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Foldit Gamers Improve Protein Design Through Crowdsourcing 25 January 2012, by Bob Yirka
Foldit gamers improve protein design through crowdsourcing 25 January 2012, by Bob Yirka chemical reactions. In earlier versions of the Foldit game, players were simply given existing proteins to play with and asked to find the minimal energy state for them by folding them in optimum ways, this latest version has gone much farther by giving players the opportunity to come up with a whole new protein design. To create the new design, gamers were given a Image: Nature Biotechnology (2012) simple beginning structure and some basic ideas doi:10.1038/nbt.2109 about the goal of the new protein, in this case to serve as a better catalyst for a class of Diels-Alder reactions, which are used to synthesize many commercial products. After offering some ideas (PhysOrg.com) -- Gamers on Foldit have such as remodeling certain sections to make them succeeded in improving the catalyst abilities of an behave in certain ways, the gamers went to work enzyme, making it 18-fold more active than the folding the proteins using the tools at hand. original version. The idea is the brainchild of University of Washington scientist Zoran Popovic The first go-round proved mostly futile, with few who is director of the Center for Game Science, gamers coming up with good improvements. To and biochemist David Baker. Together they have improve the results, the team took the best foldings created the Foldit site which is a video game from the first round and fed them back into the application that allows players to work with protein game allowing gamers to improve on them. -
Increasing Public Involvement in Structural Biology
Structure Commentary Increasing Public Involvement in Structural Biology Seth Cooper,1,* Firas Khatib,2 and David Baker2 1Department of Computer Science 2Department of Biochemistry University of Washington, Seattle, WA 98195, USA *Correspondence: [email protected] http://dx.doi.org/10.1016/j.str.2013.08.009 Public participation in scientific research can be a powerful supplement to more-traditional approaches. We discuss aspects of the public participation project Foldit that may help others interested in starting their own projects. It is now easier than ever for the public to We’re very excited about the possibility Openness to Collaboration get involved in science. The Internet has for games and other forms of public in a Variety of Forms made it feasible for research groups to involvement in science to help advance The core of the project has been a very easily connect with people all over the the field. To our knowledge, there have fruitful collaboration between the Com- world. Personal computers have also been a few other projects actively puter Science and Engineering Depart- become powerful enough to run compu- involving the public in structural biology, ment and the Biochemistry Department tationally intensive programs, giving the and we look forward to many more in at the University of Washington. Both public the opportunity to contribute to the future. Structural biology problems departments were able to bring their scientific research. Volunteer computing involving the analysis of existing mole- knowledge and skills together to make a allows the public to share their spare cules and the design of new ones are successful team. -
Algorithm Discovery by Protein Folding Game Players
Algorithm discovery by protein folding game players Firas Khatiba, Seth Cooperb, Michael D. Tykaa, Kefan Xub, Ilya Makedonb, Zoran Popovićb, David Bakera,c,1, and Foldit Players aDepartment of Biochemistry; bDepartment of Computer Science and Engineering; and cHoward Hughes Medical Institute, University of Washington, Box 357370, Seattle, WA 98195 Contributed by David Baker, October 5, 2011 (sent for review June 29, 2011) Foldit is a multiplayer online game in which players collaborate As the players themselves understand their strategies better than and compete to create accurate protein structure models. For spe- anyone, we decided to allow them to codify their algorithms cific hard problems, Foldit player solutions can in some cases out- directly, rather than attempting to automatically learn approxi- perform state-of-the-art computational methods. However, very mations. We augmented standard Foldit play with the ability to little is known about how collaborative gameplay produces these create, edit, share, and rate gameplay macros, referred to as results and whether Foldit player strategies can be formalized and “recipes” within the Foldit game (10). In the game each player structured so that they can be used by computers. To determine has their own “cookbook” of such recipes, from which they can whether high performing player strategies could be collectively invoke a variety of interactive automated strategies. Players can codified, we augmented the Foldit gameplay mechanics with tools share recipes they write with the rest of the Foldit community or for players to encode their folding strategies as “recipes” and to they can choose to keep their creations to themselves. share their recipes with other players, who are able to further mod- In this paper we describe the quite unexpected evolution of ify and redistribute them. -
A Deep Reinforcement Learning Neural Network Folding Proteins
