RNA design rules from a massive open laboratory

Analysis of natural structures and chemical mapping data reveals local stability compensation in RNA

RNA molecules adopt complex structures that perform essential biological functions across all forms of life, making them promising candidates for therapeutic applications. However, our ability to design new RNA structures remains limited by an incomplete understanding of their folding principles. While global metrics such as the minimum free energy are widely used, they are at odds with naturally occurring structures and incompatible with established design rules. Here, we introduce local stability compensation (LSC), a principle that RNA folding is governed by the local balance between destabilizing loops and their stabilizing adjacent stems, challenging the focus on global energetic optimization. Analysis of over 100,000 RNA structures revealed that LSC signatures are particularly pronounced in bulges and their adjacent stems, with distinct patterns across different RNA families that align with their biological functions. To validate LSC experimentally, we systematically analyzed thousands of RNA variants using DMS chemical mapping. Our results demonstrate that stem reactivity correlates strongly with LSC (R2 = 0.458 for hairpin loops) and that structural perturbations affect folding primarily within ~6 nucleotides from the loop. These findings establish LSC as a fundamental principle that could enhance the rational design of functional RNAs.

Authorship and Citizen Science: Seven Heuristic Rules

Citizen science (CS) is an umbrella term for research with a significant amount of contributions from volunteers. Those volunteers can occupy a hybrid role, being both 'researcher' and 'subject' at the same time. This has repercussions for questions about responsibility and credit, e.g. pertaining to the issue of authorship. In this paper, we first review some existing guidelines for authorship and their applicability to CS. Second, we assess the claim that the guidelines from the International Committee of Medical Journal Editors (ICMJE), known as 'the Vancouver guidelines', may lead to exclusion of deserving citizen scientists as authors. We maintain that the idea of including citizen scientists as authors is supported by at least two arguments: transparency and fairness. Third, we argue that it might be plausible to include groups as authors in CS. Fourth and finally, we offer a heuristic list of seven recommendations to be considered when deciding about whom to include as an author of a CS publication.

OpenASO: RNA Rescue - designing splice-modulating antisense oligonucleotides through community science

Splice-modulating antisense oligonucleotides (ASOs) are precision RNA-based drugs that are becoming an established modality to treat human disease. Previously, we reported the discovery of ASOs that target a novel, putative intronic RNA structure to rescue splicing of multiple pathogenic variants of F8 exon 16 that cause hemophilia A. However, the conventional approach to discovering splice-modulating ASOs is both laborious and expensive. Here, we describe an alternative paradigm that integrates data-driven RNA structure prediction and community science to discover splice-modulating ASOs. Using a splicing-deficient pathogenic variant of F8 exon 16 as a model, we show that 25% of the top-scoring molecules designed in the Eterna OpenASO challenge have a statistically significant impact on enhancing exon 16 splicing. Additionally, we show that a distinct combination of ASOs designed by Eterna players can additively enhance the inclusion of the splicing-deficient exon 16 variant. Together, our data suggests that crowdsourcing designs from a community of citizen scientists may accelerate the discovery of splice-modulating ASOs with potential to treat human disease.

Ribonanza: deep learning of RNA structure through dual crowdsourcing

Prediction of RNA structure from sequence remains an unsolved problem, and progress has been slowed by a paucity of experimental data. Here, we present Ribonanza, a dataset of chemical mapping measurements on two million diverse RNA sequences collected through Eterna and other crowdsourced initiatives. Ribonanza measurements enabled solicitation, training, and prospective evaluation of diverse deep neural networks through a Kaggle challenge, followed by distillation into a single, self-contained model called RibonanzaNet. When fine tuned on auxiliary datasets, RibonanzaNet achieves state-of-the-art performance in modeling experimental sequence dropout, RNA hydrolytic degradation, and RNA secondary structure, with implications for modeling RNA tertiary structure.

Improving microbial phylogeny with citizen science within a mass-market video game

Citizen science video games are designed primarily for users already inclined to contribute to science, which severely limits their accessibility for an estimated community of 3 billion gamers worldwide. We created Borderlands Science (BLS), a citizen science activity that is seamlessly integrated within a popular commercial video game played by tens of millions of gamers. This integration is facilitated by a novel game-first design of citizen science games, in which the game design aspect has the highest priority, and a suitable task is then mapped to the game design. BLS crowdsources a multiple alignment task of 1 million 16S ribosomal RNA sequences obtained from human microbiome studies. Since its initial release on 7 April 2020, over 4 million players have solved more than 135 million science puzzles, a task unsolvable by a single individual. Leveraging these results, we show that our multiple sequence alignment simultaneously improves microbial phylogeny estimations and UniFrac effect sizes compared to state-of-the-art computational methods. This achievement demonstrates that hyper-gamified scientific tasks attract massive crowds of contributors and offers invaluable resources to the scientific community.

