Computational design of receptor and sensor proteins with novel functions

Folding Kinetics and Volume Variation of the β-Hairpin Peptide Chignolin upon Ultrafast pH-Jumps

In-depth characterization of fundamental folding steps of small model peptides is crucial for a better understanding of the folding mechanisms of more complex biomacromolecules. We have previously reported on the folding/unfolding kinetics of a model α-helix. Here, we study folding transitions in chignolin (GYDPETGTWG), a short β-hairpin peptide previously used as a model to study conformational changes in β-sheet proteins. Although previously suggested, until now, the role of the Tyr2-Trp9 interaction in the folding mechanism of chignolin was not clear. In the present work, pH-dependent conformational changes of chignolin were characterized by circular dichroism (CD), nuclear magnetic resonance (NMR), ultrafast pH-jump coupled with time-resolved photoacoustic calorimetry (TR-PAC), and molecular dynamics (MD) simulations. Taken together, our results present a comprehensive view of chignolin's folding kinetics upon local pH changes and the role of the Tyr2-Trp9 interaction in the folding process. CD data show that chignolin's β-hairpin formation displays a pH-dependent skew bell-shaped curve, with a maximum close to pH 6, and a large decrease in β-sheet content at alkaline pH. The β-hairpin structure is mainly stabilized by aromatic interactions between Tyr2 and Trp9 and CH-π interactions between Tyr2 and Pro4. Unfolding of chignolin at high pH demonstrates that protonation of Tyr2 is essential for the stability of the β-hairpin. Refolding studies were triggered by laser-induced pH-jumps and detected by TR-PAC. The refolding of chignolin from high pH, mainly due to the protonation of Tyr2, is characterized by a volume expansion (10.4 mL mol-1), independent of peptide concentration, in the microsecond time range (lifetime of 1.15 μs). At high pH, the presence of the deprotonated hydroxyl (tyrosinate) hinders the formation of the aromatic interaction between Tyr2 and Trp9 resulting in a more disorganized and dynamic tridimensional structure of the peptide. This was also confirmed by comparing MD simulations of chignolin under conditions mimicking neutral and high pH.

Advances in ligand-specific biosensing for structurally similar molecules

The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.

Signaling pathways in cancer metabolism: mechanisms and therapeutic targets

A wide spectrum of metabolites (mainly, the three major nutrients and their derivatives) can be sensed by specific sensors, then trigger a series of signal transduction pathways and affect the expression levels of genes in epigenetics, which is called metabolite sensing. Life body regulates metabolism, immunity, and inflammation by metabolite sensing, coordinating the pathophysiology of the host to achieve balance with the external environment. Metabolic reprogramming in cancers cause different phenotypic characteristics of cancer cell from normal cell, including cell proliferation, migration, invasion, angiogenesis, etc. Metabolic disorders in cancer cells further create a microenvironment including many kinds of oncometabolites that are conducive to the growth of cancer, thus forming a vicious circle. At the same time, exogenous metabolites can also affect the biological behavior of tumors. Here, we discuss the metabolite sensing mechanisms of the three major nutrients and their derivatives, as well as their abnormalities in the development of various cancers, and discuss the potential therapeutic targets based on metabolite-sensing signaling pathways to prevent the progression of cancer.

A blueprint for a synthetic genetic feedback optimizer

Biomolecular control enables leveraging cells as biomanufacturing factories. Despite recent advancements, we currently lack genetically encoded modules that can be deployed to dynamically fine-tune and optimize cellular performance. Here, we address this shortcoming by presenting the blueprint of a genetic feedback module to optimize a broadly defined performance metric by adjusting the production and decay rate of a (set of) regulator species. We demonstrate that the optimizer can be implemented by combining available synthetic biology parts and components, and that it can be readily integrated with existing pathways and genetically encoded biosensors to ensure its successful deployment in a variety of settings. We further illustrate that the optimizer successfully locates and tracks the optimum in diverse contexts when relying on mass action kinetics-based dynamics and parameter values typical in Escherichia coli.

Use of whole-cell bioreporters to assess bioavailability of contaminants in aquatic systems

Water contamination has become increasingly a critical global environmental issue that threatens human and ecosystems’ health. Monitoring and risk assessment of toxic pollutants in water bodies is essential to identifying water pollution treatment needs. Compared with the traditional monitoring approaches, environmental biosensing via whole-cell bioreporters (WCBs) has exhibited excellent capabilities for detecting bioavailability of multiple pollutants by providing a fast, simple, versatile and economical way for environmental risk assessment. The performance of WCBs is determined by its elements of construction, such as host strain, regulatory and reporter genes, as well as experimental conditions. Previously, numerous studies have focused on the design and construction of WCB rather than improving the detection process and commercialization of this technology. For investigators working in the environmental field, WCB can be used to detect pollutants is more important than how they are constructed. This work provides a review of the development of WCBs and a brief introduction to genetic construction strategies and aims to summarize key studies on the application of WCB technology in detection of water contaminants, including organic pollutants and heavy metals. In addition, the current status of commercialization of WCBs is highlighted.

In-silico probing of AML related RUNX1 cancer-associated missense mutations: Predicted relationships to DNA binding and drug interactions

The molecular consequences of cancer associated mutations in Acute myeloid leukemia (AML) linked factors are not very well understood. Here, we interrogated the COSMIC database for missense mutations associated with the RUNX1 protein, that is frequently mis-regulated in AML, where we sought to identify recurrently mutated positions at the DNA-interacting interface. Indeed, six of the mutated residues, out of a total 417 residues examined within the DNA binding domain, evidenced reduced DNA association in in silico predictions. Further, given the prominence of RUNX1’s compromised function in AML, we asked the question if the mutations themselves might alter RUNX1’s interaction (off-target) with known FDA-approved drug molecules, including three currently used in treating AML. We identified several AML-associated mutations in RUNX1 that were calculated to enhance RUNX1’s interaction with specific drugs. Specifically, we retrieved data from the COSMIC database for cancer-associated mutations of RUNX1 by using R package “data.table” and “ggplot2” modules. In the presence of DNA and/or drug, we used docking scores and energetics of the complexes as tools to evaluate predicted interaction strengths with RUNX1. For example, we performed predictions of drug binding pockets involving Enasidenib, Giltertinib, and Midostaurin (AML associated), as well as ten different published cancer associated drug compounds. Docking of wild type RUNX1 with these 13 different cancer-associated drugs indicates that wild-type RUNX1 has a lower efficiency of binding while RUNX1 mutants R142K, D171N, R174Q, P176H, and R177Q suggested higher affinity of drug association. Literature evidence support our prediction and suggests the mutation R174Q affects RUNX1 DNA binding and could lead to compromised function. We conclude that specific RUNX1 mutations that lessen DNA binding facilitate the binding of a number of tested drug molecules. Further, we propose that molecular modeling and docking studies for RUNX1 in the presence of DNA and/or drugs enables evaluation of the potential impact of RUNX1 cancer associated mutations in AML.

