PROJECT #18402 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

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 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 35,698 1,142,336 AMD
2 RYZEN 7 5800X 8-CORE 16 38,940 623,040 AMD
3 RYZEN 9 5950X 16-CORE 32 13,325 426,400 AMD
4 RYZEN 7 3800X 8-CORE 16 21,023 336,368 AMD
5 CORE I9-10850K CPU @ 3.60GHZ 20 15,835 316,700 Intel
6 CORE I9-10900X CPU @ 3.70GHZ 20 13,804 276,080 Intel
7 CORE I7-8700 CPU @ 3.20GHZ 12 20,645 247,740 Intel
8 XEON CPU E5-2680 V3 @ 2.50GHZ 24 9,745 233,880 Intel
9 RYZEN 9 3900XT 12-CORE 24 8,987 215,688 AMD
10 RYZEN 5 2600X SIX-CORE 12 17,908 214,896 AMD
11 XEON CPU E5-2690 V4 @ 2.60GHZ 28 6,907 193,396 Intel
12 CORE I5-9600K CPU @ 3.70GHZ 6 25,120 150,720 Intel
13 RYZEN 5 3600 6-CORE 12 11,678 140,136 AMD
14 11TH GEN CORE I5-11400 @ 2.60GHZ 12 9,289 111,468 Intel
15 CORE I7-4770HQ CPU @ 2.20GHZ 8 7,631 61,048 Intel
16 XEON W-10855M CPU @ 2.80GHZ 12 4,657 55,884 Intel