PROJECT #18407 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 Saturday, 15 January 2022 23:40:51

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Folding@Home Identifier
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Model
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PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Saturday, 15 January 2022 23:40:51

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 30,696 982,272 AMD
2 RYZEN 7 5800X 8-CORE 16 54,179 866,864 AMD
3 RYZEN 7 5700G 16 33,427 534,832 AMD
4 11TH GEN CORE I9-11900K @ 3.50GHZ 16 27,476 439,616 Intel
5 CORE I9-10850K CPU @ 3.60GHZ 20 20,782 415,640 Intel
6 RYZEN 9 5950X 16-CORE 32 10,807 345,824 AMD
7 RYZEN 7 3800X 8-CORE 16 21,121 337,936 AMD
8 CORE I9-10900X CPU @ 3.70GHZ 20 14,901 298,020 Intel
9 RYZEN 5 5600X 6-CORE 12 24,802 297,624 AMD
10 XEON CPU E5-2680 V3 @ 2.50GHZ 24 10,695 256,680 Intel
11 CORE I9-9900K CPU @ 3.60GHZ 16 15,408 246,528 Intel
12 CORE I7-8700 CPU @ 3.20GHZ 12 20,114 241,368 Intel
13 CORE I7-10700 CPU @ 2.90GHZ 16 9,607 153,712 Intel
14 RYZEN 5 3600 6-CORE 12 11,915 142,980 AMD
15 GENUINE CPU 0000 @ 2.70GHZ 8 16,797 134,376 Intel
16 RYZEN 5 2600 SIX-CORE 12 8,671 104,052 AMD
17 RYZEN 5 1600 SIX-CORE 12 7,320 87,840 AMD
18 CORE I7-6700K CPU @ 4.00GHZ 8 9,788 78,304 Intel
19 EPYC 7251 8-CORE 16 4,618 73,888 AMD
20 CORE I7-4770HQ CPU @ 2.20GHZ 8 9,007 72,056 Intel
21 XEON W-10855M CPU @ 2.80GHZ 12 5,879 70,548 Intel
22 11TH GEN CORE I5-1135G7 @ 2.40GHZ 8 7,607 60,856 Intel
23 CORE I7-4790T CPU @ 2.70GHZ 8 7,305 58,440 Intel
24 CORE I5-8265U CPU @ 1.60GHZ 8 7,138 57,104 Intel
25 CORE I7-10610U CPU @ 1.80GHZ 8 5,880 47,040 Intel
26 CORE I7-4770K CPU @ 3.50GHZ 8 5,227 41,816 Intel
27 XEON CPU E3-1245 V2 @ 3.40GHZ 8 4,493 35,944 Intel