A natural polypeptide chain can fold into a native protein in microseconds, but predicting such stable three-dimensional structure from any given amino-acid sequence and first physical principles remains a formidable computational challenge. Aiming to recruit human visual and strategic powers to the task, Seth Cooper, David Baker and colleagues turned their 'Rosetta' structure-prediction algorithm into an online multiplayer game called Foldit, in which thousands of non-scientists competed and collaborated to produce a rich set of new algorithms and search strategies for protein structure refinement. The work shows that even computationally complex scientific problems can be effectively crowd-sourced using interactive multiplayer games. Predicting the structure of a folded protein from first principles for any given amino-acid sequence remains a formidable computational challenge. To recruit human abilities to the task, these authors turned their Rosetta structure prediction algorithm into an online multiplayer game in which thousands of non-scientists competed and collaborated to produce new algorithms and search strategies for protein structure refinement. This shows that computationally complex problems can be effectively 'crowd-sourced' through interactive multiplayer games. People exert large amounts of problem-solving effort playing computer games. Simple image- and text-recognition tasks have been successfully ‘crowd-sourced’ through games1,2,3, but it is not clear if more complex scientific problems can be solved with human-directed computing. Protein structure prediction is one such problem: locating the biologically relevant native conformation of a protein is a formidable computational challenge given the very large size of the search space. Here we describe Foldit, a multiplayer online game that engages non-scientists in solving hard prediction problems. Foldit players interact with protein structures using direct manipulation tools and user-friendly versions of algorithms from the Rosetta structure prediction methodology4, while they compete and collaborate to optimize the computed energy. We show that top-ranked Foldit players excel at solving challenging structure refinement problems in which substantial backbone rearrangements are necessary to achieve the burial of hydrophobic residues. Players working collaboratively develop a rich assortment of new strategies and algorithms; unlike computational approaches, they explore not only the conformational space but also the space of possible search strategies. The integration of human visual problem-solving and strategy development capabilities with traditional computational algorithms through interactive multiplayer games is a powerful new approach to solving computationally-limited scientific problems.

/pdf/predicting-protein-structures-with-a-multiplayer-online-game-3md0x4a9x0.pdf

Predicting protein structures with a multiplayer online game

Foldit is a multiplayer online game in which players collaborate and compete to create accurate protein structure models. For specific hard problems, Foldit player solutions can in some cases outperform state-of-the-art computational methods. However, very little is known about how collaborative gameplay produces these results and whether Foldit player strategies can be formalized and structured so that they can be used by computers. To determine whether high performing player strategies could be collectively codified, we augmented the Foldit gameplay mechanics with tools for players to encode their folding strategies as “recipes” and to share their recipes with other players, who are able to further modify and redistribute them. Here we describe the rapid social evolution of player-developed folding algorithms that took place in the year following the introduction of these tools. Players developed over 5,400 different recipes, both by creating new algorithms and by modifying and recombining successful recipes developed by other players. The most successful recipes rapidly spread through the Foldit player population, and two of the recipes became particularly dominant. Examination of the algorithms encoded in these two recipes revealed a striking similarity to an unpublished algorithm developed by scientists over the same period. Benchmark calculations show that the new algorithm independently discovered by scientists and by Foldit players outperforms previously published methods. Thus, online scientific game frameworks have the potential not only to solve hard scientific problems, but also to discover and formalize effective new strategies and algorithms.

https://www.pnas.org/content/pnas/108/47/18949.full.pdf

Algorithm discovery by protein folding game players

Incorporating the individual and collective problem solving skills of non-experts into the scientific discovery process could potentially accelerate the advancement of science. This paper discusses the design process used for Foldit, a multiplayer online biochemistry game that presents players with computationally difficult protein folding problems in the form of puzzles, allowing ordinary players to gain expertise and help solve these problems. The principle challenge of designing such scientific discovery games is harnessing the enormous collective problem-solving potential of the game playing population, who have not been previously introduced to the specific problem, or, often, the entire scientific discipline. To address this challenge, we took an iterative approach to designing the game, incorporating feedback from players and biochemical experts alike. Feedback was gathered both before and after releasing the game, to create the rules, interactions, and visualizations in Foldit that maximize contributions from game players. We present several examples of how this approach guided the game's design, and allowed us to improve both the quality of the gameplay and the application of player problem-solving.

/pdf/the-challenge-of-designing-scientific-discovery-games-3e0p3vsnjr.pdf

The challenge of designing scientific discovery games

From the Preface

As science becomes increasingly computational, the limits of what is computationally tractable become a barrier to scientific progress Many scientific problems, however, are amenable to human problem solving skills that complement computational power By leveraging these skills on a larger scale---beyond the relatively few individuals currently engaged in scientific inquiry---there is the potential for new scientific discoveries 

This book presents a framework for mapping open scientific problems into video games The game framework combines computational power with human problem solving and creativity to work toward solving scientific problems that neither computers nor humans could previously solve alone To maximize the potential contributors to scientific discovery, the framework designs a game to be played by people with no formal scientific background and incentivizes long-term engagement with a myriad of collaborative or competitive reward structures The framework allows for the continual coevolution of the players and the game to each other: as players gain expertise through gameplay, the game changes to become a better tool 

The framework is validated by being applied to proteomics problems with the video game Foldit Foldit players have contributed to novel discoveries in protein structure prediction, protein design, and protein structure refinement algorithms The coevolution of human problem solving and computer tools in an incentivized game framework is an exciting new scientific pathway that can lead to discoveries currently unreachable by other methods

A Framework for Scientific Discovery through Video Games

– We have shown in laboratory experiment that hypervelocity impacts on a solar cell produce ejecta that can be captured on aluminum (Al 1100) foil or in low density (33 kg m−3) aerogel. The origin of the secondary impacts can be determined by either analysis of the residue in the craters in the foils (which preserve an elemental signature of the solar cell components) or by their pointing direction for tracks in the aerogel (which we show align with the impact direction to ± 0.4°). This experimental evidence explains the observations of the NASA Stardust mission which has reported that the majority of tracks in the aerogel collector used to collect interstellar dust actually point at the spacecraft’s solar panels. From our results, we suggest that it should also be possible to recognize secondary ejecta craters in the Stardust mission aluminum foils, also used as dust sampling devices during the mission.

https://www.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1945-5100.2011.01294.x

Experimental investigation of impacts by solar cell secondary ejecta on silica aerogel and aluminum foil: Implications for the Stardust Interstellar Dust Collector

Here we describe the critical role that synchrotron X-ray and infrared microprobes are playing in the search for interstellar dust in the Stardust Interstellar Dust Collector (SIDC). The samples under examination are submicron particles trapped in low-density aerogel. We have found that the spatial resolution, energy range, and flux capabilities of the FTIR beamlines 1.4.3, ALS, and U2B, NSLS; the XRF microprobes ID13 and ID22NI, ESRF and 2-ID-D, APS; and the STXM beamline 11.0.2, ALS are ideally suited for studying these tiny returned samples. Using nondestructive, coordinated analyses at these microprobes, we have been able to eliminate most candidates as likely samples of interstellar dust. This in itself is a major accomplishment, since the analysis of these tiny samples is technically extremely challenging.

/pdf/non-destructive-search-for-interstellar-dust-using-43wbn6l4e7.pdf

S. Kulkarni

Papers

Non-destructive search for interstellar dust using synchrotron microprobes