ACD/Structure Elucidator: 20 Years in the History of Development

Isolation and Bioactivity of Natural Products from Streptomyces sp. MA37

The isolation and characterization of bioactive metabolites from Streptomyces species continue to represent a vital area of research, given their potential in natural product drug discovery. In this study, we characterize a new siderophore called legonoxamine I, together with a known compound, streptimidone, from the talented soil bacterium Streptomyces sp. MA37, using chromatographic techniques and spectroscopic analysis. Legonoxamine I is a new holo-siderophore, which is likely to be a derailed product from the biosynthetic pathway of legonoxamine A. We also demonstrate that legonoxamine A possesses potent anticancer activity (IC50 = 2.2 µM), exhibiting a remarkable ~30-fold increase in potency against MCF-7 ATCC HTB-22 breast cancer cells compared to desferrioxamine B, a structural analogue of legonoxamine A (IC50 = 61.1 µM). Comparing the structural difference between legonoxamine A and desferrioxamine B, it is deduced that the phenylacetyl moiety in legonoxamine A may have contributed significantly to its enhanced potency. Our findings contribute to the growing library of Streptomyces-derived metabolites and underscore the genus' potential as a promising source of lead compounds.

Shifting paradigms: The promise of allosteric inhibitors against dengue virus protease

Dengue, a mosquito-borne viral infection caused by the dengue virus (DENV), is a global health challenge. Annually, approximately 400 million cases are reported worldwide, signaling a persistent upward trend from previous years and projected a manifold increase in the future. There is a growing need for innovative and integrated approaches aimed at effective disease management. In this regard, scientific efforts are underway to find a new antiviral inhibitor that is desperately needed due to the growing prevalence of dengue, along with inadequate vector control and few vaccinations. The NS2B-NS3 protease complex within the DENV genome holds significant importance, making it an attractive target for potential interventions. Many competitive inhibitors are not clinically relevant even after extensive study, and these early hits are often not followed up to viable leads. The current focus is on exploring alternative target sites for developing effective anti-dengue compounds, resulting in the identification of various allosteric sites in recent years. While previous reviews have extensively covered active site inhibitors, this is to the best of our knowledge the first comprehensive review discussing the allosteric sites and allosteric inhibitors in greater detail. The present survey may assist researchers in understanding the key aspects and identifying new antagonists targeting the allosteric site of DENV protease.

Enhancing Chemical Reaction Monitoring with a Deep Learning Model for NMR Spectra Image Matching to Target Compounds

In the synthetic laboratory, researchers typically rely on nuclear magnetic resonance (NMR) spectra to elucidate structures of synthesized products and confirm whether they match the desired target compounds. As chemical synthesis technology evolves toward intelligence and continuity, efficient computer-assisted structure elucidation (CASE) techniques are required to replace time-consuming manual analysis and provide the necessary speed. However, current CASE methods typically aim to derive precise chemical structures from spectroscopic data, yet they suffer from drawbacks such as low accuracy, high computational cost, and reliance on chemical libraries. In meticulously designed chemical synthesis reactions, researchers prioritize confirming the attainment of the target product based on NMR spectra, rather than focusing on identifying the specific product obtained. For this purpose, we innovatively developed a binary classification model, termed as MatCS, to directly predict the relationship between NMR spectra image (including 1H NMR and 13C NMR) and the molecular structure of the target compound. After evaluating various feature extraction methods, MatCS employs a combination of the Graph Attention Networks and Graph Convolutional Networks to learn the structural features of molecular graphs and the pretrained ResNet101 network with a Convolutional Block Attention Module to extract features from NMR spectra images. The results show that on a challenging Testsim data set, which poses difficulty in distinguishing spectra of similar molecular structures, MatCS achieves comprehensive evaluation metrics with an F1-score of 0.81 and an AUC value of 0.87. Simultaneously, it exhibited commendable performance on an external SDBS data set containing experimental NMR spectra, showcasing substantial potential for structural verification tasks in real automated chemical synthesis.

Accraspiroketides A-B, Phenylnaphthacenoid-Derived Polyketides with Unprecedented [6 + 6+6 + 6] + [5 + 5] Spiro-Architecture

Two novel polyketides, accraspiroketides A (1) and B (2), which feature unprecedented [6 + 6+6 + 6] + [5 + 5] spiro chemical architectures, were isolated from Streptomyces sp. MA37 ΔaccJ mutant strain. Compounds 1-2 exhibit excellent activity against Gram-positive bacteria (MIC = 1.5-6.3 μg/mL). Notably, 1 and 2 have superior activity against clinically isolated Enterococcus faecium K60-39 (MIC = 4.0 μg/mL and 4.7 μg/mL, respectively) than ampicillin (MIC = 25 μg/mL).