DeepFoldit - A Deep Reinforcement Learning Neural Network Folding Proteins Dimitra Panou1, Martin Reczko2 1University of Athens, Department of Informatics and Telecommunications 2Biomedical Sciences Research Center “Alexander Fleming” ABSTRACT Despite considerable progress, ab initio protein structure prediction remains suboptimal. A crowdsourcing approach is the online puzzle video game Foldit [1], that provided several useful results that matched or even outperformed algorithmically computed solutions [2]. Using Foldit, the WeFold [3] crowd had several successful participations in the Critical Assessment of Techniques for Protein Structure Prediction. Based on the recent Foldit standalone version [4], we trained a deep reinforcement neural network called DeepFoldit to improve the score assigned to an unfolded protein, using the Q-learning method [5] with experience replay. This paper is focused on model improvement through hyperparameter tuning. We examined various implementations by examining different model architectures and changing hyperparameter values to improve the accuracy of the model. The new model’s hyper-parameters also improved its ability to generalize. Initial results, from the latest implementation, show that given a set of small unfolded training proteins, DeepFoldit learns action sequences that improve the score both on the training set and on novel test proteins. Our approach combines the intuitive user interface of Foldit with the efficiency of deep reinforcement learning. KEYWORDS: ab initio protein structure prediction, Reinforcement Learning, Deep Learning, Convolution Neural Networks, Q-learning 1. ALGORITHMIC BACKGROUND Machine learning (ML) is the study of algorithms and statistical models used by computer systems to accomplish a given task without using explicit guidelines, relying on inferences derived from patterns. ML is a field of artificial intelligence. -
Games As a Platform for Student Participation in Authentic Scientific Research
Games as a Platform for Student Participation in Authentic Scientific Research Rikke Magnussen1, Sidse Damgaard Hansen2, Tilo Planke2 and Jacob Friis Sherson2 AU Ideas Center for Community Driven Research, CODER 1ResearchLab: ICT and Design for Learning, Department of Communication, Aalborg University, Denmark 2Department of Physics and Astronomy, Aarhus University, Denmark [email protected] [email protected] [email protected] [email protected] Abstract: This paper presents results from the design and testing of an educational version of Quantum Moves, a Scientific Discovery Game that allows players to help solve authentic scientific challenges in the effort to develop a quantum computer. The primary aim of developing a game-based platform for student-research collaboration is to investigate if and how this type of game concept can strengthen authentic experimental practice and the creation of new knowledge in science education. Researchers and game developers tested the game in three separate high school classes (Class 1, 2, and 3). The tests were documented using video observations of students playing the game, qualitative interviews, and qualitative and quantitative questionnaires. The focus of the tests has been to study players' motivation and their experience of learning through participation in authentic scientific inquiry. In questionnaires conducted in the two first test classes students found that the aspects of doing “real scientific research” and solving physics problems were the more interesting aspects of playing the game. However, designing a game that facilitates professional research collaboration while simultaneously introducing quantum physics to high school students proved to be a challenge. A collaborative learning design was implemented in Class 3, where students were given expert roles such as experimental and theoretical physicists. -
11: Catchup II Machine Learning and Real-World Data (MLRD)
11: Catchup II Machine Learning and Real-world Data (MLRD) Ann Copestake Lent 2019 Last session: HMM in a biological application In the last session, we used an HMM as a way of approximating some aspects of protein structure. Today: catchup session 2. Very brief sketch of protein structure determination: including gamification and Monte Carlo methods (and a little about AlphaFold). Related ideas are used in many very different machine learning applications . What happens in catchup sessions? Lecture and demonstrated session scheduled as in normal session. Lecture material is non-examinable. Time for you to catch-up in demonstrated sessions or attempt some starred ticks. Demonstrators help as usual. Protein structure Levels of structure: Primary structure: sequence of amino acid residues. Secondary structure: highly regular substructures, especially α-helix, β-sheet. Tertiary structure: full 3-D structure. In the cell: an amino acid sequence (as encoded by DNA) is produced and folds itself into a protein. Secondary and tertiary structure crucial for protein to operate correctly. Some diseases thought to be caused by problems in protein folding. Alpha helix Dcrjsr - Own work, CC BY 3.0, https://commons.wikimedia.org/w/index.php?curid=9131613 Bovine rhodopsin By Andrei Lomize - Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=34114850 found in the rods in the retina of the eye a bundle of seven helices crossing the membrane (membrane surfaces marked by horizontal lines) supports a molecule of retinal, which changes structure when exposed to light, also changing the protein structure, initiating the visual pathway 7-bladed propeller fold (found naturally) http://beautifulproteins.blogspot.co.uk/ Peptide self-assembly mimic scaffold (an engineered protein) http://beautifulproteins.blogspot.co.uk/ Protein folding Anfinsen’s hypothesis: the structure a protein forms in nature is the global minimum of the free energy and is determined by the animo acid sequence. -
