De novo protein design by citizen scientists

Drugit: crowd-sourcing molecular design of non-peptidic VHL binders

Building on the role of human intuition in small molecule drug design, we explored whether crowdsourcing could recruit citizen scientists to this task while in parallel building awareness for this scientific process. Here, we introduce Drugit ( https://drugit.org ), the small molecule design mode of the online citizen science game Foldit. We demonstrate its utility by identifying distinct binders to the von Hippel Lindau E3 ligase. Several thousand molecules were suggested by players in a series of ten puzzle rounds. The proposed molecules were further evaluated in silico and manually by an expert panel. Selected candidates were synthesized and tested. One of these molecules shows dose-dependent shift perturbations in protein-observed NMR experiments. The co-crystal structure in complex with the E3 ligase reveals that the observed binding mode matches the player's original idea. The completion of one full design cycle is a proof of concept for the Drugit approach and highlights the potential of involving citizen scientists in early drug discovery.

Advancements in Lithography Techniques and Emerging Molecular Strategies for Nanostructure Fabrication

Lithography is crucial to semiconductor manufacturing, enabling the production of smaller, more powerful electronic devices. This review explores the evolution, principles, and advancements of key lithography techniques, including extreme ultraviolet (EUV) lithography, electron beam lithography (EBL), X-ray lithography (XRL), ion beam lithography (IBL), and nanoimprint lithography (NIL). Each method is analyzed based on its working principles, resolution, resist materials, and applications. EUV lithography, with sub-10 nm resolution, is vital for extending Moore's Law, leveraging high-NA optics and chemically amplified resists. EBL and IBL enable high-precision maskless patterning for prototyping but suffer from low throughput. XRL, using synchrotron radiation, achieves deep, high-resolution features, while NIL provides a cost-effective, high-throughput method for replicating nanostructures. Alignment marks play a key role in precise layer-to-layer registration, with innovations enhancing accuracy in advanced systems. The mask fabrication process is also examined, highlighting materials like molybdenum silicide for EUV and defect mitigation strategies such as automated inspection and repair. Despite challenges in resolution, defect control, and material innovation, lithography remains indispensable in semiconductor scaling, supporting applications in integrated circuits, photonics, and MEMS/NEMS devices. Various molecular strategies, mechanisms, and molecular dynamic simulations to overcome the fundamental lithographic limits are also highlighted in detail. This review offers insights into lithography's present and future, aiding researchers in nanoscale manufacturing advancements.

Massively parallel assessment of designed protein solution properties using mass spectrometry and peptide barcoding

Library screening and selection methods can determine the binding activities of individual members of large protein libraries given a physical link between protein and nucleotide sequence, which enables identification of functional molecules by DNA sequencing. However, the solution properties of individual protein molecules cannot be probed using such approaches because they are completely altered by DNA attachment. Mass spectrometry enables parallel evaluation of protein properties amenable to physical fractionation such as solubility and oligomeric state, but current approaches are limited to libraries of 1,000 or fewer proteins. Here, we improved mass spectrometry barcoding by co-synthesizing proteins with barcodes optimized to be highly multiplexable and minimally perturbative, scaling to libraries of >5,000 proteins. We use these barcodes together with mass spectrometry to assay the solution behavior of libraries of de novo-designed monomeric scaffolds, oligomers, binding proteins and nanocages, rapidly identifying design failure modes and successes.

Solving the protein folding problem…

The protein folding problem was, to paraphrase Churchill, 'A riddle wrapped in a mystery inside an enigma'. The riddle, in this context, was the folding code; what interactions at the amino acid level are driving the folding process? The mystery was the kinetic question (Levinthal's paradox); how does the folding process occur so quickly (typically in timescales ranging from μS to mS)? Finally, the enigma represents the computational problem of developing approaches to predict the final folded sate of a protein given only its amino acid sequence. Herein, I trace the path to solving this riddle wrapped in a mystery inside an enigma.

When combinations of humans and AI are useful: A systematic review and meta-analysis

Inspired by the increasing use of artificial intelligence (AI) to augment humans, researchers have studied human-AI systems involving different tasks, systems and populations. Despite such a large body of work, we lack a broad conceptual understanding of when combinations of humans and AI are better than either alone. Here we addressed this question by conducting a preregistered systematic review and meta-analysis of 106 experimental studies reporting 370 effect sizes. We searched an interdisciplinary set of databases (the Association for Computing Machinery Digital Library, the Web of Science and the Association for Information Systems eLibrary) for studies published between 1 January 2020 and 30 June 2023. Each study was required to include an original human-participants experiment that evaluated the performance of humans alone, AI alone and human-AI combinations. First, we found that, on average, human-AI combinations performed significantly worse than the best of humans or AI alone (Hedges' g = -0.23; 95% confidence interval, -0.39 to -0.07). Second, we found performance losses in tasks that involved making decisions and significantly greater gains in tasks that involved creating content. Finally, when humans outperformed AI alone, we found performance gains in the combination, but when AI outperformed humans alone, we found losses. Limitations of the evidence assessed here include possible publication bias and variations in the study designs analysed. Overall, these findings highlight the heterogeneity of the effects of human-AI collaboration and point to promising avenues for improving human-AI systems.

Critical Assessment of Protein Engineering (CAPE): A Student Challenge on the Cloud

The success of AlphaFold in protein structure prediction highlights the power of data-driven approaches in scientific research. However, developing machine learning models to design and engineer proteins with desirable functions is hampered by limited access to high-quality data sets and experimental feedback. The Critical Assessment of Protein Engineering (CAPE) challenge addresses these issues through a student-focused competition, utilizing cloud computing and biofoundries to lower barriers to entry. CAPE serves as an open platform for community learning, where mutant data sets and design algorithms from past contestants help improve overall performance in subsequent rounds. Through two competition rounds, student participants collectively designed >1500 new mutant sequences, with the best-performing variants exhibiting catalytic activity up to 5-fold higher than the wild-type parent. We envision CAPE as a collaborative platform to engage young researchers and promote computational protein engineering.

