Generation Of Libraries Research Articles

A newly developed and verified transport capacity of Monte Carlo photon particles in RMC

In reactor shielding designs, deep-penetration shielding issues, and direct photon heating, photon transport physics is crucial. In the Reactor Monte Carlo (RMC) code, a state-of-the-art photon transport capacity is implemented in this research. For criticality and fixed-source calculations, pure photon and coupled neutron-photon transport are now simulated for four photon-atomic interactions, including Rayleigh scattering, Compton scattering, Photoelectric effect, and Pair production, as well as three major processes of the secondary photons generation (Atomic relaxation, Electron-positron annihilation, and Bremsstrahlung by electrons and positrons). In order to properly balance energy release and deposition during neutron-induced photon production, the yield of photons by neutrons is scaled by a correction factor to account for the delayed gamma radiation, and the effective multiplication factor keff is used in an uncritical system. Using an Automatic Library Generator (ALG) code, a new database for photon transport is processed with the ENDF/B evaluated nuclear data library. Numerous cases, such as a point source in an infinite geometry, a photon beam source in a cylinder geometry, and a fuel assembly in the VERA benchmark, are verified against the Monte Carlo (MC) code OpenMC. Despite some notable differences in the comparison of the photon flux at certain energy ranges, which are caused by differences in the database and also the physics implementation, the results of the energy and spatial distributions of photon flux, and the photon heating show generally good agreement and relative errors are nearly within a triple statistical standard deviation. RMC now offers advanced photon transport capacity for reaction core and shielding applications.

Oktoberfest: Open-source spectral library generation and rescoring pipeline based on Prosit.

Machine learning (ML) and deep learning (DL) models for peptide property prediction such as Prosit have enabled the creation of high quality in silico reference libraries. These libraries are used in various applications, ranging from data-independent acquisition (DIA) data analysis to data-driven rescoring of search engine results. Here, we present Oktoberfest, an open source Python package of our spectral library generation and rescoring pipeline originally only available online via ProteomicsDB. Oktoberfest is largely search engine agnostic and provides access to online peptide property predictions, promoting the adoption of state-of-the-art ML/DL models in proteomics analysis pipelines. We demonstrate its ability to reproduce and even improve our results from previously published rescoring analyses on two distinct use cases. Oktoberfest is freely available on GitHub (https://github.com/wilhelm-lab/oktoberfest) and can easily be installed locally through the cross-platform PyPI Python package.

Functional Testing Scenario Library Generation Framework for Connected and Automated Vehicles

CAVs (connected and autonomous vehicles) are developing quickly and changing how people drive. Vehicle-to-anything (V2X) technology is rekindling corporate interest as 5G and 6G technologies take off. The absence of a reliable functional testing approach is one of the main issues with current technology. Currently, testing scenario libraries are created manually by testers, which has the drawback of being scarce and ineffective. Traditional automated generating algorithms provide limited-coverage scenarios that do not account for the influence of sensors. Our contributions to solving these issues are as follows. First, we extract the roads in the research region from OpenStreetMap (OSM), filter them, and annotate them using hierarchical clustering of feature values, which creates a static road library. Second, reinforcement learning is used to model dynamic situations using a partly observable Markov decision process (POMDP) in conjunction with sensor inputs. The creation process can be run concurrently with functional tests. Third, the efficiency of simulation testing is increased by integrating the static road library and the dynamic scenario section to produce a sizable library of test scenarios. This increases the realism and coverage of the library. The experimental results show that the proposed scene construction method is well suited for use in SUMO, VTD and other simulators, and has a 388% improvement in scenario coverage compared to the traditional method.

Experimental and Virtual Screening of Phenazine and Phenothiazine Derivatives in Aqueous Solution for Electrochemical Carbon Capture

