SIMILE enables alignment of tandem mass spectra with statistical significance

The changing metabolic landscape of bile acids - keys to metabolism and immune regulation

Bile acids regulate nutrient absorption and mitochondrial function, they establish and maintain gut microbial community composition and mediate inflammation, and they serve as signalling molecules that regulate appetite and energy homeostasis. The observation that there are hundreds of bile acids, especially many amidated bile acids, necessitates a revision of many of the classical descriptions of bile acids and bile acid enzyme functions. For example, bile salt hydrolases also have transferase activity. There are now hundreds of known modifications to bile acids and thousands of bile acid-associated genes, especially when including the microbiome, distributed throughout the human body (for example, there are >2,400 bile salt hydrolases alone). The fact that so much of our genetic and small-molecule repertoire, in both amount and diversity, is dedicated to bile acid function highlights the centrality of bile acids as key regulators of metabolism and immune homeostasis, which is, in large part, communicated via the gut microbiome.

ModiFinder: Tandem Mass Spectral Alignment Enables Structural Modification Site Localization

Untargeted tandem mass spectrometry (MS/MS) has become a high-throughput method to measure small molecules in complex samples. One key goal is the transformation of these MS/MS spectra into chemical structures. Computational techniques such as MS/MS library search have enabled the reidentification of known compounds. Analog library search and molecular networking extend this identification to unknown compounds. While there have been advancements in metrics for the similarity of MS/MS spectra of structurally similar compounds, there is still a lack of automated methods to provide site specific information about structural modifications. Here we introduce ModiFinder which leverages the alignment of peaks in MS/MS spectra between structurally related known and unknown small molecules. Specifically, ModiFinder focuses on shifted MS/MS fragment peaks in the MS/MS alignment. These shifted peaks putatively represent substructures of the known molecule that contain the site of the modification. ModiFinder synthesizes this information together and scores the likelihood for each atom in the known molecule to be the modification site. We demonstrate in this manuscript how ModiFinder can effectively localize modifications which extends the capabilities of MS/MS analog searching and molecular networking to accelerate the discovery of novel compounds.

TransExION: a transformer based explainable similarity metric for comparing IONS in tandem mass spectrometry

Small molecule identification is a crucial task in analytical chemistry and life sciences. One of the most commonly used technologies to elucidate small molecule structures is mass spectrometry. Spectral library search of product ion spectra (MS/MS) is a popular strategy to identify or find structural analogues. This approach relies on the assumption that spectral similarity and structural similarity are correlated. However, popular spectral similarity measures, usually calculated based on identical fragment matches between the MS/MS spectra, do not always accurately reflect the structural similarity. In this study, we propose TransExION, a Transformer based Explainable similarity metric for IONS. TransExION detects related fragments between MS/MS spectra through their mass difference and uses these to estimate spectral similarity. These related fragments can be nearly identical, but can also share a substructure. TransExION also provides a post-hoc explanation of its estimation, which can be used to support scientists in evaluating the spectral library search results and thus in structure elucidation of unknown molecules. Our model has a Transformer based architecture and it is trained on the data derived from GNPS MS/MS libraries. The experimental results show that it improves existing spectral similarity measures in searching and interpreting structural analogues as well as in molecular networking. SCIENTIFIC CONTRIBUTION: We propose a transformer-based spectral similarity metrics that improves the comparison of small molecule tandem mass spectra. We provide a post hoc explanation that can serve as a good starting point for unknown spectra annotation based on database spectra.

The hidden treasures in endophytic fungi: a comprehensive review on the diversity of fungal bioactive metabolites, usual analytical methodologies, and applications

This review provides a comprehensive overview of the key aspects of the natural metabolite production by endophytic fungi, which has attracted significant attention due to its diverse biological activities and wide range of applications. Synthesized by various fungal species, these metabolites encompass compounds with therapeutic, agricultural, and commercial significance. We delved into strategies and advancements aimed at optimizing fungal metabolite production. Fungal cultivation, especially by Aspergillus, Penicillium, and Fusarium, plays a pivotal role in metabolite biosynthesis, and researchers have explored both submerged and solid-state cultivation processes to harness the full potential of fungal species. Nutrient optimization, pH, and temperature control are critical factors in ensuring high yields of the targeted bioactive metabolites especially for scaling up processes. Analytical methods that includes High-Performance Liquid Chromatography (HPLC), Liquid Chromatography-Mass Spectrometry (LC-MS), Gas Chromatography-Mass Spectrometry (GC-MS), Nuclear Magnetic Resonance (NMR), and Mass Spectrometry (MS), are indispensable for the identification and quantification of the compounds. Moreover, genetic engineering and metabolic pathway manipulation have emerged as powerful tools to enhance metabolite production and develop novel fungal strains with increased yields. Regulation and control mechanisms at the genetic, epigenetic, and metabolic levels are explored to fine-tune the biosynthesis of fungal metabolites. Ongoing research aims to overcome the complexity of the steps involved to ensure the efficient production and utilization of fungal metabolites.

Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics

Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of MS/MS spectra originating from published untargeted metabolomics experiments. Entries in this library, or "suspects," were derived from unannotated spectra that could be linked in a molecular network to an annotated spectrum. Annotations were propagated to unknowns based on structural relationships to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimer's brain phenotype. The nearest neighbor suspect spectral library is openly available for download or for data analysis through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.

BLINK enables ultrafast tandem mass spectrometry cosine similarity scoring

Metabolomics has a long history of using cosine similarity to match experimental tandem mass spectra to databases for compound identification. Here we introduce the Blur-and-Link (BLINK) approach for scoring cosine similarity. By bypassing fragment alignment and simultaneously scoring all pairs of spectra using sparse matrix operations, BLINK is over 3000 times faster than MatchMS, a widely used loop-based alignment and scoring implementation. Using a similarity cutoff of 0.7, BLINK and MatchMS had practically equivalent identification agreement, and greater than 99% of their scores and matching ion counts were identical. This performance improvement can enable calculations to be performed that would typically be limited by time and available computational resources.