Structural diversity and clustering of bacterial flagellar outer domains

Supercoiled flagellar filaments function as mechanical propellers within the bacterial flagellum complex, playing a crucial role in motility. Flagellin, the building block of the filament, features a conserved inner D0/D1 core domain across different bacterial species. In contrast, approximately half of the flagellins possess additional, highly divergent outer domain(s), suggesting varied functional potential. In this study, we report atomic structures of flagellar filaments from three distinct bacterial species: Cupriavidus gilardii, Stenotrophomonas maltophilia, and Geovibrio thiophilus. Our findings reveal that the flagella from the facultative anaerobic G. thiophilus possesses a significantly more negatively charged surface, potentially enabling adhesion to positively charged minerals. Furthermore, we analyze all AlphaFold predicted structures for annotated bacterial flagellins, categorizing the flagellin outer domains into 682 structural clusters. This classification provides insights into the prevalence and experimental verification of these outer domains. Remarkably, two of the flagellar structures reported herein belong to a distinct cluster, indicating additional opportunities on the study of the functional diversity of flagellar outer domains. Our findings underscore the complexity of bacterial flagellins and open up possibilities for future studies into their varied roles beyond motility.

Scalable protein design using optimization in a relaxed sequence space

Machine learning (ML)-based design approaches have advanced the field of de novo protein design, with diffusion-based generative methods increasingly dominating protein design pipelines. Here, we report a "hallucination"-based protein design approach that functions in relaxed sequence space, enabling the efficient design of high-quality protein backbones over multiple scales and with broad scope of application without the need for any form of retraining. We experimentally produced and characterized more than 100 proteins. Three high-resolution crystal structures and two cryo-electron microscopy density maps of designed single-chain proteins comprising up to 1000 amino acids validate the accuracy of the method. Our pipeline can also be used to design synthetic protein-protein interactions, as validated experimentally by a set of protein heterodimers. Relaxed sequence optimization offers attractive performance with respect to designability, scope of applicability for different design problems, and scalability across protein sizes.

Citizen involvement in research on technological innovations for health, care or well-being: a scoping review

Citizen science can be a powerful approach to foster the successful implementation of technological innovations in health, care or well-being. Involving experience experts as co-researchers or co-designers of technological innovations facilitates mutual learning, community building, and empowerment. By utilizing the expert knowledge of the intended users, innovations have a better chance to get adopted and solve complex health-related problems. As citizen science is still a relatively new practice for health and well-being, little is known about effective methods and guidelines for successful collaboration. This scoping review aims to provide insight in (1) the levels of citizen involvement in current research on technological innovations for health, care or well-being, (2) the used participatory methodologies, and (3) lesson's learned by the researchers.A scoping review was conducted and reported in accordance with the PRISMA-ScR guidelines. The search was performed in SCOPUS in January 2021 and included peer-reviewed journal and conference papers published between 2016 and 2020. The final selection (N = 83) was limited to empirical studies that had a clear focus on technological innovations for health, care or well-being and involved citizens at the level of collaboration or higher. Our results show a growing interest in citizens science as an inclusive research approach. Citizens are predominantly involved in the design phase of innovations and less in the preparation, data-analyses or reporting phase. Eight records had citizens in the lead in one of the research phases.Researcher use different terms to describe their methodological approach including participatory design, co-design, community based participatory research, co-creation, public and patient involvement, partcipatory action research, user-centred design and citizen science. Our selection of cases shows that succesful citizen science projects develop a structural and longitudinal partnership with their collaborators, use a situated and adaptive research approach, and have researchers that are willing to abandon traditional power dynamics and engage in a mutual learning experience.

Design of amyloidogenic peptide traps

Segments of proteins with high β-strand propensity can self-associate to form amyloid fibrils implicated in many diseases. We describe a general approach to bind such segments in β-strand and β-hairpin conformations using de novo designed scaffolds that contain deep peptide-binding clefts. The designs bind their cognate peptides in vitro with nanomolar affinities. The crystal structure of a designed protein-peptide complex is close to the design model, and NMR characterization reveals how the peptide-binding cleft is protected in the apo state. We use the approach to design binders to the amyloid-forming proteins transthyretin, tau, serum amyloid A1 and amyloid β1-42 (Aβ42). The Aβ binders block the assembly of Aβ fibrils as effectively as the most potent of the clinically tested antibodies to date and protect cells from toxic Aβ42 species.

The State-of-the-Art Overview to Application of Deep Learning in Accurate Protein Design and Structure Prediction

In recent years, there has been a notable increase in the scientific community's interest in rational protein design. The prospect of designing an amino acid sequence that can reliably fold into a desired three-dimensional structure and exhibit the intended function is captivating. However, a major challenge in this endeavor lies in accurately predicting the resulting protein structure. The exponential growth of protein databases has fueled the advancement of the field, while newly developed algorithms have pushed the boundaries of what was previously achievable in structure prediction. In particular, using deep learning methods instead of brute force approaches has emerged as a faster and more accurate strategy. These deep-learning techniques leverage the vast amount of data available in protein databases to extract meaningful patterns and predict protein structures with improved precision. In this article, we explore the recent developments in the field of protein structure prediction. We delve into the newly developed methods that leverage deep learning approaches, highlighting their significance and potential for advancing our understanding of protein design.

