---
title: "Rosetta@home"
chunk: 2/6
source: "https://en.wikipedia.org/wiki/Rosetta@home"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:25:41.390646+00:00"
instance: "kb-cron"
---

A primary feature of the Rosetta@home graphical user interface (GUI) is a screensaver which shows a current workunit's progress during the simulated protein folding process.  In the upper-left of the current screensaver, the target protein is shown adopting different shapes (conformations) in its search for the lowest energy structure.  Depicted immediately to the right is the structure of the most recently accepted.  On the upper right the lowest energy conformation of the current decoy is shown; below that is the true, or native, structure of the protein if it has already been determined.  Three graphs are included in the screensaver.  Near the middle, a graph for the accepted model's thermodynamic free energy is displayed, which fluctuates as the accepted model changes.  A graph of the accepted model's root-mean-square deviation (RMSD), which measures how structurally similar the accepted model is to the native model, is shown far right.  On the right of the accepted energy graph and below the RMSD graph, the results from these two functions are used to produce an energy vs. RMSD plot as the model is progressively refined.
Like all BOINC projects, Rosetta@home runs in the background of the user's computer, using idle computer power, either at or before logging into an account on the host operating system.  The program frees resources from the CPU as they are needed by other applications so that normal computer use is unaffected.  Many program settings can be specified via user account preferences, including: the maximum percentage of CPU resources the program can use (to control power consumption or heat production from a computer running at sustained capacity), the times of day during which the program can run, and many more.

== Project significance ==

With the proliferation of genome sequencing projects, scientists can infer the amino acid sequence, or primary structure, of many proteins that carry out functions within the cell.  To better understand a protein's function and aid in rational drug design, scientists need to know the protein's three-dimensional tertiary structure.

Protein 3D structures are currently determined experimentally via X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy. The process is slow (it can take weeks or even months to figure out how to crystallize a protein for the first time) and costly (around US$100,000 per protein). Unfortunately, the rate at which new sequences are discovered far exceeds the rate of structure determination – out of more than 7,400,000 protein sequences available in the National Center for Biotechnology Information (NCBI) nonredundant (nr) protein database, fewer than 52,000 proteins' 3D structures have been solved and deposited in the Protein Data Bank, the main repository for structural information on proteins. One of the main goals of Rosetta@home is to predict protein structures with the same accuracy as existing methods, but in a way that requires significantly less time and money.  Rosetta@home also develops methods to determine the structure and docking of membrane proteins (e.g., G protein–coupled receptors (GPCRs)), which are exceptionally difficult to analyze with traditional techniques like X-ray crystallography and NMR spectroscopy, yet represent the majority of targets for modern drugs.
Progress in protein structure prediction is evaluated in the biannual Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiment, in which researchers from around the world attempt to derive a protein's structure from the protein's amino acid sequence.  High scoring groups in this sometimes competitive experiment are considered the de facto standard-bearers for what is the state of the art in protein structure prediction.  Rosetta, the program on which Rosetta@home is based, has been used since CASP5 in 2002.  In the 2004 CASP6 experiment, Rosetta made history by being the first to produce a close to atomic-level resolution, ab initio protein structure prediction in its submitted model for CASP target T0281.  Ab initio modeling is considered an especially difficult category of protein structure prediction, as it does not use information from structural homology and must rely on information from sequence homology and modeling physical interactions within the protein. Rosetta@home has been used in CASP since 2006, where it was among the top predictors in every category of structure prediction in CASP7. These high quality predictions were enabled by the computing power made available by Rosetta@home volunteers. Increasing computing power allows Rosetta@home to sample more regions of conformation space (the possible shapes a protein can assume), which, according to Levinthal's paradox, is predicted to increase exponentially with protein length.
Rosetta is also used in protein–protein docking prediction, which determines the structure of multiple complexed proteins, or quaternary structure.  This type of protein interaction affects many cellular functions, including antigen–antibody and enzyme–inhibitor binding and cellular import and export.  Determining these interactions is critical for drug design.  Rosetta is used in the Critical Assessment of Prediction of Interactions (CAPRI) experiment, which evaluates the state of the protein docking field similar to how CASP gauges progress in protein structure prediction.  The computing power made available by Rosetta@home's project volunteers has been cited as a major factor in Rosetta's performance in CAPRI 2007, where its docking predictions have been among the most accurate and complete.
In early 2008, Rosetta was used to computationally design a protein with a function never before observed in nature. This was inspired in part by the retraction of a high-profile paper from 2004 which originally described the computational design of a protein with improved enzymatic activity relative to its natural form. The 2008 research paper from David Baker's group describing how the protein was made, which cited Rosetta@home for the computing resources it made available, represented an important proof of concept for this protein design method. This type of protein design could have future applications in drug discovery, green chemistry, and bioremediation.
The Rosetta computer program was cited in the 2024 Scientific Background to the Nobel Prize in Chemistry. 