A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health

Modern machine learning approaches have led to performant diagnostic models for a variety of health conditions. Several machine learning approaches, such as decision trees and deep neural networks, can, in principle, approximate any function. However, this power can be considered to be both a gift and a curse, as the propensity toward overfitting is magnified when the input data are heterogeneous and high dimensional and the output class is highly nonlinear. This issue can especially plague diagnostic systems that predict behavioral and psychiatric conditions that are diagnosed with subjective criteria. An emerging solution to this issue is crowdsourcing, where crowd workers are paid to annotate complex behavioral features in return for monetary compensation or a gamified experience. These labels can then be used to derive a diagnosis, either directly or by using the labels as inputs to a diagnostic machine learning model. This viewpoint describes existing work in this emerging field and discusses ongoing challenges and opportunities with crowd-powered diagnostic systems, a nascent field of study. With the correct considerations, the addition of crowdsourcing to human-in-the-loop machine learning workflows for the prediction of complex and nuanced health conditions can accelerate screening, diagnostics, and ultimately access to care.

When online citizen science meets teaching: Storyfication of a science discovery game to teach, learn, and contribute to genomic research

In the last decade, video games became a common vehicle for citizen science initiatives in life science, allowing participants to contribute to real scientific data analysis while learning about it. Since 2010, our scientific discovery game (SDG) Phylo enlists participants in comparative genomic data analysis. It is frequently used as a learning tool, but the activities were difficult to aggregate to build a coherent teaching activity. Here, we describe a strategy and series of recipes to facilitate the integration of SDGs in courses and implement this approach in Phylo. We developed new roles and functionalities enabling instructors to create assignments and monitor the progress of students. A story mode progressively introduces comparative genomics concepts, allowing users to learn and contribute to the analysis of real genomic sequences. Preliminary results from a user study suggest this framework may help to boost user motivation and clarify pedagogical objectives.

Minimization of the E. coli ribosome, aided and optimized by community science

The ribosome is a ribonucleoprotein complex found in all domains of life. Its role is to catalyze protein synthesis, the messenger RNA (mRNA)-templated formation of amide bonds between α-amino acid monomers. Amide bond formation occurs within a highly conserved region of the large ribosomal subunit known as the peptidyl transferase center (PTC). Here we describe the step-wise design and characterization of mini-PTC 1.1, a 284-nucleotide RNA that recapitulates many essential features of the Escherichia coli PTC. Mini-PTC 1.1 folds into a PTC-like structure under physiological conditions, even in the absence of r-proteins, and engages small molecule analogs of A- and P-site tRNAs. The sequence of mini-PTC 1.1 differs from the wild type E. coli ribosome at 12 nucleotides that were installed by a cohort of citizen scientists using the on-line video game Eterna. These base changes improve both the secondary structure and tertiary folding of mini-PTC 1.1 as well as its ability to bind small molecule substrate analogs. Here, the combined input from Eterna citizen-scientists and RNA structural analysis provides a robust workflow for the design of a minimal PTC that recapitulates many features of an intact ribosome.

Compact RNA sensors for increasingly complex functions of multiple inputs

Designing single molecules that compute general functions of input molecular partners represents a major unsolved challenge in molecular design. Here, we demonstrate that high-throughput, iterative experimental testing of diverse RNA designs crowdsourced from Eterna yields sensors of increasingly complex functions of input oligonucleotide concentrations. After designing single-input RNA sensors with activation ratios beyond our detection limits, we created logic gates, including challenging XOR and XNOR gates, and sensors that respond to the ratio of two inputs. Finally, we describe the OpenTB challenge, which elicited 85-nucleotide sensors that compute a score for diagnosing active tuberculosis, based on the ratio of products of three gene segments. Building on OpenTB design strategies, we created an algorithm Nucleologic that produces similarly compact sensors for the three-gene score based on RNA and DNA. These results open new avenues for diverse applications of compact, single molecule sensors previously limited by design complexity.

Developing Complex RNA Design Applications in the Infrared Framework

Applications in biotechnology and bio-medical research call for effective strategies to design novel RNAs with very specific properties. Such advanced design tasks require support by computational tools but at the same time put high demands on their flexibility and expressivity to model the application-specific requirements. To address such demands, we present the computational framework Infrared. It supports developing advanced customized design tools, which generate RNA sequences with specific properties, often in a few lines of Python code. This text guides the reader in tutorial format through the development of complex design applications. Thanks to the declarative, compositional approach of Infrared, we can describe this development as a step-by-step extension of an elementary design task. Thus, we start with generating sequences that are compatible with a single RNA structure and go all the way to RNA design targeting complex positive and negative design objectives with respect to single or even multiple target structures. Finally, we present a "real-world" application of computational design to create an RNA device for biotechnology: we use Infrared to generate design candidates of an artificial "AND" riboswitch, which activates gene expression in the simultaneous presence of two different small metabolites. In these applications, we exploit that the system can generate, in an efficient (fixed-parameter tractable) way, multiple diverse designs that satisfy a number of constraints and have high quality w.r.t. to an objective (by sampling from a Boltzmann distribution).

Estimating RNA Secondary Structure Folding Free Energy Changes with efn2

A number of analyses require estimates of the folding free energy changes of specific RNA secondary structures. These predictions are often based on a set of nearest neighbor parameters that models the folding stability of a RNA secondary structure as the sum of folding stabilities of the structural elements that comprise the secondary structure. In the software suite RNAstructure, the free energy change calculation is implemented in the program efn2. The efn2 program estimates the folding free energy change and the experimental uncertainty in the folding free energy change. It can be run through the graphical user interface for RNAstructure, from the command line, or a web server. This chapter provides detailed protocols for using efn2.

Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors

We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. We discovered a noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>490 ligand-bound x-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2400 compounds) for this campaign were shared rapidly and openly, creating a rich, open, and intellectual property-free knowledge base for future anticoronavirus drug discovery.

Generative and predictive neural networks for the design of functional RNA molecules

RNA is a remarkably versatile molecule that has been engineered for applications in therapeutics, diagnostics, and in vivo information-processing systems. However, the complex relationship between the sequence and structural properties of an RNA molecule and its ability to perform specific functions often necessitates extensive experimental screening of candidate sequences. Here we present a generalized neural network architecture that utilizes the sequence and structure of RNA molecules (SANDSTORM) to inform functional predictions. We demonstrate that this approach achieves state-of-the-art performance across several distinct RNA prediction tasks, while learning interpretable abstractions of RNA secondary structure. We paired these predictive models with generative adversarial RNA design networks (GARDN), allowing the generative modelling of novel mRNA 5' untranslated regions and toehold switch riboregulators exhibiting a predetermined fitness. This approach enabled the design of novel toehold switches with a 43-fold increase in experimentally characterized dynamic range compared to those designed using classic thermodynamic algorithms. SANDSTORM and GARDN thus represent powerful new predictive and generative tools for the development of diagnostic and therapeutic RNA molecules with improved function.

NanoAdventure: Development of a Text-Based Adventure Game in English, Spanish, and Chinese for Communicating about Nanotechnology and the Nanoscale

Video games and immersive, narrative experiences are often called upon to help students understand difficult scientific concepts, such as sense of scale. However, the development of educational video games requires expertise and, frequently, a sizable budget. Here, we report on the use of an interactive text-style video game, NanoAdventure, to communicate about sense of scale and nanotechnology to the public. NanoAdventure was developed on an open-source, free-to-use platform with simple coding and enhanced with free or low-cost assets. NanoAdventure was launched in three languages (English, Spanish, Chinese) and compared to textbook-style and blog-style control texts in a randomized study. Participants answered questions on their knowledge of nanotechnology and their attitudes toward nanotechnology before and after reading one randomly assigned text (textbook, blog, or NanoAdventure game). Our results demonstrate that interactive fiction is effective in communicating about sense of scale and nanotechnology as well as the relevance of nanotechnology to a general public. NanoAdventure was found to be the most "fun" and easy to read of all text styles by participants in a randomized trial. Here, we make the case for interactive "Choose Your Own Adventure" style games as another effective tool among educational game models for chemistry and science communication.

Meet the Medicines-A Crowdsourced Approach to Collecting and Communicating Information about Essential Medicines Online

The World Health Organization (WHO) maintains a list of medicines and medical devices, essential medicines, that should be available to everyone, to form a functioning healthcare system. Yet, many of these medicines remain out of reach for people around the world. One significant barrier to improving the accessibility of essential medicines is a paucity of information about both the extent and causes of this problem. E$$ENTIAL MEDICINE$ (E$$) is a citizen science project designed to investigate this deficit of information by recruiting members of the public to find, validate, compile and share information on essential medicines through an open, online database. Herein, we report an approach to crowdsourcing both the collection of information on the accessibility of essential medicines and the subsequent communication of these findings to diverse audiences. The Meet the Medicines initiative encourages members of the public to share information from the E$$ database, in a short video format appropriate for social media. This communication details the design and implementation of our crowdsourced approach and strategies for recruiting and supporting participants. We discuss data on participant engagement, consider the benefits and challenges of this approach and suggest ways to promote crowdsourcing practices for social and scientific good.

Community science designed ribosomes with beneficial phenotypes

Functional design of ribosomes with mutant ribosomal RNA (rRNA) can expand opportunities for understanding molecular translation, building cells from the bottom-up, and engineering ribosomes with altered capabilities. However, such efforts are hampered by cell viability constraints, an enormous combinatorial sequence space, and limitations on large-scale, 3D design of RNA structures and functions. To address these challenges, we develop an integrated community science and experimental screening approach for rational design of ribosomes. This approach couples Eterna, an online video game that crowdsources RNA sequence design to community scientists in the form of puzzles, with in vitro ribosome synthesis, assembly, and translation in multiple design-build-test-learn cycles. We apply our framework to discover mutant rRNA sequences that improve protein synthesis in vitro and cell growth in vivo, relative to wild type ribosomes, under diverse environmental conditions. This work provides insights into rRNA sequence-function relationships and has implications for synthetic biology.

Practical recommendations from a multi-perspective needs and challenges assessment of citizen science games

Citizen science games are an increasingly popular form of citizen science, in which volunteer participants engage in scientific research while playing a game. Their success depends on a diverse set of stakeholders working together-scientists, volunteers, and game developers. Yet the potential needs of these stakeholder groups and their possible tensions are poorly understood. To identify these needs and possible tensions, we conducted a qualitative data analysis of two years of ethnographic research and 57 interviews with stakeholders from 10 citizen science games, following a combination of grounded theory and reflexive thematic analysis. We identify individual stakeholder needs as well as important barriers to citizen science game success. These include the ambiguous allocation of developer roles, limited resources and funding dependencies, the need for a citizen science game community, and science-game tensions. We derive recommendations for addressing these barriers.