Molecular redox-active compounds, including quinone and aza-aromatics, have emerged as tools for capturing carbon dioxide using renewable electricity. Given the vast and highly diverse chemical space of the candidate compounds, accessing their electrochemical properties in a rapid way is attractive for both experimental and virtual screening approaches.Here we present a study on the experimental screening of a series of commercial redox-active compounds. Phenazine and phenothiazine dyes have been experimentally tested for applications in electrochemical carbon capture, for which they are finding increasing applications as redox mediators due to their unique redox properties in aqueous solution. Nicotinamide has been used as a redox-inactive solubilizing agent to increase the solubilities of general phenazine and phenothiazine derivatives for rapid experimental screening of commercial compounds without time-consuming synthetic modifications. Cyclic voltammetry in aqueous solutions under nitrogen and carbon dioxide indicated reversible behavior in the pH swing of an aqueous solution. Bulk electrolysis experiments demonstrated the chemical stability of phenazine and phenothiazine derivatives. The redox properties and the chemical stability make phenazine and phenothiazine derivatives interesting candidates for future use as redox-active materials in electrochemical carbon capture.We also present progress towards a virtual screening for the optimization of electrochemical and physicochemical properties of redox-active compounds. Based on our experimental screening of commercial phenazine and phenothiazine compounds identifying neutral red as the lead compound, a virtual screening was performed for optimization of electrochemical and physicochemical properties using machine learning. The virtual screening development steps include (1) chemical library generation using neutral red as a lead compound, (2) molecular property prediction based on quantum chemical calculations, (3) aqueous solubility prediction using machine learning, (4) data processing and database creation, and (5) experimental validation of the optimized compounds for the use of electrochemical carbon capture. Figure 1

Multi-physics coupling simulation of small mobile nuclear reactor with finite element-based models

This paper describes the multi-physics coupling simulation of a small transportable nuclear reactor, SIMONS, using the neutronics/thermo-mechanics coupling three-dimensional high-fidelity code platform FEMAS (FEM-based Multiphysics Analysis Software for Nuclear Reactor). FEMAS builds multi-physics field models utilizing the open-source finite element codes deal.II and FEniCS. Using coupling simulations of neutronics, thermal-conduction, and thermo-mechanics, the SIMONS micro mobile reactor is examined. For the generation of cross-sections libraries, the open-source Monte Carlo code OpenMC is utilized, and the (n,xn) correction and SPH method is developed to maintain neutron equilibrium and eliminate homogenization errors. The approaches yield neutronic solutions that are in good agreement with the reference solutions, as the keff error decreases from 5066 pcm to 7.2 pcm and the maximum relative power error decreases from 5.2% to 0.3%. With a thermal power of 240 kW, the local temperature difference of the entire reactor is 354.68 K, and the deformation rate due to thermal stress is 2.4%, resulting in a reactivity feedback of 4459.6 pcm. At the wall in contact with the outermost fuel rods, where the temperature gradient grows substantially and thermal stress rises, the monolith stress is measured to peak at 9.65 MPa. This research has paved the way for a more thorough multi-physics examination of micro nuclear reactors in the era of high-precision computation.

DNA-Programmed Lipid Nanoreactors for Synthesis of Carbohydrate Mimetics by Fusion of Aqueous Sub-attoliter Compartments.

Lipid nanoreactors are biomimetic reaction vessels (nanoreactors) that can host aqueous or membrane-associated chemical and enzymatic reactions. Nanoreactors provide ultra-miniaturization from atto- to zeptoliter volumes per reaction vessel with the major challenge of encoding and spatio-temporal control over reactions at the individual nanoreactor or population level, thereby controlling volumes several orders of magnitude below advanced microfluidic devices. We present DNA-programmed lipid nanoreactors (PLNs) functionalized with lipidated oligonucleotides (LiNAs) that allow programming and encoding of nanoreactor interactions by controlled membrane fusion, exemplified for a set of carbohydrate mimetics with mono- to hexasaccharide azide building blocks connected by click-chemistry. Programmed reactions are initiated by fusion of distinct populations of nanoreactors with individually encapsulated building blocks. A focused library of triazole-linked carbohydrate-Cy5 conjugates formed by strain-promoted azide-alkyne cycloadditions demonstrated LiNA-programmed chemistry, including two-step reaction schemes. The PLN method is developed toward a robust platform for synthesis in confined space employing fully programmable nanoreactors, applicable to multistep synthesis for the generation of combinatorial libraries with subsequent analysis of the molecules formed, based on the addressability of the lipid nanoreactors.

Design of Tetra-Peptide Ligands of Antibody Fc Regions Using In Silico Combinatorial Library Screening.

