Improved Bst DNA Polymerase Variants Derived via a Machine Learning Approach

Bringing the heat: Thermostable analogs of Bst polymerase allow high-temperature LAMP

BACKGROUND

Loop-mediated isothermal amplification (LAMP) is a nucleic acid amplification method that gained prominence during the early months of the COVID-19 pandemic due to its simplicity, sensitivity and robustness. However, this technique is susceptible to non-specific amplifications, raising concerns about false-positive results and reduced diagnostic accuracy. A primary contributor to false-positive testing is primer dimerization, which can theoretically be mitigated by performing reactions at higher temperatures. Unfortunately, the strand-displacing DNA polymerases typically used in LAMP, such as Bst, exhibit reduced efficiency at elevated temperatures. To address this limitation, we hypothesised that naturally occurring thermophilic analogs of Bst may be capable of supporting LAMP at higher temperatures, thereby improving reaction specificity.

METHODS

Bioinformatics and recombinant enzyme production allowed the identification and synthesis of several Bst analogs. These were tested in real-time LAMP assays to detect diverse targets, in a wide range of reaction temperatures (63°C-75°C) and in the presence of typical qPCR inhibitors.

RESULTS

Three polymerases-Bst_7, Bst_8 and Bst_15-demonstrated exceptional activity and robust stability at higher temperature conditions (up to 72.5°C), while displaying considerable resistance to common qPCR inhibitors.

CONCLUSIONS

The identified thermophilic Bst analogs represent a potential solution for the mitigation of non-specific amplification in LAMP, further boosting the application of this technique in molecular diagnostic settings.

Artificial Intelligence Technology Assists Enzyme Prediction and Rational Design

Since the structure of enzymes determines their function, elucidating the structure of enzymes lays a solid foundation for deciphering their catalytic mechanism and enabling rational design. The development of artificial intelligence (AI) has sparked a technological revolution, infusing new vitality into theoretical studies of enzymology and the advancement of enzyme engineering techniques. This Review outlines the development process and main methods of AI applied in the structural elucidation and functional prediction of enzymes. Furthermore, it emphasizes AI-based rational design of enzymes and provides a detailed exposition of representative AI algorithms and case studies. With the support of AI technology, the comprehension of enzyme structure and function and their relationship will become deeper and more efficient, thereby promoting the widespread application of enzyme engineering in various fields.

Enhancing recombinant growth factor and serum protein production for cultivated meat manufacturing

The commercial growth factors (GFs) and serum proteins (SPs) contribute to the high cost associated with the serum-free media for cultivated meat production. Producing recombinant GFs and SPs in scale from microbial cell factories can reduce the cost of culture media. Escherichia coli is a frequently employed host in the expression recombinant GFs and SPs. This review explores critical strategies for cost reduction in GFs and SPs production, focusing on yield enhancement, product improvement, purification innovation, and process innovation. Firstly, the review discusses the use of fusion tags to increase the solubility and yield of GFs & SPs, highlighting various studies that have successfully employed these tags for yield enhancement. We then explore how tagging strategies can streamline and economize the purification process, further reducing production costs. Additionally, we address the challenge of low half-life in GFs and SPs and propose potential strategies that can enhance their stability. Furthermore, improvements in the E. coli chassis and cell engineering strategies are also described, with an emphasis on the key areas that can improve yield and identify areas for cost minimization. Finally, we discuss key bioprocessing areas which can facilitate easier scale-up, enhance yield, titer, and productivity, and ultimately lower long-term production costs. It is crucial to recognize that not all suggested approaches can be applied simultaneously, as their relevance varies with different GFs and SPs. However, integrating of multiple strategies is anticipated to yield a cumulative effect, significantly reducing production costs. This collective effort is expected to substantially decrease the price of cultivated meat, contributing to the broader goal of developing sustainable and affordable meat.

Rational Design of a Novel DNA Polymerase From Clostridium thermocellum to Improve LAMP Detection Performance