An Ethics Framework for Evaluating Ownership Practices in Biomedical Citizen Science

The collaborative nature of citizen science raises important questions about managing ownership of its research outputs. Potential citizen science research outputs include data sets, findings, publications, and discoveries of new ideas, methods, products, and technologies. Unlike citizen science projects conducted in other disciplines, biomedical citizen science projects often include features, such as contribution of personal health data, that might heighten citizen scientists' expectations that they will be able to access, control, or share in the benefits of project outputs. Here, we refer to moral claims of access, control, and benefit as ownership claims, and a project's management of ownership claims as its ownership practices. Ethical management of ownership is widely recognized as an important consideration for citizen science projects, and practitioners and scholars have described helpful recommendations for preempting issues and engaging stakeholders on practices. Building on this literature, we propose a framework to help biomedical citizen science projects systematically evaluate the ethical soundness of their ownership practices based on four considerations: reciprocal treatment, relative treatment, risk-benefit assessment, and reasonable expectations.

RNA secondary structure packages evaluated and improved by high-throughput experiments

Despite the popularity of computer-aided study and design of RNA molecules, little is known about the accuracy of commonly used structure modeling packages in tasks sensitive to ensemble properties of RNA. Here, we demonstrate that the EternaBench dataset, a set of more than 20,000 synthetic RNA constructs designed on the RNA design platform Eterna, provides incisive discriminative power in evaluating current packages in ensemble-oriented structure prediction tasks. We find that CONTRAfold and RNAsoft, packages with parameters derived through statistical learning, achieve consistently higher accuracy than more widely used packages in their standard settings, which derive parameters primarily from thermodynamic experiments. We hypothesized that training a multitask model with the varied data types in EternaBench might improve inference on ensemble-based prediction tasks. Indeed, the resulting model, named EternaFold, demonstrated improved performance that generalizes to diverse external datasets including complete messenger RNAs, viral genomes probed in human cells and synthetic designs modeling mRNA vaccines.

Large scale screening of CRISPR guide RNAs using an optimized high throughput robotics system

All CRISPR/CAS systems utilize CRISPR guide RNAs (crRNAs), the design of which depend on the type of CAS protein, genetic target and the environment/matrix. While machine learning approaches have recently been developed to optimize some crRNA designs, candidate crRNAs must still be screened for efficacy under relevant conditions. Here, we demonstrate a high-throughput method to screen hundreds of candidate crRNAs for activation of Cas13a collateral RNA cleavage. Entire regions of a model gene transcript (Y. pestis lcrV gene) were tiled to produce overlapping crRNA sets. We tested for possible effects that included crRNA/target sequence, size and secondary structures, and the commercial source of DNA oligomers used to generate crRNAs. Detection of a 981 nt target RNA was initially successful with 271 out of 296 tested guide RNAs, and that was improved to 287 out of 296 (97%) after protocol optimizations. For this specific example, we determined that crRNA efficacy did not strongly depend on the target region or crRNA physical properties, but was dependent on the source of DNA oligomers used for RNA preparation. Our high-throughput methods for screening crRNAs has general applicability to the optimization of Cas12 and Cas13 guide RNA designs.

Deep learning models for RNA secondary structure prediction (probably) do not generalize across families

MOTIVATION

The secondary structure of RNA is of importance to its function. Over the last few years, several papers attempted to use machine learning to improve de novo RNA secondary structure prediction. Many of these papers report impressive results for intra-family predictions but seldom address the much more difficult (and practical) inter-family problem.

RESULTS

We demonstrate that it is nearly trivial with convolutional neural networks to generate pseudo-free energy changes, modelled after structure mapping data that improve the accuracy of structure prediction for intra-family cases. We propose a more rigorous method for inter-family cross-validation that can be used to assess the performance of learning-based models. Using this method, we further demonstrate that intra-family performance is insufficient proof of generalization despite the widespread assumption in the literature and provide strong evidence that many existing learning-based models have not generalized inter-family.

AVAILABILITY AND IMPLEMENTATION

Source code and data are available at https://github.com/marcellszi/dl-rna.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Games for Teaching/Learning Quantum Mechanics: A Pilot Study with High-School Students

The teaching of quantum physics is challenging, not the least because teachers must overcome the traditional narrative approach, students must gain a conceptual understanding of fundamentals, and citizens must become aware of quantum technologies. Quantum games are powerful tools to overcome obstacles and push one’s limits without fear of failure. We report on a pilot study involving twenty high-school student volunteers, consisting of a compact intervention module on the concepts of quantum states, properties, measurement, superposition, and entanglement within the framework of the Model of Educational Reconstruction, followed by playing a game, quantum TiqTaqToe. The outcomes of this research-based learning environment are discussed via the qualitative analysis of students’ answers to two open questionnaires. We find that students grasped the concepts of superposition and, with special awareness, entanglement, the game proving effective to help students experience their implications in quantum behavior. The informal and stimulating tournament atmosphere favored intertwining of the game with learning goals. Our central message is that the use of quantum game tools fits a teaching/learning environment in manners often not well understood in the literature; it enhances awareness of the nature of new and non-intuitive concepts, increases complementarity with other languages within the process of thinking about physics, boosts student engagement, and improves intervention efficiency and effectiveness.

