PROJECT #18411 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: 64,500

Core: GRO_A8

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

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

Rank
Project
Model Name
Folding@Home Identifier
Make
Brand
GPU
Model
PPD
Average
Points WU
Average
WUs Day
Average
WU Time
Average

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 9 3950X 16-CORE 32 27,199 870,368 AMD
2 RYZEN 9 5950X 16-CORE 32 22,269 712,608 AMD
3 RYZEN 9 3900X 12-CORE 24 16,572 397,728 AMD
4 11TH GEN CORE I9-11900K @ 3.50GHZ 16 19,600 313,600 Intel
5 RYZEN 7 5800X 8-CORE 16 18,530 296,480 AMD
6 RYZEN 7 3800X 8-CORE 16 16,586 265,376 AMD
7 CORE I9-9900K CPU @ 3.60GHZ 16 16,358 261,728 Intel
8 RYZEN 7 3700X 8-CORE 16 15,049 240,784 AMD
9 XEON CPU E5-2680 V3 @ 2.50GHZ 24 9,133 219,192 Intel
10 EPYC 7251 8-CORE 16 6,596 105,536 AMD
11 CORE I9-9880H CPU @ 2.30GHZ 16 6,523 104,368 Intel
12 RYZEN 7 2700X EIGHT-CORE 16 4,570 73,120 AMD