Structural diversity and clustering of bacterial flagellar outer domains

Supercoiled flagellar filaments function as mechanical propellers within the bacterial flagellum complex, playing a crucial role in motility. Flagellin, the building block of the filament, features a conserved inner D0/D1 core domain across different bacterial species. In contrast, approximately half of the flagellins possess additional, highly divergent outer domain(s), suggesting varied functional potential. In this study, we elucidate atomic structures of flagellar filaments from three distinct bacterial species: Cupriavidus gilardii , Stenotrophomonas maltophilia , and Geovibrio thiophilus . Our findings reveal that the flagella from the facultative anaerobic G. thiophilus possesses a significantly more negatively charged surface, potentially enabling adhesion to positively charged minerals. Furthermore, we analyzed all AlphaFold predicted structures for annotated bacterial flagellins, categorizing the flagellin outer domains into 682 structural clusters. This classification provides insights into the prevalence and experimental verification of these outer domains. Remarkably, two of the flagellar structures reported herein belong to a previously unexplored cluster, indicating new opportunities on the study of the functional diversity of flagellar outer domains. Our findings underscore the complexity of bacterial flagellins and open up possibilities for future studies into their varied roles beyond motility.

Artificial structural proteins: Synthesis, assembly and material applications

Structural proteins have evolved over billions of years and offer outstanding mechanical properties, such as resilience, toughness and stiffness. Advances in modular protein engineering, polypeptide modification, and synthetic biology have led to the development of novel biomimetic structural proteins to perform in biomedical and military fields. However, the development of customized structural proteins and assemblies with superior performance remains a major challenge, due to the inherent limitations of biosynthesis, difficulty in mimicking the complexed macroscale assembly, etc. This review summarizes the approaches for the design and production of biomimetic structural proteins, and their chemical modifications for multiscale assembly. Furthermore, we discuss the function tailoring and current applications of biomimetic structural protein assemblies. A perspective of future research is to reveal how the mechanical properties are encoded in the sequences and conformations. This review, therefore, provides an important reference for the development of structural proteins-mimetics from replication of nature to even outperforming nature.

Characterization and Diversification of AraC/XylS Family Regulators Guided by Transposon Sequencing

In this study, we explored the development of engineered inducible systems. Publicly available data from previous transposon sequencing assays were used to identify regulators of metabolism in Pseudomonas putida KT2440. For AraC family regulators (AFRs) represented in these data, we posited AFR/promoter/inducer groupings. Twelve promoters were characterized for a response to their proposed inducers in P. putida, and the resultant data were used to create and test nine two-plasmid sensor systems in Escherichia coli. Several of these were further developed into a palette of single-plasmid inducible systems. From these experiments, we observed an unreported inducer response from a previously characterized AFR, demonstrated that the addition of a P. putida transporter improved the sensor dynamics of an AFR in E. coli, and identified an uncharacterized AFR with a novel potential inducer specificity. Finally, targeted mutations in an AFR, informed by structural predictions, enabled the further diversification of these inducible plasmids.

Programmable synthetic receptors: the next-generation of cell and gene therapies

Cell and gene therapies hold tremendous promise for treating a range of difficult-to-treat diseases. However, concerns over the safety and efficacy require to be further addressed in order to realize their full potential. Synthetic receptors, a synthetic biology tool that can precisely control the function of therapeutic cells and genetic modules, have been rapidly developed and applied as a powerful solution. Delicately designed and engineered, they can be applied to finetune the therapeutic activities, i.e., to regulate production of dosed, bioactive payloads by sensing and processing user-defined signals or biomarkers. This review provides an overview of diverse synthetic receptor systems being used to reprogram therapeutic cells and their wide applications in biomedical research. With a special focus on four synthetic receptor systems at the forefront, including chimeric antigen receptors (CARs) and synthetic Notch (synNotch) receptors, we address the generalized strategies to design, construct and improve synthetic receptors. Meanwhile, we also highlight the expanding landscape of therapeutic applications of the synthetic receptor systems as well as current challenges in their clinical translation.

Inequality persists in a large citizen science programme despite increased participation through ICT innovations

Biological recording is a prominent and widely practised form of citizen science, but few studies explore long-term demographic trends in participation and knowledge production. We studied long-term demographic trends of age and gender of participants reporting to a large online citizen science multi-taxon biodiversity platform ( www.artportalen.se ). Adoption by user communities and continually developing Information and Communications Technologies (ICTs) greatly increased the number of participants reporting data, but profound long-term imbalances in gender contribution across species groups persisted over time. Reporters identifying as male dominated in numbers, spent more days in the field reporting and reported more species on each field day. Moreover, an age imbalance towards older participants amplified over time. As the first long-term study of citizen participation by age and gender, our results show that it is important for citizen science project developers to account for cultural and social developments that might exclude participants, and to engage with underrepresented and younger participants. This could facilitate the breadth of engagement and learning across a larger societal landscape, ensure project longevity and biodiversity data representation (e.g. mitigate gender bias influence on the number of reports of different species groups).

Directing polymorph specific calcium carbonate formation with de novo protein templates

Biomolecules modulate inorganic crystallization to generate hierarchically structured biominerals, but the atomic structure of the organic-inorganic interfaces that regulate mineralization remain largely unknown. We hypothesized that heterogeneous nucleation of calcium carbonate could be achieved by a structured flat molecular template that pre-organizes calcium ions on its surface. To test this hypothesis, we design helical repeat proteins (DHRs) displaying regularly spaced carboxylate arrays on their surfaces and find that both protein monomers and protein-Ca2+ supramolecular assemblies directly nucleate nano-calcite with non-natural {110} or {202} faces while vaterite, which forms first in the absence of the proteins, is bypassed. These protein-stabilized nanocrystals then assemble by oriented attachment into calcite mesocrystals. We find further that nanocrystal size and polymorph can be tuned by varying the length and surface chemistry of the designed protein templates. Thus, bio-mineralization can be programmed using de novo protein design, providing a route to next-generation hybrid materials.