Reagentless electrochemical biosensors through incorporation of unnatural amino acids on the protein structure

Typical protein biosensors employ chemical or genetic labeling of the protein, thus introducing an extraneous molecule to the wild-type parent protein, often changing the overall structure and properties of the protein. While these labeling methods have proven successful in many cases, they also have a series of disadvantages associated with their preparation and function. An alternative route for labeling proteins is the incorporation of unnatural amino acid (UAA) analogues, capable of acting as a label, into the structure of a protein. Such an approach, while changing the local microenvironment, poses less of a burden on the overall structure of the protein. L-DOPA is an analog of phenylalanine and contains a catechol moiety that participates in a quasi-reversible, two-electron redox process, thus making it suitable as an electrochemical label/reporter. The periplasmic glucose/galactose binding protein (GBP) was chosen to demonstrate this detection principle. Upon glucose binding, GBP undergoes a significant conformational change that is manifested as a change in the electrochemistry of L-DOPA. The electroactive GBP was immobilized onto gold nanoparticle-modified, polymerized caffeic acid, screen-printed carbon electrodes (GBP-LDOPA/AuNP/PCA/SPCE) for the purpose of direct measurement of glucose levels and serves as a proof-of-concept of the use of electrochemically-active unnatural amino acids as the label. The resulting reagentless GBP biosensors exhibited a highly selective and sensitive binding affinity for glucose in the micromolar range, laying the foundation for a new biosensing methodology based on global incorporation of an electroactive amino acid into the protein's primary sequence for highly selective electrochemical detection of compounds of interest.

Subcellular Localization Defects Characterize Ribose-Binding Mutant Proteins with New Ligand Properties in Escherichia coli

Periplasmic binding proteins have been previously proclaimed as a general scaffold to design sensor proteins with new recognition specificities for nonnatural compounds. Such proteins can be integrated in bacterial bioreporter chassis with hybrid chemoreceptors to produce a concentration-dependent signal after ligand binding to the sensor cell. However, computationally designed new ligand-binding properties ignore the more general properties of periplasmic binding proteins, such as their periplasmic translocation, dynamic transition of open and closed forms, and interactions with membrane receptors. In order to better understand the roles of such general properties in periplasmic signaling behavior, we studied the subcellular localization of ribose-binding protein (RbsB) in Escherichia coli in comparison to a recently evolved set of mutants designed to bind 1,3-cyclohexanediol. As proxies for localization, we calibrated and deployed C-terminal end mCherry fluorescent protein fusions. Whereas RbsB-mCherry coherently localized to the periplasmic space and accumulated in (periplasmic) polar regions depending on chemoreceptor availability, mutant RbsB-mCherry expression resulted in high fluorescence cell-to-cell variability. This resulted in higher proportions of cells devoid of clear polar foci and of cells with multiple fluorescent foci elsewhere, suggesting poorer translocation, periplasmic autoaggregation, and mislocalization. Analysis of RbsB mutants and mutant libraries at different stages of directed evolution suggested overall improvement to more RbsB-wild-type-like characteristics, which was corroborated by structure predictions. Our results show that defects in periplasmic localization of mutant RbsB proteins partly explain their poor sensing performance. Future efforts should be directed to predicting or selecting secondary mutations outside computationally designed binding pockets, taking folding, translocation, and receptor interactions into account. IMPORTANCE Biosensor engineering relies on transcription factors or signaling proteins to provide the actual sensory functions for the target chemicals. Since for many compounds there are no natural sensory proteins, there is a general interest in methods that could unlock routes to obtaining new ligand-binding properties. Bacterial periplasmic binding proteins (PBPs) form an interesting family of proteins to explore for this purpose, because there is a large natural variety suggesting evolutionary trajectories to bind new ligands. PBPs are conserved and amenable to accurate computational binding pocket predictions. However, studying ribose-binding protein in Escherichia coli, we discovered that designed variants have defects in their proper localization in the cell, which can impair appropriate sensor signaling. This indicates that functional sensing capacity of PBPs cannot be obtained solely through computational design of the ligand-binding pocket but must take other properties of the protein into account, which are currently very difficult to predict.

Strategies for Improving Small-Molecule Biosensors in Bacteria

In recent years, small-molecule biosensors have become increasingly important in synthetic biology and biochemistry, with numerous new applications continuing to be developed throughout the field. For many biosensors, however, their utility is hindered by poor functionality. Here, we review the known types of mechanisms of biosensors within bacterial cells, and the types of approaches for optimizing different biosensor functional parameters. Discussed approaches for improving biosensor functionality include methods of directly engineering biosensor genes, considerations for choosing genetic reporters, approaches for tuning gene expression, and strategies for incorporating additional genetic modules.

Directed Evolution of Transcription Factor-Based Biosensors for Altered Effector Specificity

Transcription factor-based biosensors are important tools in Synthetic Biology for the sensing of industrially valuable molecules and clinically important metabolites, therefore presenting applications in the bioremediation, industrial biotechnology, and biomedical fields. The directed evolution of allosteric transcription factors (aTFs) with the aim of altering effector specificity has the potential for the development of new biosensors to detect natural and nonnatural molecules, expanding the scope of available aTF-based biosensors. In this chapter, we delineate a general method for the directed evolution of aTFs. The theory of library design is discussed, along with the detailed methodology for an improved transformation of combined libraries, and the experimental search space by counterselection using fluorescence-activated cell sorting (FACS) is presented.

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.