A Structure-Guided Molecular Network Strategy for Global Untargeted Metabolomics Data Annotation

Large-scale metabolite annotation is a bottleneck in untargeted metabolomics. Here, we present a structure-guided molecular network strategy (SGMNS) for deep annotation of untargeted ultra-performance liquid chromatography-high resolution mass spectrometry (MS) metabolomics data. Different from the current network-based metabolite annotation method, SGMNS is based on a global connectivity molecular network (GCMN), which was constructed by molecular fingerprint similarity of chemical structures in metabolome databases. Neighbor metabolites with similar structures in GCMN are expected to produce similar spectra. Network annotation propagation of SGMNS is performed using known metabolites as seeds. The experimental MS/MS spectra of seeds are assigned to corresponding neighbor metabolites in GCMN as their "pseudo" spectra; the propagation is done by searching predicted retention times, MS1, and "pseudo" spectra against metabolite features in untargeted metabolomics data. Then, the annotated metabolite features were used as new seeds for annotation propagation again. Performance evaluation of SGMNS showed its unique advantages for metabolome annotation. The developed method was applied to annotate six typical biological samples; a total of 701, 1557, 1147, 1095, 1237, and 2041 metabolites were annotated from the cell, feces, plasma (NIST SRM 1950), tissue, urine, and their pooled sample, respectively, and the annotation accuracy was >83% with RSD <2%. The results show that SGMNS fully exploits the chemical space of the existing metabolomes for metabolite deep annotation and overcomes the shortcoming of insufficient reference MS/MS spectra.

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.

BUDDY: molecular formula discovery via bottom-up MS/MS interrogation

A substantial fraction of metabolic features remains undetermined in mass spectrometry (MS)-based metabolomics, and molecular formula annotation is the starting point for unraveling their chemical identities. Here we present bottom-up tandem MS (MS/MS) interrogation, a method for de novo formula annotation. Our approach prioritizes MS/MS-explainable formula candidates, implements machine-learned ranking and offers false discovery rate estimation. Compared with the mathematically exhaustive formula enumeration, our approach shrinks the formula candidate space by 42.8% on average. Method benchmarking on annotation accuracy was systematically carried out on reference MS/MS libraries and real metabolomics datasets. Applied on 155,321 recurrent unidentified spectra, our approach confidently annotated >5,000 novel molecular formulae absent from chemical databases. Beyond the level of individual metabolic features, we combined bottom-up MS/MS interrogation with global optimization to refine formula annotations while revealing peak interrelationships. This approach allowed the systematic annotation of 37 fatty acid amide molecules in human fecal data. All bioinformatics pipelines are available in a standalone software, BUDDY ( https://github.com/HuanLab/BUDDY ).

Artificial intelligence in microbial natural product drug discovery: current and emerging role

Covering: up to the end of 2022Microorganisms are exceptional sources of a wide array of unique natural products and play a significant role in drug discovery. During the golden era, several life-saving antibiotics and anticancer agents were isolated from microbes; moreover, they are still widely used. However, difficulties in the isolation methods and repeated discoveries of the same molecules have caused a setback in the past. Artificial intelligence (AI) has had a profound impact on various research fields, and its application allows the effective performance of data analyses and predictions. With the advances in omics, it is possible to obtain a wealth of information for the identification, isolation, and target prediction of secondary metabolites. In this review, we discuss drug discovery based on natural products from microorganisms with the help of AI and machine learning.

The critical role that spectral libraries play in capturing the metabolomics community knowledge

BACKGROUND

Spectral library searching is currently the most common approach for compound annotation in untargeted metabolomics. Spectral libraries applicable to liquid chromatography mass spectrometry have grown in size over the past decade to include hundreds of thousands to millions of mass spectra and tens of thousands of compounds, forming an essential knowledge base for the interpretation of metabolomics experiments.

AIM OF REVIEW

We describe existing spectral library resources, highlight different strategies for compiling spectral libraries, and discuss quality considerations that should be taken into account when interpreting spectral library searching results. Finally, we describe how spectral libraries are empowering the next generation of machine learning tools in computational metabolomics, and discuss several opportunities for using increasingly accessible large spectral libraries.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review focuses on the current state of spectral libraries for untargeted LC-MS/MS based metabolomics. We show how the number of entries in publicly accessible spectral libraries has increased more than 60-fold in the past eight years to aid molecular interpretation and we discuss how the role of spectral libraries in untargeted metabolomics will evolve in the near future.

Comparison of Cosine, Modified Cosine, and Neutral Loss Based Spectrum Alignment For Discovery of Structurally Related Molecules

Spectrum alignment of tandem mass spectrometry (MS/MS) data using the modified cosine similarity and subsequent visualization as molecular networks have been demonstrated to be a useful strategy to discover analogs of molecules from untargeted MS/MS-based metabolomics experiments. Recently, a neutral loss matching approach has been introduced as an alternative to MS/MS-based molecular networking with an implied performance advantage in finding analogs that cannot be discovered using existing MS/MS spectrum alignment strategies. To comprehensively evaluate the scoring properties of neutral loss matching, the cosine similarity, and the modified cosine similarity, similarity measures of 955 228 peptide MS/MS spectrum pairs and 10 million small molecule MS/MS spectrum pairs were compared. This comparative analysis revealed that the modified cosine similarity outperformed neutral loss matching and the cosine similarity in all cases. The data further indicated that the performance of MS/MS spectrum alignment depends on the location and type of the modification, as well as the chemical compound class of fragmented molecules.