Genetically encoded ATP and NAD(P)H biosensors: potential tools in metabolic engineering

To date, many metabolic engineering tools and strategies have been developed, including tools for cofactor engineering, which is a common strategy for bioproduct synthesis. Cofactor engineering is used for the regulation of pyridine nucleotides, including NADH/NAD+ and NADPH/NADP+, and adenosine triphosphate/adenosine diphosphate (ATP/ADP), which is crucial for maintaining redox and energy balance. However, the intracellular levels of NADH/NAD+, NADPH/NADP+, and ATP/ADP cannot be monitored in real time using traditional methods. Recently, many biosensors for detecting, monitoring, and regulating the intracellular levels of NADH/NAD+, NADPH/NADP+, and ATP/ADP have been developed. Although cofactor biosensors have been mainly developed for use in mammalian cells, the potential application of cofactor biosensors in metabolic engineering in bacterial and yeast cells has received recent attention. Coupling cofactor biosensors with genetic circuits is a promising strategy in metabolic engineering for optimizing the production of biochemicals. In this review, we focus on the development of biosensors for NADH/NAD+, NADPH/NADP+, and ATP/ADP and the potential application of these biosensors in metabolic engineering. We also provide critical perspectives, identify current research challenges, and provide guidance for future research in this promising field.

Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data

Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open-source protein NMR data sets for such "blind" targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15N-1H residual dipolar coupling data. For these nine small (70-108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research.

Global Wheat Head Detection Challenges: Winning Models and Application for Head Counting

Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science problems. Data competitions have a rich history in plant phenotyping, and new outdoor field datasets have the potential to embrace solutions across research and commercial applications. We developed the Global Wheat Challenge as a generalization competition in 2020 and 2021 to find more robust solutions for wheat head detection using field images from different regions. We analyze the winning challenge solutions in terms of their robustness when applied to new datasets. We found that the design of the competition had an influence on the selection of winning solutions and provide recommendations for future competitions to encourage the selection of more robust solutions.

Molecular Complementarity of Proteomimetic Materials for Target-Specific Recognition and Recognition-Mediated Complex Functions

As biomolecules essential for sustaining life, proteins are generated from long chains of 20 different α-amino acids that are folded into unique 3D structures. In particular, many proteins have molecular recognition functions owing to their binding pockets, which have complementary shapes, charges, and polarities for specific targets, making these biopolymers unique and highly valuable for biomedical and biocatalytic applications. Based on the understanding of protein structures and microenvironments, molecular complementarity can be exhibited by synthesizable and modifiable materials. This has prompted researchers to explore the proteomimetic potentials of a diverse range of materials, including biologically available peptides and oligonucleotides, synthetic supramolecules, inorganic molecules, and related coordination networks. To fully resemble a protein, proteomimetic materials perform the molecular recognition to mediate complex molecular functions, such as allosteric regulation, signal transduction, enzymatic reactions, and stimuli-responsive motions; this can also expand the landscape of their potential bio-applications. This review focuses on the recognitive aspects of proteomimetic designs derived for individual materials and their conformations. Recent progress provides insights to help guide the development of advanced protein mimicry with material heterogeneity, design modularity, and tailored functionality. The perspectives and challenges of current proteomimetic designs and tools are also discussed in relation to future applications.

Rational design of mini protein mimicking uricase: Encapsulation in ZIF-8 for uric acid detection

Five mini proteins mimicking uricase comprising 20, 40, 60, 80, and 100 amino acids were designed based on the conserved active site residues within the same dimer, using the crystal structure of tetrameric uricase from Arthrobacter globiformis (PDB ID: 2yzb) as a template. Based on molecular docking analysis, the smallest mini protein, mp20, shared similar residues to that of native uricase that formed hydrogen bonds with uric acid and was chosen for further studies. Although purified recombinant mp20 did not exhibit uricase activity, it showed specific binding towards uric acid and evinced excellent thermotolerance and structural stability at temperatures ranging from 10°C to 100°C, emulating its natural origin. To explore the potential of mp20 as a bioreceptor in uric acid sensing, mp20 was encapsulated within zeolitic imidazolate framework-8 (mp20@ZIF-8) followed by the modification on rGO-screen printed electrode (rGO/SPCE) to maintain the structural stability. An irreversible anodic peak and increased semicircular arcs of the Nyquist plot with an increase of the analyte concentrations were observed by utilizing cyclic voltammetry and electrochemical impedance spectroscopy (EIS), suggesting the detection of uric acid occurred, which is based on substrate-mp20 interaction.

Strengthening microbial cell factories for efficient production of bioactive molecules

High demand of bioactive molecules (food additives, antibiotics, plant growth enhancers, cosmetics, pigments and other commercial products) is the prime need for the betterment of human life where the applicability of the synthetic chemical product is on the saturation due to associated toxicity and ornamentations. It has been noticed that the discovery and productivity of such molecules in natural scenarios are limited due to low cellular yields as well as less optimized conventional methods. In this respect, microbial cell factories timely fulfilling the requirement of synthesizing bioactive molecules by improving production yield and screening more promising structural homologues of the native molecule. Where the robustness of the microbial host can be potentially achieved by taking advantage of cell engineering approaches such as tuning functional and adjustable factors, metabolic balancing, adapting cellular transcription machinery, applying high throughput OMICs tools, stability of genotype/phenotype, organelle optimizations, genome editing (CRISPER/Cas mediated system) and also by developing accurate model systems via machine-learning tools. In this article, we provide an overview from traditional to recent trends and the application of newly developed technologies, for strengthening the systemic approaches and providing future directions for enhancing the robustness of microbial cell factories to speed up the production of biomolecules for commercial purposes.

