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

Identifying the DNA methylation preference of transcription factors using ProtBERT and SVM

Transcription factors (TFs) can affect gene expression by binding to certain specific DNA sequences. This binding process of TFs may be modulated by DNA methylation. A subset of TFs that serve as methylation readers preferentially binds to certain methylated DNA and is defined as TFPM. The identification of TFPMs enhances our understanding of DNA methylation's role in gene regulation. However, their experimental identification is resource-demanding. In this study, we propose a novel two-step computational approach to classify TFs and TFPMs. First, we employed a fine-tuned ProtBERT model to differentiate between the classes of TFs and non-TFs. Second, we combined the Reduced Amino Acid Category (RAAC) with K-mer and SVM to predict the potential of TFs to bind to methylated DNA. Comparative experiments demonstrate that our proposed methods outperform all existing approaches and emphasize the efficiency of our computational framework in classifying TFs and TFPMs. Cross-species validation on an independent mouse dataset further demonstrates the generalizability of our proposed framework In addition, we conducted predictions on all human transcription factors and found that most of the top 20 proteins belong to the Krueppel C2H2-type Zinc-finger family. So far, some studies have demonstrated a partial correlation between this family and DNA methylation and confirmed the preference of some of its members, thereby showing the robustness of our approach.

Composite Mapping for Peptide-Based Data Storage with Higher Coding Density and Fewer Synthesis Cycles

Peptides are natural information-bearing mediums and are promising for high-density data storage. However, conventional mapping of one amino acid (AA) to one binary code has limited the improvement of coding density by increasing the total number of different AAs. Here, a novel composite mapping strategy is developed, where each position in the peptide sequence is a composite letter consisting of several different AAs, and thousands of composite letters are available for mapping, thus breaking the limit of conventional mapping. When 20 different AAs are used, the coding density of six-AAs composite mapping achieves 15 bits/letter, while conventional mapping only reaches 4 bits/AA. The whole process of encoding data into composite letter sequences, synthesizing composite letter sequences via solid-phase peptide synthesis, sequencing composite letter sequences by mass spectrometry, and decoding data from composite letter sequences is successfully demonstrated for the first time. Composite mapping also demonstrates several distinct advantages, including high coding density, few synthesis cycles, high reliability against errors, low probability of homopolymers, and good compatibility with other encoding algorithms. The developed composite mapping strategy provides a novel way for peptide-based data storage to increase the coding density and reduce the synthesis cycles, showing great potential for large-scale data storage.

Development of chemokine network inhibitors using combinatorial saturation mutagenesis

Targeting chemokine-driven inflammation has been elusive due to redundant pathways constituting chemokine-immune cell networks. Tick evasins overcome redundant pathways by broadly targeting either CC or CXC-chemokine classes. Recently identified evasin-derived peptides inhibiting both chemokine classes provide a starting point for developing agents with enhanced potency and breadth of action. Structure-guided and affinity maturation approaches to achieve this are unsuitable when multiple targets are concerned. Here we develop a combinatorial saturation mutagenesis optimisation strategy (CoSMOS). This identifies a combinatorially mutated evasin-derived peptide with significantly enhanced pIC50 against three different inflammatory disease chemokine pools. Using AlphaFold 3 to model peptide - chemokine interactions, we show that the combinatorially mutated peptide has increased total and hydrophobic inter-chain bonding via tryptophan residues and is predicted to sterically hinder chemokine interactions required for immune cell migration. We suggest that CoSMOS-generated promiscuous binding activities could target disease networks where structurally related proteins drive redundant signalling pathways.

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.

Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery

Due to the spread of antibiotic resistance, global attention is focused on its inhibition and the expansion of effective medicinal compounds. The novel functional properties of peptides have opened up new horizons in personalized medicine. With artificial intelligence methods combined with therapeutic peptide products, pharmaceuticals and biotechnology advance drug development rapidly and reduce costs. Short-chain peptides inhibit a wide range of pathogens and have great potential for targeting diseases. To address the challenges of synthesis and sustainability, artificial intelligence methods, namely machine learning, must be integrated into their production. Learning methods can use complicated computations to select the active and toxic compounds of the drug and its metabolic activity. Through this comprehensive review, we investigated the artificial intelligence method as a potential tool for finding peptide-based drugs and providing a more accurate analysis of peptides through the introduction of predictable databases for effective selection and development.

Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products

Covering: 1995 to 2023Advances in bioanalytical methods, particularly mass spectrometry, have provided valuable molecular insights into the mechanisms of life. Non-targeted metabolomics aims to detect and (relatively) quantify all observable small molecules present in a biological system. By comparing small molecule abundances between different conditions or timepoints in a biological system, researchers can generate new hypotheses and begin to understand causes of observed phenotypes. Functional metabolomics aims to investigate the functional roles of metabolites at the scale of the metabolome. However, most functional metabolomics studies rely on indirect measurements and correlation analyses, which leads to ambiguity in the precise definition of functional metabolomics. In contrast, the field of natural products has a history of identifying the structures and bioactivities of primary and specialized metabolites. Here, we propose to expand and reframe functional metabolomics by integrating concepts from the fields of natural products and chemical biology. We highlight emerging functional metabolomics approaches that shift the focus from correlation to physical interactions, and we discuss how this allows researchers to uncover causal relationships between molecules and phenotypes.

Synthetic Peptides: Promising Modalities for the Targeting of Disease-Related Nucleic Acids

DNA and RNA play pivotal roles in life processes by storing and transferring genetic information, modulating gene expression, and contributing to essential cellular machinery such as ribosomes. Dysregulation and mutations in nucleic acid-related processes are implicated in numerous diseases. Despite the critical impact on health of nucleic acid mutations or dysregulation, therapeutic compounds addressing these biomolecules remain limited. Peptides have emerged as a promising class of molecules for biomedical research, offering potential solutions for challenging drug targets. This review focuses on the use of synthetic peptides to target disease-related nucleic acids. We discuss examples of peptides targeting double-stranded DNA, including the clinical candidate Omomyc, and compounds designed for regulatory G-quadruplexes. Further, we provide insights into both library-based screenings and the rational design of peptides to target regulatory human RNA scaffolds and viral RNAs, emphasizing the potential of peptides in addressing nucleic acid-related diseases.

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.

Precision Sequence-Defined Polymers: From Sequencing to Biological Functions

Precise sequence-defined polymers (SDPs) with uniform chain-to-chain structure including chain length, unit sequence, and end functionalities represent the pinnacle of sophistication in the realm of polymer science. For example, the absolute control over the unit sequence of SDPs allows for the bottom-up design of polymers with hierarchical microstructures and functions. Accompanied with the development of synthetic techniques towards precision SDPs, the decoding of SDP sequences and construction of advanced functions irreplaceable by other synthetic materials is of central importance. In this Minireview, we focus on recent advances in SDP sequencing techniques including tandem mass spectrometry (MS), chemically assisted primary MS, as well as other non-destructive sequencing methods such as nuclear magnetic resonance (NMR) spectroscopy, circular dichroism (CD), and nanopore sequencing. Additionally, we delve into the promising prospects of SDP functions in the area of cutting-edge biological research. Topics of exploration include gene delivery systems, the development of hybrid materials combining SDPs and nucleic acids, protein recognition and regulation, as well as the interplay between chirality and biological functions. A brief outlook towards the future directions of SDPs is also presented.

Cyclic Peptides for Drug Development

Cyclic peptides are fascinating molecules abundantly found in nature and exploited as molecular format for drug development as well as other applications, ranging from research tools to food additives. Advances in peptide technologies made over many years through improved methods for synthesis and drug development have resulted in a steady stream of new drugs, with an average of around one cyclic peptide drug approved per year. Powerful technologies for screening random peptide libraries, and de novo generating ligands, have enabled the development of cyclic peptide drugs independent of naturally derived molecules and now offer virtually unlimited development opportunities. In this review, we feature therapeutically relevant cyclic peptides derived from nature and discuss the unique properties of cyclic peptides, the enormous technological advances in peptide ligand development in recent years, and current challenges and opportunities for developing cyclic peptides that address unmet medical needs.