Overcoming NMR line broadening of nitrogen containing compounds: A simple solution

This study presents a straightforward solution to the challenge of elucidating the structures of nitrogen containing compounds undergoing isomerization. When spectral line broadening occurs related to isomerization, be it prototropic tautomerism or bond rotations, this poses a significant obstacle to structural elucidation. By adding acids, we demonstrate a simple approach to overcome this issue and effectively sharpen NMR signals for acid stable prototropic tautomers as well as the conformational isomers containing a morpholine or piperazine ring.

Design of rigid protein-protein interaction inhibitors enables targeting of undruggable Mcl-1

The structure-based design of small-molecule inhibitors targeting protein-protein interactions (PPIs) remains a huge challenge as the drug must bind typically wide and shallow protein sites. A PPI target of high interest for hematological cancer therapy is myeloid cell leukemia 1 (Mcl-1), a prosurvival guardian protein from the Bcl-2 family. Despite being previously considered undruggable, seven small-molecule Mcl-1 inhibitors have recently entered clinical trials. Here, we report the crystal structure of the clinical-stage inhibitor AMG-176 bound to Mcl-1 and analyze its interaction along with clinical inhibitors AZD5991 and S64315. Our X-ray data reveal high plasticity of Mcl-1 and a remarkable ligand-induced pocket deepening. Nuclear Magnetic Resonance (NMR)-based free ligand conformer analysis demonstrates that such unprecedented induced fit is uniquely achieved by designing highly rigid inhibitors, preorganized in their bioactive conformation. By elucidating key chemistry design principles, this work provides a roadmap for targeting the largely untapped PPI class more successfully.

Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation

Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.

Enhancing Efficiency of Natural Product Structure Revision: Leveraging CASE and DFT over Total Synthesis

Natural products remain one of the major sources of coveted, biologically active compounds. Each isolated compound undergoes biological testing, and its structure is usually established using a set of spectroscopic techniques (NMR, MS, UV-IR, ECD, VCD, etc.). However, the number of erroneously determined structures remains noticeable. Structure revisions are very costly, as they usually require extensive use of spectroscopic data, computational chemistry, and total synthesis. The cost is particularly high when a biologically active compound is resynthesized and the product is inactive because its structure is wrong and remains unknown. In this paper, we propose using Computer-Assisted Structure Elucidation (CASE) and Density Functional Theory (DFT) methods as tools for preventive verification of the originally proposed structure, and elucidation of the correct structure if the original structure is deemed to be incorrect. We examined twelve real cases in which structure revisions of natural products were performed using total synthesis, and we showed that in each of these cases, time-consuming total synthesis could have been avoided if CASE and DFT had been applied. In all described cases, the correct structures were established within minutes of using the originally published NMR and MS data, which were sometimes incomplete or had typos.

Challenging Structure Elucidation of Lumnitzeralactone, an Ellagic Acid Derivative from the Mangrove Lumnitzera racemosa

The previously undescribed natural product lumnitzeralactone (1), which represents a derivative of ellagic acid, was isolated from the anti-bacterial extract of the Indonesian mangrove species Lumnitzera racemosa Willd. The structure of lumnitzeralactone (1), a proton-deficient and highly challenging condensed aromatic ring system, was unambiguously elucidated by extensive spectroscopic analyses involving high-resolution mass spectrometry (HRMS), 1D ¹H and ¹³C nuclear magnetic resonance spectroscopy (NMR), and 2D NMR (including 1,1-ADEQUATE and 1,n-ADEQUATE). Determination of the structure was supported by computer-assisted structure elucidation (CASE system applying ACD-SE), density functional theory (DFT) calculations, and a two-step chemical synthesis. Possible biosynthetic pathways involving mangrove-associated fungi have been suggested.

Sherlock-A Free and Open-Source System for the Computer-Assisted Structure Elucidation of Organic Compounds from NMR Data

The structure elucidation of small organic molecules (<1500 Dalton) through 1D and 2D nuclear magnetic resonance (NMR) data analysis is a potentially challenging, combinatorial problem. This publication presents Sherlock, a free and open-source Computer-Assisted Structure Elucidation (CASE) software where the user controls the chain of elementary operations through a versatile graphical user interface, including spectral peak picking, addition of automatically or user-defined structure constraints, structure generation, ranking and display of the solutions. A set of forty-five compounds was selected in order to illustrate the new possibilities offered to organic chemists by Sherlock for improving the reliability and traceability of structure elucidation results.

Drug discovery inspired by bioactive small molecules from nature

Natural products (NPs) have greatly contributed to the development of novel treatments for human diseases such as cancer, metabolic disorders, and infections. Compared to synthetic chemical compounds, primary and secondary metabolites from medicinal plants, fungi, microorganisms, and our bodies are promising resources with immense chemical diversity and favorable properties for drug development. In addition to the well-validated significance of secondary metabolites, endogenous small molecules derived from central metabolism and signaling events have shown great potential as drug candidates due to their unique metabolite-protein interactions. In this short review, we highlight the values of NPs, discuss recent scientific and technological advances including metabolomics tools, chemoproteomics approaches, and artificial intelligence-based computation platforms, and explore potential strategies to overcome the current challenges in NP-driven drug discovery.