Diachenko Karine Karapetivna

Student IA-42 group 

Faculty of Information and Computer Engineering

National Technical University of Ukraine”Igor Sykorsky Kyiv Polytechnic Institute”

Kyiv, Ukraine


Research Supervisor: Boyko I.

Teacher of English



  Annotation: this article examines the problem of the project "Folding@home". Specific features in the process of finding cure of diseases are considered by statistics means.

  Keywords: protein, folding, disease, structure, computer, calculating, molecule, algorithm, modeling, resource, project, cancer, treatment, therapies, virus, method, process, combination, analysis, scientist, volunteer, software, feature, capacity, network, interface, smartphone, unit, database, server, point.


  Proteins are biological molecules that perform thousands of specific functions within each cell of each biological organism. Proteins are synthesized in ribosomes in the form of a long polypeptide filament, but then quickly fold into their native spatial structure. This process is called protein folding. It may seem to be surprising, but this fundamental process is still not properly discovered at molecular level. 

  Poor understanding of the nature of proteins folding results in lots of not fully discovered diseases including Alzheimer's disease, cancer, Creutzfeldt–Jakob disease, cystic fibrosis, Huntington's disease, sickle-cell anemia, and type II diabetes. Cellular infection by viruses such as HIV and influenza also involve folding events on cell membranes. Better understanding of protein misfolding will significantly improve therapies and can open new methods of treatment of those diseases to the world. Such therapies include the use of engineered molecules to alter the production of a given protein, help destroy a misfolded protein or assist in the folding process. The combination of computational molecular modeling and experimental analysis can accelerate the development of molecular medicine.

  Folding@home (FAH or F@h) is a distributed computing project for computer modeling of protein molecule folding. The project was launched on the 1st of October 2000 by scientists from Stanford University. At the time of July 2008 – it is the largest project of distributed computing, both in terms of capacity and number of participants. Folding@home is developed and operated by the Pande Laboratory at Stanford University, under the direction of Prof. Vijay Pande and is shared by a huge number of scientific institutions and research laboratories all around the world.

  The project uses the idle processing resources of thousands of personal computers owned by volunteers who have installed the software on their systems. Many people may consider that the installed software may slow down the computers, but the software loads to the maximum efficiency only when user doesn’t need the computer’s resources. Also an important feature is low power consumption. The software is based on optimized computing algorithm. To a lesser extent, Folding@home also tries to predict a protein's final structure and determine how other molecules may interact with it, which has applications in drug design. 

  By the time of the 4th of February 2015 about 8,200,000 computing cores are active in the Folding@home project. The total capacity is 9.3 petaflops. In 2007 the Guinness Book of Records recognized the Folding@home project as the most powerful network of distributed computing. As of December 2011, the Folding@home project ranked second in the world ranking of the most powerful distributed computing systems, being behind only Bitcoin, whose capacity is 161 petaflops. For comparison, the "Tianhe-2" system with a capacity about 33.9 petaflops occupies the first line in the world rankings of TOP500 supercomputers, the second place takes "Titan" (17.6 petaflops).

  The project was the first to use graphics processing units (GPUs), PlayStation 3s, Message Passing Interface (used for computing on multi-core processors), and some Sony Xperia smartphones for distributed computing and scientific research. In most cases graphics processing units (GPUs) have higher capacity than central processor units (CPUs). The project uses statistical simulation methodology that is a paradigm shift from traditional computing methods. While being a part of the client–server model network architecture, the volunteered machines each receive pieces of a simulation (work units), complete them and return to the project's database servers, where the units are compiled into an overall simulation. If one unit fails the task, any of the other ones can recalculate the task again. 

  Volunteers can track their contributions on the Folding@home website, which makes volunteers' participation competitive and encourages long-term involvement. As of the end of October 2016 Ukrainian team overclockers.UA is ranked #88 in TOP-150 teams with up to 25 members per team.

  Between 2000 and 2010, the length of the proteins Folding@home studied increased by a factor of four, while its timescales for protein folding simulations increased by six orders of magnitude. In 2002, Folding@home used Markov state models to complete approximately a million CPU days of simulations over the span of several months, and in 2011, MSMs parallelized another simulation that required an aggregate 10 million CPU hours of computing.

  Also there are other different distributed computing projects. For example, Rosetta@home is a distributed computing project aimed at predicting the structure of a protein and is one of the most accurate systems for predicting a tertiary structure. Since Rosetta only predicts a finite collapsed state and does not model the process of folding itself, Rosetta@home and Folding@home are accented on different molecular issues. The Pande lab can use conformational states from the Rosetta software in the Markov state model as starting points for modeling in Folding@home. On the contrary, structure prediction algorithms can be improved using thermodynamic and kinetic models and sampling aspects to model protein folding. Thus, Folding@home and Rosetta@home complement each other.



1. n official web-site [Electronic resource] / Access mode: http://folding.stanford.edu/

2. Gen_X_Accord, Vijay Pande. Folding@home vs. Rosetta@home. Rosetta@home forums. University of Washington (June 11, 2006)

3. Bojan Zagrovic, Christopher D. Snow, Siraj Khaliq, Michael R. Shirts, and Vijay S. Pande (2002). «Native-like Mean Structure in the Unfolded Ensemble of Small Proteins». Journal of Molecular Biology 323 (1): 153–164. 

4. Lensink MF, Méndez R, Wodak SJ (December 2007). «Docking and scoring protein complexes: CAPRI 3rd Edition». Proteins 69 (4): 704–18.

5. Del Lucent; V. Vishal; Vijay S. Pande (2007). "Protein folding under confinement: A role for solvent". Proceedings of the National Academy of Sciences of the United States of America. 104 (25): 10430–10434