PROJECT #18414 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 46,876 750,016 AMD
2 RYZEN 9 5950X 16-CORE 32 18,748 599,936 AMD
3 CORE I9-9900X CPU @ 3.50GHZ 20 22,796 455,920 Intel
4 RYZEN 7 5700G 16 23,885 382,160 AMD
5 RYZEN 9 3950X 16-CORE 32 11,366 363,712 AMD
6 RYZEN THREADRIPPER 2950X 16-CORE 32 9,240 295,680 AMD
7 RYZEN 7 3800X 8-CORE 16 17,516 280,256 AMD
8 CORE I7-10870H CPU @ 2.20GHZ 16 15,997 255,952 Intel
9 RYZEN 5 5600X 6-CORE 12 20,800 249,600 AMD
10 11TH GEN CORE I9-11900K @ 3.50GHZ 16 15,154 242,464 Intel
11 RYZEN 7 3700X 8-CORE 16 14,571 233,136 AMD
12 CORE I9-10900X CPU @ 3.70GHZ 20 11,440 228,800 Intel
13 XEON W-2175 CPU @ 2.50GHZ 28 7,761 217,308 Intel
14 CORE I9-9900K CPU @ 3.60GHZ 16 13,407 214,512 Intel
15 CORE I7-8700 CPU @ 3.20GHZ 12 16,891 202,692 Intel
16 RYZEN 5 2600X SIX-CORE 12 15,993 191,916 AMD
17 CORE I7-5960X CPU @ 3.00GHZ 16 11,150 178,400 Intel
18 11TH GEN CORE I7-11800H @ 2.30GHZ 16 8,076 129,216 Intel
19 RYZEN 5 3600X 6-CORE 12 10,621 127,452 AMD
20 RYZEN 5 3600 6-CORE 12 9,916 118,992 AMD
21 CORE I7-9700K CPU @ 3.60GHZ 8 13,850 110,800 Intel
22 RYZEN 5 1600 SIX-CORE 12 8,478 101,736 AMD
23 CORE I7-9700KF CPU @ 3.60GHZ 8 12,351 98,808 Intel
24 RYZEN 5 3600XT 6-CORE 12 7,584 91,008 AMD
25 11TH GEN CORE I5-11400 @ 2.60GHZ 12 7,330 87,960 Intel
26 RYZEN 5 2600 SIX-CORE 12 6,509 78,108 AMD
27 XEON W-10855M CPU @ 2.80GHZ 12 5,989 71,868 Intel
28 CORE I9-8950HK CPU @ 2.90GHZ 12 5,263 63,156 Intel
29 RYZEN 7 2700X EIGHT-CORE 16 3,638 58,208 AMD
30 CORE I7-4770HQ CPU @ 2.20GHZ 8 6,281 50,248 Intel
31 CORE I7-3770 CPU @ 3.40GHZ 8 5,795 46,360 Intel
32 XEON CPU E5-1620 0 @ 3.60GHZ 8 5,081 40,648 Intel
33 CORE I7 CPU 975 @ 3.33GHZ 8 2,322 18,576 Intel