3.3.3 Categorization as a game
3.4.3 Scoring
3.4.4 Introductory Levels
3.5 Rewards and Social Interaction
3.5.1 Rewards and ranking types
3.6 Conclusion
Chapter 4Protein Structure Prediction
4.1 Introduction
4.2 Quest to the Natives
4.3 CASP8 Experiments
4.4 Evaluation
4.5 Rebuild and Refine Comparison
4.5.1 First strand swap example
4.5.2 Player contribution and expertise
4.6 Alignment Tool and CASP9
4.7 Solution of Crystal Structure
4.8 Conclusion
Chapter 5Protein Design
5.1 Introduction
5.2 Framework Extension
5.2.1 Foldit
5.2.2 Iteration Strategy
5.3 Science Transfer
5.3.1 Visualizations
5.3.2 Tools
5.3.3 Conditions
5.4 Introductory Levels
5.5 Examples
5.5.1 Fibronectin
5.5.2 Diels-Alder
5.6 Conclusion
Chapter 6Protein Structure Refinement Algorithms
6.1 Introduction
6.2 RelatedWork
6.3 Overview
6.3.1 Cookbook
6.4 CASP9 Analysis
6.4.1 Recipe Sharing
6.4.2 Inheritance Relationships
6.4.3 Ratings
6.5 Script Recipe Adoption Analysis
6.6 Algorithm Categories
6.7 Context Dependence
6.8 Recipe Evolution
6.9 Performance Comparison
6.10 Conclusion
Chapter 7Conclusion
7.1 Contributions
7.2 Future Work
Bibliography
Author’s Biography
Preface
When we first set out to create Foldit over six years ago, it wasn’t clear that a game-based approach to scientific discovery would work. So we planned from the start for the game to be continually adapting and changing, in order to keep improving based on the lessons we’d learn. It took several years of design, development, and continued iteration from a team of computer scientists and biochemists until the game was at a point where we made our first exciting discovery. The nature of the challenging problems we were facing required this time and refinement to solve.
We are now seeing a number of other games that allow players to contribute to scientific research. Muchof this growth has been in fields related to biology and biochemistry: EteRNA for designing RNA shapes, EyeWire for mapping neurons, and Phylo for aligning genetic sequences. Each of these games has had exciting scientific results produced by gameplay. Games are being applied in other areas as well, such as in the Algoraph suite of games for solving graph theory problems. I have been involved in the development of two more science games:Nanocrafter, which aims to push the frontiers of DNA-based synthetic biology, and Flow Jam, which allows players to help formally verify software.
The Foldit community has continued to grow and adapt to the new scientific challenges that we have posed. We have continued to look further into the design of synthetic proteins and their applications for health and understanding proteins. The Foldit team is continually grateful to the player community for their creativity, problem solving, and enthusiasm. Their brainpower and fresh insights