Loop-mediated isothermal amplification (LAMP) is a detection method widely used in pathogen detection and clinical diagnosis. Nevertheless, it is highly constrained by thermal stability, catalytic activity, and resistance to inhibitors of Bst DNA polymerase. In this study, a novel DNA polymerase was characterized from Clostridium thermocellum, exhibiting potential in LAMP detection. Through bioinformatics analysis, the enzyme and the DNA-binding domain (DBD) from Pyrococcus abyssi were mutated for enhanced interaction between proteins and DNA. A chimeric mutant DBDE146K-S738R reaches the detection threshold 13 min earlier than wild-type Cth DNA polymerase in real-time LAMP detection with a template concentration of 1.58 × 105 fg/µL. It also showed the highest enzymatic activity at pH 9.0 and 65°C. The chimeric enzyme DBDE146K-S738R exhibits good thermal stability, capable of performing LAMP reactions after treatment at 73°C or 70°C for 8 h. Moreover, it maintains high activity even under the inhibitory conditions of 50 U/mL heparin, 1.6 mM EDTA, 200 mM NaCl, 10% ethanol, 1.2 M urea, or 0.8% phenol. Notably, it was able to detect 1.58 × 102 ag/µL of the genome and 1.03 CFU/mL of the colony in Salmonella typhimurium detection. The enzyme's performance is superior to commercial Bst 2.0 and comparable to commercial Bst 3.0. The results suggest that DBDE146K-S738R in LAMP exhibits great potential for molecular biological studies and clinical diagnostic analysis.

Machine Learning Guided Rational Design of a Non-Heme Iron-Based Lysine Dioxygenase Improves its Total Turnover Number

Highly selective C-H functionalization remains an ongoing challenge in organic synthetic methodologies. Biocatalysts are robust tools for achieving these difficult chemical transformations. Biocatalyst engineering has often required directed evolution or structure-based rational design campaigns to improve their activities. In recent years, machine learning has been integrated into these workflows to improve the discovery of beneficial enzyme variants. In this work, we combine a structure-based self-supervised machine learning framework, MutComputeX, with classical molecular dynamics simulations to down select mutations for rational design of a non-heme iron-dependent lysine dioxygenase, LDO. This approach consistently resulted in functional LDO mutants and circumvents the need for extensive study of mutational activity before-hand. Our rationally designed single mutants purified with up to 2-fold higher expression yields than WT and displayed higher total turnover numbers (TTN). Combining five such single mutations into a pentamutant variant, LPNYI LDO, leads to a 40 % improvement in the TTN (218±3) as compared to WT LDO (TTN=160±2). Overall, this work offers a low-barrier approach for those seeking to synergize machine learning algorithms with pre-existing protein engineering strategies.

Protein representations: Encoding biological information for machine learning in biocatalysis

Enzymes offer a more environmentally friendly and low-impact solution to conventional chemistry, but they often require additional engineering for their application in industrial settings, an endeavour that is challenging and laborious. To address this issue, the power of machine learning can be harnessed to produce predictive models that enable the in silico study and engineering of improved enzymatic properties. Such machine learning models, however, require the conversion of the complex biological information to a numerical input, also called protein representations. These inputs demand special attention to ensure the training of accurate and precise models, and, in this review, we therefore examine the critical step of encoding protein information to numeric representations for use in machine learning. We selected the most important approaches for encoding the three distinct biological protein representations - primary sequence, 3D structure, and dynamics - to explore their requirements for employment and inductive biases. Combined representations of proteins and substrates are also introduced as emergent tools in biocatalysis. We propose the division of fixed representations, a collection of rule-based encoding strategies, and learned representations extracted from the latent spaces of large neural networks. To select the most suitable protein representation, we propose two main factors to consider. The first one is the model setup, which is influenced by the size of the training dataset and the choice of architecture. The second factor is the model objectives such as consideration about the assayed property, the difference between wild-type models and mutant predictors, and requirements for explainability. This review is aimed at serving as a source of information and guidance for properly representing enzymes in future machine learning models for biocatalysis.

Click, Compute, Create: A Review of Web-based Tools for Enzyme Engineering

Enzyme engineering, though pivotal across various biotechnological domains, is often plagued by its time-consuming and labor-intensive nature. This review aims to offer an overview of supportive in silico methodologies for this demanding endeavor. Starting from methods to predict protein structures, to classification of their activity and even the discovery of new enzymes we continue with describing tools used to increase thermostability and production yields of selected targets. Subsequently, we discuss computational methods to modulate both, the activity as well as selectivity of enzymes. Last, we present recent approaches based on cutting-edge machine learning methods to redesign enzymes. With exception of the last chapter, there is a strong focus on methods easily accessible via web-interfaces or simple Python-scripts, therefore readily useable for a diverse and broad community.

Integrating Genomic Data with the Development of CRISPR-Based Point-of-Care-Testing for Bacterial Infections

Purpose of Review

Bacterial infections and antibiotic resistance contribute to global mortality. Despite many infections being preventable and treatable, the lack of reliable and accessible diagnostic tools exacerbates these issues. CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)-based diagnostics has emerged as a promising solution. However, the development of CRISPR diagnostics has often occurred in isolation, with limited integration of genomic data to guide target selection. In this review, we explore the synergy between bacterial genomics and CRISPR-based point-of-care tests (POCT), highlighting how genomic insights can inform target selection and enhance diagnostic accuracy.