A bacterial bioreporter for the detection of 1,3,5-trinitro-1,3,5-triazinane (RDX)

We report the design, construction, and testing of Escherichia coli-based bioluminescent bioreporters for the detection of 1,3,5-trinitro-1,3,5-triazinane (RDX), one of the most prevalent military-grade explosives in use today. These sensor strains are based on a fusion between the promoter of either the hmp (nitric oxide dioxygenase) or the hcp (a high-affinity nitric oxide reductase) E. coli gene, to the microbial bioluminescence luxCDABEG gene cassette. Signal intensity was enhanced in ∆hmp and ∆hcp mutants, and detection sensitivity was improved when the two gene promoters were cloned in tandem. The Photobacterium leiognathi luxCDABEG reporter genes were superior to those of Aliivibrio fischeri in terms of signal intensity, but in most cases inferior in terms of detection sensitivity, due to a higher background signal. Both sensor strains were also induced by additional nitro-organic explosives, as well as by nitrate salts. Sensitive detection of RDX in a solid matrix (either LB agar or sand) was also demonstrated, with the bioreporters encapsulated in 1.5-mm calcium alginate beads. Lowest RDX concentration detected in sand was 1.67 mg/kg sand. The bioreporter strains described herein may serve as a basis for a standoff detection technology of RDX-based explosive devices, including buried landmines.

Modular tuning engineering and versatile applications of genetically encoded biosensors

Genetically encoded biosensors have a diverse range of detectable signals and potential applications in many fields, including metabolism control and high-throughput screening. Their ability to be used in situ with minimal interference to the bioprocess of interest could revolutionize synthetic biology and microbial cell factories. The performance and functions of these biosensors have been extensively studied and have been rapidly improved. We review here current biosensor tuning strategies and attempt to unravel how to obtain ideal biosensor functions through experimental adjustments. Strategies for expanding the biosensor input signals that increases the number of detectable compounds have also been summarized. Finally, different output signals and their practical requirements for biotechnology and biomedical applications and environmental safety concerns have been analyzed. This in-depth review of the responses and regulation mechanisms of genetically encoded biosensors will assist to improve their design and optimization in various application scenarios.

Exploiting enzyme evolution for computational protein design

Recent years have seen an explosion of interest in understanding the physicochemical parameters that shape enzyme evolution, as well as substantial advances in computational enzyme design. This review discusses three areas where evolutionary information can be used as part of the design process: (i) using ancestral sequence reconstruction (ASR) to generate new starting points for enzyme design efforts; (ii) learning from how nature uses conformational dynamics in enzyme evolution to mimic this process in silico; and (iii) modular design of enzymes from smaller fragments, again mimicking the process by which nature appears to create new protein folds. Using showcase examples, we highlight the importance of incorporating evolutionary information to continue to push forward the boundaries of enzyme design studies.

Rosetta:MSF:NN: Boosting performance of multi-state computational protein design with a neural network

Rational protein design aims at the targeted modification of existing proteins. To reach this goal, software suites like Rosetta propose sequences to introduce the desired properties. Challenging design problems necessitate the representation of a protein by means of a structural ensemble. Thus, Rosetta multi-state design (MSD) protocols have been developed wherein each state represents one protein conformation. Computational demands of MSD protocols are high, because for each of the candidate sequences a costly three-dimensional (3D) model has to be created and assessed for all states. Each of these scores contributes one data point to a complex, design-specific energy landscape. As neural networks (NN) proved well-suited to learn such solution spaces, we integrated one into the framework Rosetta:MSF instead of the so far used genetic algorithm with the aim to reduce computational costs. As its predecessor, Rosetta:MSF:NN administers a set of candidate sequences and their scores and scans sequence space iteratively. During each iteration, the union of all candidate sequences and their Rosetta scores are used to re-train NNs that possess a design-specific architecture. The enormous speed of the NNs allows an extensive assessment of alternative sequences, which are ranked on the scores predicted by the NN. Costly 3D models are computed only for a small fraction of best-scoring sequences; these and the corresponding 3D-based scores replace half of the candidate sequences during each iteration. The analysis of two sets of candidate sequences generated for a specific design problem by means of a genetic algorithm confirmed that the NN predicted 3D-based scores quite well; the Pearson correlation coefficient was at least 0.95. Applying Rosetta:MSF:NN:enzdes to a benchmark consisting of 16 ligand-binding problems showed that this protocol converges ten-times faster than the genetic algorithm and finds sequences with comparable scores.

Ribose-Binding Protein Mutants With Improved Interaction Towards the Non-natural Ligand 1,3-Cyclohexanediol

Bioreporters consist of genetically modified living organisms that respond to the presence of target chemical compounds by production of an easily measurable signal. The central element in a bioreporter is a sensory protein or aptamer, which, upon ligand binding, modifies expression of the reporter signal protein. A variety of naturally occurring or modified versions of sensory elements has been exploited, but it has proven to be challenging to generate elements that recognize non-natural ligands. Bacterial periplasmic binding proteins have been proposed as a general scaffold to design receptor proteins for non-natural ligands, but despite various efforts, with only limited success. Here, we show how combinations of randomized mutagenesis and reporter screening improved the performance of a set of mutants in the ribose binding protein (RbsB) of Escherichia coli, which had been designed based on computational simulations to bind the non-natural ligand 1,3-cyclohexanediol (13CHD). Randomized mutant libraries were constructed that used the initially designed mutants as scaffolds, which were cloned in an appropriate E. coli bioreporter system and screened for improved induction of the GFPmut2 reporter fluorescence in presence of 1,3-cyclohexanediol. Multiple rounds of library screening, sorting, renewed mutagenesis and screening resulted in 4.5-fold improvement of the response to 1,3-cyclohexanediol and a lower detection limit of 0.25 mM. All observed mutations except one were located outside the direct ligand-binding pocket, suggesting they were compensatory and helping protein folding or functional behavior other than interaction with the ligand. Our results thus demonstrate that combinations of ligand-binding-pocket redesign and randomized mutagenesis can indeed lead to the selection and recovery of periplasmic-binding protein mutants with non-natural compound recognition. However, current lack of understanding of the intermolecular movement and ligand-binding in periplasmic binding proteins such as RbsB are limiting the rational production of further and better sensory mutants.

