PROJECT #18410 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, 03 October 2022 12:16:28

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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 XEON CPU E5-2680 V4 @ 2.40GHZ 28 28,967 811,076 Intel
2 RYZEN 9 5950X 16-CORE 32 21,820 698,240 AMD
3 12TH GEN CORE I7-12700K 20 31,361 627,220 Intel
4 RYZEN THREADRIPPER PRO 5965WX 24-CORES 48 12,927 620,496 AMD
5 12TH GEN CORE I9-12900K 24 24,560 589,440 Intel
6 RYZEN 9 3950X 16-CORE 32 17,458 558,656 AMD
7 RYZEN 7 5800X 8-CORE 16 28,793 460,688 AMD
8 RYZEN 9 3900X 12-CORE 24 18,473 443,352 AMD
9 RYZEN THREADRIPPER 2950X 16-CORE 32 13,322 426,304 AMD
10 11TH GEN CORE I7-11700K @ 3.60GHZ 16 24,934 398,944 Intel
11 12TH GEN CORE I7-12700 20 19,242 384,840 Intel
12 RYZEN 9 3900 12-CORE 24 15,963 383,112 AMD
13 RYZEN 9 5900X 12-CORE 24 14,703 352,872 AMD
14 GENUINE CPU 0000 @ 2.10GHZ 44 7,803 343,332 Intel
15 XEON CPU E5-2690 V4 @ 2.60GHZ 28 11,667 326,676 Intel
16 RYZEN 7 3800X 8-CORE 16 19,211 307,376 AMD
17 RYZEN 9 3900XT 12-CORE 24 12,468 299,232 AMD
18 CORE I9-10850K CPU @ 3.60GHZ 20 14,448 288,960 Intel
19 CORE I7-10870H CPU @ 2.20GHZ 16 17,541 280,656 Intel
20 CORE I9-10900X CPU @ 3.70GHZ 20 13,590 271,800 Intel
21 RYZEN 5 3600 6-CORE 12 22,567 270,804 AMD
22 CORE I9-7920X CPU @ 2.90GHZ 24 10,683 256,392 Intel
23 CORE I9-9900K CPU @ 3.60GHZ 16 15,946 255,136 Intel
24 XEON CPU E5-2680 V3 @ 2.50GHZ 24 10,237 245,688 Intel
25 XEON CPU E5-2650 V2 @ 2.60GHZ 32 7,472 239,104 Intel
26 RYZEN 7 3700X 8-CORE 16 10,722 171,552 AMD
27 RYZEN 5 5600X 6-CORE 12 13,787 165,444 AMD
28 CORE I7-10700 CPU @ 2.90GHZ 16 10,193 163,088 Intel
29 RYZEN 7 PRO 4750G 16 9,654 154,464 AMD
30 11TH GEN CORE I9-11900F @ 2.50GHZ 16 8,739 139,824 Intel
31 CORE I7-10700T CPU @ 2.00GHZ 16 7,917 126,672 Intel
32 RYZEN 7 1700 EIGHT-CORE 16 6,331 101,296 AMD
33 XEON CPU E5-2680 0 @ 2.70GHZ 16 5,763 92,208 Intel
34 XEON CPU E5-2698 V4 @ 2.20GHZ 16 2,935 46,960 Intel
35 XEON CPU E5-2697 V2 @ 2.70GHZ 24 1,763 42,312 Intel