PROJECT #18423 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

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 04:36:07

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 CORE I9-8950HK CPU @ 2.90GHZ 12 239,861 2,878,332 Intel
2 RYZEN 9 3950X 16-CORE 32 23,909 765,088 AMD
3 RYZEN 7 5800X 8-CORE 16 24,554 392,864 AMD
4 CORE I7-10870H CPU @ 2.20GHZ 16 23,738 379,808 Intel
5 XEON CPU E5-2680 V3 @ 2.50GHZ 24 15,429 370,296 Intel
6 RYZEN 7 3800X 8-CORE 16 22,951 367,216 AMD
7 CORE I9-10850K CPU @ 3.60GHZ 20 17,618 352,360 Intel
8 EPYC 7401P 24-CORE 48 7,102 340,896 AMD
9 RYZEN THREADRIPPER 2970WX 24-CORE 48 6,536 313,728 AMD
10 CORE I9-10900X CPU @ 3.70GHZ 20 12,499 249,980 Intel
11 CORE I7-8700 CPU @ 3.20GHZ 12 19,811 237,732 Intel
12 CORE I9-9900X CPU @ 3.50GHZ 20 11,234 224,680 Intel
13 RYZEN 5 5600X 6-CORE 12 18,137 217,644 AMD
14 CORE I5-10600 CPU @ 3.30GHZ 12 17,294 207,528 Intel
15 CORE I5-10400 CPU @ 2.90GHZ 12 15,016 180,192 Intel
16 RYZEN 7 2700X EIGHT-CORE 16 11,028 176,448 AMD
17 RYZEN 5 3600 6-CORE 12 14,410 172,920 AMD
18 RYZEN 5 2600 SIX-CORE 12 11,499 137,988 AMD
19 RYZEN 5 1600 SIX-CORE 12 9,373 112,476 AMD
20 11TH GEN CORE I5-11400 @ 2.60GHZ 12 8,966 107,592 Intel