A Convolutional Neural Network-Based Approach for the Rapid Annotation of Molecularly Diverse Natural Products

Abstract

This report describes the first application of the novel NMR-based machine learning tool "Small Molecule Accurate Recognition Technology" (SMART 2.0) for mixture analysis and subsequent accelerated discovery and characterization of new natural products. The concept was applied to the extract of a filamentous marine cyanobacterium known to be a prolific producer of cytotoxic natural products. This environmental Symploca extract was roughly fractionated, and then prioritized and guided by cancer cell cytotoxicity, NMR-based SMART 2.0, and MS²-based molecular networking. This led to the isolation and rapid identification of a new chimeric swinholide-like macrolide, symplocolide A, as well as the annotation of swinholide A, samholides A-I, and several new derivatives. The planar structure of symplocolide A was confirmed to be a structural hybrid between swinholide A and luminaolide B by 1D/2D NMR and LC-MS² analysis. A second example applies SMART 2.0 to the characterization of structurally novel cyclic peptides, and compares this approach to the recently appearing "atomic sort" method. This study exemplifies the revolutionary potential of combined traditional and deep learning-assisted analytical approaches to overcome longstanding challenges in natural products drug discovery.

Figure 1.

(a,h) Cytotoxicity assay against H460 lung cancer cells reveals VLC fraction H (a) and subfractions H4–H6 (h), respectively, as the most potent at 1 and 10 μg/mL (see also Figure S3). (b) Digitized HSQC spectrum of most cytotoxic fraction H. (c) t-SNE embedding of the HSQC spectrum into the 180 dimensional cluster space of 53,076 nodes (reduced here to 10,000 for clarity) representing natural products trained on the basis of their ¹H-¹³C-HSQC spectra. Swinholide A and its closest neighbors are highlighted (purple nodes, black rectangle). (d) SMART 2.0 results (top12 structures based on cosine similarity score) of fraction H suggests that it contains macrolides from the swinholide class. (e) Molecular networking analysis of cyanobacterial subfractions H3–H7. Highlighted cluster with putatively new m/z feature 1395.9 (H4, green nodes). (f) MS² analysis of swinholide A and comparison to (g) the feature detected as new m/z 1395.9 (symplocolide A) show fragmentation patterns that reveal structural insights (same fragment highlighted in green, distinctive fragment highlighted in blue) for both compounds.

Figure 2.

Comparison of the ‘atomic sort’ method and SMART 2.0 to detect novel/rare structural elements. (a) HSQC correlations suggesting structural novelty by ‘atomic sort’ method (highlighted in blue). (b) Top two results of SMART 2.0 analysis querying cyclomarin A (experimental HSQC data from reference) to detect related compounds that include rare structural moieties (highlighted in red, purple).

Scheme 1.

Structures of symplocolide A (1), luminaolide (2), luminaolide B (3), swinholide A (4), and samholides A-I (5–13). All compounds except 2 and 3 were detected in this study.