An Overview of Computer-aided Drug Design Tools and Recent Applications in Designing of Anti-diabetic Agents

BACKGROUND

In this fast-growing era, high throughput data is now being easily accessed by getting transformed into datasets which store the information. Such information is valuable to optimize the hypothesis and drug design via computer-aided drug design (CADD). Nowadays, we can explore the role of CADD in various disciplines like Nanotechnology, Biochemistry, Medical Sciences, Molecular Biology, etc. Methods: We searched the valuable literature using a pertinent database with given keywords like computer-aided drug design, anti-diabetic, drug design, etc. We retrieved all valuable articles which are recent and discussing the role of computation in the designing of anti-diabetic agents.

RESULTS

To facilitate the drug discovery process, the computational approach has set landmarks in the whole pipeline for drug discovery from target identification and mechanism of action to the identification of leads and drug candidates. Along with this, there is a determined endeavor to describe the significance of in-silico studies in predicting the absorption, distribution, metabolism, excretion, and toxicity profile. Thus, globally, CADD is accepted with a variety of tools for studying QSAR, virtual screening, protein structure prediction, quantum chemistry, material design, physical and biological property prediction.

CONCLUSION

Computer-assisted tools are used as the drug discovery tool in the area of different diseases, and here we reviewed the collaborative aspects of information technologies and chemoinformatic tools in the discovery of anti-diabetic agents, keeping in view the growing importance for treating diabetes.

Bacterial bioreporters for the detection of trace explosives: performance enhancement by DNA shuffling and random mutagenesis

Landmines and other explosive remnants of war pose a global humanitarian problem that claims numerous casualties long after the conflict has ended. As there are no acceptable methodologies for the remote discovery of such devices, current detection practices still require the risky presence of personnel in the minefield. We have recently described bacterial sensor strains capable of reporting the existence of 2,4-dinitrotoluene (DNT) vapors in the soil above 2,4,6-trinitrotoluene (TNT)-based landmines, by generating a bioluminescent or a fluorescent signal. This may allow the identification of landmine location by remote imaging of an area over which the bacteria have been spread. In the study reported herein, we have improved the DNT-detection capabilities of these sensor strains by combining two DNT-responsive Escherichia coli gene promoters, yqjF and azoR, and subjecting them to three cycles of random mutagenesis by error-prone PCR, combined with segmentation and rearrangement ("DNA shuffling"). The activity of selected modified promoters was evaluated with the Aliivibrio fischeri and Photobacterium leiognathi luxCDABEG gene cassettes as the bioluminescent reporters, exhibiting a ten-fold background reduction that has led to a three-fold decrease in detection threshold. Signal intensity was further enhanced by modifying the ribosomal binding site of the yqjF gene promoter. The superior DNT detection capabilities on a solid matrix by the improved sensor strain were demonstrated. KEY POINTS: • Performance of microbial sensor strains for buried explosives was molecularly enhanced. • Manipulations included random mutagenesis, "DNA shuffling," and RBS reprogramming. • The re-engineered constructs exhibited superior detection of trace explosives.

Hybrid MC/MD for protein design

Computational protein design relies on simulations of a protein structure, where selected amino acids can mutate randomly, and mutations are selected to enhance a target property, such as stability. Often, the protein backbone is held fixed and its degrees of freedom are modeled implicitly to reduce the complexity of the conformational space. We present a hybrid method where short molecular dynamics (MD) segments are used to explore conformations and alternate with Monte Carlo (MC) moves that apply mutations to side chains. The backbone is fully flexible during MD. As a test, we computed side chain acid/base constants or pK_a's in five proteins. This problem can be considered a special case of protein design, with protonation/deprotonation playing the role of mutations. The solvent was modeled as a dielectric continuum. Due to cost, in each protein we allowed just one side chain position to change its protonation state and the other position to change its type or mutate. The pK_a's were computed with a standard method that scans a range of pH values and with a new method that uses adaptive landscape flattening (ALF) to sample all protonation states in a single simulation. The hybrid method gave notably better accuracy than standard, fixed-backbone MC. ALF decreased the computational cost a factor of 13.

Transcription factor-based biosensors: a molecular-guided approach for natural product engineering

Natural products and their derivatives offer a rich source of chemical and biological diversity; however, traditional engineering of their biosynthetic pathways to improve yields and access to unnatural derivatives requires a precise understanding of their enzymatic processes. High-throughput screening platforms based on allosteric transcription-factor based biosensors can be leveraged to overcome the screening bottleneck to enable searching through large libraries of pathway/strain variants. Herein, the development and application of engineered allosteric transcription factor-based biosensors is described that enable optimization of precursor availability, product titers, and downstream product tailoring for advancing the natural product bioeconomy. We discuss recent successes for tailoring biosensor design, including computationally-based approaches, and present our future outlook with the integration of cell-free technologies and de novo protein design for rapidly generating biosensor tools.

Detection of buried explosives with immobilized bacterial bioreporters

The unchecked dispersal of antipersonnel landmines since the late 19th century has resulted in large areas contaminated with these explosive devices, creating a substantial worldwide humanitarian safety risk. The main obstacle to safe and effective landmine removal is the identification of their exact location, an activity that currently requires entry of personnel into the minefields; to date, there is no commercialized technology for an efficient stand-off detection of buried landmines. In this article, we describe the optimization of a microbial sensor strain, genetically engineered for the remote detection of 2,4,6-trinitrotoloune (TNT)-based mines. This bioreporter, designed to bioluminescence in response to minute concentrations of either TNT or 2,4-dinitotoluene (DNT), was immobilized in hydrogel beads and optimized for dispersion over the minefield. Following modifications of the hydrogel matrix in which the sensor bacteria are encapsulated, as well as their genetic reporting elements, these sensor bacteria sensitively detected buried 2,4-dinitrotoluene in laboratory experiments. Encapsulated in 1.5 mm 2% alginate beads containing 1% polyacrylic acid, they also detected the location of a real metallic antipersonnel landmine under field conditions. To the best of our knowledge, this is the first report demonstrating the detection of a buried landmine with a luminescent microbial bioreporter.