Recent Findings

We review recent advances in CRISPR-based technologies, focusing on the critical role of target sequence selection in improving the sensitivity of CRISPR-based diagnostics. Additionally, we examine the implementation of these technologies in resource-limited settings across Asia and Africa, presenting successful case studies that demonstrate their potential.

Summary

The integration of bacterial genomics with CRISPR technology offers significant promise for the development of effective point-of-care diagnostics.

Exploring the molecular aspect and updating evolutionary approaches to the DNA polymerase enzymes for biotechnological needs: A comprehensive review

DNA polymerases are essential enzymes that play a key role in living organisms, as they participate in the synthesis and maintenance of the DNA molecule. The intrinsic properties of these enzymes have been widely observed and studied to understand their functions, activities, and behavior, which has allowed their natural power in DNA synthesis to be exploited in modern biotechnology, to the point of making them true pillars of the field. In this context, the laboratory evolution of these enzymes, either by directed evolution or rational design, has led to the generation of a wide range of new DNA polymerases with novel properties, suitable for a variety of biotechnological needs. In this review, we examine DNA polymerases at the molecular level, their biotechnological use, and their evolutionary methods in relation to the novel properties sought, providing a chronological selection of evolved DNA polymerases cited in the literature that we consider to be of great interest. To our knowledge, this work is the first to bring together the molecular, functional and evolutionary aspects of the DNA polymerase enzyme. We believe it will be of great interest to researchers whose aim is to produce new lines of evolved DNA polymerases.

Strategies and procedures to generate chimeric DNA polymerases for improved applications

Chimeric DNA polymerase with notable performance has been generated for wide applications including DNA amplification and molecular diagnostics. This rational design method aims to improve specific enzymatic characteristics or introduce novel functions by fusing amino acid sequences from different proteins with a single DNA polymerase to create a chimeric DNA polymerase. Several strategies prove to be efficient, including swapping homologous domains between polymerases to combine benefits from different species, incorporating additional domains for exonuclease activity or enhanced binding ability to DNA, and integrating functional protein along with specific protein structural pattern to improve thermal stability and tolerance to inhibitors, as many cases in the past decade shown. The conventional protocol to develop a chimeric DNA polymerase with desired traits involves a Design-Build-Test-Learn (DBTL) cycle. This procedure initiates with the selection of a parent polymerase, followed by the identification of relevant domains and devising a strategy for fusion. After recombinant expression and purification of chimeric polymerase, its performance is evaluated. The outcomes of these evaluations are analyzed for further enhancing and optimizing the functionality of the polymerase. This review, centered on microorganisms, briefly outlines typical instances of chimeric DNA polymerases categorized, and presents a general methodology for their creation. KEY POINTS: • Chimeric DNA polymerase is generated by rational design method. • Strategies include domain exchange and addition of proteins, domains, and motifs. • Chimeric DNA polymerase exhibits improved enzymatic properties or novel functions.

Innovative PNA-LB mediated allele-specific LAMP for KRAS mutation profiling on a compact lab-on-a-disc device

Tailored healthcare, an approach focused on individual patients, requires integrating emerging interdisciplinary technologies to develop accurate and user-friendly diagnostic tools. KRAS mutations, prevalent in various common cancers, are crucial determinants in selecting patients for novel KRAS inhibitor therapies. This study presents a novel state-of-the-art Lab-on-a-Disc system utilizing peptide nucleic acids-loop backward (PNA-LB) mediated allele-specific loop-mediated isothermal amplification (LAMP) for detecting the frequent G12D KRAS mutation, signifying its superiority over alternative mutation detection approaches. The designed Lab-on-a-Disc system demonstrated exceptional preclinical and technical precision, accuracy, and versatility. By applying varying cutoff values to PNA- LB LAMP reactions, the assay's sensitivity and specificity were increased by 80 % and 90 %, respectively. The device's key advantages include a robust microfluidic Lab-on-a-Disc design, precise rotary control, and a cutting-edge induction heating module. These features enable multiplexing of LAMP reactions with high reproducibility and repeatability, with CV% values less than 3.5 % and 5.5 %, respectively. The device offers several methods for accurate endpoint result detection, including naked-eye observation, RGB image analysis using Python code, and time of fluorescence (Tf) values. Preclinical specificity and sensitivity, assessed using different cutoffs for Eva-Green fluorescence Tf values and pH-sensitive dyes, demonstrated comparable performance to the best standard methods. Overall, this study represents a significant step towards tailoring treatment strategies for cancer patients through precise and efficient mutation detection technologies.

Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations

Engineering stabilized proteins is a fundamental challenge in the development of industrial and pharmaceutical biotechnologies. We present Stability Oracle: a structure-based graph-transformer framework that achieves SOTA performance on accurately identifying thermodynamically stabilizing mutations. Our framework introduces several innovations to overcome well-known challenges in data scarcity and bias, generalization, and computation time, such as: Thermodynamic Permutations for data augmentation, structural amino acid embeddings to model a mutation with a single structure, a protein structure-specific attention-bias mechanism that makes transformers a viable alternative to graph neural networks. We provide training/test splits that mitigate data leakage and ensure proper model evaluation. Furthermore, to examine our data engineering contributions, we fine-tune ESM2 representations (Prostata-IFML) and achieve SOTA for sequence-based models. Notably, Stability Oracle outperforms Prostata-IFML even though it was pretrained on 2000X less proteins and has 548X less parameters. Our framework establishes a path for fine-tuning structure-based transformers to virtually any phenotype, a necessary task for accelerating the development of protein-based biotechnologies.

Machine learning guided rational design of a non-heme iron-based lysine dioxygenase improves its total turnover number

Highly selective C-H functionalization remains an ongoing challenge in organic synthetic methodologies. Biocatalysts are robust tools for achieving these difficult chemical transformations. Biocatalyst engineering has often required directed evolution or structure-based rational design campaigns to improve their activities. In recent years, machine learning has been integrated into these workflows to improve the discovery of beneficial enzyme variants. In this work, we combine a structure-based machine-learning algorithm with classical molecular dynamics simulations to down select mutations for rational design of a non-heme iron-dependent lysine dioxygenase, LDO. This approach consistently resulted in functional LDO mutants and circumvents the need for extensive study of mutational activity before-hand. Our rationally designed single mutants purified with up to 2-fold higher yields than WT and displayed higher total turnover numbers (TTN). Combining five such single mutations into a pentamutant variant, LPNYI LDO, leads to a 40% improvement in the TTN (218±3) as compared to WT LDO (TTN = 160±2). Overall, this work offers a low-barrier approach for those seeking to synergize machine learning algorithms with pre-existing protein engineering strategies.

Asymmetric Synthesis of α-Chloroamides via Photoenzymatic Hydroalkylation of Olefins

Photoenzymatic intermolecular hydroalkylations of olefins are highly enantioselective for chiral centers formed during radical termination but poorly selective for centers set in the C-C bond-forming event. Here, we report the evolution of a flavin-dependent "ene"-reductase to catalyze the coupling of α,α-dichloroamides with alkenes to afford α-chloroamides in good yield with excellent chemo- and stereoselectivity. These products can serve as linchpins in the synthesis of pharmaceutically valuable motifs. Mechanistic studies indicate that radical formation occurs by exciting a charge-transfer complex templated by the protein. Precise control over the orientation of molecules within the charge-transfer complex potentially accounts for the observed stereoselectivity. The work expands the types of motifs that can be prepared using photoenzymatic catalysis.

Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme

A major challenge to achieving industry-scale biomanufacturing of therapeutic alkaloids is the slow process of biocatalyst engineering. Amaryllidaceae alkaloids, such as the Alzheimer's medication galantamine, are complex plant secondary metabolites with recognized therapeutic value. Due to their difficult synthesis they are regularly sourced by extraction and purification from the low-yielding daffodil Narcissus pseudonarcissus. Here, we propose an efficient biosensor-machine learning technology stack for biocatalyst development, which we apply to engineer an Amaryllidaceae enzyme in Escherichia coli. Directed evolution is used to develop a highly sensitive (EC50 = 20 μM) and specific biosensor for the key Amaryllidaceae alkaloid branchpoint 4'-O-methylnorbelladine. A structure-based residual neural network (MutComputeX) is subsequently developed and used to generate activity-enriched variants of a plant methyltransferase, which are rapidly screened with the biosensor. Functional enzyme variants are identified that yield a 60% improvement in product titer, 2-fold higher catalytic activity, and 3-fold lower off-product regioisomer formation. A solved crystal structure elucidates the mechanism behind key beneficial mutations.

Changes in Coding and Efficiency through Modular Modifications to a One Pot PURE System for In Vitro Transcription and Translation

The incorporation of unnatural amino acids is an attractive method for improving or bringing new and novel functions in peptides and proteins. Cell-free protein synthesis using the Protein Synthesis Using Recombinant Elements (PURE) system is an attractive platform for efficient unnatural amino acid incorporation. In this work, we further adapted and modified the One Pot PURE to obtain a robust and modular system for enzymatic single-site-specific incorporation of an unnatural amino acid. We demonstrated the flexibility of this system through the introduction of two different orthogonal aminoacyl tRNA synthetase:tRNA pairs that suppressed two distinctive stop codons in separate reaction mixtures.