Towards de novo design of transmembrane α-helical assemblies using structural modelling and molecular dynamics simulation

Computational de novo protein design involves iterative processes consisting of amino acid sequence design, structural modelling and scoring, and design validation by synthesis and experimental characterisation. Recent advances in protein structure prediction and modelling methods have enabled the highly efficient and accurate design of water-soluble proteins. However, the design of membrane proteins remains a major challenge. To advance membrane protein design, considering the higher complexity of membrane protein folding, stability, and dynamic interactions between water, ions, lipids, and proteins is an important task. For introducing explicit solvents and membranes to these design methods, all-atom molecular dynamics (MD) simulations of designed proteins provide useful information that cannot be obtained experimentally. In this review, we first describe two major approaches to designing transmembrane α-helical assemblies, consensus and de novo design. We further illustrate recent MD studies of membrane protein folding related to protein design, as well as advanced treatments in molecular models and conformational sampling techniques in the simulations. Finally, we discuss the possibility to introduce MD simulations after the existing static modelling and screening of design decoys as an additional step for refinement of the design, which considers membrane protein folding dynamics and interactions with explicit membranes.

Profiling of peripheral blood B-cell transcriptome in children who developed coeliac disease in a prospective study

Background

In coeliac disease (CoD), the role of B-cells has mainly been considered to be production of antibodies. The functional role of B-cells has not been analysed extensively in CoD.

Methods

We conducted a study to characterize gene expression in B-cells from children developing CoD early in life using samples collected before and at the diagnosis of the disease. Blood samples were collected from children at risk at 12, 18, 24 and 36 months of age. RNA from peripheral blood CD19⁺ cells was sequenced and differential gene expression was analysed using R package Limma.

Findings

Overall, we found one gene, HNRNPL, modestly downregulated in all patients (logFC -0·7; q = 0·09), and several others downregulated in those diagnosed with CoD already by the age of 2 years.

Interpretation

The data highlight the role of B-cells in CoD development. The role of HNRPL in suppressing enteroviral replication suggests that the predisposing factor for both CoD and enteroviral infections is the low level of HNRNPL expression.

Funding

EU FP7 grant no. 202063, EU Regional Developmental Fund and research grant PRG712, The Academy of Finland Centre of Excellence in Molecular Systems Immunology and Physiology Research (SyMMyS) 2012-2017, grant no. 250114) and, AoF Personalized Medicine Program (grant no. 292482), AoF grants 292335, 294337, 319280, 31444, 319280, 329277, 331790) and grants from the Sigrid Jusélius Foundation (SJF).

Improvements and new functionalities of UNRES server for coarse-grained modeling of protein structure, dynamics, and interactions

In this paper we report the improvements and extensions of the UNRES server (https://unres-server.chem.ug.edu.pl) for physics-based simulations with the coarse-grained UNRES model of polypeptide chains. The improvements include the replacement of the old code with the recently optimized one and adding the recent scale-consistent variant of the UNRES force field, which performs better in the modeling of proteins with the β and the α+β structures. The scope of applications of the package was extended to data-assisted simulations with restraints from nuclear magnetic resonance (NMR) and chemical crosslink mass-spectroscopy (XL-MS) measurements. NMR restraints can be input in the NMR Exchange Format (NEF), which has become a standard. Ambiguous NMR restraints are handled without expert intervention owing to a specially designed penalty function. The server can be used to run smaller jobs directly or to prepare input data to run larger production jobs by using standalone installations of UNRES.

A coarse-grained approach to NMR-data-assisted modeling of protein structures

The ESCASA algorithm for analytical estimation of proton positions from coarse-grained geometry developed in our recent work has been implemented in modeling protein structures with the highly coarse-grained UNRES model of polypeptide chains (two sites per residue) and nuclear magnetic resonance (NMR) data. A penalty function with the shape of intersecting gorges was applied to treat ambiguous distance restraints, which automatically selects consistent restraints. Hamiltonian replica exchange molecular dynamics was used to carry out the conformational search. The method was tested with both unambiguous and ambiguous restraints producing good-quality models with GDT_TS from 7.4 units higher to 14.4 units lower than those obtained with the CYANA or MELD software for protein-structure determination from NMR data at the all-atom resolution. The method can thus be applied in modeling the structures of flexible proteins, for which extensive conformational search enabled by coarse-graining is more important than high modeling accuracy.

Exploring Novel Innovation Strategies to Close a Technology Gap in Neurosurgery: The HORAO Crowdsourcing Campaign (Preprint)

BACKGROUND

Scientific research is typically performed by expert individuals or groups who investigate potential solutions in a sequential manner. Given the current worldwide exponential increase in technical innovations, potential solutions for any new problem might already exist, even though they were developed to solve a different problem. Therefore, in crowdsourcing ideation, a research question is explained to a much larger group of individuals beyond the specialist community to obtain a multitude of diverse, outside-the-box solutions. These are then assessed in parallel by a group of experts for their capacity to solve the new problem. The 2 key problems in brain tumor surgery are the difficulty of discerning the exact border between a tumor and the surrounding brain, and the difficulty of identifying the function of a specific area of the brain. Both problems could be solved by a method that visualizes the highly organized fiber tracts within the brain; the absence of fibers would reveal the tumor, whereas the spatial orientation of the tracts would reveal the area's function. To raise awareness about our challenge of developing a means of intraoperative, real-time, noninvasive identification of fiber tracts and tumor borders to improve neurosurgical oncology, we turned to the crowd with a crowdsourcing ideation challenge.