Peptides, or short chains of amino-acid residues, are becoming increasingly important as active ingredients of drugs and as crucial probes and/or tools in medical, biotechnological, and pharmaceutical research. Situated at the interface between small molecules and larger macromolecular systems, they pose a difficult challenge for computational methods. We report an in silico peptide library generation and prioritization workflow using CmDock for identifying tetrapeptide ligands that bind to Fc regions of antibodies that is analogous to known in vitro recombinant peptide libraries' display and expression systems. The results of our in silico study are in accordance with existing scientific literature on in vitro peptides that bind to antibody Fc regions. In addition, we postulate an evolving in silico library design workflow that will help circumvent the combinatorial problem of in vitro comprehensive peptide libraries by focusing on peptide subunits that exhibit favorable interaction profiles in initial in silico peptide generation and testing.

Cloning of Affibody Libraries for Display Methods.

Affibody molecules are small (6-kDa) affinity proteins folded in a three-helical bundle and generated by directed evolution for specific binding to various target molecules. The most advanced affibody molecules are currently tested in the clinic, and data from more than 300 subjects show excellent activity and safety profiles. The generation of affibody molecules against a particular target starts with the generation of an affibody library, which can then be used for panning using multiple methods and selection systems. This protocol describes the molecular cloning of DNA-encoded affibody libraries to a display vector of choice, for either phage, Escherichia coli, or Staphylococcus carnosus display. The DNA library can come from different sources, such as error-prone polymerase chain reaction (PCR), molecular shuffling of mutations from previous selections, or, more commonly, from DNA synthesis using various methods. Restriction enzyme-based subcloning is the most common strategy for affibody libraries of higher diversity (e.g., >107 variants) and is described here.

Selection of Affibody Molecules Using Staphylococcal Display.

Affibody molecules are small (6-kDa) affinity proteins generated by directed evolution for specific binding to various target molecules. The first step in this workflow involves the generation of an affibody library, which can then be used for biopanning using multiple display methods. This protocol describes selection from affibody libraries using display on Staphylococcus carnosus Display of affibodies on staphylococci is very efficient and straightforward because of the single cell membrane and the use of a construct with a constitutive promoter. The workflow involves display of affibody libraries on the surface of S. carnosus cells, followed by screening and selection of binders using fluorescence-activated cell sorting (FACS). The transformation of DNA libraries into S. carnosus is less efficient and more complicated than for Escherichia coli. Because of this, staphylococcal display is suitable for affinity maturation or other protein-engineering efforts that are not dependent on very high diversity, and thus magnetic-activated cell sorting (MACS) is often not required before FACS. However, MACS is an option, and MACS procedures used for E. coli can easily be adapted for use in S. carnosus if needed.

Selection of Affibody Molecules Using Phage Display.

Affibody molecules are small (6-kDa) affinity proteins generated by directed evolution for specific binding to various target molecules. The first step in this workflow involves the generation of an affibody library. This is then followed by amplification of the library, which can then be used for biopanning using multiple methods. This protocol describes amplification of affibody libraries, followed by biopanning using phage display and analysis of the selection output. The general procedure is mainly for selection of first-generation affibody molecules from large naive (unbiased) libraries, typically yielding affibody hits with affinities in the low nanomolar range. For selection from affinity maturation libraries with the aim of isolating variants of even higher affinities, the procedure is similar, but parameters such as target concentration and washing are adjusted to achieve the proper stringency.

Selection of Affibody Molecules Using Escherichia coli Display.

Affibody molecules are small (6-kDa) affinity proteins generated by directed evolution for specific binding to various target molecules. The first step in this workflow involves the generation of an affibody library, which can then be used for selection via multiple display methods. This protocol describes selection from affibody libraries by Escherichia coli cell surface display. With this method, high-diversity libraries of 1011 can be displayed on the cell surface. The method involves two steps for selection of binders from high-diversity libraries: magnetic-activated cell sorting (MACS) and fluorescence-activated cell sorting (FACS). MACS is used first to enrich the library in target-binding clones and to decrease diversity to a size that can be effectively screened and sorted in the flow cytometer in a reasonable time (typically <107 cells). The protocol is based on methodology using an AIDA-I autotransporter for display on the outer membrane, but the general procedures can also be adjusted and used for other types of autotransporters or alternative E. coli display methods.

Targeted In Vivo Mutagenesis in Yeast Using CRISPR/Cas9 and Hyperactive Cytidine and Adenine Deaminases.