Machine Learning-Guided Protein Engineering

Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid in the discovery and annotation of promising enzymes, as well as in suggesting beneficial mutations for improving known targets. The field of machine learning for protein engineering is gathering steam, driven by recent success stories and notable progress in other areas. It already encompasses ambitious tasks such as understanding and predicting protein structure and function, catalytic efficiency, enantioselectivity, protein dynamics, stability, solubility, aggregation, and more. Nonetheless, the field is still evolving, with many challenges to overcome and questions to address. In this Perspective, we provide an overview of ongoing trends in this domain, highlight recent case studies, and examine the current limitations of machine learning-based methods. We emphasize the crucial importance of thorough experimental validation of emerging models before their use for rational protein design. We present our opinions on the fundamental problems and outline the potential directions for future research.

An enhanced activity and thermostability of chimeric Bst DNA polymerase for isothermal amplification applications

Loop-mediated isothermal amplification (LAMP) is a widely used method for clinical diagnosis, customs quarantine, and disease prevention. However, the low catalytic activity of Bst DNA polymerase has made it challenging to develop rapid and reliable point-of-care testing. Herein, we developed a series of Bst DNA polymerase mutants with enhanced activity by predicting and analyzing the activity sites. Among these mutants, single mutants K431D and K431E showed a 1.93- and 2.03-fold increase in catalytic efficiency, respectively. We also created a chimeric protein by fusing the DNA-binding domain of DNA ligase from Pyrococcus abyssi (DBD), namely DBD-K431E, which enabled real-time LAMP at high temperatures up to 73 ℃ and remained active after heating at 70 ℃ for 8 h. The chimeric DBD-K431E remained active in the presence of 50 U/mL heparin, 10% ethanol, and up to 100 mM NaCl, and showed higher activity in 110 mM (NH4)2SO4, 110 mM KCl, and 12 mM MgSO4. Notably, it generated a fluorescence signal during the detection of Salmonella typhimurium at 2 × 102 ag/μL of genomic DNA and 1.24 CFU/mL of bacterial colony, outperforming the wild type and the commercial counterpart Bst 2.0. Our results suggest that the DBD-K431E variant could be a promising tool for general molecular biology research and clinical diagnostics. KEY POINTS: • Residue K431 is probably a key site of Bst DNA polymerase activity • The chimeric DBD-K431E is more inhibitor tolerant and thermostable than Bst-LF • The DBD-K431E variant can detect Salmonella typhimurium at 102 ag/μL or 100 CFU/mL.

Advancing Enzyme's Stability and Catalytic Efficiency through Synergy of Force-Field Calculations, Evolutionary Analysis, and Machine Learning

Thermostability is an essential requirement for the use of enzymes in the bioindustry. Here, we compare different protein stabilization strategies using a challenging target, a stable haloalkane dehalogenase DhaA115. We observe better performance of automated stabilization platforms FireProt and PROSS in designing multiple-point mutations over the introduction of disulfide bonds and strengthening the intra- and the inter-domain contacts by in silico saturation mutagenesis. We reveal that the performance of automated stabilization platforms was still compromised due to the introduction of some destabilizing mutations. Notably, we show that their prediction accuracy can be improved by applying manual curation or machine learning for the removal of potentially destabilizing mutations, yielding highly stable haloalkane dehalogenases with enhanced catalytic properties. A comparison of crystallographic structures revealed that current stabilization rounds were not accompanied by large backbone re-arrangements previously observed during the engineering stability of DhaA115. Stabilization was achieved by improving local contacts including protein-water interactions. Our study provides guidance for further improvement of automated structure-based computational tools for protein stabilization.

Bst polymerase - a humble relative of Taq polymerase

DNA polymerases are a superfamily of enzymes synthesizing DNA using DNA as a template. They are essential for nucleic acid metabolism and for DNA replication and repair. Modern biotechnology and molecular diagnostics rely heavily on DNA polymerases in analyzing nucleic acids. Among a variety of discovered DNA polymerases, Bst polymerase, a large fragment of DNA polymerase I from Geobacillus stearothermophilus, is one of the most commonly used but is not as well studied as Taq polymerase. The ability of Bst polymerase to displace an upstream DNA strand during synthesis, coupled with its moderate thermal stability, has provided the basis for several isothermal DNA amplification methods, including LAMP, WGA, RCA, and many others. Bst polymerase is one of the key components defining the robustness and analytical characteristics of diagnostic test systems based on isothermal amplification. Here, we present an overview of the biochemical and structural features of Bst polymerase and provide information on its mutated analogs.