129Xe NMR-Protein Sensor Reveals Cellular Ribose Concentration

Dysregulation of cellular ribose uptake can be indicative of metabolic abnormalities or tumorigenesis. However, analytical methods are currently limited for quantifying ribose concentration in complex biological samples. Here, we utilize the highly specific recognition of ribose by ribose-binding protein (RBP) to develop a single-protein ribose sensor detectable via a sensitive NMR technique known as hyperpolarized 129Xe chemical exchange saturation transfer (hyper-CEST). We demonstrate that RBP, with a tunable ribose-binding site and further engineered to bind xenon, enables the quantitation of ribose over a wide concentration range (nM to mM). Ribose binding induces the RBP "closed" conformation, which slows Xe exchange to a rate detectable by hyper-CEST. Such detection is remarkably specific for ribose, with the minimal background signal from endogenous sugars of similar size and structure, for example, glucose or ribose-6-phosphate. Ribose concentration was measured for mammalian cell lysate and serum, which led to estimates of low-mM ribose in a HeLa cell line. This highlights the potential for using genetically encoded periplasmic binding proteins such as RBP to measure metabolites in different biological fluids, tissues, and physiologic states.

A novel and effective natural product-based immunodetection tool for TNT-like compounds

This study aimed to develop a fast and reliable protocol for Trinitrophenol-Tris(hydroxymethyl)aminomethane (TNP-Tris) detection applying a β-lactamase-fusion protein of choice, the natural product-based immunoreagent tool of competitive sensitivity developed herein for the first time. Since the fusion protein 11B3-scFv-β-lactamase is constructed from a scFv-antibody (11B3) linked to an enzyme (β-lactamase), the step related to the use of secondary antibody in the enzyme-linked immunosorbent assay (ELISA) is completely omitted. Indeed, this fusion protein itself serves both as binding mean of the antigen model and detecting agent, due to the presence of the naturally occurring enzyme. In such a way, it actually affords the one-step TNP-Tris detection reaching a promising LOD value of 45 ± 2 fmol or 157 ± 6 pg/mL. Taken all together, the current protocol does represent much cheaper and significantly less-time consuming alternative compared both to the recombinant antibodies and recombinant phages, previously designed means in our labs for the same purpose.

Redesigning HVEM Interface for Selective Binding to LIGHT, BTLA, and CD160

Herpes virus entry mediator (HVEM) regulates positive and negative signals for T cell activation through co-signaling pathways. Dysfunction of the HVEM co-signaling network is associated with multiple pathologies related to autoimmunity, infectious disease, and cancer, making the associated molecules biologically and therapeutically attractive targets. HVEM interacts with three ligands from two different superfamilies using two different binding interfaces. The engagement with ligands CD160 and B- and T-lymphocyte attenuator (BTLA), members of immunoglobulin superfamily, is associated with inhibitory signals, whereas inflammatory responses are regulated through the interaction with LIGHT from the TNF superfamily. We computationally redesigned the HVEM recognition interfaces using a residue-specific pharmacophore approach, ProtLID, to achieve switchable-binding specificity. In subsequent cell-based binding assays the new interfaces, designed with only single or double mutations, exhibited selective binding to only one or two out of the three cognate ligands.

Genome-wide gene-deletion screening identifies mutations that significantly enhance explosives vapor detection by a microbial sensor

Genetically engineered microbial biosensors, capable of detecting traces of explosives residues above buried military ordnance and emitting an optical signal in response, may potentially serve for the standoff detection of buried landmines. A promising candidate for such an application is a previously reported Escherichia coli-based reporter strain that employs the yqjF gene promoter as its sensing element; however, for this sensor to be able to detect actual landmines reliably, it was necessary for its detection sensitivity and signal intensity to be enhanced. In this study, a high-throughput approach was employed to screen the effects of individual gene deletions on yqjF activation by 2,4-dinitrotoluene (DNT). Several genes were identified, the deletion of which elicited a significant enhancement of yqjF induction by DNT. The most promising of these mutations were introduced into the sensor strain, individually or in pairs, yielding a considerable increase in signal intensity and a lowering of the detection threshold. A strain harboring two of the identified mutations, ygdD and eutE, appears to be the most sensitive microbial biosensor currently described for the detection of traces of landmine explosives.

Beyond natural: synthetic expansions of botanical form and function

Powered by developments that enabled genome-scale investigations, systems biology emerged as a field aiming to understand how phenotypes emerge from network functions. These advances fuelled a new engineering discipline focussed on synthetic reconstructions of complex biological systems with the goal of predictable rational design and control. Initially, progress in the nascent field of synthetic biology was slow due to the ad hoc nature of molecular biology methods such as cloning. The application of engineering principles such as standardisation, together with several key technical advances, enabled a revolution in the speed and accuracy of genetic manipulation. Combined with mathematical and statistical modelling, this has improved the predictability of engineering biological systems of which nonlinearity and stochasticity are intrinsic features leading to remarkable achievements in biotechnology as well as novel insights into biological function. In the past decade, there has been slow but steady progress in establishing foundations for synthetic biology in plant systems. Recently, this has enabled model-informed rational design to be successfully applied to the engineering of plant gene regulation and metabolism. Synthetic biology is now poised to transform the potential of plant biotechnology. However, reaching full potential will require conscious adjustments to the skillsets and mind sets of plant scientists.

Dynamics, a Powerful Component of Current and Future in Silico Approaches for Protein Design and Engineering

Computational prediction has become an indispensable aid in the processes of engineering and designing proteins for various biotechnological applications. With the tremendous progress in more powerful computer hardware and more efficient algorithms, some of in silico tools and methods have started to apply the more realistic description of proteins as their conformational ensembles, making protein dynamics an integral part of their prediction workflows. To help protein engineers to harness benefits of considering dynamics in their designs, we surveyed new tools developed for analyses of conformational ensembles in order to select engineering hotspots and design mutations. Next, we discussed the collective evolution towards more flexible protein design methods, including ensemble-based approaches, knowledge-assisted methods, and provable algorithms. Finally, we highlighted apparent challenges that current approaches are facing and provided our perspectives on their further development.

Supramammillary nucleus synchronizes with dentate gyrus to regulate spatial memory retrieval through glutamate release

The supramammillary nucleus (SuM) provides substantial innervation to the dentate gyrus (DG). It remains unknown how the SuM and DG coordinate their activities at the circuit level to regulate spatial memory. Additionally, SuM co-releases GABA and glutamate to the DG, but the relative role of GABA versus glutamate in regulating spatial memory remains unknown. Here we report that SuM-DG Ca²⁺ activities are highly correlated during spatial memory retrieval as compared to the moderate correlation during memory encoding when mice are performing a location discrimination task. Supporting this evidence, we demonstrate that the activity of SuM neurons or SuM-DG projections is required for spatial memory retrieval. Furthermore, we show that SuM glutamate transmission is necessary for both spatial memory retrieval and highly-correlated SuM-DG activities during spatial memory retrieval. Our studies identify a long-range SuM-DG circuit linking two highly correlated subcortical regions to regulate spatial memory retrieval through SuM glutamate release.

