Research section Research projects and references

Research: EBOLA Folding Project #18292

Project #18292 overview

Project Summary AI Beta

Scientists use computer simulations to understand how molecules move and interact. The project relates to testing different simulation models (force fields) by studying a protein from the Ebola virus. This helps researchers better understand how viruses work and develop new treatments.
Automated summary; simplified and may not be fully accurate.

Project team

Manager(s)
Justin Miller
Institution
University of Pennsylvania

Work unit

Atoms
21,989
Core
0xaa
Status
Beta
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. p18291 - amber14sb with tip3p water. p18292 - charmm36m with tip3p water.

Performance data

Hardware Performance for Project 18292

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

Data as of Sunday, 06 September 2026 12:23:44

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 5600 6-CORE 12 45,107 541,284 AMD
2 RYZEN 5 5500 12 40,951 491,412 AMD
3 13TH GEN CORE I9-13900KF 32 14,489 463,648 Intel
4 RYZEN 9 3900X 12-CORE 24 13,204 316,896 AMD
5 12TH GEN CORE I5-12500T 12 15,224 182,688 Intel
6 11TH GEN CORE I5-11400F @ 2.60GHZ 12 14,479 173,748 Intel
7 CORE I5-10400 CPU @ 2.90GHZ 12 12,173 146,076 Intel
8 RYZEN 5 3600 6-CORE 12 11,500 138,000 AMD
9 CORE I5-10500T CPU @ 2.30GHZ 12 10,773 129,276 Intel
10 CORE I5-7400 CPU @ 3.00GHZ 4 15,241 60,964 Intel
11 CORE I7-7500U CPU @ 2.70GHZ 4 11,809 47,236 Intel
12 CORE I7-7700HQ CPU @ 2.80GHZ 8 2,837 22,696 Intel
13 CORE I5-3570S CPU @ 3.10GHZ 4 4,092 16,368 Intel