Abiotic peptides as carriers of information for the encoding of small-molecule library synthesis

Encoded Display of Chemical Libraries on Nanoparticles as a Versatile Selection Tool To Discover Protein Ligands

DNA-encoded library (DEL) technology is a powerful tool for discovering potent ligands for biological targets but constrained by limitations, including the insolubility of DNA in organic solvents and its instability under various reaction conditions, which restrict the reactivity scope and structural diversity achievable in library synthesis. Here, we present a new strategy called nanoDEL, where library molecules and DNA tags are displayed on the surface of nanoparticles. Since nanoparticles disperse well in both organic solvents and aqueous solutions, DEL synthesis can be accomplished using well-established organic solvent-based conditions, eliminating the need for aqueous conditions. Moreover, nanoDEL enables air-sensitive reactions that are inaccessible with conventional DEL methods relying on aqueous conditions. Notably, in nanoDEL, multiple copies of a DNA tag are attached to an individual nanoparticle to encode a single compound, significantly enhancing tolerance to DNA-damaging conditions. Even when most DNA tags are damaged, sequence analysis remains feasible via amplification of intact tags. Consequently, nanoDEL facilitates the convenient use of existing organic reactions without the necessity to develop DNA-compatible reactions. The potential of nanoDEL was validated by affinity selection against streptavidin as a model system and successfully applied to the discovery of potent small-molecule inhibitors for a kinase and stapled peptide inhibitors targeting a protein-protein interaction, exhibiting dissociation constants in the nanomolar range. Furthermore, we demonstrated that a large combinatorial library can be efficiently synthesized on nanoparticles using a synthetic scheme including moisture-sensitive reaction steps, which are not feasible with conventional DELs.

A Workflow Enabling the Automated Synthesis, Chain-End Degradation, and Rapid Mass Spectrometry Analysis for Molecular Information Storage in Sequence-Defined Oligourethanes

The field of molecular information storage has recently expanded to include abiotic sequence-defined polymers. While robust methods have been developed, there is a current bottleneck in the throughput of this work as information density is increased. Herein, we introduce an automated workflow in which a commercial peptide synthesizer composed of a single XYZ liquid-handling robot was adapted to both synthesize and sequence sequence-defined oligourethanes. Our sequencing method was improved to cut down the number of samples required for each oligomer from 13 to one. Additionally, we introduce the use of desorption electrospray ionization mass spectrometry as our analysis method for sequencing, which allowed for simplified and increased speed of data acquisition. Finally, we created a Python script that is able to reconstruct the sequence information from the MS data in an automated fashion. We demonstrate this new workflow by encoding and decoding a quote from the late Maya Angelou: "When you learn, teach, when you get, give".

pyBinder: Quantitation to Advance Affinity Selection-Mass Spectrometry

Affinity selection-mass spectrometry (AS-MS) is a ligand discovery platform that relies upon mass spectrometry to identify molecules bound to a biomolecular target. When utilized with large peptide libraries (108 members), AS-MS sample complexity can surpass the sequencing capacity of modern mass spectrometers, resulting in incomplete data, identification of few target-specific ligands, and/or incomplete sequencing. To address this challenge, we introduce pyBinder to perform quantitation on AS-MS data to process primary MS1 data and develop two scores to rank the peptides from the integration of their peak area: target selectivity and concentration-dependent enrichment. We benchmark pyBinder utilizing AS-MS data developed against antihemagglutinin antibody 12ca5, revealing that peptides that contain a motif known for target-specific high-affinity binding are well characterized by these two scores. AS-MS data from a second protein target, WD Repeat Domain 5 (WDR5), is analyzed to confirm the two pyBinder scores reliably capture the target-specific motif-containing peptides. From the results delivered by pyBinder, a list of target-selective features is developed and fed back into subsequent MS experiments to facilitate expanded data generation and the targeted discovery of selective ligands. pyBinder analysis resulted in a 4-fold increase in motif-containing sequence identification for WDR5 (from 3 to 14 ligands discovered), showing the utility of the two scores. This work establishes an improved approach for AS-MS to enable discovery outcomes (i.e., more ligands identified), but also a way to compare AS-MS data across samples, protocols, and conditions broadly.