Rational design of affinity ligands for bioseparation

Affinity adsorbents have been the cornerstone in protein purification. The selective nature of the molecular recognition interactions established between an affinity ligands and its target provide the basis for efficient capture and isolation of proteins. The plethora of affinity adsorbents available in the market reflects the importance of affinity chromatography in the bioseparation industry. Ligand discovery relies on the implementation of rational design techniques, which provides the foundation for the engineering of novel affinity ligands. The main goal for the design of affinity ligands is to discover or improve functionality, such as increased stability or selectivity. However, the methodologies must adapt to the current needs, namely to the number and diversity of biologicals being developed, and the availability of new tools for big data analysis and artificial intelligence. In this review, we offer an overview on the development of affinity ligands for bioseparation, including the evolution of rational design techniques, dating back to the years of early discovery up to the current and future trends in the field.

Spectroscopic and molecular modeling investigation on inhibition effect of nitroaromatic compounds on acetylcholinesterase activity

Nitroaromatic compounds (NACs) are widely distributed in the environment and are considered toxic or carcinogenic. However, little attention has been paid to the binding interactions between NACs and biomacromolecules (e.g., proteins). Here we investigated the effects of three model NACs, nitrobenzene (NB), 1,3-dinitrobenzene (DNB), and 1,3,5-trinitrobenzene (TNB), on the activity of acetylcholinesterase (AChE). The presence of NACs (up to 0.5 mM) effectively suppressed the AChE-catalyzed hydrolysis of acetylthiocholine iodide, with the suppression effect increasing with the nitro-group substitution (TNB > DNB > NB). Consistently, the UV absorption of AChE at 206 nm arising from the skeleton structure decreased by the addition NACs, and the decrease exhibited the same compound sequence, reflecting the perturbing interactions with the skeleton enzyme structure. However, no changes were made on the secondary structure of AChE, as evidenced by the circular dichroism analysis. The fluorescence quenching analysis of AChE demonstrated that NB and DNB interacted with both tryptophan (Trp) and tyrosine (Tyr) residues, whereas TNB interacted only with Trp. The UV absorption and fluorescence quenching analyses both reflected that the interactions with the non-skeleton aromatic amino acids were weak. ¹H NMR analysis confirmed the strong π-π coupling interactions between TNB and model Trp. Molecular simulation indicated that the DNB or TNB molecule was sandwiched between Trp84 and Phe330 at the catalytic site via π-π coupling interactions. The findings highlight the importance of specific interactions of NACs with proteins to cause them to malfunction.

Computational redesign of the Escherichia coli ribose-binding protein ligand binding pocket for 1,3-cyclohexanediol and cyclohexanol

Bacterial periplasmic-binding proteins have been acclaimed as general biosensing platform, but their range of natural ligands is too limited for optimal development of chemical compound detection. Computational redesign of the ligand-binding pocket of periplasmic-binding proteins may yield variants with new properties, but, despite earlier claims, genuine changes of specificity to non-natural ligands have so far not been achieved. In order to better understand the reasons of such limited success, we revisited here the Escherichia coli RbsB ribose-binding protein, aiming to achieve perceptible transition from ribose to structurally related chemical ligands 1,3-cyclohexanediol and cyclohexanol. Combinations of mutations were computationally predicted for nine residues in the RbsB binding pocket, then synthesized and tested in an E. coli reporter chassis. Two million variants were screened in a microcolony-in-bead fluorescence-assisted sorting procedure, which yielded six mutants no longer responsive to ribose but with 1.2-1.5 times induction in presence of 1 mM 1,3-cyclohexanediol, one of which responded to cyclohexanol as well. Isothermal microcalorimetry confirmed 1,3-cyclohexanediol binding, although only two mutant proteins were sufficiently stable upon purification. Circular dichroism spectroscopy indicated discernable structural differences between these two mutant proteins and wild-type RbsB. This and further quantification of periplasmic-space abundance suggested most mutants to be prone to misfolding and/or with defects in translocation compared to wild-type. Our results thus affirm that computational design and library screening can yield RbsB mutants with recognition of non-natural but structurally similar ligands. The inherent arisal of protein instability or misfolding concomitant with designed altered ligand-binding pockets should be overcome by new experimental strategies or by improved future protein design algorithms.

Degradation of High Energy Materials Using Biological Reduction: A Rational Way to Reach Bioremediation

Explosives molecules have been widely used since World War II, leading to considerable contamination of soil and groundwater. Recently, bioremediation has emerged as an environmentally friendly approach to solve such contamination issues. However, the 1,3,5,7-tetranitro-1,3,5,7-tetrazocane (HMX) explosive, which has very low solubility in water, does not provide satisfying results with this approach. In this study, we used a rational design strategy for improving the specificity of the nitroreductase from E. Cloacae (PDB ID 5J8G) toward HMX. We used the Coupled Moves algorithm from Rosetta to redesign the active site around HMX. Molecular Dynamics (MD) simulations and affinity calculations allowed us to study the newly designed protein. Five mutations were performed. The designed nitroreductase has a better fit with HMX. We observed more H-bonds, which productively stabilized the HMX molecule for the mutant than for the wild type enzyme. Thus, HMX’s nitro groups are close enough to the reductive cofactor to enable a hydride transfer. Also, the HMX affinity for the designed enzyme is better than for the wild type. These results are encouraging. However, the total reduction reaction implies numerous HMX derivatives, and each of them has to be tested to check how far the reaction can’ go.

