PROJECT #18424 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.

.

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

Rank
Project
Model Name
Folding@Home Identifier
Make
Brand
GPU
Model
PPD
Average
Points WU
Average
WUs Day
Average
WU Time
Average

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 CORE I7-8700 CPU @ 3.20GHZ 12 142,494 1,709,928 Intel
2 11TH GEN CORE I5-11400F @ 2.60GHZ 12 60,307 723,684 Intel
3 RYZEN 9 5950X 16-CORE 32 16,788 537,216 AMD
4 11TH GEN CORE I9-11900K @ 3.50GHZ 16 30,360 485,760 Intel
5 RYZEN 7 5800X 8-CORE 16 30,313 485,008 AMD
6 RYZEN 9 3950X 16-CORE 32 14,886 476,352 AMD
7 RYZEN 7 3800X 8-CORE 16 27,160 434,560 AMD
8 XEON CPU E5-2690 V4 @ 2.60GHZ 28 15,027 420,756 Intel
9 RYZEN 9 3900 12-CORE 24 17,468 419,232 AMD
10 RYZEN 7 5700G 16 25,488 407,808 AMD
11 12TH GEN CORE I7-12700 20 19,849 396,980 Intel
12 RYZEN 9 3900X 12-CORE 24 14,772 354,528 AMD
13 CORE I5-9600K CPU @ 3.70GHZ 6 57,209 343,254 Intel
14 CORE I7-10700K CPU @ 3.80GHZ 16 20,813 333,008 Intel
15 XEON W-1290P CPU @ 3.70GHZ 20 16,426 328,520 Intel
16 CORE I9-9900K CPU @ 3.60GHZ 16 15,784 252,544 Intel
17 RYZEN 5 5600X 6-CORE 12 20,834 250,008 AMD
18 XEON CPU E5-2650 V2 @ 2.60GHZ 32 7,561 241,952 Intel
19 CORE I7-5960X CPU @ 3.00GHZ 16 14,868 237,888 Intel
20 CORE I9-10900X CPU @ 3.70GHZ 20 11,866 237,320 Intel
21 XEON CPU E5-2680 V3 @ 2.50GHZ 24 9,195 220,680 Intel
22 CORE I5-10400 CPU @ 2.90GHZ 12 14,019 168,228 Intel
23 11TH GEN CORE I9-11900F @ 2.50GHZ 16 10,498 167,968 Intel
24 RYZEN 7 PRO 4750G 16 10,293 164,688 AMD
25 RYZEN 5 3600 6-CORE 12 13,688 164,256 AMD
26 CORE I7-10700 CPU @ 2.90GHZ 16 9,740 155,840 Intel
27 RYZEN 7 3700X 8-CORE 16 9,557 152,912 AMD
28 11TH GEN CORE I5-11400 @ 2.60GHZ 12 11,494 137,928 Intel
29 CORE I7-8700K CPU @ 3.70GHZ 12 10,835 130,020 Intel
30 RYZEN 5 1600 SIX-CORE 12 9,443 113,316 AMD
31 CORE I7-10700T CPU @ 2.00GHZ 16 7,016 112,256 Intel
32 RYZEN 9 5900HX 16 6,749 107,984 AMD
33 CORE I7-5820K CPU @ 3.30GHZ 12 8,919 107,028 Intel
34 RYZEN 5 2600 SIX-CORE 12 8,786 105,432 AMD
35 XEON CPU E5-2680 0 @ 2.70GHZ 16 5,851 93,616 Intel
36 XEON CPU E5-2450 0 @ 2.10GHZ 10 6,224 62,240 Intel
37 12TH GEN CORE I7-12700K 20 2,154 43,080 Intel