Micro-HBI: Human-Biology Interaction With Living Cells, Viruses, and Molecules

Human-Biology Interaction (HBI) is a field that aims to provide first-hand experience with living matter and the modern life-sciences to the lay public. Advances in optical, bioengineering, and digital technologies as well as interaction design now also enable real and direct experiences at the microscale, such as with living cells and molecules, motivating the sub-field of “micro-HBI.” This is distinct from simulating any biological processes. There is a significant need for HBI as new educational modalities are required to enable all strata of society to become informed about new technologies and biology in general, as we face challenges like global pandemics, environmental loss, and species extinctions. Here we review this field in order to provide a jump-off point for future work and to bring stakeholder from different disciplines together. By now, the field has explored and demonstrated many such interactive systems, the use of different microorganisms, new interaction design principles, and versatile applications, such as museum exhibits, biotic games, educational cloud labs, citizen science platforms, and hands-on do-it-yourself (DIY) Bio maker activities. We close with key open questions for the field to move forward.

Crowdsourced RNA design discovers diverse, reversible, efficient, self-contained molecular switches

Internet-based scientific communities promise a means to apply distributed, diverse human intelligence toward previously intractable scientific problems. However, current implementations have not allowed communities to propose experiments to test all emerging hypotheses at scale or to modify hypotheses in response to experiments. We report high-throughput methods for molecular characterization of nucleic acids that enable the large-scale video game–based crowdsourcing of RNA sensor design, followed by high-throughput functional characterization. Iterative design testing of thousands of crowdsourced RNA sensor designs produced near–thermodynamically optimal and reversible RNA switches that act as self-contained molecular sensors and couple five distinct small molecule inputs to three distinct protein binding and fluorogenic outputs. This work suggests a paradigm for widely distributed experimental bioscience.

Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics

Therapeutic mRNAs and vaccines are being developed for a broad range of human diseases, including COVID-19. However, their optimization is hindered by mRNA instability and inefficient protein expression. Here, we describe design principles that overcome these barriers. We develop an RNA sequencing-based platform called PERSIST-seq to systematically delineate in-cell mRNA stability, ribosome load, as well as in-solution stability of a library of diverse mRNAs. We find that, surprisingly, in-cell stability is a greater driver of protein output than high ribosome load. We further introduce a method called In-line-seq, applied to thousands of diverse RNAs, that reveals sequence and structure-based rules for mitigating hydrolytic degradation. Our findings show that highly structured "superfolder" mRNAs can be designed to improve both stability and expression with further enhancement through pseudouridine nucleoside modification. Together, our study demonstrates simultaneous improvement of mRNA stability and protein expression and provides a computational-experimental platform for the enhancement of mRNA medicines.

Deep learning models for predicting RNA degradation via dual crowdsourcing

Medicines based on messenger RNA (mRNA) hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition ('Stanford OpenVaccine') on Kaggle, involving single-nucleotide resolution measurements on 6,043 diverse 102-130-nucleotide RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504-1,588 nucleotides) with improved accuracy compared with previously published models. These results indicate that such models can represent in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for dataset creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales.

RiboDraw: semiautomated two-dimensional drawing of RNA tertiary structure diagrams

Publishing, discussing, envisioning, modeling, designing and experimentally determining RNA three-dimensional (3D) structures involve preparation of two-dimensional (2D) drawings that depict critical functional features of the subject molecules, such as noncanonical base pairs and protein contacts. Here, we describe RiboDraw, new software for crafting these drawings. We illustrate the features of RiboDraw by applying it to several RNAs, including the Escherichia coli tRNA-Phe, the P4-P6 domain of Tetrahymena ribozyme, a -1 ribosomal frameshift stimulation element from beet western yellows virus and the 5' untranslated region of SARS-CoV-2. We show secondary structure diagrams of the 23S and 16S subunits of the E. coli ribosome that reflect noncanonical base pairs, ribosomal proteins and structural motifs, and that convey the relative positions of these critical features in 3D space. This software is a MATLAB package freely available at https://github.com/DasLab/RiboDraw.

Deep learning models for predicting RNA degradation via dual crowdsourcing

Messenger RNA-based medicines hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition ("Stanford OpenVaccine") on Kaggle, involving single-nucleotide resolution measurements on 6043 102-130-nucleotide diverse RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504-1588 nucleotides) with improved accuracy compared to previously published models. Top teams integrated natural language processing architectures and data augmentation techniques with predictions from previous dynamic programming models for RNA secondary structure. These results indicate that such models are capable of representing in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for data set creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales.

Individual and collective human intelligence in drug design: evaluating the search strategy

In recent years, individual and collective human intelligence, defined as the knowledge, skills, reasoning and intuition of individuals and groups, have been used in combination with computer algorithms to solve complex scientific problems. Such approach was successfully used in different research fields such as: structural biology, comparative genomics, macromolecular crystallography and RNA design. Herein we describe an attempt to use a similar approach in small-molecule drug discovery, specifically to drive search strategies of de novo drug design. This is assessed with a case study that consists of a series of public experiments in which participants had to explore the huge chemical space in silico to find predefined compounds by designing molecules and analyzing the score associate with them. Such a process may be seen as an instantaneous surrogate of the classical design-make-test cycles carried out by medicinal chemists during the drug discovery hit to lead phase but not hindered by long synthesis and testing times. We present first findings on (1) assessing human intelligence in chemical space exploration, (2) comparing individual and collective human intelligence performance in this task and (3) contrasting some human and artificial intelligence achievements in de novo drug design.

