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 Sunday, 16 January 2022 11:42:01

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Folding@Home Identifier
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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 9 3950X 16-CORE 32 35,496 1,135,872 AMD
2 RYZEN 7 5800X 8-CORE 16 47,770 764,320 AMD
3 RYZEN 7 5700G 16 43,705 699,280 AMD
4 11TH GEN CORE I9-11900K @ 3.50GHZ 16 26,527 424,432 Intel
5 RYZEN 7 3800X 8-CORE 16 20,747 331,952 AMD
6 RYZEN 9 5950X 16-CORE 32 9,693 310,176 AMD
7 CORE I9-10900X CPU @ 3.70GHZ 20 14,101 282,020 Intel
8 RYZEN 5 5600X 6-CORE 12 21,610 259,320 AMD
9 RYZEN THREADRIPPER 3960X 24-CORE 48 5,226 250,848 AMD
10 RYZEN 5 3600X 6-CORE 12 20,499 245,988 AMD
11 RYZEN 9 3900X 12-CORE 24 9,941 238,584 AMD
12 CORE I7-8700 CPU @ 3.20GHZ 12 19,428 233,136 Intel
13 XEON CPU E5-2690 V4 @ 2.60GHZ 28 7,192 201,376 Intel
14 CORE I7-5960X CPU @ 3.00GHZ 16 12,310 196,960 Intel
15 RYZEN 7 2700X EIGHT-CORE 16 12,218 195,488 AMD
16 CORE I7-9700 CPU @ 3.00GHZ 8 22,827 182,616 Intel
17 CORE I9-9900 CPU @ 3.10GHZ 16 11,163 178,608 Intel
18 EPYC 7401P 24-CORE 48 2,931 140,688 AMD
19 RYZEN 9 3900XT 12-CORE 24 5,841 140,184 AMD
20 RYZEN 5 3600 6-CORE 12 11,639 139,668 AMD
21 11TH GEN CORE I5-11400 @ 2.60GHZ 12 9,197 110,364 Intel
22 CORE I7-8705G CPU @ 3.10GHZ 8 10,582 84,656 Intel
23 CORE I7-6700K CPU @ 4.00GHZ 8 10,573 84,584 Intel
24 CORE I9-8950HK CPU @ 2.90GHZ 12 6,945 83,340 Intel
25 11TH GEN CORE I5-1135G7 @ 2.40GHZ 8 8,703 69,624 Intel
26 CORE I3-10100 CPU @ 3.60GHZ 8 8,678 69,424 Intel
27 CORE I7-10750H CPU @ 2.60GHZ 12 5,568 66,816 Intel
28 RYZEN 7 3700X 8-CORE 16 3,793 60,688 AMD
29 CORE I7-6700 CPU @ 3.40GHZ 8 6,973 55,784 Intel
30 CORE I7-4770HQ CPU @ 2.20GHZ 8 5,717 45,736 Intel