OBJECTIVE

Our objective was to evaluate the feasibility of a crowdsourcing ideation campaign for finding novel solutions to challenges in neuroscience. The purpose of this paper is to introduce our chosen crowdsourcing method and discuss it in the context of the current literature.

METHODS

We ran a prize-based crowdsourcing ideation competition called HORAO on the commercial platform HeroX. Prize money previously collected through a crowdfunding campaign was offered as an incentive. Using a multistage approach, an expert jury first selected promising technical solutions based on broad, predefined criteria, coached the respective solvers in the second stage, and finally selected the winners in a conference setting. We performed a postchallenge web-based survey among the solvers crowd to find out about their backgrounds and demographics.

RESULTS

Our web-based campaign reached more than 20,000 people (views). We received 45 proposals from 32 individuals and 7 teams, working in 26 countries on 4 continents. The postchallenge survey revealed that most of the submissions came from single solvers or teams working in engineering or the natural sciences, with additional submissions from other nonmedical fields. We engaged in further exchanges with 3 out of the 5 finalists and finally initiated a successful scientific collaboration with the winner of the challenge.

CONCLUSIONS

This open innovation competition is the first of its kind in medical technology research. A prize-based crowdsourcing ideation campaign is a promising strategy for raising awareness about a specific problem, finding innovative solutions, and establishing new scientific collaborations beyond strictly disciplinary domains.

Design and Engineering of Miniproteins

The potential of miniproteins in the biological and chemical sciences is constantly increasing. Significant progress in the design methodologies has been achieved over the last 30 years. Early approaches based on propensities of individual amino acid residues to form individual secondary structures were subsequently improved by structural analyses using NMR spectroscopy and crystallography. Consequently, computational algorithms were developed, which are now highly successful in designing structures with accuracy often close to atomic range. Further perspectives include construction of miniproteins incorporating non-native secondary structures derived from sequences with units other than α-amino acids. Noteworthy, miniproteins with extended structures, which are now feasibly accessible, are excellent scaffolds for construction of functional molecules.

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.

Protein design via deep learning

Proteins with desired functions and properties are important in fields like nanotechnology and biomedicine. De novo protein design enables the production of previously unseen proteins from the ground up and is believed as a key point for handling real social challenges. Recent introduction of deep learning into design methods exhibits a transformative influence and is expected to represent a promising and exciting future direction. In this review, we retrospect the major aspects of current advances in deep-learning-based design procedures and illustrate their novelty in comparison with conventional knowledge-based approaches through noticeable cases. We not only describe deep learning developments in structure-based protein design and direct sequence design, but also highlight recent applications of deep reinforcement learning in protein design. The future perspectives on design goals, challenges and opportunities are also comprehensively discussed.

Lessons from a breast cell annotation competition series for school pupils

Due to COVID-19 outbreaks, most school pupils have had to be home-schooled for long periods of time. Two editions of a web-based competition “Beat the Pathologists” for school age participants in the UK ran to fill up pupils’ spare time after home-schooling and evaluate their ability on contributing to AI annotation. The two editions asked the participants to annotate different types of cells on Ki67 stained breast cancer images. The Main competition was at four levels with different level of complexity. We obtained annotations of four kinds of cells entered by school pupils and ground truth from expert pathologists. In this paper, we analyse school pupils’ performance on differentiating different kinds of cells and compare their performance with two neural networks (AlexNet and VGG16). It was observed that children tend to get very good performance in tumour cell annotation with the best F1 measure 0.81 which is a metrics taking both false positives and false negatives into account. Low accuracy was achieved with F1 score 0.75 on positive non-tumour cells and 0.59 on negative non-tumour cells. Superior performance on non-tumour cell detection was achieved by neural networks. VGG16 with training from scratch achieved an F1 score over 0.70 in all cell categories and 0.92 in tumour cell detection. We conclude that non-experts like school pupils have the potential to contribute to large-scale labelling for AI algorithm development if sufficient training activities are organised. We hope that competitions like this can promote public interest in pathology and encourage participation by more non-experts for annotation.

Algorithms for protein design

Computational Protein Design has the potential to contribute to major advances in enzyme technology, vaccine design, receptor-ligand engineering, biomaterials, nanosensors, and synthetic biology. Although Protein Design is a challenging problem, proteins can be designed by experts in Protein Design, as well as by non-experts whose primary interests are in the applications of Protein Design. The increased accessibility of Protein Design technology is attributable to the accumulated knowledge and experience with Protein Design as well as to the availability of software and online resources. The objective of this review is to serve as a guide to the relevant literature with a focus on the novel methods and algorithms that have been developed or applied for Protein Design, and to assist in the selection of algorithms for Protein Design. Novel algorithms and models that have been introduced to utilize the enormous amount of experimental data and novel computational hardware have the potential for producing substantial increases in the accuracy, reliability and range of applications of designed proteins.

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.

A Brave New World: Virtual Reality and Augmented Reality in Systems Biology

How we interact with computer graphics has not changed significantly from viewing 2D text and images on a flatscreen since their invention. Yet, recent advances in computing technology, internetworked devices and gaming are driving the design and development of new ideas in other modes of human-computer interfaces (HCIs). Virtual Reality (VR) technology uses computers and HCIs to create the feeling of immersion in a three-dimensional (3D) environment that contains interactive objects with a sense of spatial presence, where objects have a spatial location relative to, and independent of the users. While this virtual environment does not necessarily match the real world, by creating the illusion of reality, it helps users leverage the full range of human sensory capabilities. Similarly, Augmented Reality (AR), superimposes virtual images to the real world. Because humans learn the physical world through a gradual sensory familiarization, these immersive visualizations enable gaining familiarity with biological systems not realizable in the physical world (e.g., allosteric regulatory networks within a protein or biomolecular pathways inside a cell). As VR/AR interfaces are anticipated to be explosive in consumer markets, systems biologists will be more immersed into their world. Here we introduce a brief history of VR/AR, their current roles in systems biology, and advantages and disadvantages in augmenting user abilities. We next argue that in systems biology, VR/AR technologies will be most useful in visually exploring and communicating data; performing virtual experiments; and education/teaching. Finally, we discuss our perspective on future directions for VR/AR in systems biology.