Getting Humans to Do Quantum Optimization - User Acquisition, Engagement and Early Results from the Citizen Cyberscience Game Quantum Moves
Human Computation (2014) 1:2:221-246 © 2014, Lieberoth et al. CC-BY-3.0 ISSN: 2330-8001, DOI: 10.15346/hc.v1i2.11 Getting Humans to do Quantum Optimization - User Acquisition, Engagement and Early Results from the Citizen Cyberscience Game Quantum Moves ANDREAS LIEBEROTH, Aarhus University MADS KOCK PEDERSEN, Aarhus University ANDREEA CATALINA MARIN, Aarhus University TILO PLANKE, Aarhus University JACOB FRIIS SHERSON, Aarhus University ABSTRACT The game Quantum Moves was designed to pit human players against computer algorithms, combining their solutions into hybrid optimization to control a scalable quantum computer. In this midstream report, we open our design process and describe the series of constitutive building stages going into a quantum physics citizen science game. We present our approach from designing a core gameplay around quantum simulations, to putting extra game elements in place in order to frame, structure, and motivate players’ difficult path from curious visitors to competent science contributors. The player base is extremely diverse – for instance, two top players are a 40 year old female accountant and a male taxi driver. Among statistical predictors for retention and in-game high scores, the data from our first year suggest that people recruited based on real-world physics interest and via real-world events, but only with an intermediate science education, are more likely to become engaged and skilled contributors. Interestingly, female players tended to perform better than male players, even though men played more games per day. To understand this relationship, we explore the profiles of our top players in more depth. We discuss in-world and in-game performance factors departing in psychological theories of intrinsic and extrinsic motivation, and the implications for using real live humans to do hybrid optimization via initially simple, but ultimately very cognitively complex games. -
Protein Folding CMSC 423 Proteins
Protein Folding CMSC 423 Proteins mRNA AGG GUC UGU CGA ∑ = {A,C,G,U} protein R V C R |∑| = 20 amino acids Amino acids with flexible side chains strung R V together on a backbone C residue R Function depends on 3D shape Examples of Proteins Alcohol dehydrogenase Antibodies TATA DNA binding protein Collagen: forms Trypsin: breaks down tendons, bones, etc. other proteins Examples of “Molecules of the Month” from the Protein Data Bank http://www.rcsb.org/pdb/ Protein Structure Backbone Protein Structure Backbone Side-chains http://www.jalview.org/help/html/misc/properties.gif Alpha helix Beta sheet 1tim Alpha Helix C’=O of residue n bonds to NH of residue n + 4 Suggested from theoretical consideration by Linus Pauling in 1951. Beta Sheets antiparallel parallel Structure Prediction Given: KETAAAKFERQHMDSSTSAASSSN… Determine: Folding Ubiquitin with Rosetta@Home http://boinc.bakerlab.org/rah_about.php CASP8 Best Target Prediction Ben-David et al, 2009 Critical Assessment of protein Structure Prediction Structural Genomics Determined structure Space of all protein structures Structure Prediction & Design Successes FoldIt players determination the structure of the retroviral protease of Mason-Pfizer monkey virus (causes AIDS-like disease in monkeys). [Khatib et al, 2011] Top7: start with unnatural, novel fold at left, designed a sequence of amino acids that will fold into it. (Khulman et al, Science, 2003) Determining the Energy + - 0 electrostatics van der Waals • Energy of a protein conformation is the sum of several energy terms. bond lengths -
Genetic Analyses of Human Fetal Retinal Pigment Epithelium Gene Expression Suggest Ocular Disease Mechanisms
ARTICLE https://doi.org/10.1038/s42003-019-0430-6 OPEN Genetic analyses of human fetal retinal pigment epithelium gene expression suggest ocular disease mechanisms Boxiang Liu 1,6, Melissa A. Calton2,6, Nathan S. Abell2, Gillie Benchorin2, Michael J. Gloudemans 3, 1234567890():,; Ming Chen2, Jane Hu4, Xin Li 5, Brunilda Balliu5, Dean Bok4, Stephen B. Montgomery 2,5 & Douglas Vollrath2 The retinal pigment epithelium (RPE) serves vital roles in ocular development and retinal homeostasis but has limited representation in large-scale functional genomics datasets. Understanding how common human genetic variants affect RPE gene expression could elu- cidate the sources of phenotypic variability in selected monogenic ocular diseases and pin- point causal genes at genome-wide association study (GWAS) loci. We interrogated the genetics of gene expression of cultured human fetal RPE (fRPE) cells under two metabolic conditions and discovered hundreds of shared or condition-specific expression or splice quantitative trait loci (e/sQTLs). Co-localizations of fRPE e/sQTLs with age-related macular degeneration (AMD) and myopia GWAS data suggest new candidate genes, and mechan- isms by which a common RDH5 allele contributes to both increased AMD risk and decreased myopia risk. Our study highlights the unique transcriptomic characteristics of fRPE and provides a resource to connect e/sQTLs in a critical ocular cell type to monogenic and complex eye disorders. 1 Department of Biology, Stanford University, Stanford, CA 94305, USA. 2 Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA. 3 Program in Biomedical Informatics, Stanford University School of Medicine, Stanford 94305 CA, USA. 4 Department of Ophthalmology, Jules Stein Eye Institute, UCLA, Los Angeles 90095 CA, USA. -
