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The Synthetic Biology Open Language (SBOL) is a community-developed data standard that allows knowledge about biological designs to be captured using a machine-tractable, ontology-backed representation that is built using Semantic Web technologies. While early versions of SBOL focused only on the description of DNA-based components and their sub-components, SBOL can now be used to represent knowledge across multiple scales and throughout the entire synthetic biology workflow, from the specification of a single molecule or DNA fragment through to multicellular systems containing multiple interacting genetic circuits. The third major iteration of the SBOL standard, SBOL3, is an effort to streamline and simplify the underlying data model with a focus on real-world applications, based on experience from the deployment of SBOL in a variety of scientific and industrial settings. Here, we introduce the SBOL3 specification both in comparison to previous versions of SBOL and through practical examples of its use.

Synthetic biology builds upon genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. When designing a synthetic system, synthetic biologists need to exchange information about multiple types of molecules, the intended behavior of the system, and actual experimental measurements. The Synthetic Biology Open Language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, following an open community process involving both bench scientists and scientific modelers and software developers, across academia, industry, and other institutions. This document describes SBOL 3.1.0, which improves on version 3.0.0 by including a number of corrections and clarifications as well as several other updates and enhancements. First, this version includes a complete set of validation rules for checking whether documents are valid SBOL 3. Second, the best practices section has been moved to an online repository that allows for more rapid and interactive of sharing these conventions. Third, it includes updates based upon six community approved enhancement proposals. Two enhancement proposals are related to the representation of an object’s namespace. In particular, the Namespace class has been removed and replaced with a namespace property on each class. Another enhancement is the generalization of the CombinatorialDeriviation class to allow direct use of Features and Measures. Next, the Participation class now allow Interactions to be participants to describe higher-order interactions. Another change is the use of Sequence Ontology terms for Feature orientation. Finally, this version of SBOL has generalized from using Unique Reference Identifiers (URIs) to Internationalized Resource Identifiers (IRIs) to support international character sets.

The discovery of safe platforms that can circumvent the endocytic pathway is of great significance for biological therapeutics that are usually degraded during endocytosis. Here we show that a self‐assembled and dynamic macrocycle can passively diffuse through the cell membrane and deliver a broad range of biologics, including proteins, CRISPR Cas9, and ssDNA, directly to the cytosol while retaining their bioactivity. Cell‐penetrating macrocycle CPM can be easily prepared from the room temperature condensation of diketopyrrolopyrrole lactams with diamines. We attribute the high cellular permeability of CPM to its amphiphilic nature and chameleonic properties. It adopts conformations that partially bury polar groups and expose hydrophobic side chains, thus self‐assembling into micellar‐like structures. Its superior fluorescence makes CPM trackable inside cells where it follows the endomembrane system. CPM outperformed commercial reagents for biologics delivery and showed high RNA knockdown efficiency of CRISPR Cas9. We envisage that this macrocycle will be an ideal starting point to design and synthesize biomimetic macrocyclic tags that can readily facilitate the interaction and uptake of biomolecules and overcome endosomal digestion. [ABSTRACT FROM AUTHOR]

Electronic immunosensors are indispensable tools for diagnostics, particularly in scenarios demanding immediate results. Conventionally, these sensors rely on the chemical immobilization of antibodies onto electrodes. However, globular proteins tend to adsorb and unfold on these surfaces. Therefore, self‐assembled monolayers (SAMs) of thiolated alkyl molecules are commonly used for indirect gold–antibody coupling. Here, a limitation associated with SAMs is revealed, wherein they curtail the longevity of protein sensors, particularly when integrated into the state‐of‐the‐art transducer of organic bioelectronics—the organic electrochemical transistor. The SpyDirect method is introduced, generating an ultrahigh‐density array of oriented nanobody receptors stably linked to the gold electrode without any SAMs. It is accomplished by directly coupling cysteine‐terminated and orientation‐optimized spyTag peptides, onto which nanobody‐spyCatcher fusion proteins are autocatalytically attached, yielding a dense and uniform biorecognition layer. The structure‐guided design optimizes the conformation and packing of flexibly tethered nanobodies. This biolayer enhances shelf‐life and reduces background noise in various complex media. SpyDirect functionalization is faster and easier than SAM‐based methods and does not necessitate organic solvents, rendering the sensors eco‐friendly, accessible, and amenable to scalability. SpyDirect represents a broadly applicable biofunctionalization method for enhancing the cost‐effectiveness, sustainability, and longevity of electronic biosensors, all without compromising sensitivity. [ABSTRACT FROM AUTHOR]

Synthetic biology builds upon genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. When designing a synthetic system, synthetic biologists need to exchange information about multiple types of molecules, the intended behavior of the system, and actual experimental measurements. The Synthetic Biology Open Language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, following an open community process involving both wet bench scientists and dry scientific modelers and software developers, across academia, industry, and other institutions. This document describes SBOL 3.0.0, which condenses and simplifies previous versions of SBOL based on experiences in deployment across a variety of scientific and industrial settings. In particular, SBOL 3.0.0, (1) separates sequence features from part/sub-part relationships, (2) renames Component Definition/Component to Component/Sub-Component, (3) merges Component and Module classes, (4) ensures consistency between data model and ontology terms, (5) extends the means to define and reference Sub-Components, (6) refines requirements on object URIs, (7) enables graph-based serialization, (8) moves Systems Biology Ontology (SBO) for Component types, (9) makes all sequence associations explicit, (10) makes interfaces explicit, (11) generalizes Sequence Constraints into a general structural Constraint class, and (12) expands the set of allowed constraints.

Synthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long development times, high rates of failure, and poor reproducibility. One method to ameliorate these problems is to improve the exchange of information about designed systems between laboratories. The synthetic biology open language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, filling a need not satisfied by other pre-existing standards. This document details version 2.3.0 of SBOL, which builds upon version 2.2.0 published in last year’s JIB Standards in Systems Biology special issue. In particular, SBOL 2.3.0 includes means of succinctly representing sequence modifications, such as insertion, deletion, and replacement, an extension to support organization and attachment of experimental data derived from designs, and an extension for describing numerical parameters of design elements. The new version also includes specifying types of synthetic biology activities, unambiguous locations for sequences with multiple encodings, refinement of a number of validation rules, improved figures and examples, and clarification on a number of issues related to the use of external ontology terms.

Synthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long development times, high rates of failure, and poor reproducibility. One method to ameliorate these problems would be to improve the exchange of information about designed systems between laboratories. The Synthetic Biology Open Language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, filling a need not satisfied by other pre-existing standards. This document details version 2.1 of SBOL that builds upon version 2.0 published in last year’s JIB special issue. In particular, SBOL 2.1 includes improved rules for what constitutes a valid SBOL document, new role fields to simplify the expression of sequence features and how components are used in context, and new best practices descriptions to improve the exchange of basic sequence topology information and the description of genetic design provenance, as well as miscellaneous other minor improvements.

Synthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long development times, high rates of failure, and poor reproducibility. One method to ameliorate these problems would be to improve the exchange of information about designed systems between laboratories. The synthetic biology open language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, filling a need not satisfied by other pre-existing standards. This document details version 2.2.0 of SBOL that builds upon version 2.1.0 published in last year’s JIB special issue. In particular, SBOL 2.2.0 includes improved description and validation rules for genetic design provenance, an extension to support combinatorial genetic designs, a new class to add non-SBOL data as attachments, a new class for genetic design implementations, and a description of a methodology to describe the entire design-build-test-learn cycle within the SBOL data model.

People who are engineering biological organisms often find it useful to communicate in diagrams, both about the structure of the nucleic acid sequences that they are engineering and about the functional relationships between sequence features and other molecular species. Some typical practices and conventions have begun to emerge for such diagrams. The Synthetic Biology Open Language Visual (SBOL Visual) has been developed as a standard for organizing and systematizing such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 2.0 of SBOL Visual, which builds on the prior SBOL Visual 1.0 standard by expanding diagram syntax to include functional interactions and molecular species, making the relationship between diagrams and the SBOL data model explicit, supporting families of symbol variants, clarifying a number of requirements and best practices, and significantly expanding the collection of diagram glyphs.

Fluorescence resonance energy transfer (FRET)-based detection of protein interactions is limited by the very narrow range of FRET-permitting distances. We show two different strategies for the rational design of weak helper interactions that co-recruit donor and acceptor fluorophores for a more robust detection of bimolecular FRET: (i) in silico design of electrostatically driven encounter complexes and (ii) fusion of tunable domain-peptide interaction modules based on WW or SH3 domains. We tested each strategy for optimization of FRET between (m)Citrine and mCherry, which do not natively interact. Both approaches yielded comparable and large increases in FRET efficiencies with little or no background. Helper-interaction modules can be fused to any pair of fluorescent proteins and could, we found, enhance FRET between mTFP1 and mCherry as well as between mTurquoise2 and mCitrine. We applied enhanced helper-interaction FRET (hiFRET) probes to study the binding between full-length H-Ras and Raf1 as well as the drug-induced interaction between Raf1 and B-Raf. [ABSTRACT FROM AUTHOR]

Protein tyrosine kinases (PTKs) are key signaling molecules and important drug targets. Although the efficient recombinant production of active PTKs is important for both pharmaceutical industry and academic research, most PTKs are still obtained from conventional, expensive and time-consuming insect-cell based expression. Host toxicity, kinase inactivity, insolubility and heterogeneity are among the reasons thought to preclude PTK expression in Escherichia coli. Herein we review these presumed roadblocks and their possible solutions for bacterial expression of PTKs, and give an overview on kinase activity assays. Finally, we report our experiences and observations with the kinases Src, Lyn and FAK as examples to illustrate implementation, effects and pitfalls of E. coli expression and in vitro assaying of PTKs., (© 2019 Elsevier Inc. All rights reserved.)