PROJECT #18420 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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Folding@Home Identifier
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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 11TH GEN CORE I5-11400 @ 2.60GHZ 12 235,038 2,820,456 Intel
2 RYZEN 9 5950X 16-CORE 32 16,390 524,480 AMD
3 RYZEN 7 5800X 8-CORE 16 30,423 486,768 AMD
4 RYZEN 9 3950X 16-CORE 32 14,513 464,416 AMD
5 12TH GEN CORE I7-12700 20 21,351 427,020 Intel
6 11TH GEN CORE I7-11700K @ 3.60GHZ 16 24,707 395,312 Intel
7 RYZEN 9 5900X 12-CORE 24 16,046 385,104 AMD
8 RYZEN 9 3900XT 12-CORE 24 15,249 365,976 AMD
9 RYZEN 9 3900X 12-CORE 24 14,507 348,168 AMD
10 RYZEN 7 5700G 16 20,970 335,520 AMD
11 XEON CPU E5-2690 V4 @ 2.60GHZ 28 11,699 327,572 Intel
12 CORE I9-8950HK CPU @ 2.90GHZ 12 26,233 314,796 Intel
13 RYZEN 7 3800X 8-CORE 16 18,191 291,056 AMD
14 RYZEN 5 5600X 6-CORE 12 24,104 289,248 AMD
15 RYZEN 7 PRO 4750G 16 16,971 271,536 AMD
16 CORE I9-9900K CPU @ 3.60GHZ 16 16,652 266,432 Intel
17 XEON CPU E5-2650 V2 @ 2.60GHZ 32 7,625 244,000 Intel
18 RYZEN 7 2700 EIGHT-CORE 16 15,133 242,128 AMD
19 RYZEN 7 3700X 8-CORE 16 14,645 234,320 AMD
20 RYZEN THREADRIPPER 2950X 16-CORE 32 7,196 230,272 AMD
21 XEON CPU E5-2680 V3 @ 2.50GHZ 24 9,486 227,664 Intel
22 CORE I7-8700 CPU @ 3.20GHZ 12 18,131 217,572 Intel
23 RYZEN 5 3600 6-CORE 12 15,358 184,296 AMD
24 CORE I7-6800K CPU @ 3.40GHZ 12 15,043 180,516 Intel
25 RYZEN 5 3600X 6-CORE 12 14,619 175,428 AMD
26 RYZEN 5 5600G 12 13,781 165,372 AMD
27 CORE I9-9900KF CPU @ 3.60GHZ 16 9,493 151,888 Intel
28 RYZEN 5 2600 SIX-CORE 12 12,220 146,640 AMD
29 CORE I7-10700 CPU @ 2.90GHZ 16 7,735 123,760 Intel
30 CORE I7-10700T CPU @ 2.00GHZ 16 7,421 118,736 Intel
31 RYZEN 5 1600 SIX-CORE 12 9,193 110,316 AMD
32 RYZEN 7 1700 EIGHT-CORE 16 6,866 109,856 AMD
33 XEON CPU E5-2680 0 @ 2.70GHZ 16 6,533 104,528 Intel
34 RYZEN 7 2700X EIGHT-CORE 16 5,355 85,680 AMD
35 CORE I5-10400 CPU @ 2.90GHZ 12 6,901 82,812 Intel
36 XEON CPU E5-2620 0 @ 2.00GHZ 12 2,692 32,304 Intel