Reconfigurable asymmetric protein assemblies through implicit negative design

Asymmetric multiprotein complexes that undergo subunit exchange play central roles in biology but present a challenge for design because the components must not only contain interfaces that enable reversible association but also be stable and well behaved in isolation. We use implicit negative design to generate β sheet-mediated heterodimers that can be assembled into a wide variety of complexes. The designs are stable, folded, and soluble in isolation and rapidly assemble upon mixing, and crystal structures are close to the computational models. We construct linearly arranged hetero-oligomers with up to six different components, branched hetero-oligomers, closed C4-symmetric two-component rings, and hetero-oligomers assembled on a cyclic homo-oligomeric central hub and demonstrate that such complexes can readily reconfigure through subunit exchange. Our approach provides a general route to designing asymmetric reconfigurable protein systems.

Prediction of tuberculosis using an automated machine learning platform for models trained on synthetic data

High-quality medical data is critical to the development and implementation of machine learning (ML) algorithms in healthcare; however, security, and privacy concerns continue to limit access. We sought to determine the utility of “synthetic data” in training ML algorithms for the detection of tuberculosis (TB) from inflammatory biomarker profiles. A retrospective dataset (A) comprised of 278 patients was used to generate synthetic datasets (B, C, and D) for training models prior to secondary validation on a generalization dataset. ML models trained and validated on the Dataset A (real) demonstrated an accuracy of 90%, a sensitivity of 89% (95% CI, 83–94%), and a specificity of 100% (95% CI, 81–100%). Models trained using the optimal synthetic dataset B showed an accuracy of 91%, a sensitivity of 93% (95% CI, 87–96%), and a specificity of 77% (95% CI, 50–93%). Synthetic datasets C and D displayed diminished performance measures (respective accuracies of 71% and 54%). This pilot study highlights the promise of synthetic data as an expedited means for ML algorithm development.

Development of a potent high-affinity human therapeutic antibody via novel application of recombination signal sequence-based affinity maturation

Therapeutic antibody development requires discovery of an antibody molecule with desired specificities and drug-like properties. For toxicological studies, a therapeutic antibody must bind the ortholog antigen with a similar affinity to the human target to enable relevant dosing regimens, and antibodies falling short of this affinity design goal may not progress as therapeutic leads. Herein, we report the novel use of mammalian recombination signal sequence (RSS)–directed recombination for complementarity-determining region–targeted protein engineering combined with mammalian display to close the species affinity gap of human interleukin (IL)-13 antibody 731. This fully human antibody has not progressed as a therapeutic in part because of a 400-fold species affinity gap. Using this nonhypothesis-driven affinity maturation method, we generated multiple antibody variants with improved IL-13 affinity, including the highest affinity antibody reported to date (34 fM). Resolution of a cocrystal structure of the optimized antibody with the cynomolgus monkey (or nonhuman primate) IL-13 protein revealed that the RSS-derived mutations introduced multiple successive amino-acid substitutions resulting in a de novo formation of a π–π stacking–based protein–protein interaction between the affinity-matured antibody heavy chain and helix C on IL-13, as well as an introduction of an interface-distant residue, which enhanced the light chain–binding affinity to target. These mutations synergized binding of heavy and light chains to the target protein, resulting in a remarkably tight interaction, and providing a proof of concept for a new method of protein engineering, based on synergizing a mammalian display platform with novel RSS-mediated library generation.

A Structural Perspective of Reps from CRESS-DNA Viruses and Their Bacterial Plasmid Homologues

Rolling circle replication (RCR) is ubiquitously used by cellular and viral systems for genome and plasmid replication. While the molecular mechanism of RCR has been described, the structural mechanism is desperately lacking. Circular-rep encoded single stranded DNA (CRESS-DNA) viruses employ a viral encoded replicase (Rep) to initiate RCR. The recently identified prokaryotic homologues of Reps may also be responsible for initiating RCR. Reps are composed of an endonuclease, oligomerization, and ATPase domain. Recent structural studies have provided structures for all these domains such that an overall mechanism of RCR initiation can begin to be synthesized. However, structures of Rep in complex with its various DNA substrates and/or ligands are lacking. Here we provide a 3D bioinformatic review of the current structural information available for Reps. We combine an excess of 1590 sequences with experimental and predicted structural data from 22 CRESS-DNA groups to identify similarities and differences between Reps that lead to potentially important functional sites. Experimental studies of these sites may shed light on how Reps execute their functions. Furthermore, we identify Rep-substrate or Rep-ligand structures that are urgently needed to better understand the structural mechanism of RCR.

Protein Assembly by Design

Proteins are nature's primary building blocks for the construction of sophisticated molecular machines and dynamic materials, ranging from protein complexes such as photosystem II and nitrogenase that drive biogeochemical cycles to cytoskeletal assemblies and muscle fibers for motion. Such natural systems have inspired extensive efforts in the rational design of artificial protein assemblies in the last two decades. As molecular building blocks, proteins are highly complex, in terms of both their three-dimensional structures and chemical compositions. To enable control over the self-assembly of such complex molecules, scientists have devised many creative strategies by combining tools and principles of experimental and computational biophysics, supramolecular chemistry, inorganic chemistry, materials science, and polymer chemistry, among others. Owing to these innovative strategies, what started as a purely structure-building exercise two decades ago has, in short order, led to artificial protein assemblies with unprecedented structures and functions and protein-based materials with unusual properties. Our goal in this review is to give an overview of this exciting and highly interdisciplinary area of research, first outlining the design strategies and tools that have been devised for controlling protein self-assembly, then describing the diverse structures of artificial protein assemblies, and finally highlighting the emergent properties and functions of these assemblies.