Construction of Bacterial Cells with an Active Transport System for Unnatural Amino Acids

Engineered organisms with an expanded genetic code have attracted much attention in chemical and synthetic biology research. In this work, engineered bacterial organisms with enhanced unnatural amino acid (UAA) uptake abilities were developed by screening periplasmic binding protein (PBP) mutants for recognition of UAAs. A FRET-based assay was used to identify a mutant PBP (LBP-AEL) with excellent binding affinity ( K_d ≈ 500 nM) to multiple UAAs from 37 mutants. Bacterial cells expressing LBP-AEL showed up to 5-fold enhanced uptake of UAAs, which was determined by genetic incorporation of UAAs into a green fluorescent protein and measuring UAA concentration in cell lysates. To the best of our knowledge, this work is the first report of engineering cellular uptake of UAAs and could provide an impetus for designing advanced unnatural organisms with an expanded genetic code, which function with the efficiency comparable to that of natural organisms. The system would be useful to increase mutant protein yield from lower concentrations of UAAs for industrial and large-scale applications. In addition, the techniques used in this report such as the sensor design and the measurement of UAA concentration in cell lysates could be useful for other biochemical applications.

Computational Redesign of PD-1 Interface for PD-L1 Ligand Selectivity

Chronic or persistent stimulation of the programmed cell death-1 (PD-1) pathway prevents T cells from mounting anti-tumor and anti-viral immune responses. Blockade of this inhibitory checkpoint pathway has shown therapeutic importance by rescuing T cells from their exhausted state. Cognate ligands of the PD-1 receptor include the tissue-specific PD-L1 and PD-L2 proteins. Engineering a human PD-1 interface specific for PD-L1 or PD-L2 can provide a specific reagent and therapeutic advantage for tissue-specific disruption of the PD-1 pathway. We utilized ProtLID, a computational framework, which constitutes a residue-based pharmacophore approach, to custom-design a human PD-1 interface specific to human PD-L1 without any significant affinity to PD-L2. In subsequent cell assay experiments, half of all single-point mutant designs proved to introduce a statistically significant selectivity, with nine of these maintaining a close to wild-type affinity to PD-L1. This proof-of-concept study suggests a general approach to re-engineer protein interfaces for specificity.

Whole-Cell Biosensor with Tunable Limit of Detection Enables Low-Cost Agglutination Assays for Medical Diagnostic Applications

Whole-cell biosensors can form the basis of affordable, easy-to-use diagnostic tests that can be readily deployed for point-of-care (POC) testing, but to date the detection of analytes such as proteins that cannot easily diffuse across the cell membrane has been challenging. Here we developed a novel biosensing platform based on cell agglutination using an E. coli whole-cell biosensor surface-displaying nanobodies which bind selectively to a target protein analyte. As a proof-of-concept, we show the feasibility of this design to detect a model analyte at nanomolar concentrations. Moreover, we show that the design architecture is flexible by building assays optimized to detect a range of model analyte concentrations using straightforward design rules and a mathematical model. Finally, we re-engineer our whole-cell biosensor for the detection of a medically relevant biomarker by the display of two different nanobodies against human fibrinogen and demonstrate a detection limit as low as 10 pM in diluted human plasma. Overall, we demonstrate that our agglutination technology fulfills the requirement of POC testing by combining low-cost nanobody production, customizable detection range and low detection limits. This technology has the potential to produce affordable diagnostics for field-testing in the developing world, emergency or disaster relief sites, as well as routine medical testing and personalized medicine.

Chimeric LysR-Type Transcriptional Biosensors for Customizing Ligand Specificity Profiles toward Flavonoids

Transcriptional biosensors enable key applications in both metabolic engineering and synthetic biology. Due to nature's immense variety of metabolites, these applications require biosensors with a ligand specificity profile customized to the researcher's needs. In this work, chimeric biosensors were created by introducing parts of a donor regulatory circuit from Sinorhizobium meliloti, delivering the desired luteolin-specific response, into a nonspecific biosensor chassis from Herbaspirillum seropedicae. Two strategies were evaluated for the development of chimeric LysR-type biosensors with customized ligand specificity profiles toward three closely related flavonoids, naringenin, apigenin, and luteolin. In the first strategy, chimeric promoter regions were constructed at the biosensor effector module, while in the second strategy, chimeric transcription factors were created at the biosensor detector module. Via both strategies, the biosensor repertoire was expanded with luteolin-specific chimeric biosensors demonstrating a variety of response curves and ligand specificity profiles. Starting from the nonspecific biosensor chassis, a shift from 27.5% to 95.3% luteolin specificity was achieved with the created chimeric biosensors. Both strategies provide a compelling, faster, and more accessible route for the customization of biosensor ligand specificity, compared to de novo design and construction of each biosensor circuit for every desired ligand specificity.

Structure-Based Design of Versatile Biosensors for Small Molecules Based on the PAS Domain of a Thermophilic Histidine Kinase

The development of biosensors for in vitro quantification of small molecules such as metabolites or man-made chemicals is still a major challenge. Here we show that engineered variants of the sensory PAS domain of the histidine kinase CitA of the thermophilic bacterium Geobacillus thermoleovorans represent promising alternatives to established biorecognition elements. By combining binding site grafting and rational design we constructed protein variants binding l-malate, ethylmalonate, or the aromatic compound phthalate instead of the native ligand citrate. Due to more favorable entropy contributions, the wild-type protein and its engineered variants exhibited increased (nano- to micromolar) affinities and improved enantioselectivity compared to CitA homologues of mesophilic organisms. Ligand binding was directly converted into an optical signal that was preserved after immobilization of the protein. A fluorescently labeled variant was used to quantify ethylmalonate, an urinary biomarker for ethylmalonic encephalopathy, in synthetic urine, thereby demonstrating the applicability of the sensor in complex samples.

Metabolite sensing and signaling in cell metabolism

Metabolite sensing is one of the most fundamental biological processes. During evolution, multilayered mechanisms developed to sense fluctuations in a wide spectrum of metabolites, including nutrients, to coordinate cellular metabolism and biological networks. To date, AMPK and mTOR signaling are among the best-understood metabolite-sensing and signaling pathways. Here, we propose a sensor-transducer-effector model to describe known mechanisms of metabolite sensing and signaling. We define a metabolite sensor by its specificity, dynamicity, and functionality. We group the actions of metabolite sensing into three different modes: metabolite sensor-mediated signaling, metabolite-sensing module, and sensing by conjugating. With these modes of action, we provide a systematic view of how cells sense sugars, lipids, amino acids, and metabolic intermediates. In the future perspective, we suggest a systematic screen of metabolite-sensing macromolecules, high-throughput discovery of biomacromolecule-metabolite interactomes, and functional metabolomics to advance our knowledge of metabolite sensing and signaling. Most importantly, targeting metabolite sensing holds great promise in therapeutic intervention of metabolic diseases and in improving healthy aging.