Electron-rich anilines as cleavable linkers for peptides

We report the development of a new electron-rich aniline (ERA)-based cleavable linker. Anilines can be incorporated into peptides during SPPS and are stable to most reaction conditions. ERA-containing peptides can be cleaved rapidly in the presence of oxidants, such as DDQ, CAN, and NaIO4, in 30 min at room temperature. The compatibility of these conditions is demonstrated on peptides containing natural and unnatural amino acids. While the cleavages of other oxidatively labile linkers is known to cause decomposition of Tyr and Trp, these sensitive residues are stable to DDQ oxidations. An ERA-linker also enables the capture and release of a biotinylated peptide from immobilized streptavidin. ERA-linkers may serve as an excellent tool for peptide library screening applications.

Quantitative Characterization of Partitioning Stringency in SELEX

Maintaining stringent conditions in SELEX (Systematic Evolution of Ligands by EXponential enrichment) is crucial for obtaining high-affinity aptamers. However, excessive stringency greatly increases the risk of SELEX failure. Controlling stringency has remained a technical challenge, largely dependent on intuition, due to the absence of a clear, quantitative measure of stringency. This study was motivated by our insight that, while stringency is influenced by multiple factors, it can be quantified by its effect: increasing stringency reduces the quantity of binders normalized to that of nonbinders after partitioning. Based on this insight, we propose measuring stringency using the binder-to-nonbinder ratio (BNR), where a lower BNR indicates higher stringency. We derive an experimental method for determining BNR via quantitative PCR. Our theoretical analysis and SELEX experiments using two distinct proteins as selection targets underscore the importance of maintaining a BNR significantly greater than zero to avoid failure, a principle we call the SELEX nonfailure criterion. By employing inverse BNR to quantify stringency and applying this criterion, researchers can more rationally control SELEX progress. The quantitative stringency measure and nonfailure criterion can also be applied to other artificial evolution methods, provided that selected binders are quantifiable.

High density information storage through isotope ratio encoding

The need for reliable information storage is on a steep rise. Sequence-defined polymers, particularly oligonucleotides, are already in use in several areas, while compound mixtures also offer a simple way for storing information. We investigated the use of a set of isotopologues in information storage by mixing, where the information is stored in the form of a mass spectrometric (MS) fingerprint of the mixture. A small molecule with 24 non-labile and replaceable hydrogen atoms was selected as a model, and a set of components covering the D0-D24 deuteration range were synthesized. Theoretical analysis predicted that by mixing up to 10 out of the prepared components, one can encode over 130 million different combinations and distinguish their MS fingerprints. As a proof of principle, several mixtures predicted to have similar fingerprints were prepared and their MS fingerprints were recorded. From each measured MS fingerprint, we were able to unambiguously identify the actual composition of the mixture. It was also demonstrated that one can make the MS fingerprints of a given mixture unique, thereby making counterfeiting of the stored information very difficult. Finally, the utility of isotope ratio encoding in covalent tagging was also demonstrated.

Encoding and display technologies for combinatorial libraries in drug discovery: The coming of age from biology to therapy

Drug discovery is a sophisticated process that incorporates scientific innovations and cutting-edge technologies. Compared to traditional bioactivity-based screening methods, encoding and display technologies for combinatorial libraries have recently advanced from proof-of-principle experiments to promising tools for pharmaceutical hit discovery due to their high screening efficiency, throughput, and resource minimization. This review systematically summarizes the development history, typology, and prospective applications of encoding and displayed technologies, including phage display, ribosomal display, mRNA display, yeast cell display, one-bead one-compound, DNA-encoded, peptide nucleic acid-encoded, and new peptide-encoded technologies, and examples of preclinical and clinical translation. We discuss the progress of novel targeted therapeutic agents, covering a spectrum from small-molecule inhibitors and nonpeptidic macrocycles to linear, monocyclic, and bicyclic peptides, in addition to antibodies. We also address the pending challenges and future prospects of drug discovery, including the size of screening libraries, advantages and disadvantages of the technology, clinical translational potential, and market space. This review is intended to establish a comprehensive high-throughput drug discovery strategy for scientific researchers and clinical drug developers.

