 12-CORE 24 15,198 364,752 AMD
11 CORE I9-7920X CPU @ 2.90GHZ 24 15,074 361,776 Intel
12 RYZEN 9 3900X 12-CORE 24 14,792 355,008 AMD
13 RYZEN 7 5700G 16 21,826 349,216 AMD
14 XEON CPU E5-2690 V4 @ 2.60GHZ 28 12,093 338,604 Intel
15 RYZEN 7 3800X 8-CORE 16 19,674 314,784 AMD
16 EPYC 7401P 24-CORE 48 6,450 309,600 AMD
17 RYZEN 9 5900X 12-CORE 24 12,750 306,000 AMD
18 11TH GEN CORE I5-11400F @ 2.60GHZ 12 25,316 303,792 Intel
19 11TH GEN CORE I5-11600K @ 3.90GHZ 12 24,897 298,764 Intel
20 11TH GEN CORE I9-11900K @ 3.50GHZ 16 18,000 288,000 Intel
21 XEON CPU E5-2680 V3 @ 2.50GHZ 24 10,632 255,168 Intel
22 RYZEN 5 5600X 6-CORE 12 20,913 250,956 AMD
23 CORE I9-9900 CPU @ 3.10GHZ 16 14,965 239,440 Intel
24 CORE I9-9900K CPU @ 3.60GHZ 16 14,738 235,808 Intel
25 CORE I5-10400 CPU @ 2.90GHZ 12 18,929 227,148 Intel
26 CORE I7-8700 CPU @ 3.20GHZ 12 18,922 227,064 Intel
27 RYZEN 5 3600 6-CORE 12 17,560 210,720 AMD
28 CORE I9-8950HK CPU @ 2.90GHZ 12 16,617 199,404 Intel
29 RYZEN 7 PRO 4750G 16 10,883 174,128 AMD
30 CORE I9-9900KF CPU @ 3.60GHZ 16 10,481 167,696 Intel
31 RYZEN 7 3700X 8-CORE 16 9,935 158,960 AMD
32 CORE I7-10700 CPU @ 2.90GHZ 16 9,542 152,672 Intel
33 11TH GEN CORE I5-11400 @ 2.60GHZ 12 10,986 131,832 Intel
34 CORE I7-10700T CPU @ 2.00GHZ 16 7,631 122,096 Intel
35 CORE I5-10400F CPU @ 2.90GHZ 12 9,682 116,184 Intel
36 XEON CPU E5-2698 V4 @ 2.20GHZ 16 7,083 113,328 Intel
37 RYZEN 5 1600 SIX-CORE 12 9,390 112,680 AMD
38 RYZEN 7 2700X EIGHT-CORE 16 7,012 112,192 AMD
39 RYZEN 9 5900HS 16 6,238 99,808 AMD
40 RYZEN 5 2600X SIX-CORE 12 8,303 99,636 AMD
41 XEON CPU E5-2620 0 @ 2.00GHZ 12 2,965 35,580 Intel