Protein Sequence Design by Conformational Landscape Optimization
Protein sequence design by conformational landscape optimization Christoffer Norna,b,1, Basile I. M. Wickya,b,1, David Juergensa,b,c, Sirui Liud, David Kima,b, Doug Tischera,b, Brian Koepnicka,b, Ivan Anishchenkoa,b, Foldit Players2, David Bakera,b,e,3, and Sergey Ovchinnikovd,f,3 aDepartment of Biochemistry, University of Washington, Seattle, WA 98105; bInstitute for Protein Design, University of Washington, Seattle, WA 98105; cGraduate Program in Molecular Engineering, University of Washington, Seattle, WA 98105; dFaculty of Arts and Sciences, Division of Science, Harvard University, Cambridge, MA 02138; eHoward Hughes Medical Institute, University of Washington, Seattle, WA 98105; and fJohn Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA 02138 Edited by Barry Honig, Columbia University, New York, NY, and approved January 27, 2021 (received for review August 14, 2020) The protein design problem is to identify an amino acid sequence scale stochastic folding calculations, searching over possible that folds to a desired structure. Given Anfinsen’s thermodynamic structures with the sequence held fixed (9). This two-step pro- hypothesis of folding, this can be recast as finding an amino acid cedure has the disadvantage that the structure prediction cal- sequence for which the desired structure is the lowest energy culations are very slow, requiring many central processing unit state. As this calculation involves not only all possible amino acid (CPU) days for adequate sampling of protein conformational sequences but also, all possible structures, most current ap- space. Moreover, there is no immediate recipe for updating the proaches focus instead on the more tractable problem of finding designed sequence based on the prediction results—instead, the lowest-energy amino acid sequence for the desired structure, sequences that do not have the designed structure as their often checking by protein structure prediction in a second step lowest-energy state are typically discarded. -
Professional Vision in a Citizen Science Game Marisa Ponti
Chefs Know More than Just Recipes: Professional Vision in a Citizen Science Game Marisa Ponti, Igor Stankovic, Wolmet Barendregt, Department of Applied Information Technology, University of Gothenburg, Bruno Kestemont, Lyn Bain (Foldit Players, Go Science Team). Introduction Over the past decade there has been a rapid increase in the number of citizen science projects. Citizen science is now an accepted term for a range of practices that involve members of the general public, many of which are are not trained as scientists, in collecting, categorizing, transcribing or analyzing scientific data (Bonney et al., 2014). Successful citizen science projects include involving participants in classifying astronomical images, reporting bird sightings, counting invasive species in the field, using spatial reasoning skills to align genomes or folding proteins. These projects feature tasks that can only be performed by people (e.g., making an observation) or can be performed more accurately by people (e.g., classifying an image) with the support of computational means to organize these efforts. As such, citizen science often relies on some form of socio-computational system (Prestopnik & Crowston, 2012). Games are one form of such socio-computational systems used in citizen science to entice and sustain participation. The list of citizen science games that people can play while contributing to science is growing. Games, and gamified websites or applications, take a range of forms within citizen science. Some projects, like MalariaSpot, include just a few game elements such as points, badges, and leaderboards. Other projects, like Foldit and Eyewire, are full immersive experiences. Still others, such as Forgotten Island, are beginning to use narrative- based gamification approaches. -
Comparative Host-Coronavirus Protein Interaction Networks Reveal Pan
Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms David Gordon, Joseph Hiatt, Mehdi Bouhaddou, Veronica Rezelj, Svenja Ulferts, Hannes Braberg, Alexander Jureka, Kirsten Obernier, Jeffrey Guo, Jyoti Batra, et al. To cite this version: David Gordon, Joseph Hiatt, Mehdi Bouhaddou, Veronica Rezelj, Svenja Ulferts, et al.. Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms. Science, American Association for the Advancement of Science, 2020, 370 (6521), pp.eabe9403. 10.1126/science.abe9403. hal-03102291 HAL Id: hal-03102291 https://hal.archives-ouvertes.fr/hal-03102291 Submitted on 7 Jan 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Corrected 21 December 2020. See full text. RESEARCH ◥ RESULTS: Quantitative comparison of the 389 RESEARCH ARTICLE SUMMARY interactors of SARS-CoV-2, 366 of SARS-CoV-1, and 296 of MERS-CoV highlighted interactions CORONAVIRUS with host processes that are conserved across all three viruses, including where nonortholo- Comparative host-coronavirus protein interaction gous proteins from different virus strains seem to fill similar roles. We also localized each networks reveal pan-viral disease mechanisms individually-expressed viral protein by micros- copy and then raised and validated antisera David E.