Theoretical basis for stabilizing messenger RNA through secondary structure design

RNA hydrolysis presents problems in manufacturing, long-term storage, world-wide delivery and in vivo stability of messenger RNA (mRNA)-based vaccines and therapeutics. A largely unexplored strategy to reduce mRNA hydrolysis is to redesign RNAs to form double-stranded regions, which are protected from in-line cleavage and enzymatic degradation, while coding for the same proteins. The amount of stabilization that this strategy can deliver and the most effective algorithmic approach to achieve stabilization remain poorly understood. Here, we present simple calculations for estimating RNA stability against hydrolysis, and a model that links the average unpaired probability of an mRNA, or AUP, to its overall hydrolysis rate. To characterize the stabilization achievable through structure design, we compare AUP optimization by conventional mRNA design methods to results from more computationally sophisticated algorithms and crowdsourcing through the OpenVaccine challenge on the Eterna platform. We find that rational design on Eterna and the more sophisticated algorithms lead to constructs with low AUP, which we term 'superfolder' mRNAs. These designs exhibit a wide diversity of sequence and structure features that may be desirable for translation, biophysical size, and immunogenicity. Furthermore, their folding is robust to temperature, computer modeling method, choice of flanking untranslated regions, and changes in target protein sequence, as illustrated by rapid redesign of superfolder mRNAs for B.1.351, P.1 and B.1.1.7 variants of the prefusion-stabilized SARS-CoV-2 spike protein. Increases in in vitro mRNA half-life by at least two-fold appear immediately achievable.

Game of Cruxes: co-designing a game for scientists and stakeholders for identifying joint problems

Scientists increasingly cross their disciplinary boundaries and connect with local stakeholders to jointly solve complex problems. Working with stakeholders means higher legitimacy and supports practical impact of research. Games provide a tool to achieve such transdisciplinary collaboration. In this paper, we explore the use of a game in a participatory project where scientists and local stakeholders are seeking and defining a joint problem. The literature is clear that this step is essential but remains short on concrete methods. Here, we explore this potential in practice. We conducted parallel participatory processes in two alpine regions considered as socio-ecological system (SES) in Switzerland and France, both vulnerable to global change. Based on these two case studies, we co-constructed a game, integrating scientific concerns about key land use, climate change and socio-economic elements of a mountain SES (tourism, agriculture, housing and demography). With the game, we assessed the existence of joint problems connecting scientific and local interests. The game successfully engaged participants at both sites over 11 game sessions, showing potential of use in other transdisciplinary settings. By covering a wide array of issues, the game created a discussion space for listing problems and identifying where scientist and stakeholder interests overlap. In Switzerland, the game revealed no pressing joint problem to be addressed. In France, game sessions revealed, among other problems, an enduring and complex issue regarding the co-existence of inhabitants and powerful institutions. Having demonstrated the capacity of this game for joint-problem assessment, we believe other participatory research in similar SES could benefit from an early use of such an approach to frame the potential for collaboration.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11625-021-00983-2.

Engineering molecular translation systems

Molecular translation systems provide a genetically encoded framework for protein synthesis, which is essential for all life. Engineering these systems to incorporate non-canonical amino acids (ncAAs) into peptides and proteins has opened many exciting opportunities in chemical and synthetic biology. Here, we review recent advances that are transforming our ability to engineer molecular translation systems. In cell-based systems, new processes to synthesize recoded genomes, tether ribosomal subunits, and engineer orthogonality with high-throughput workflows have emerged. In cell-free systems, adoption of flexizyme technology and cell-free ribosome synthesis and evolution platforms are expanding the limits of chemistry at the ribosome's RNA-based active site. Looking forward, innovations will deepen understanding of molecular translation and provide a path to polymers with previously unimaginable structures and functions.

MoiRNAiFold: a novel tool for complex in silico RNA design

Novel tools for in silico design of RNA constructs such as riboregulators are required in order to reduce time and cost to production for the development of diagnostic and therapeutic advances. Here, we present MoiRNAiFold, a versatile and user-friendly tool for de novo synthetic RNA design. MoiRNAiFold is based on Constraint Programming and it includes novel variable types, heuristics and restart strategies for Large Neighborhood Search. Moreover, this software can handle dozens of design constraints and quality measures and improves features for RNA regulation control of gene expression, such as Translation Efficiency calculation. We demonstrate that MoiRNAiFold outperforms any previous software in benchmarking structural RNA puzzles from EteRNA. Importantly, with regard to biologically relevant RNA designs, we focus on RNA riboregulators, demonstrating that the designed RNA sequences are functional both in vitro and in vivo. Overall, we have generated a powerful tool for de novo complex RNA design that we make freely available as a web server (https://moiraibiodesign.com/design/).