Two sequence- and two structure-based ML models have learned different aspects of protein biochemistry

Deep learning models are seeing increased use as methods to predict mutational effects or allowed mutations in proteins. The models commonly used for these purposes include large language models (LLMs) and 3D Convolutional Neural Networks (CNNs). These two model types have very different architectures and are commonly trained on different representations of proteins. LLMs make use of the transformer architecture and are trained purely on protein sequences whereas 3D CNNs are trained on voxelized representations of local protein structure. While comparable overall prediction accuracies have been reported for both types of models, it is not known to what extent these models make comparable specific predictions and/or generalize protein biochemistry in similar ways. Here, we perform a systematic comparison of two LLMs and two structure-based models (CNNs) and show that the different model types have distinct strengths and weaknesses. The overall prediction accuracies are largely uncorrelated between the sequence- and structure-based models. Overall, the two structure-based models are better at predicting buried aliphatic and hydrophobic residues whereas the two LLMs are better at predicting solvent-exposed polar and charged amino acids. Finally, we find that a combined model that takes the individual model predictions as input can leverage these individual model strengths and results in significantly improved overall prediction accuracy.

Two sequence- and two structure-based ML models have learned different aspects of protein biochemistry

Deep learning models are seeing increased use as methods to predict mutational effects or allowed mutations in proteins. The models commonly used for these purposes include large language models (LLMs) and 3D Convolutional Neural Networks (CNNs). These two model types have very different architectures and are commonly trained on different representations of proteins. LLMs make use of the transformer architecture and are trained purely on protein sequences whereas 3D CNNs are trained on voxelized representations of local protein structure. While comparable overall prediction accuracies have been reported for both types of models, it is not known to what extent these models make comparable specific predictions and/or generalize protein biochemistry in similar ways. Here, we perform a systematic comparison of two LLMs and two structure-based models (CNNs) and show that the different model types have distinct strengths and weaknesses. The overall prediction accuracies are largely uncorrelated between the sequence- and structure-based models. Overall, the two structure-based models are better at predicting buried aliphatic and hydrophobic residues whereas the two LLMs are better at predicting solvent-exposed polar and charged amino acids. Finally, we find that a combined model that takes the individual model predictions as input can leverage these individual model strengths and results in significantly improved overall prediction accuracy.

DNA Polymerases for Whole Genome Amplification: Considerations and Future Directions

In the same way that specialized DNA polymerases (DNAPs) replicate cellular and viral genomes, only a handful of dedicated proteins from various natural origins as well as engineered versions are appropriate for competent exponential amplification of whole genomes and metagenomes (WGA). Different applications have led to the development of diverse protocols, based on various DNAPs. Isothermal WGA is currently widely used due to the high performance of Φ29 DNA polymerase, but PCR-based methods are also available and can provide competent amplification of certain samples. Replication fidelity and processivity must be considered when selecting a suitable enzyme for WGA. However, other properties, such as thermostability, capacity to couple replication, and double helix unwinding, or the ability to maintain DNA replication opposite to damaged bases, are also very relevant for some applications. In this review, we provide an overview of the different properties of DNAPs widely used in WGA and discuss their limitations and future research directions.

Nested Phosphorothioated Hybrid Primer-Mediated Isothermal Amplification for Specific and Dye-Based Subattomolar Nucleic Acid Detection at Low Temperatures

Developing dye-based isothermal nucleic acid amplification (INAA) at low temperatures such as 37 °C remains a technical challenge. Here, we describe a nested phosphorothioated (PS) hybrid primer-mediated isothermal amplification (NPSA) assay which only utilizes EvaGreen (a DNA-binding dye) to achieve specific and dye-based subattomolar nucleic acid detection at 37 °C. The success of low-temperature NPSA essentially depends on employing Bacillus smithii DNA polymerase, a strand-displacing DNA polymerase with wide range of activation temperature. However, the NPSA's high efficiency entails nested PS-modified hybrid primers and the additives of urea and T4 Gene 32 Protein. To address the inhibition of urea on reverse transcription (RT), one-tube two-stage recombinase-aided RT-NPSA (rRT-NPSA) is established. By targeting human Kirsten rat sarcoma viral (KRAS) oncogene, NPSA (rRT-NPSA) stably detects 0.2 aM of KRAS gene (mRNA) within 90 (60) min. In addition, rRT-NPSA possesses subattomolar sensitivity to detect human ribosomal protein L13 mRNA. The NPSA/rRT-NPSA assays are also validated to obtain consistent results with PCR/RT-PCR methods on qualitatively detecting DNA/mRNA targets extracted from cultured cells and clinical samples. As a dye-based, low-temperature INAA method, NPSA inherently facilitates the development of miniaturized diagnostic biosensors.