Highly pure DNA-encoded chemical libraries by dual-linker solid-phase synthesis

The first drugs discovered using DNA-encoded chemical library (DEL) screens have entered late-stage clinical development. However, DEL technology as a whole still suffers from poor chemical purity resulting in suboptimal performance. In this work, we report a technique to overcome this issue through self-purifying release of the DEL after magnetic bead-based synthesis. Both the first and last building blocks of each assembled library member were linked to the beads by tethers that could be cleaved by mutually orthogonal chemistry. Sequential cleavage of the first and last tether, with washing in between, ensured that the final library comprises only the fully complete compounds. The outstanding purity attained by this approach enables a direct correlation of chemical display and encoding, allows for an increased chemical reaction scope, and facilitates the use of more diversity elements while achieving greatly improved signal-to-noise ratios in selections.

Proteomic Barcoding Platform for Macromolecular Screening and Delivery

Engineered macromolecules offer compelling means for the therapy of conventionally undruggable interactions in human disease. However, their efficacy is limited by barriers to tissue and intracellular delivery. Inspired by recent advances in molecular barcoding and evolution, we developed BarcodeBabel, a generalized method for the design of libraries of peptide barcodes suitable for high-throughput mass spectrometry proteomics. Combined with PeptideBabel, a Monte Carlo sampling algorithm for the design of peptides with evolvable physicochemical properties and sequence complexity, we developed a barcoded library of cell penetrating peptides (CPPs) with distinct physicochemical features. Using quantitative targeted mass spectrometry, we identified CPPS with improved nuclear and cytoplasmic delivery exceeding hundreds of millions of molecules per human cell while maintaining minimal membrane disruption and negligible toxicity in vitro. These studies provide a proof of concept for peptide barcoding as a homogeneous high-throughput method for macromolecular screening and delivery. BarcodeBabel and PeptideBabel are available open-source from https://github.com/kentsisresearchgroup/.

Intelligent Biomaterialomics: Molecular Design, Manufacturing, and Biomedical Applications

Materialomics integrates experiment, theory, and computation in a high-throughput manner, and has changed the paradigm for the research and development of new functional materials. Recently, with the rapid development of high-throughput characterization and machine-learning technologies, the establishment of biomaterialomics that tackles complex physiological behaviors has become accessible. Breakthroughs in the clinical translation of nanoparticle-based therapeutics and vaccines have been observed. Herein, recent advances in biomaterials, including polymers, lipid-like materials, and peptides/proteins, discovered through high-throughput screening or machine learning-assisted methods, are summarized. The molecular design of structure-diversified libraries; high-throughput characterization, screening, and preparation; and, their applications in drug delivery and clinical translation are discussed in detail. Furthermore, the prospects and main challenges in future biomaterialomics and high-throughput screening development are highlighted.

Optimizing Nucleic Acid Delivery Systems through Barcode Technology

Conventional biological experiments often focus on in vitro assays because of the inherent limitations when handling multiple variables in vivo, including labor-intensive and time-consuming procedures. Often only a subset of samples demonstrating significant efficacy in the in vitro assays can be evaluated in vivo. Nonetheless, because of the low correlation between the in vitro and in vivo tests, evaluation of the variables under examination in vivo and not solely in vitro is critical. An emerging approach to achieve high-throughput in vivo tests involves using a barcode system consisting of various nucleotide combinations. Unique barcodes for each variant enable the simultaneous testing of multiple entities, eliminating the need for separate individual tests. Subsequently, to identify crucial parameters, samples were collected and analyzed using barcode sequencing. This review explores the development of barcode design and its applications, including the evaluation of nucleic acid delivery systems and the optimization of gene expression in vivo.

AI is a viable alternative to high throughput screening: a 318-target study

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.

Mirror-image trypsin digestion and sequencing of D-proteins

The development of mirror-image biology systems and related applications is hindered by the lack of effective methods to sequence mirror-image (D-) proteins. Although natural-chirality (L-) proteins can be sequenced by bottom-up liquid chromatography-tandem mass spectrometry (LC-MS/MS), the sequencing of long D-peptides and D-proteins with the same strategy requires digestion by a site-specific D-protease before mass analysis. Here we apply solid-phase peptide synthesis and native chemical ligation to chemically synthesize a mirror-image version of trypsin, a widely used protease for site-specific protein digestion. Using mirror-image trypsin digestion and LC-MS/MS, we sequence a mirror-image large subunit ribosomal protein (L25) and a mirror-image Sulfolobus solfataricus P2 DNA polymerase IV (Dpo4), and distinguish between different mutants of D-Dpo4. We also perform writing and reading of digital information in a long D-peptide of 50 amino acids. Thus, mirror-image trypsin digestion in conjunction with LC-MS/MS may facilitate practical applications of D-peptides and D-proteins as potential therapeutic and informational tools.

