PROJECT #18418 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 Friday, 03 December 2021 04:36:08

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PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Friday, 03 December 2021 04:36:08

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 RYZEN 7 5800X 8-CORE 16 47,424 758,784 AMD
2 11TH GEN CORE I9-11900K @ 3.50GHZ 16 24,140 386,240 Intel
3 RYZEN 9 3950X 16-CORE 32 11,485 367,520 AMD
4 RYZEN 7 3700X 8-CORE 16 22,557 360,912 AMD
5 11TH GEN CORE I7-11700K @ 3.60GHZ 16 21,585 345,360 Intel
6 RYZEN 9 3900XT 12-CORE 24 13,430 322,320 AMD
7 RYZEN 7 3800X 8-CORE 16 19,580 313,280 AMD
8 RYZEN 5 5600X 6-CORE 12 22,449 269,388 AMD
9 CORE I9-10850K CPU @ 3.60GHZ 20 13,033 260,660 Intel
10 RYZEN 5 3600X 6-CORE 12 20,373 244,476 AMD
11 CORE I9-10900X CPU @ 3.70GHZ 20 10,898 217,960 Intel
12 XEON CPU E31245 @ 3.30GHZ 8 25,730 205,840 Intel
13 CORE I7-8700 CPU @ 3.20GHZ 12 16,615 199,380 Intel
14 CORE I9-9900K CPU @ 3.60GHZ 16 12,312 196,992 Intel
15 RYZEN 5 2600X SIX-CORE 12 15,959 191,508 AMD
16 CORE I7-5960X CPU @ 3.00GHZ 16 10,965 175,440 Intel
17 CORE I7-9700 CPU @ 3.00GHZ 8 21,848 174,784 Intel
18 RYZEN 5 3600 6-CORE 12 10,285 123,420 AMD
19 11TH GEN CORE I9-11900F @ 2.50GHZ 16 7,277 116,432 Intel
20 RYZEN 5 1600 SIX-CORE 12 6,921 83,052 AMD
21 CORE I9-8950HK CPU @ 2.90GHZ 12 6,899 82,788 Intel
22 RYZEN 5 2600 SIX-CORE 12 6,785 81,420 AMD
23 CORE I7-10750H CPU @ 2.60GHZ 12 6,564 78,768 Intel
24 CORE I7-8705G CPU @ 3.10GHZ 8 9,677 77,416 Intel
25 CORE I7-4770HQ CPU @ 2.20GHZ 8 6,363 50,904 Intel
26 CORE I7-4790T CPU @ 2.70GHZ 8 6,151 49,208 Intel
27 CORE I5-1035G4 CPU @ 1.10GHZ 8 6,096 48,768 Intel
28 CORE I7-3770 CPU @ 3.40GHZ 8 6,044 48,352 Intel
29 CORE I5-4590 CPU @ 3.30GHZ 4 11,920 47,680 Intel
30 XEON CPU E3-1245 V2 @ 3.40GHZ 8 5,137 41,096 Intel
31 CORE I7-4770K CPU @ 3.50GHZ 8 4,399 35,192 Intel