Kinetics of elementary steps in loop-mediated isothermal amplification (LAMP) show that strand invasion during initiation is rate-limiting

Loop-mediated isothermal amplification (LAMP) has proven to be easier to implement than PCR for point-of-care diagnostic tests. However, the underlying mechanism of LAMP is complicated and the kinetics of the major steps in LAMP have not been fully elucidated, which prevents rational improvements in assay development. Here we present our work to characterize the kinetics of the elementary steps in LAMP and show that: (i) strand invasion / initiation is the rate-limiting step in the LAMP reaction; (ii) the loop primer plays an important role in accelerating the rate of initiation and does not function solely during the exponential amplification phase and (iii) strand displacement synthesis by Bst-LF polymerase is relatively fast (125 nt/s) and processive on both linear and hairpin templates, although with some interruptions on high GC content templates. Building on these data, we were able to develop a kinetic model that relates the individual kinetic experiments to the bulk LAMP reaction. The assays developed here provide important insights into the mechanism of LAMP, and the overall model should be crucial in engineering more sensitive and faster LAMP reactions. The kinetic methods we employ should likely prove useful with other isothermal DNA amplification methods.

Loop-Mediated Isothermal Amplification: From Theory to Practice

Increasing the accuracy of pathogen identification and reducing the duration of analysis remain relevant for modern molecular diagnostics up to this day. In laboratory and clinical practice, detection of pathogens mostly relies on methods of nucleic acid amplification, among which the polymerase chain reaction (PCR) is considered the "gold standard." Nevertheless, in some cases, isothermal amplification methods act as an alternative to PCR diagnostics. Upon more than thirty years of the development of isothermal DNA synthesis, the appearance of loop-mediated isothermal amplification (LAMP) has enabled new directions of in-field diagnostics of bacterial and viral infections. This review examines the key characteristics of the LAMP method and corresponding features in practice. We discuss the structure of LAMP amplicons with single-stranded loops, which have the sites for primer annealing under isothermal conditions. The latest achievements in the modification of the LAMP method are analyzed, which allow considering it as a unique platform for creating the next-generation diagnostic assays.

Thermophilic Nucleic Acid Polymerases and Their Application in Xenobiology

Thermophilic nucleic acid polymerases, isolated from organisms that thrive in extremely hot environments, possess great DNA/RNA synthesis activities under high temperatures. These enzymes play indispensable roles in central life activities involved in DNA replication and repair, as well as RNA transcription, and have already been widely used in bioengineering, biotechnology, and biomedicine. Xeno nucleic acids (XNAs), which are analogs of DNA/RNA with unnatural moieties, have been developed as new carriers of genetic information in the past decades, which contributed to the fast development of a field called xenobiology. The broad application of these XNA molecules in the production of novel drugs, materials, and catalysts greatly relies on the capability of enzymatic synthesis, reverse transcription, and amplification of them, which have been partially achieved with natural or artificially tailored thermophilic nucleic acid polymerases. In this review, we first systematically summarize representative thermophilic and hyperthermophilic polymerases that have been extensively studied and utilized, followed by the introduction of methods and approaches in the engineering of these polymerases for the efficient synthesis, reverse transcription, and amplification of XNAs. The application of XNAs facilitated by these polymerases and their mutants is then discussed. In the end, a perspective for the future direction of further development and application of unnatural nucleic acid polymerases is provided.

Charge Engineering Improves the Performance of Bst DNA Polymerase Fusions

The charge states of proteins can greatly influence their stabilities and interactions with substrates, and the addition of multiple charges (supercharging) has been shown to be a successful approach for engineering protein stability and function. The addition of a fast-folding fusion domain to the Bacillus stearothermophilus DNA polymerase improved its functionality in isothermal amplification assays, and further charge engineering of this domain has increased both protein stability and diagnostics performance. When combined with mutations that stabilize the core of the protein, the charge-engineered fusion domain leads to the ability to carry out loop-mediated isothermal amplification (LAMP) at temperatures up to 74° C or in the presence of high concentrations of urea, with detection times under 10 min. Adding both positive and negative charges to the fusion domain led to changes in the relative reverse transcriptase and DNA polymerase activities of the polymerase. Overall, the development of a modular fusion domain whose charged surface can be modified at will should prove to be of use in the engineering of other polymerases and, in general, may prove useful for protein stabilization.

