PROJECT #18400 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: 24,700
Core: GRO_A8
Status: Public
PROJECT FOLDING PPD AVERAGES BY GPU
PPDDB data as of Wednesday, 29 March 2023 06:15:36
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 Wednesday, 29 March 2023 06:15:36
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
---|---|---|---|---|---|
1 | RYZEN 9 3950X 16-CORE | 32 | 35,146 | 1,124,672 | AMD |
2 | RYZEN 7 5800X 8-CORE | 16 | 30,101 | 481,616 | AMD |
3 | RYZEN 9 5950X 16-CORE | 32 | 13,646 | 436,672 | AMD |
4 | RYZEN 7 3800X 8-CORE | 16 | 21,559 | 344,944 | AMD |
5 | CORE I9-10850K CPU @ 3.60GHZ | 20 | 17,169 | 343,380 | Intel |
6 | RYZEN 9 3900X 12-CORE | 24 | 12,252 | 294,048 | AMD |
7 | CORE I7-8700 CPU @ 3.20GHZ | 12 | 19,719 | 236,628 | Intel |
8 | XEON CPU E5-2690 V4 @ 2.60GHZ | 28 | 7,037 | 197,036 | Intel |
9 | RYZEN 7 2700X EIGHT-CORE | 16 | 11,053 | 176,848 | AMD |
10 | CORE I5-10400 CPU @ 2.90GHZ | 12 | 12,277 | 147,324 | Intel |
11 | RYZEN 9 3900XT 12-CORE | 24 | 6,071 | 145,704 | AMD |
12 | RYZEN 5 3600 6-CORE | 12 | 10,614 | 127,368 | AMD |
13 | RYZEN 5 2600 SIX-CORE | 12 | 8,349 | 100,188 | AMD |
14 | CORE I7-6700K CPU @ 4.00GHZ | 8 | 11,013 | 88,104 | Intel |
15 | CORE I7-8705G CPU @ 3.10GHZ | 8 | 10,783 | 86,264 | Intel |
16 | CORE I7-10750H CPU @ 2.60GHZ | 12 | 4,367 | 52,404 | Intel |
17 | CORE I5-7200U CPU @ 2.50GHZ | 4 | 12,066 | 48,264 | Intel |