Biomolecular Modeling and Simulation: A Prospering Multidisciplinary Field

We reassess progress in the field of biomolecular modeling and simulation, following up on our perspective published in 2011. By reviewing metrics for the field's productivity and providing examples of success, we underscore the productive phase of the field, whose short-term expectations were overestimated and long-term effects underestimated. Such successes include prediction of structures and mechanisms; generation of new insights into biomolecular activity; and thriving collaborations between modeling and experimentation, including experiments driven by modeling. We also discuss the impact of field exercises and web games on the field's progress. Overall, we note tremendous success by the biomolecular modeling community in utilization of computer power; improvement in force fields; and development and application of new algorithms, notably machine learning and artificial intelligence. The combined advances are enhancing the accuracy andscope of modeling and simulation, establishing an exemplary discipline where experiment and theory or simulations are full partners.

RNA Engineering for Public Health: Innovations in RNA-Based Diagnostics and Therapeutics

RNA is essential for cellular function: From sensing intra- and extracellular signals to controlling gene expression, RNA mediates a diverse and expansive list of molecular processes. A long-standing goal of synthetic biology has been to develop RNA engineering principles that can be used to harness and reprogram these RNA-mediated processes to engineer biological systems to solve pressing global challenges. Recent advances in the field of RNA engineering are bringing this to fruition, enabling the creation of RNA-based tools to combat some of the most urgent public health crises. Specifically, new diagnostics using engineered RNAs are able to detect both pathogens and chemicals while generating an easily detectable fluorescent signal as an indicator. New classes of vaccines and therapeutics are also using engineered RNAs to target a wide range of genetic and pathogenic diseases. Here, we discuss the recent breakthroughs in RNA engineering enabling these innovations and examine how advances in RNA design promise to accelerate the impact of engineered RNA systems.

PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design

Biomolecular structure drives function, and computational capabilities have progressed such that the prediction and computational design of biomolecular structures is increasingly feasible. Because computational biophysics attracts students from many different backgrounds and with different levels of resources, teaching the subject can be challenging. One strategy to teach diverse learners is with interactive multimedia material that promotes self-paced, active learning. We have created a hands-on education strategy with a set of sixteen modules that teach topics in biomolecular structure and design, from fundamentals of conformational sampling and energy evaluation to applications like protein docking, antibody design, and RNA structure prediction. Our modules are based on PyRosetta, a Python library that encapsulates all computational modules and methods in the Rosetta software package. The workshop-style modules are implemented as Jupyter Notebooks that can be executed in the Google Colaboratory, allowing learners access with just a web browser. The digital format of Jupyter Notebooks allows us to embed images, molecular visualization movies, and interactive coding exercises. This multimodal approach may better reach students from different disciplines and experience levels as well as attract more researchers from smaller labs and cognate backgrounds to leverage PyRosetta in their science and engineering research. All materials are freely available at https://github.com/RosettaCommons/PyRosetta.notebooks.

Quantum Physics Literacy Aimed at K12 and the General Public

Educating K12 students and general public in quantum physics represents an evitable must no longer since quantum technologies are going to revolutionize our lives. Quantum literacy is a formidable challenge and an extraordinary opportunity for a massive cultural uplift, where citizens learn how to engender creativity and practice a new way of thinking, essential for smart community building. Scientific thinking hinges on analyzing facts and creating understanding, and it is then formulated with the dense mathematical language for later fact checking. Within classical physics, learners’ intuition may in principle be educated via classroom demonstrations of everyday-life phenomena. Their understanding can even be framed with the mathematics suited to their instruction degree. For quantum physics, on the contrary, we have no experience of quantum phenomena and the required mathematics is beyond non-expert reach. Therefore, educating intuition needs imagination. Without rooting to experiments and some degree of formal framing, educators face the risk to provide only evanescent tales, often misled, while resorting to familiar analogies. Here, we report on the realization of QPlayLearn, an online platform conceived to explicitly address challenges and opportunities of massive quantum literacy. QPlayLearn’s mission is to provide multilevel education on quantum science and technologies to anyone, regardless of age and background. To this aim, innovative interactive tools enhance the learning process effectiveness, fun, and accessibility, while remaining grounded on scientific correctness. Examples are games for basic quantum physics teaching, on-purpose designed animations, and easy-to-understand explanations on terminology and concepts by global experts. As a strategy for massive cultural change, QPlayLearn offers diversified content for different target groups, from primary school all the way to university physics students. It is addressed also to companies wishing to understand the potential of the emergent quantum industry, journalists, and policymakers needing to seize what quantum technologies are about, as well as all quantum science enthusiasts.

Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics

Therapeutic mRNAs and vaccines are being developed for a broad range of human diseases, including COVID-19. However, their optimization is hindered by mRNA instability and inefficient protein expression. Here, we describe design principles that overcome these barriers. We develop a new RNA sequencing-based platform called PERSIST-seq to systematically delineate in-cell mRNA stability, ribosome load, as well as in-solution stability of a library of diverse mRNAs. We find that, surprisingly, in-cell stability is a greater driver of protein output than high ribosome load. We further introduce a method called In-line-seq, applied to thousands of diverse RNAs, that reveals sequence and structure-based rules for mitigating hydrolytic degradation. Our findings show that "superfolder" mRNAs can be designed to improve both stability and expression that are further enhanced through pseudouridine nucleoside modification. Together, our study demonstrates simultaneous improvement of mRNA stability and protein expression and provides a computational-experimental platform for the enhancement of mRNA medicines.