Development of an oxazole-based cleavable linker for peptides

We report the development of a new oxazole-based cleavable linker to release peptides from attached cargo. Oxazoles are stable to most reaction conditions, yet they can be rapidly cleaved in the presence of single-electron oxidants like cerium ammonium nitrate (CAN). An oxazole linker could be synthesized and attached to peptides through standard solid-phase peptide coupling reactions. Cleavage of these peptide-oxazole conjugates is demonstrated on a broad scope of peptides containing various natural and unnatural amino acids. These results represent the first example of a peptide-based linker that is cleaved through single-electron oxidation. The oxazole is also demonstrated to be a suitable linker for both the release of a peptide from a conjugated small molecule and the orthogonal release of cargo from a peptide containing multiple cleavable linkers. Oxazole linkers could serve as a promising tool for peptide screening platforms such as peptide-encoded libraries.

Recent advances in bioaffinity strategies for preclinical and clinical drug discovery: Screening natural products, small molecules and antibodies

Bioaffinity drug screening strategies have gained popularity in preclinical and clinical drug discovery for natural products, small molecules and antibodies owing to their superior selectivity, the large number of compounds to be screened and their ability to minimize the time and expenses of the drug discovery process. This paper provides a systematic summary of the principles of commonly used bioaffinity-based screening methods, elaborates on the success of bioaffinity in clinical drug development and summarizes the active compounds, preclinical drugs and marketed drugs obtained through affinity screening methods. Owing to the high demand for new drugs, bioaffinity-guided screening techniques will play a greater part in clinical drug development.

Navigating the Expansive Landscapes of Soft Materials: A User Guide for High-Throughput Workflows

Synthetic polymers are highly customizable with tailored structures and functionality, yet this versatility generates challenges in the design of advanced materials due to the size and complexity of the design space. Thus, exploration and optimization of polymer properties using combinatorial libraries has become increasingly common, which requires careful selection of synthetic strategies, characterization techniques, and rapid processing workflows to obtain fundamental principles from these large data sets. Herein, we provide guidelines for strategic design of macromolecule libraries and workflows to efficiently navigate these high-dimensional design spaces. We describe synthetic methods for multiple library sizes and structures as well as characterization methods to rapidly generate data sets, including tools that can be adapted from biological workflows. We further highlight relevant insights from statistics and machine learning to aid in data featurization, representation, and analysis. This Perspective acts as a "user guide" for researchers interested in leveraging high-throughput screening toward the design of multifunctional polymers and predictive modeling of structure-property relationships in soft materials.

Targeting carbonic anhydrases for the management of hypoxic metastatic tumors

INTRODUCTION

Several isoforms of the metalloenzyme carbonic anhydrase (CA, EC 4.2.1.1) are connected with tumorigenesis. Hypoxic tumors overexpress CA IX and XII as a consequence of HIF activation cascade, being involved in pH regulation, metabolism, and metastases formation. Other isoforms (CA I, II, III, IV) were also reported to be present in some tumors.

AREAS COVERED

Some CA isoforms are biomarkers for disease progression or response to therapy. Inhibitors, antibodies, and other procedures for targeting these enzymes for the treatment of tumors/metastases are discussed. Sulfonamides and coumarins represent the most investigated classes of inhibitors, but carboxylates, selenium, and tellurium-containing inhibitors were also investigated. Hybrid drugs of CA inhibitors with other antitumor agents for multitargeted therapy were reported.

EXPERT OPINION

Targeting CAs present in solid or hematological tumors with selective, targeted inhibitors is a validated approach, which has been consolidated in the last years. A host of new preclinical data and several clinical trials of antibodies and small-molecule inhibitors are ongoing, which connected with the large number of new chemotypes/procedures discovered to be effective, may lead to a breakthrough in this therapeutic area. The scientific/patent literature has been searched for on PubMed, ScienceDirect, Espacenet, and PatentGuru, from 2018 to 2023.