Directed evolution is a preferred strategy to improve the function of proteins such as enzymes that act as bottlenecks in metabolic pathways. Common directed evolution approaches rely on error-prone PCR-based libraries where the number of possible variants is usually limited by cellular transformation efficiencies. Targeted in vivo mutagenesis can advance directed evolution approaches and help to overcome limitations in library generation. In the current study, we aimed to develop a high-efficiency time-controllable targeted mutagenesis toolkit in the yeast Saccharomyces cerevisiae by employing the CRISPR/Cas9 technology. To that end, we fused the dCas9 protein with hyperactive variants of adenine and cytidine deaminases aiming to create an inducible CRISPR-based mutagenesis tool targeting a specific DNA sequence in vivo with extended editing windows and high mutagenesis efficiency. We also investigated the effect of guide RNA multiplexing on the mutagenesis efficiency both phenotypically and on the DNA level.

Identification of GDF15 peptide fragments inhibiting GFRAL receptor signaling

Growth differentiation factor 15 (GDF15) is believed to be a major causative factor for cancer-induced cachexia. Recent elucidation of the central circuits involved in GDF15 function and its signaling through the glial cell-derived neurotrophic factor family receptor α-like (GFRAL) has prompted the interest of targeting the GDF15-GFRAL signaling for energy homeostasis and body weight regulation. Here, we applied advanced peptide technologies to identify GDF15 peptide fragments inhibiting GFRAL signaling. SPOT peptide arrays revealed binding of GDF15 C-terminal peptide fragments to the extracellular domain of GFRAL. Parallel solid-phase peptide synthesis allowed for generation of complementary GDF15 peptide libraries and their subsequent functional evaluation in cells expressing the GFRAL/RET receptor complex. We identified a series of C-terminal fragments of GDF15 inhibiting GFRAL activity in the micromolar range. These novel GFRAL peptide inhibitors could serve as valuable tools for further development of peptide therapeutics towards the treatment of cachexia and other wasting disorders.

Droplet microfluidic technologies for next-generation high-throughput screening

Droplet microfluidics has evolved into a promising platform for high-throughput screening (HTS), allowing for rapid and precise analysis of thousands of samples encapsulated within droplets. Droplet microfluidic platform offers versatility, high-throughput, and the ability to compartmentalize reactions for a wide range of applications including pharmaceutics, cell analysis, and combinatorial chemical analysis. While droplet-based microfluidics has made considerable advances in automating basic laboratory tasks, for instance, manipulation, storage, and analysis, there has been comparatively little advancement toward HTS applications. The complexity of the technology, the lack of standardization, and the challenges associated with screening large numbers of samples are all factors that have contributed to the limited adoption of droplet-based microfluidics in HTS. In this perspective, we provide a comprehensive overview of the progress of droplet microfluidics as a potential platform for next-generation HTS, specifically in the domain of droplet separation and library generation. We hope that this perspective will inspire further research in relevant academic fields and contribute to the development of innovative HTS strategies based on droplet microfluidic technologies.

The pComb3 Phagemid Family of Phage Display Vectors.

A phagemid is a plasmid that contains the origin of replication and packaging signal of a filamentous phage. Following bacterial transformation, a phagemid can be replicated and amplified as a plasmid, using a double-stranded DNA origin of replication, or it can be replicated as single-stranded DNA for packaging into filamentous phage particles. The use of phagemids enables phage display of large proteins, such as antibody fragments. Phagemid pComb3 was among the first phage display vectors used for the generation and selection of antibody libraries in the 50-kDa Fab format, a monovalent proxy of natural antibodies. Affording a robust and versatile tool for more than three decades, phage display vectors of the pComb3 phagemid family have been widely used for the discovery, affinity maturation, and humanization of antibodies in Fab, scFv, and single-domain formats from naive, immune, and synthetic antibody repertoires. In addition, they have been used for broadening phage display to the mining of nonimmunoglobulin repertoires. This review examines conceptual, functional, and molecular features of the first-generation phage display vector pComb3 and its successors, pComb3H, pComb3X, and pC3C.

Which boronic acids are used most frequently for synthesis of bioactive molecules?

Boronic acids are essential building blocks used for the synthesis of bioactive molecules, the generation of chemical libraries and the exploration of structure–activity relationships. As a result, more than ten thousand boronic acids are commercially available. Medicinal chemists are therefore facing a challenge; which of them should they select to maximize information obtained by the synthesis of new target molecules. The present article aims to help them to make the right choices. The boronic acids used frequently in the synthesis of bioactive molecules were identified by mining several large molecular and reaction databases and their properties were analyzed. Based on the results a diverse set of boronic acids covering well the bioactive chemical space was selected and is suggested as a basis for library design for the efficient exploration of structure–activity relationships. A Boronic Acid Navigator web tool which helps chemists to make their own selection is also made available at https://bit.ly/boronics.

