PROJECT #18422 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, 03 October 2022 12:16:28

Rank
Project
Model Name
Folding@Home Identifier
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Brand
GPU
Model
PPD
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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 RYZEN 9 5950X 16-CORE 32 26,385 844,320 AMD
2 12TH GEN CORE I7-12700K 20 31,408 628,160 Intel
3 12TH GEN CORE I9-12900K 24 23,920 574,080 Intel
4 RYZEN 9 5900X 12-CORE 24 20,595 494,280 AMD
5 RYZEN 7 5800X 8-CORE 16 29,586 473,376 AMD
6 RYZEN 9 3950X 16-CORE 32 13,682 437,824 AMD
7 RYZEN 7 5700X 8-CORE 16 26,894 430,304 AMD
8 RYZEN 9 3900 12-CORE 24 17,924 430,176 AMD
9 11TH GEN CORE I7-11700K @ 3.60GHZ 16 26,851 429,616 Intel
10 CORE I9-7920X CPU @ 2.90GHZ 24 17,442 418,608 Intel
11 12TH GEN CORE I7-12700 20 20,245 404,900 Intel
12 RYZEN 9 3900XT 12-CORE 24 15,344 368,256 AMD
13 RYZEN 9 3900X 12-CORE 24 13,699 328,776 AMD
14 XEON CPU E5-2690 V4 @ 2.60GHZ 28 11,248 314,944 Intel
15 RYZEN 7 3800X 8-CORE 16 18,785 300,560 AMD
16 RYZEN 5 5600X 6-CORE 12 23,385 280,620 AMD
17 RYZEN 5 5600G 12 21,142 253,704 AMD
18 XEON CPU E5-2680 V3 @ 2.50GHZ 24 10,279 246,696 Intel
19 CORE I7-8700 CPU @ 3.20GHZ 12 19,018 228,216 Intel
20 CORE I9-10900X CPU @ 3.70GHZ 20 11,326 226,520 Intel
21 CORE I9-9900 CPU @ 3.10GHZ 16 13,906 222,496 Intel
22 CORE I7-10700 CPU @ 2.90GHZ 16 13,556 216,896 Intel
23 RYZEN 7 PRO 4750G 16 12,715 203,440 AMD
24 XEON CPU E5-2698 V4 @ 2.20GHZ 16 12,116 193,856 Intel
25 RYZEN 5 3600 6-CORE 12 16,073 192,876 AMD
26 11TH GEN CORE I9-11900F @ 2.50GHZ 16 10,194 163,104 Intel
27 EPYC 7401P 24-CORE 48 3,188 153,024 AMD
28 RYZEN 7 3700X 8-CORE 16 9,304 148,864 AMD
29 CORE I5-10400 CPU @ 2.90GHZ 12 12,161 145,932 Intel
30 11TH GEN CORE I5-11400 @ 2.60GHZ 12 10,673 128,076 Intel
31 RYZEN 7 2700X EIGHT-CORE 16 7,872 125,952 AMD
32 CORE I7-10700T CPU @ 2.00GHZ 16 7,607 121,712 Intel
33 CORE I9-8950HK CPU @ 2.90GHZ 12 9,803 117,636 Intel
34 RYZEN 5 1600 SIX-CORE 12 9,080 108,960 AMD
35 APPLE M1 PRO 10 7,772 77,720 Apple
36 XEON CPU E5-2680 0 @ 2.70GHZ 16 4,595 73,520 Intel
37 XEON CPU E5-2697 V2 @ 2.70GHZ 24 2,183 52,392 Intel