Exploring human-guided strategies for reaction network exploration: Interactive molecular dynamics in virtual reality as a tool for citizen scientists

The emerging fields of citizen science and gamification reformulate scientific problems as games or puzzles to be solved. Through engaging the wider non-scientific community, significant breakthroughs may be made by analyzing citizen-gathered data. In parallel, recent advances in virtual reality (VR) technology are increasingly being used within a scientific context and the burgeoning field of interactive molecular dynamics in VR (iMD-VR) allows users to interact with dynamical chemistry simulations in real time. Here, we demonstrate the utility of iMD-VR as a medium for gamification of chemistry research tasks. An iMD-VR "game" was designed to encourage users to explore the reactivity of a particular chemical system, and a cohort of 18 participants was recruited to playtest this game as part of a user study. The reaction game encouraged users to experiment with making chemical reactions between a propyne molecule and an OH radical, and "molecular snapshots" from each game session were then compiled and used to map out reaction pathways. The reaction network generated by users was compared to existing literature networks demonstrating that users in VR capture almost all the important reaction pathways. Further comparisons between humans and an algorithmic method for guiding molecular dynamics show that through using citizen science to explore these kinds of chemical problems, new approaches and strategies start to emerge.

Computational and Experimental Assessment of Backbone Templates for Computational Redesign of the Thioredoxin Fold

Computational protein design has taken big strides in recent years; however, the tools available are still not at a state where a sequence can be designed to fold into a given protein structure at will and with high probability. We have applied here a recent release of Rosetta Design to redesign a set of structurally very similar proteins belonging to the thioredoxin fold. We used a genetic screening tool to estimate solubility/folding of the designed proteins in E. coli and to select the best hits from this for further biochemical characterization. We have previously used this set of template proteins for redesign and found that success was highly dependent on template structure, a trait which was also found in this study. Nevertheless, state-of-the-art design software is now able to predict the best template, most likely due to the introduction of an energy term that reports on stress in covalent bond lengths and angles. The template that led to the greatest fraction of successful designs was the same (a thioredoxin from spinach) as that identified in our previous study. Our previously described redesign of thioredoxin, which also used the spinach protein as a template, however also performed well as a template. In the present study, both of these templates yielded proteins with compact folded structures and enforced the conclusion that any design project must carefully consider different design templates. Fortunately, selecting designs based on energies appears to correctly identify such templates.

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.

Large-scale citizen science provides high-resolution nitrogen dioxide values and health impact while enhancing community knowledge and collective action

We present outcomes from a large-scale air quality citizen science campaign (xAire, 725 measurements) to demonstrate its positive contribution in the interplay between advances in exposure assessment and developments in policy or collective action. A broad partnership with 1,650 people from communities around 18 primary schools across Barcelona provided the capacity to obtain unprecedented high-resolution NO₂ levels and an updated asthma Health Impact Assessment. It is shown that NO₂ levels vary considerably with at some cases very high levels. More than a 1,000 new cases of childhood asthma could be prevented each year by lowering NO₂ levels. Representativity of site selection and the minimal number of samplers for land use regression modelling are considered. Enhancement of community knowledge and attitudes towards collective response were observed and identified as key drivers for successful large-scale monitoring campaigns. The results encourage strengthening collaboration with local communities when exploring environmental health issues.

Synthetic Biology Approaches for Engineering Next-Generation Adenoviral Gene Therapies

Synthetic biology centers on the design and modular assembly of biological parts so as to construct artificial biological systems. Over the past decade, synthetic biology has blossomed into a highly productive field, yielding advances in diverse areas such as neuroscience, cell-based therapies, and chemical manufacturing. Similarly, the field of gene therapy has made enormous strides both in proof-of-concept studies and in the clinical setting. One viral vector of increasing interest for gene therapy is the adenovirus (Ad). A major part of the Ad's increasing momentum comes from synthetic biology approaches to Ad engineering. Convergence of gene therapy and synthetic biology has enhanced Ad vectors by mitigating Ad toxicity in vivo, providing precise Ad tropisms, and incorporating genetic circuits to make smart therapies which adapt to environmental stimuli. Synthetic biology engineering of Ad vectors may lead to superior gene delivery and editing platforms which could find applications in a wide range of therapeutic contexts.

ESCASA: Analytical estimation of atomic coordinates from coarse-grained geometry for nuclear-magnetic-resonance-assisted protein structure modeling. I. Backbone and Hβ protons

A method for the estimation of coordinates of atoms in proteins from coarse-grained geometry by simple analytical formulas (ESCASA), for use in nuclear-magnetic-resonance (NMR) data-assisted coarse-grained simulations of proteins is proposed. In this paper, the formulas for the backbone H^α and amide (H^N ) protons, and the side-chain H^β protons, given the C^α -trace, have been derived and parameterized, by using the interproton distances calculated from a set of 140 high-resolution non-homologous protein structures. The mean standard deviation over all types of proton pairs in the set was 0.44 Å after fitting. Validation against a set of 41 proteins with NMR-determined structures, which were not considered in parameterization, resulted in average standard deviation from average proton-proton distances of the NMR-determined structures of 0.25 Å, compared to 0.21 Å obtained with the PULCHRA all-atom-chain reconstruction algorithm and to the 0.12 Å standard deviation of the average-structure proton-proton distance of NMR-determined ensembles. The formulas provide analytical forces and can, therefore, be used in coarse-grained molecular dynamics.