PROJECT #18405 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: 24,700

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 3950X 16-CORE 32 34,263 1,096,416 AMD
2 RYZEN 9 5950X 16-CORE 32 24,268 776,576 AMD
3 RYZEN 7 5800X 8-CORE 16 47,422 758,752 AMD
4 11TH GEN CORE I9-11900K @ 3.50GHZ 16 26,495 423,920 Intel
5 RYZEN 9 3900 12-CORE 24 15,811 379,464 AMD
6 RYZEN 7 3800X 8-CORE 16 21,425 342,800 AMD
7 CORE I9-10850K CPU @ 3.60GHZ 20 16,758 335,160 Intel
8 RYZEN 9 5900X 12-CORE 24 13,323 319,752 AMD
9 CORE I9-9900K CPU @ 3.60GHZ 16 18,239 291,824 Intel
10 RYZEN 9 3900X 12-CORE 24 10,281 246,744 AMD
11 RYZEN THREADRIPPER 3960X 24-CORE 48 4,849 232,752 AMD
12 XEON CPU E5-2680 V3 @ 2.50GHZ 24 9,631 231,144 Intel
13 RYZEN 5 5600X 6-CORE 12 18,788 225,456 AMD
14 RYZEN 5 2600X SIX-CORE 12 17,811 213,732 AMD
15 CORE I7-8700 CPU @ 3.20GHZ 12 17,319 207,828 Intel
16 CORE I5-10400 CPU @ 2.90GHZ 12 12,608 151,296 Intel
17 RYZEN 5 3600 6-CORE 12 12,191 146,292 AMD
18 11TH GEN CORE I5-11400 @ 2.60GHZ 12 9,353 112,236 Intel
19 CORE I7-6700K CPU @ 4.00GHZ 8 11,108 88,864 Intel
20 11TH GEN CORE I9-11900F @ 2.50GHZ 16 4,900 78,400 Intel
21 CORE I9-8950HK CPU @ 2.90GHZ 12 5,692 68,304 Intel
22 XEON W-10855M CPU @ 2.80GHZ 12 4,690 56,280 Intel
23 CORE I7-4770HQ CPU @ 2.20GHZ 8 6,742 53,936 Intel
24 XEON CPU E31245 @ 3.30GHZ 8 4,696 37,568 Intel
25 11TH GEN CORE I7-1165G7 @ 2.80GHZ 8 1,169 9,352 Intel