PROJECT #18425 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: 80,500

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 XEON CPU E5-2620 0 @ 2.00GHZ 12 4,661,813 55,941,756 Intel
2 RYZEN THREADRIPPER 2950X 16-CORE 32 1,553,143 49,700,576 AMD
3 RYZEN 9 5950X 16-CORE 32 22,456 718,592 AMD
4 RYZEN 9 3900X 12-CORE 24 19,571 469,704 AMD
5 CORE I9-10850K CPU @ 3.60GHZ 20 23,438 468,760 Intel
6 RYZEN 5 5500U 12 38,206 458,472 AMD
7 12TH GEN CORE I7-12700 20 22,762 455,240 Intel
8 RYZEN 9 3900 12-CORE 24 18,659 447,816 AMD
9 RYZEN THREADRIPPER 2970WX 24-CORE 48 9,253 444,144 AMD
10 RYZEN 9 3950X 16-CORE 32 13,599 435,168 AMD
11 RYZEN 7 5800X 8-CORE 16 26,923 430,768 AMD
12 RYZEN 9 3900XT 12-CORE 24 15,831 379,944 AMD
13 RYZEN 9 5900X 12-CORE 24 15,445 370,680 AMD
14 XEON CPU E5-2690 V4 @ 2.60GHZ 28 12,376 346,528 Intel
15 RYZEN 7 3800X 8-CORE 16 19,285 308,560 AMD
16 CORE I9-9900K CPU @ 3.60GHZ 16 17,397 278,352 Intel
17 RYZEN 5 5600X 6-CORE 12 21,617 259,404 AMD
18 CORE I7-10700K CPU @ 3.80GHZ 16 14,961 239,376 Intel
19 CORE I9-10900X CPU @ 3.70GHZ 20 11,942 238,840 Intel
20 12TH GEN CORE I7-12700K 20 11,760 235,200 Intel
21 CORE I7-8700 CPU @ 3.20GHZ 12 19,030 228,360 Intel
22 XEON CPU E5-2680 V3 @ 2.50GHZ 24 9,485 227,640 Intel
23 RYZEN 5 3600 6-CORE 12 18,425 221,100 AMD
24 XEON CPU E5-2660 V3 @ 2.60GHZ 20 10,744 214,880 Intel
25 CORE I5-10400 CPU @ 2.90GHZ 12 17,279 207,348 Intel
26 RYZEN 7 3700X 8-CORE 16 12,376 198,016 AMD
27 RYZEN 5 5600G 12 15,979 191,748 AMD
28 RYZEN 7 PRO 4750G 16 9,827 157,232 AMD
29 CORE I7-10700 CPU @ 2.90GHZ 16 8,931 142,896 Intel
30 CORE I9-8950HK CPU @ 2.90GHZ 12 11,506 138,072 Intel
31 11TH GEN CORE I5-11400 @ 2.60GHZ 12 10,176 122,112 Intel
32 CORE I7-10750H CPU @ 2.60GHZ 12 9,929 119,148 Intel
33 CORE I7-10700T CPU @ 2.00GHZ 16 6,861 109,776 Intel
34 XEON CPU E5-2680 0 @ 2.70GHZ 16 5,323 85,168 Intel
35 XEON CPU E5-2450 0 @ 2.10GHZ 10 6,100 61,000 Intel
36 CORE I7-8750H CPU @ 2.20GHZ 12 3,183 38,196 Intel