A simple, three-part model provides a systematic view of how cells sense sugars, lipids, amino acids and metabolic intermediates. Cells quickly and accurately perceive changes in intra- and extracellular molecules such as nutrients to respond to changing environments. Drawing on existing knowledge about AMPK and MTORC1 signaling, Yi-Ping Wang and Qun-Ying Lei at Fudan University in Shanghai propose a model in which three components: a sensor, transducer and effector enable metabolic sensing and signaling to proceed. The sensor detects the metabolite, and, through conjugation, conformational changes or protein–protein interactions, transmits this information to the transducer, which decides the appropriate response. The transducer then issues orders to effector proteins which coordinate the action. The future identification of novel metabolic sensors through systematic screening could lead to new therapeutic interventions for metabolic and age-related diseases.

DNA-Based Nanodevices Controlled by Purely Entropic Linker Domains

We demonstrate here the rational design of purely entropic domains as a versatile approach to achieve control of the input/output response of synthetic molecular receptors. To do so and to highlight the versatility and generality of this approach, we have rationally re-engineered two model DNA-based receptors: a clamp-like DNA-based switch that recognizes a specific DNA sequence and an ATP-binding aptamer. We show that, by varying the length of the linker domain that connects the two recognition portions of these receptors, it is possible to finely control their affinity for their specific ligand. Through mathematical modeling and thermodynamic characterization, we also demonstrate for both systems that entropy changes associated with changes in linker length are responsible for affinity modulation and that the linker we have designed behaves as a disordered random-coil polymer. The approach also allows us to regulate the ligand concentration range at which the receptors respond and show optimal specificity. Given these attributes, the use of purely entropic domains appears as a versatile and general approach to finely control the activity of synthetic receptors in a highly predictable and controlled fashion.

Structural Analysis of Variability and Interaction of the N-terminal of the Oncogenic Effector CagA of Helicobacter pylori with Phosphatidylserine

Helicobacter pylori cytotoxin-associated gene A protein (CagA) has been associated with the increase in virulence and risk of cancer. It has been demonstrated that CagA’s translocation is dependent on its interaction with phosphatidylserine. We evaluated the variability of the N-terminal CagA in 127 sequences reported in NCBI, by referring to molecular interaction forces with the phosphatidylserine and the docking of three mutations chosen from variations in specific positions. The major sites of conservation of the residues involved in CagA–Phosphatidylserine interaction were 617, 621 and 626 which had no amino acid variation. Position 636 had the lowest conservation score; mutations in this position were evaluated to observe the differences in intermolecular forces for the CagA–Phosphatidylserine complex. We evaluated the docking of three mutations: K636A, K636R and K636N. The crystal and mutation models presented a ΔG of −8.919907, −8.665261, −8.701923, −8.515097 Kcal/mol, respectively, while mutations K636A, K636R, K636N and the crystal structure presented 0, 3, 4 and 1 H-bonds, respectively. Likewise, the bulk effect of the ΔG and amount of H-bonds was estimated in all of the docking models. The type of mutation affected both the ΔG (χ2(1)=93.82, p-value <2.2×10−16) and the H-bonds (χ2(1)=91.93, p-value <2.2×10−16). Overall, 76.9% of the strains that exhibit the K636N mutation produced a severe pathology. The average H-bond count diminished when comparing the mutations with the crystal structure of all the docking models, which means that other molecular forces are involved in the CagA–Phosphatidylserine complex interaction.

Building a minimal and generalizable model of transcription factor-based biosensors: Showcasing flavonoids

Progress in synthetic biology tools has transformed the way we engineer living cells. Applications of circuit design have reached a new level, offering solutions for metabolic engineering challenges that include developing screening approaches for libraries of pathway variants. The use of transcription-factor-based biosensors for screening has shown promising results, but the quantitative relationship between the sensors and the sensed molecules still needs more rational understanding. Herein, we have successfully developed a novel biosensor to detect pinocembrin based on a transcriptional regulator. The FdeR transcription factor (TF), known to respond to naringenin, was combined with a fluorescent reporter protein. By varying the copy number of its plasmid and the concentration of the biosensor TF through a combinatorial library, different responses have been recorded and modeled. The fitted model provides a tool to understand the impact of these parameters on the biosensor behavior in terms of dose-response and time curves and offers guidelines to build constructs oriented to increased sensitivity and or ability of linear detection at higher titers. Our model, the first to explicitly take into account the impact of plasmid copy number on biosensor sensitivity using Hill-based formalism, is able to explain uncharacterized systems without extensive knowledge of the properties of the TF. Moreover, it can be used to model the response of the biosensor to different compounds (here naringenin and pinocembrin) with minimal parameter refitting.

IDeAS: automated design tool for hetero-chiral protein folds

Incorporating D amino acids in the protein design alphabet can in principle multiply the design space by many orders of magnitude. All native proteins are polymers composed of L chiral amino acids. Practically limitless in diversity over amino acid sequences, protein structure is limited in folds and thus shapes, principally due to the poly L stereochemistry of their backbone. To diversify shapes, we introduced both L- and D α-amino acids as design alphabets to explore the possibility of generating novel folds, varied in chemical as well as stereo-chemical sequence. Now, to have stereochemically-defined proteins tuned chemically, we present the Inverse Design and Automation Software, IDeAS. Retro-fitting side chains on a backbone with L and D stereochemistry, the software demonstrate functional fits over stereo-chemically diverse folds in a range of applications of interest in protein design.

Deep learning to predict the lab-of-origin of engineered DNA

Genetic engineering projects are rapidly growing in scale and complexity, driven by new tools to design and construct DNA. There is increasing concern that widened access to these technologies could lead to attempts to construct cells for malicious intent, illegal drug production, or to steal intellectual property. Determining the origin of a DNA sequence is difficult and time-consuming. Here deep learning is applied to predict the lab-of-origin of a DNA sequence. A convolutional neural network was trained on the Addgene plasmid dataset that contained 42,364 engineered DNA sequences from 2230 labs as of February 2016. The network correctly identifies the source lab 48% of the time and 70% it appears in the top 10 predicted labs. Often, there is not a single "smoking gun" that affiliates a DNA sequence with a lab. Rather, it is a combination of design choices that are individually common but collectively reveal the designer.