Modular Cloning by Golden Gate Assembly and Possible Application in Pathway Design.

Preparation of expression vectors using conventional cloning strategies is laborious and not suitable for the design of metabolic pathways or enzyme cascades, which usually requires the preparation of a vector library to identify productive clones. Recently, Modular Cloning as a novel cloning technique in synthetic biology has been developed. Modular Cloning relies on Golden Gate assembly and supports preparation of individual expression vectors in one-step and one-pot reactions, thus allowing rapid generation of vector libraries. A number of Modular Cloning toolkits for specific applications has been established, providing a collection of distinct genetic elements such as promoters, ribosome binding sites and tags, that can be combined individually in one-step using defined fusion sites. Modular Cloning has been successfully applied to generate various strains for producing value-added compounds. This was achieved by orchestrating complex pathways involving up to 20 enzymes. Due to the novelty of the genetic approach, industrial applications are still rare. In addition, some applications are limited due to the lack of high-throughput screening methods. This shifts the bottleneck from library preparation to screening capacity and needs to be addressed by future developments to pave the path for the establishment of Modular Cloning in industrial applications.

Optimizing Linear Ion-Trap Data-Independent Acquisition toward Single-Cell Proteomics.

A linear ion trap (LIT) is an affordable, robust mass spectrometer that provides fast scanning speed and high sensitivity, where its primary disadvantage is inferior mass accuracy compared to more commonly used time-of-flight or orbitrap (OT) mass analyzers. Previous efforts to utilize the LIT for low-input proteomics analysis still rely on either built-in OTs for collecting precursor data or OT-based library generation. Here, we demonstrate the potential versatility of the LIT for low-input proteomics as a stand-alone mass analyzer for all mass spectrometry (MS) measurements, including library generation. To test this approach, we first optimized LIT data acquisition methods and performed library-free searches with and without entrapment peptides to evaluate both the detection and quantification accuracy. We then generated matrix-matched calibration curves to estimate the lower limit of quantification using only 10 ng of starting material. While LIT-MS1 measurements provided poor quantitative accuracy, LIT-MS2 measurements were quantitatively accurate down to 0.5 ng on the column. Finally, we optimized a suitable strategy for spectral library generation from low-input material, which we used to analyze single-cell samples by LIT-DIA using LIT-based libraries generated from as few as 40 cells.

Generation and Selection of Synthetic Human Antibody Libraries via Phage Display.

Synthetic antibody libraries enable the development of antibodies that can recognize virtually any antigen, with affinity and specificity profiles that are superior to those of natural antibodies. By using highly stable and optimized frameworks, synthetic antibody libraries can be rapidly generated by precisely designing synthetic DNA, allowing absolute control over the position and chemical diversity introduced while expanding the sequence space for antigen recognition. Here, we describe a detailed protocol for the generation of highly diverse synthetic antibody phage display libraries based on a single framework, with diversity genetically incorporated by using finely designed mutagenic oligonucleotides. This general method enables the facile construction of large antibody libraries with precisely tunable features, resulting in the rapid development of recombinant antibodies for virtually any antigen.

Modular Software for Generating and Modeling Diverse Polymer Databases.

Machine learning methods offer the opportunity to design new functional materials on an unprecedented scale; however, building the large, diverse databases of molecules on which to train such methods remains a daunting task. Automated computational chemistry modeling workflows are therefore becoming essential tools in this data-driven hunt for new materials with novel properties, since they offer a means by which to create and curate molecular databases without requiring significant levels of user input. This ensures that well-founded concerns regarding data provenance, reproducibility, and replicability are mitigated. We have developed a versatile and flexible software package, PySoftK (Python Soft Matter at King's College London) that provides flexible, automated computational workflows to create, model, and curate libraries of polymers with minimal user intervention. PySoftK is available as an efficient, fully tested, and easily installable Python package. Key features of the software include the wide range of different polymer topologies that can be automatically generated and its fully parallelized library generation tools. It is anticipated that PySoftK will support the generation, modeling, and curation of large polymer libraries to support functional materials discovery in the nanotechnology and biotechnology arenas.