Theoretical basis for stabilizing messenger RNA through secondary structure design

RNA hydrolysis presents problems in manufacturing, long-term storage, world-wide delivery, and in vivo stability of messenger RNA (mRNA)-based vaccines and therapeutics. A largely unexplored strategy to reduce mRNA hydrolysis is to redesign RNAs to form double-stranded regions, which are protected from in-line cleavage and enzymatic degradation, while coding for the same proteins. The amount of stabilization that this strategy can deliver and the most effective algorithmic approach to achieve stabilization remain poorly understood. Here, we present simple calculations for estimating RNA stability against hydrolysis, and a model that links the average unpaired probability of an mRNA, or AUP, to its overall hydrolysis rate. To characterize the stabilization achievable through structure design, we compare AUP optimization by conventional mRNA design methods to results from more computationally sophisticated algorithms and crowdsourcing through the OpenVaccine challenge on the Eterna platform. These computational tests were carried out on both model mRNAs and COVID-19 mRNA vaccine candidates. We find that rational design on Eterna and the more sophisticated algorithms lead to constructs with low AUP, which we term 'superfolder' mRNAs. These designs exhibit wide diversity of sequence and structure features that may be desirable for translation, biophysical size, and immunogenicity, and their folding is robust to temperature, choice of flanking untranslated regions, and changes in target protein sequence, as illustrated by rapid redesign of superfolder mRNAs for B.1.351, P.1, and B.1.1.7 variants of the prefusion-stabilized SARS-CoV-2 spike protein. Increases in in vitro mRNA half-life by at least two-fold appear immediately achievable.

Citizen Science in the Natural Sciences

The natural sciences include the life and physical sciences and study nature through observing and understanding phenomena, testing hypotheses, and performing experiments. Key principles such as reliability, validity, objectivity, and predictability are achieved through transparent assumptions, methods, data, and interpretations as well as multidisciplinarity.

In this chapter we present insights into the genesis of citizen science in the natural sciences and reflect on the intellectual history of the natural sciences in relation to citizen science today. Further, we consider the current scientific approaches and achievements of natural science projects, which are applying citizen science to address empirical and/or theoretical research, focusing on monitoring programmes. Presenting examples and case studies, we focus on the key characteristics of the scientific inquiries being investigated in the natural sciences through citizen science. Finally, we discuss the consequences of engagement in scientific processes in relation to the future of natural scientists in a complex world.

Single enzyme RT-PCR of full-length ribosomal RNA

The ribosome is a two-subunit, macromolecular machine composed of RNA and proteins that carries out the polymerization of α-amino acids into polypeptides. Efforts to engineer ribosomal RNA (rRNA) deepen our understanding of molecular translation and provide opportunities to expand the chemistry of life by creating ribosomes with altered properties. Toward these efforts, reverse transcription PCR (RT-PCR) of the entire 16S and 23S rRNAs, which make up the 30S small subunit and 50S large subunit, respectively, is important for isolating desired phenotypes. However, reverse transcription of rRNA is challenging due to extensive secondary structure and post-transcriptional modifications. One key challenge is that existing commercial kits for RT-PCR rely on reverse transcriptases that lack the extreme thermostability and processivity found in many commercial DNA polymerases, which can result in subpar performance on challenging templates. Here, we develop methods employing a synthetic thermostable reverse transcriptase (RTX) to enable and optimize RT-PCR of the complete Escherichia coli 16S and 23S rRNAs. We also characterize the error rate of RTX when traversing the various post-transcriptional modifications of the 23S rRNA. We anticipate that this work will facilitate efforts to study and characterize many naturally occurring long RNAs and to engineer the translation apparatus for synthetic biology.

Crowdsourcing and open innovation in drug discovery: recent contributions and future directions

The past decade has seen significant growth in the use of 'crowdsourcing' and open innovation approaches to engage 'citizen scientists' to perform novel scientific research. Here, we quantify and summarize the current state of adoption of open innovation by major pharmaceutical companies. We also highlight recent crowdsourcing and open innovation research contributions to the field of drug discovery, and interesting future directions.

Credit for and Control of Research Outputs in Genomic Citizen Science

Citizen science encompasses activities with scientific objectives in which members of the public participate as more than passive research subjects from whom personal data or biospecimens are collected and analyzed by others. Citizen science is increasingly common in the biomedical sciences, including the fields of genetics and human genomics. Genomic citizen science initiatives are diverse and involve citizen scientists in collecting genetic data, solving genetic puzzles, and conducting experiments in community laboratories. At the same time that genomic citizen science is presenting new opportunities for individuals to participate in scientific discovery, it is also challenging norms regarding the manner in which scientific research outputs are managed. In this review, we present a typology of genomic citizen science initiatives, describe ethical and legal foundations for recognizing genomic citizen scientists' claims of credit for and control of research outputs, and detail how such claims are or might be addressed in practice across a variety of initiatives.

A constructive model for collective intelligence

Natural phenomena of collective intelligence (CI) occurring in physical space show a potential approach to effective large-scale human collaboration in cyberspace. Based on existing explanatory understanding of CI, this perspective proposes a constructive model for building artificial CI systems, i.e., problem-oriented CI phenomena with AI-powered information integration and feedback.