PROJECT #18403 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 Sunday, 16 January 2022 11:42:01

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 Sunday, 16 January 2022 11:42:01

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 RYZEN 7 5700G 16 47,014 752,224 AMD
2 RYZEN 7 5800X 8-CORE 16 44,465 711,440 AMD
3 RYZEN 9 3950X 16-CORE 32 20,115 643,680 AMD
4 RYZEN 9 5950X 16-CORE 32 14,528 464,896 AMD
5 11TH GEN CORE I9-11900K @ 3.50GHZ 16 27,325 437,200 Intel
6 RYZEN 7 3800X 8-CORE 16 20,302 324,832 AMD
7 CORE I9-7920X CPU @ 2.90GHZ 24 13,494 323,856 Intel
8 CORE I9-10850K CPU @ 3.60GHZ 20 15,712 314,240 Intel
9 CORE I9-10900X CPU @ 3.70GHZ 20 15,022 300,440 Intel
10 CORE I7-8700 CPU @ 3.20GHZ 12 20,494 245,928 Intel
11 RYZEN 5 2600X SIX-CORE 12 18,132 217,584 AMD
12 RYZEN THREADRIPPER 3960X 24-CORE 48 4,513 216,624 AMD
13 XEON CPU E5-2690 V4 @ 2.60GHZ 28 7,106 198,968 Intel
14 RYZEN 7 PRO 4750G 16 11,539 184,624 AMD
15 RYZEN 5 5600X 6-CORE 12 13,603 163,236 AMD
16 CORE I5-10400 CPU @ 2.90GHZ 12 12,391 148,692 Intel
17 RYZEN 5 3600 6-CORE 12 11,120 133,440 AMD
18 11TH GEN CORE I5-11400 @ 2.60GHZ 12 8,860 106,320 Intel
19 CORE I7-5820K CPU @ 3.30GHZ 12 8,686 104,232 Intel
20 RYZEN 5 1600 SIX-CORE 12 7,305 87,660 AMD
21 RYZEN 5 2600 SIX-CORE 12 7,243 86,916 AMD
22 CORE I7-6700K CPU @ 4.00GHZ 8 10,048 80,384 Intel
23 CORE I9-8950HK CPU @ 2.90GHZ 12 6,394 76,728 Intel
24 CORE I7-8705G CPU @ 3.10GHZ 8 9,297 74,376 Intel
25 CORE I7-4770HQ CPU @ 2.20GHZ 8 7,143 57,144 Intel
26 CORE I5-8259U CPU @ 2.30GHZ 8 4,809 38,472 Intel