PROJECT #18424 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: 80,500

Core: GRO_A8

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Friday, 03 December 2021 04:36:07

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PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Friday, 03 December 2021 04:36:07

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 RYZEN 9 5950X 16-CORE 32 18,107 579,424 AMD
2 11TH GEN CORE I9-11900K @ 3.50GHZ 16 30,360 485,760 Intel
3 XEON W-1290P CPU @ 3.70GHZ 20 16,426 328,520 Intel
4 RYZEN 9 3900X 12-CORE 24 13,676 328,224 AMD
5 CORE I9-9900K CPU @ 3.60GHZ 16 15,784 252,544 Intel
6 RYZEN 5 5600X 6-CORE 12 20,745 248,940 AMD
7 CORE I7-8700 CPU @ 3.20GHZ 12 20,185 242,220 Intel
8 RYZEN 7 3800X 8-CORE 16 15,018 240,288 AMD
9 CORE I7-5960X CPU @ 3.00GHZ 16 14,868 237,888 Intel
10 CORE I9-10900X CPU @ 3.70GHZ 20 11,866 237,320 Intel
11 RYZEN 5 3600 6-CORE 12 17,408 208,896 AMD
12 CORE I7-10700 CPU @ 2.90GHZ 16 9,801 156,816 Intel
13 CORE I7-8700K CPU @ 3.70GHZ 12 10,835 130,020 Intel
14 11TH GEN CORE I5-11400 @ 2.60GHZ 12 10,670 128,040 Intel
15 RYZEN 5 2600 SIX-CORE 12 8,690 104,280 AMD
16 RYZEN 7 3700X 8-CORE 16 5,757 92,112 AMD
17 XEON CPU E5-2450 0 @ 2.10GHZ 10 6,224 62,240 Intel