Preparation and Use of Cellular Reagents: A Low-resource Molecular Biology Reagent Platform

Protein reagents are indispensable for most molecular and synthetic biology procedures. Most conventional protocols rely on highly purified protein reagents that require considerable expertise, time, and infrastructure to produce. In consequence, most proteins are acquired from commercial sources, reagent expense is often high, and accessibility may be hampered by shipping delays, customs barriers, geopolitical constraints, and the need for a constant cold chain. Such limitations to the widespread availability of protein reagents, in turn, limit the expansion and adoption of molecular biology methods in research, education, and technology development and application. Here, we describe protocols for producing a low-resource and locally sustainable reagent delivery system, termed "cellular reagents," in which bacteria engineered to overexpress proteins of interest are dried and can then be used directly as reagent packets in numerous molecular biology reactions, without the need for protein purification or a constant cold chain. As an example of their application, we describe the execution of polymerase chain reaction (PCR) and loop-mediated isothermal amplification (LAMP) using cellular reagents, detailing how to replace pure protein reagents with optimal amounts of rehydrated cellular reagents. We additionally describe a do-it-yourself fluorescence visualization device for using these cellular reagents in common molecular biology applications. The methods presented in this article can be used for low-cost, on-site production of commonly used molecular biology reagents (including DNA and RNA polymerases, reverse transcriptases, and ligases) with minimal instrumentation and expertise, and without the need for protein purification. Consequently, these methods should generally make molecular biology reagents more affordable and accessible. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Preparation of cellular reagents Alternate Protocol 1: Preparation of lyophilized cellular reagents Alternate Protocol 2: Evaluation of bacterial culture growth via comparison to McFarland turbidity standards Support Protocol 1: SDS-PAGE for protein expression analysis of cellular reagents Basic Protocol 2: Using Taq DNA polymerase cellular reagents for PCR Basic Protocol 3: Using Br512 DNA polymerase cellular reagents for loop-mediated isothermal amplification (LAMP) Support Protocol 2: Building a fluorescence visualization device.

Clinical validation of engineered CRISPR/Cas12a for rapid SARS-CoV-2 detection

Background

The coronavirus disease (COVID-19) caused by SARS-CoV-2 has swept through the globe at an unprecedented rate. CRISPR-based detection technologies have emerged as a rapid and affordable platform that can shape the future of diagnostics.

Methods

We developed ENHANCEv2 that is composed of a chimeric guide RNA, a modified LbCas12a enzyme, and a dual reporter construct to improve the previously reported ENHANCE system. We validated both ENHANCE and ENHANCEv2 using 62 nasopharyngeal swabs and compared the results to RT-qPCR. We created a lyophilized version of ENHANCEv2 and characterized its detection capability and stability.

Results

Here we demonstrate that when coupled with an RT-LAMP step, ENHANCE detects COVID-19 samples down to a few copies with 95% accuracy while maintaining a high specificity towards various isolates of SARS-CoV-2 against 31 highly similar and common respiratory pathogens. ENHANCE works robustly in a wide range of magnesium concentrations (3 mM-13 mM), allowing for further assay optimization. Our clinical validation results for both ENHANCE and ENHANCEv2 show 60/62 (96.7%) sample agreement with RT-qPCR results while only using 5 µL of sample and 20 minutes of CRISPR reaction. We show that the lateral flow assay using paper-based strips displays 100% agreement with the fluorescence-based reporter assay during clinical validation. Finally, we demonstrate that a lyophilized version of ENHANCEv2 shows high sensitivity and specificity for SARS-CoV-2 detection while reducing the CRISPR reaction time to as low as 3 minutes while maintaining its detection capability for several weeks upon storage at room temperature.

Conclusions

CRISPR-based diagnostic platforms offer many advantages as compared to conventional qPCR-based detection methods. Our work here provides clinical validation of ENHANCE and its improved form ENHANCEv2 for the detection of COVID-19.

Nguyen et al. describe the clinical validation of the ENHANCE system, a method to detect SARS-CoV-2 based on engineered crRNAs for Cas12a and preceded by an RT-LAMP amplification step. Authors also describe the development and clinical validation of a version of this system, ENHANCEv2, that can be lyophilized and that uses another mutated Cas12a for further signal amplification.

The COVID-19 pandemic has underscored the need for rapid and accurate tests to detect SARS-CoV-2 infection. The tests commonly used have limitations, and a detection system based on CRISPR technology could offer a useful alternative. CRISPR is a technology derived from bacteria that can specifically detect pieces of DNA. We have previously developed ENHANCE, a detection system that converts the SARS-CoV-2 genetic material into DNA that is then detected by an engineered CRISPR technology. Here, we develop an improved version of this method, ENHANCEv2, that has an extended shelf life and less need for refrigeration, facilitating transportation of the components required for the test and its use. We show that both ENHANCE and ENHACEv2 can quickly and accurately detect SARS-CoV-2 in swabs from infected people. This is a step towards having more versatile tools to detect SARS-CoV-2 infection quickly and accurately.