PROJECT #18409 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 Monday, 03 October 2022 12:16:28

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, 03 October 2022 12:16:28

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 CORE I9-10850K CPU @ 3.60GHZ 20 36,403 728,060 Intel
2 RYZEN 9 5900X 12-CORE 24 22,572 541,728 AMD
3 12TH GEN CORE I9-12900K 24 22,428 538,272 Intel
4 RYZEN 9 3950X 16-CORE 32 14,958 478,656 AMD
5 RYZEN 9 3900 12-CORE 24 19,548 469,152 AMD
6 12TH GEN CORE I7-12700K 20 23,038 460,760 Intel
7 EPYC 7V12 64-CORE 64 7,178 459,392 AMD
8 RYZEN 7 5800X 8-CORE 16 27,268 436,288 AMD
9 RYZEN 7 5700X 8-CORE 16 26,774 428,384 AMD
10 RYZEN 9 5950X 16-CORE 32 13,262 424,384 AMD
11 11TH GEN CORE I7-11700K @ 3.60GHZ 16 24,920 398,720 Intel
12 CORE I9-7920X CPU @ 2.90GHZ 24 15,282 366,768 Intel
13 12TH GEN CORE I7-12700 20 17,202 344,040 Intel
14 RYZEN 9 3900XT 12-CORE 24 13,800 331,200 AMD
15 12TH GEN CORE I5-12600K 16 19,454 311,264 Intel
16 RYZEN 7 5700G 16 18,972 303,552 AMD
17 RYZEN 9 3900X 12-CORE 24 12,309 295,416 AMD
18 RYZEN 7 5800X3D 8-CORE 16 18,188 291,008 AMD
19 RYZEN 7 3800X 8-CORE 16 18,029 288,464 AMD
20 CORE I9-10900X CPU @ 3.70GHZ 20 13,481 269,620 Intel
21 CORE I9-9900K CPU @ 3.60GHZ 16 16,622 265,952 Intel
22 RYZEN 5 3600 6-CORE 12 22,085 265,020 AMD
23 CORE I9-9900X CPU @ 3.50GHZ 20 12,095 241,900 Intel
24 CORE I9-9900 CPU @ 3.10GHZ 16 14,721 235,536 Intel
25 XEON CPU E5-2680 V3 @ 2.50GHZ 24 9,428 226,272 Intel
26 XEON CPU E5-2650 V2 @ 2.60GHZ 32 6,958 222,656 Intel
27 11TH GEN CORE I9-11900K @ 3.50GHZ 16 13,799 220,784 Intel
28 11TH GEN CORE I7-11850H @ 2.50GHZ 16 13,190 211,040 Intel
29 RYZEN 7 PRO 4750G 16 12,001 192,016 AMD
30 11TH GEN CORE I5-11400F @ 2.60GHZ 12 14,262 171,144 Intel
31 CORE I7-10700T CPU @ 2.00GHZ 16 10,493 167,888 Intel
32 11TH GEN CORE I9-11900F @ 2.50GHZ 16 10,385 166,160 Intel
33 RYZEN 7 3700X 8-CORE 16 9,718 155,488 AMD
34 CORE I7-10870H CPU @ 2.20GHZ 16 8,399 134,384 Intel
35 XEON CPU E5-2690 V2 @ 3.00GHZ 20 6,453 129,060 Intel
36 11TH GEN CORE I7-11800H @ 2.30GHZ 16 8,065 129,040 Intel
37 CORE I9-9880H CPU @ 2.30GHZ 16 6,290 100,640 Intel
38 XEON CPU E5-2680 0 @ 2.70GHZ 16 6,181 98,896 Intel
39 RYZEN 7 4800H 16 6,046 96,736 AMD