Emerging Trends in the Chemistry of End-to-End Depolymerization

Over the past couple of decades, polymers that depolymerize end-to-end upon cleavage of their backbone or activation of a terminal functional group, sometimes referred to as "self-immolative" polymers, have been attracting increasing attention. They are of growing interest in the context of enhancing polymer degradability but also in polymer recycling as they allow monomers to be regenerated in a controlled manner under mild conditions. Furthermore, they are highly promising for applications as smart materials due to their ability to provide an amplified response to a specific signal, as a single sensing event is translated into the generation of many small molecules through a cascade of reactions. From a chemistry perspective, end-to-end depolymerization relies on the principles of self-immolative linkers and polymer ceiling temperature (Tc). In this article, we will introduce the key chemical concepts and foundations of the field and then provide our perspective on recent exciting developments. For example, over the past few years, new depolymerizable backbones, including polyacetals, polydisulfides, polyesters, polythioesters, and polyalkenamers, have been developed, while modern approaches to depolymerize conventional backbones such as polymethacrylates have also been introduced. Progress has also been made on the topological evolution of depolymerizable systems, including the introduction of fully depolymerizable block copolymers, hyperbranched polymers, and polymer networks. Furthermore, precision sequence-defined oligomers have been synthesized and studied for data storage and encryption. Finally, our perspectives on future opportunities and challenges in the field will be discussed.

DNA-Compatible Cyanomethylation of (Hetero)aryl Halides or Triflates under a Tandem Reaction for DNA-Encoded Library Synthesis

A DNA-compatible reaction has been developed for the cyanomethylation of (hetero)aryl halides or triflates via a tandem process involving palladium-mediated Suzuki-Miyaura coupling and base-promoted isoxazole fragmentation. This one-pot protocol employs easily accessible starting materials, exhibits a wide substrate scope, and results in no significant DNA damage. Additionally, the resulting (hetero)arylacetonitriles can be converted into the corresponding carboxylic acids, which may be utilized for the synthesis of DNA-encoded chemical libraries.

Advances in Ultrahigh Throughput Hit Discovery with Tandem Mass Spectrometry Encoded Libraries

Discovering new bioactive molecules is crucial for drug development. Finding a hit compound for a new drug target usually requires screening of millions of molecules. Affinity selection based technologies have revolutionized early hit discovery by enabling the rapid screening of libraries with millions or billions of compounds in short timeframes. In this Perspective, we describe recent technology breakthroughs that enable the screening of ultralarge synthetic peptidomimetic libraries with a barcode-free tandem mass spectrometry decoding strategy. A combination of combinatorial synthesis, affinity selection, automated de novo peptide sequencing algorithms, and advances in mass spectrometry instrumentation now enables hit discovery from synthetic libraries with over 100 million members. We provide a perspective on this powerful technology and showcase success stories featuring the discovery of high affinity binders for a number of drug targets including proteins, nucleic acids, and specific cell types. Further, we show the usage of the technology to discover synthetic peptidomimetics with specific functions and reactivity. We predict that affinity selection coupled with tandem mass spectrometry and automated de novo decoding will rapidly evolve further and become a broadly used drug discovery technology.

Leveraging artificial intelligence in the fight against infectious diseases

Despite advances in molecular biology, genetics, computation, and medicinal chemistry, infectious disease remains an ominous threat to public health. Addressing the challenges posed by pathogen outbreaks, pandemics, and antimicrobial resistance will require concerted interdisciplinary efforts. In conjunction with systems and synthetic biology, artificial intelligence (AI) is now leading to rapid progress, expanding anti-infective drug discovery, enhancing our understanding of infection biology, and accelerating the development of diagnostics. In this Review, we discuss approaches for detecting, treating, and understanding infectious diseases, underscoring the progress supported by AI in each case. We suggest future applications of AI and how it might be harnessed to help control infectious disease outbreaks and pandemics.

Peptide barcodes meet drug discovery

Small-molecule libraries encoded by peptide tags may accelerate the search for therapeutics.