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Research: EBOLA Folding Project #18291

Project #18291 overview

Project Summary AI Beta

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Project team

Manager(s)
Justin Miller
Institution
University of Pennsylvania

Work unit

Atoms
21,989
Core
0xaa
Status
Public

No related projects listed.

Source material

Official Project Description

Force fields aren't only a thing in far off galaxies, but are also an integral part of molecular dynamics simulations.

Principally, molecular dynamics simulations are evaluating Newton's laws of motion iteratively.

Each atom in the simulation is given a position, velocity, and has some forces acting upon it.

We then take a short step forward in time (often 2-4 femtoseconds), update the positions of each atom based on the last known position, velocity, and acceleration, before re-evaluating the forces acting upon each atom.

Repeating this millions to trillions of times (or more), gives us a physics-based movie of atoms moving which we use to give insight into the behavior of our favorite proteins. One of the fundamental steps of this process is calculating the forces on each atom.

The collective model describing how to calculate these forces is called a force field.

Through the years, many force fields have been derived and refined, each one focusing on improving certain forces or behaviors of the simulation.

While tests are usually performed when force fields are redeveloped, it is difficult to achieve robust sampling (e.g.

many observations of rare events).

Here, we are continuing our efforts to catalog the performance and accuracy of these force fields.

In this project series, we use the ebolavirus protein VP35, as our test model.

VP35 is used by ebolavirus to protect viral RNA from recognition by the immune system which the Bowman lab has extensively characterized.

Notably, we have identified a cryptic pocket which we have experimentally characterized, along with several mutations that both close and open the pocket.

This suite of data provides a robust means to characterize the ability of force fields to both identify cryptic pockets as well as the sensitivity of force fields to mutations in proteins. In 18291 we test amber14sb with tip3p water.

Performance data

Hardware Performance for Project 18291

Compare community-sampled Folding@Home output for the GPUs and CPUs processing this project.

Data as of Sunday, 02 August 2026 21:52:27

CPU PPD Averages Beta

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 RYZEN 5 5500 12 37,265 447,180 AMD
2 13TH GEN CORE I9-13900KF 32 10,158 325,056 Intel
3 12TH GEN CORE I9-12900K 24 13,283 318,792 Intel
4 CORE I5-10400 CPU @ 2.90GHZ 12 11,592 139,104 Intel
5 11TH GEN CORE I5-11400F @ 2.60GHZ 12 9,484 113,808 Intel
6 CORE I5-10500T CPU @ 2.30GHZ 12 9,055 108,660 Intel
7 CORE I7-4790K CPU @ 4.00GHZ 8 13,140 105,120 Intel
8 RYZEN 5 3600 6-CORE 12 8,087 97,044 AMD
9 XEON CPU E3-1270 V5 @ 3.60GHZ 8 9,150 73,200 Intel
10 CORE I5-7400 CPU @ 3.00GHZ 4 16,167 64,668 Intel
11 CORE I5-2400 CPU @ 3.10GHZ 4 11,404 45,616 Intel
12 CORE I7-7700HQ CPU @ 2.80GHZ 8 1,940 15,520 Intel