PROJECT #18404 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 Monday, 27 September 2021 17:59:03

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

PPDDB data as of Monday, 27 September 2021 17:59:03

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 35,496 1,135,872 AMD
2 RYZEN 7 5800X 8-CORE 16 53,645 858,320 AMD
3 RYZEN 9 5950X 16-CORE 32 14,953 478,496 AMD
4 CORE I9-10900X CPU @ 3.70GHZ 20 14,101 282,020 Intel
5 RYZEN THREADRIPPER 3960X 24-CORE 48 5,226 250,848 AMD
6 RYZEN 9 3900X 12-CORE 24 9,941 238,584 AMD
7 XEON CPU E5-2690 V4 @ 2.60GHZ 28 7,192 201,376 Intel
8 CORE I7-9700 CPU @ 3.00GHZ 8 22,892 183,136 Intel
9 RYZEN 5 3600 6-CORE 12 10,554 126,648 AMD
10 11TH GEN CORE I5-11400 @ 2.60GHZ 12 9,197 110,364 Intel
11 CORE I7-10750H CPU @ 2.60GHZ 12 7,164 85,968 Intel
12 CORE I7-8705G CPU @ 3.10GHZ 8 10,648 85,184 Intel
13 CORE I7-6700K CPU @ 4.00GHZ 8 10,249 81,992 Intel
14 CORE I9-8950HK CPU @ 2.90GHZ 12 6,360 76,320 Intel
15 11TH GEN CORE I5-1135G7 @ 2.40GHZ 8 8,918 71,344 Intel
16 CORE I7-6700 CPU @ 3.40GHZ 8 6,973 55,784 Intel