PROJECT #18412 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.

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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, 23 May 2022 03:34:39

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PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Monday, 23 May 2022 03:34:39

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 RYZEN 9 5950X 16-CORE 32 20,602 659,264 AMD
2 RYZEN 9 3950X 16-CORE 32 15,940 510,080 AMD
3 12TH GEN CORE I7-12700K 20 25,489 509,780 Intel
4 RYZEN 7 5700G 16 30,655 490,480 AMD
5 RYZEN 7 5800X 8-CORE 16 28,899 462,384 AMD
6 RYZEN 9 5900X 12-CORE 24 18,737 449,688 AMD
7 12TH GEN CORE I7-12700 20 21,936 438,720 Intel
8 RYZEN 9 3900 12-CORE 24 17,283 414,792 AMD
9 RYZEN 9 3900XT 12-CORE 24 14,067 337,608 AMD
10 RYZEN 7 3800X 8-CORE 16 20,426 326,816 AMD
11 CORE I9-10850K CPU @ 3.60GHZ 20 15,447 308,940 Intel
12 XEON CPU E5-2650 V2 @ 2.60GHZ 32 8,122 259,904 Intel
13 RYZEN 9 3900X 12-CORE 24 10,470 251,280 AMD
14 CORE I9-9900K CPU @ 3.60GHZ 16 15,015 240,240 Intel
15 RYZEN 7 3700X 8-CORE 16 14,280 228,480 AMD
16 RYZEN 7 2700X EIGHT-CORE 16 14,218 227,488 AMD
17 XEON CPU E5-2680 V3 @ 2.50GHZ 24 9,107 218,568 Intel
18 CORE I9-7920X CPU @ 2.90GHZ 24 8,822 211,728 Intel
19 XEON CPU E5-2660 V3 @ 2.60GHZ 20 10,566 211,320 Intel
20 CORE I9-10900X CPU @ 3.70GHZ 20 10,271 205,420 Intel
21 11TH GEN CORE I7-11700K @ 3.60GHZ 16 12,368 197,888 Intel
22 CORE I9-9900 CPU @ 3.10GHZ 16 12,228 195,648 Intel
23 11TH GEN CORE I7-11850H @ 2.50GHZ 16 11,476 183,616 Intel
24 RYZEN 9 5900HX 16 9,998 159,968 AMD
25 RYZEN 7 PRO 4750G 16 9,845 157,520 AMD
26 CORE I7-10700 CPU @ 2.90GHZ 16 7,632 122,112 Intel
27 XEON CPU E5-2690 V2 @ 3.00GHZ 20 6,017 120,340 Intel
28 XEON CPU E5-2697 V2 @ 2.70GHZ 24 4,822 115,728 Intel
29 CORE I7-10700T CPU @ 2.00GHZ 16 6,942 111,072 Intel
30 XEON CPU E5-2680 0 @ 2.70GHZ 16 5,273 84,368 Intel
31 XEON CPU E5-2450 0 @ 2.10GHZ 10 6,157 61,570 Intel