Biology subjects

Ross, D. H.

Publications and source records attributed to Ross, D. H..

7 recordsLinked to original sources

bioRxiv2026

Reference-free compound identification using computational prediction of molecular properties and multi-dimensional spectrometric measurements: a fentanyl case study

Mass spectrometry is used to identify chemicals to which humans are exposed, but it cannot directly determine molecular structures. Instead, structures are inferred by matching experimental spectra to libraries of spectra constructed from analyses of pure reference compounds. However, the chemical space of human exposures far exceeds the amount of experimental library spectra. Here, we evaluate a reference-free strategy for confident identification of unknown molecules. Using fentanyl as a case study, we created a suspect library of over 1 billion computationally predicted fentanyl analogs and predicted molecular properties through machine learning, molecular dynamics, and density functional theory. Multi-dimensional spectra from a blinded analysis of a mock fentanyl tablet were matched with the predicted library, yielding an average of three candidate structures per measured analog, with six exact identifications. This work emphasizes the promise of reference-free molecular measurements for assessing human exposure by merging computational predictions with high-dimensional measurements.

scientific communication and education↗

bioRxiv2026

VaLPAS: Leveraging variation in experimental multi-omics data to elucidate protein function

MotivationDespite continuing advances in sequencing and computational function determination, large parts of the studied gene, protein, and metabolite space remain functionally undetermined. Most function assignment is driven by homology searches and annotation transfer from known and extensively studied proteins but often fails to leverage available experimental omics data generated via technologies like mass-spectrometry. ResultsThe VaLPAS (Variation-Leveraged Phenomic Association Screen) framework is available as a Python package and provides a user-friendly platform for calculation of associations between expression patterns of genes or proteins in multi-omic datasets based on various statistical and learning methods. The goal of this approach is to shed light on the functional dark matter of protein space by elucidating previously unknown functions of molecules using guilt by association with molecules of known function. We present results demonstrating the utility of VaLPAS to identify high-confidence predictions for a subset of genes/proteins of unknown function in a previously published multi-omics dataset from the oleaginous yeast, Rhodotorula toruloides. AvailabilityVaLPAS is written in Python. The code is hosted on github (https://github.com/PNNL-Predictive-Phenomics/valpas/).

bioinformatics↗

bioRxiv2026

A comprehensive reference database to support untargeted metabolomics in Pseuudomonas putida

Pseudomonas putida strain KT2440 is a crucial model organism for synthetic biology and bioengineering applications, yet there currently exists no comprehensive metabolomics database comparable to those available for other model organisms. This gap hinders the use of untargeted metabolomics for exploratory analyses in this system. We developed the P. putida metabolome reference database (PPMDB v1) to address this limitation by consolidating metabolite information from multiple sources and expanding coverage through computational predictions. The database was constructed by curating metabolites from BioCyc, BiGG, and other literature sources, then computationally expanding this collection using BioTransformer environmental transformation predictions to generate additional predicted metabolites. We enhanced the databases utility for molecular annotation in metabolomics studies by incorporating analytical properties including collision cross-sections, tandem mass spectra, and gas-phase infrared spectra. These analytical properties were gathered from existing measurement data or predicted using computational tools. We further augmented the database through inclusion of reaction information and pathway annotations, facilitating biological interpretation of metabolomics data. This publicly available resource fills a critical gap in P. putida research infrastructure, supporting metabolite annotation and biological interpretation in untargeted metabolomics studies and enabling in-depth exploratory analyses of this important synthetic biology platform at the molecular level. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/713193v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@c8828forg.highwire.dtl.DTLVardef@1f3a5c5org.highwire.dtl.DTLVardef@1084535org.highwire.dtl.DTLVardef@1f7ca4a_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Empirically establishing drug exposure records directly from untargeted metabolomics data

Despite extensive efforts, extracting information on medication exposure from clinical records remains challenging. To complement this approach, we developed the tandem mass spectrometry (MS/MS) based GNPS Drug Library. This resource integrates MS/MS data for drugs and their metabolites/analogs with controlled vocabularies on exposure sources, pharmacologic classes, therapeutic indications, and mechanisms of action. It enables direct analysis of drug exposure and metabolism from untargeted metabolomics data independent of clinical records. Our library facilitates stratification of individuals in clinical studies based on the empirically detected medications, exemplified by drug-dependent microbiota-derived N-acyl lipid changes in a cohort with human immunodeficiency virus. The GNPS Drug Library holds potential for broader applications in drug discovery and precision medicine.

bioinformatics↗

bioRxiv2024

Introducing identification probability for automated and transferable assessment of metabolite identification confidence in metabolomics and related studies

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence - the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in context of the chemical space being considered, are easily automated, or are transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a reference library or chemical space that match to an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multi-property reference libraries constructed from the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

biochemistry↗

bioRxiv2023

Elucidating the Impact of Bacterial Lipases, Human Serum Albumin, and FASII Inhibition on the Utilization of Exogenous Fatty Acids by Staphylococcus aureus

Staphylococcus aureus only synthesizes straight-chain or branched-chain saturated fatty acids (SCFAs or BCFAs) via the type II fatty acid synthesis (FASII) pathway, but as a highly adaptive pathogen, S. aureus can also utilize host-derived exogenous fatty acids (eFAs), including SCFAs and unsaturated fatty acids (UFAs). S. aureus secretes three lipases, Geh, sal1, and SAUSA300_0641, which could perform the function of releasing fatty acids from host lipids. Once released, the FAs are phosphorylated by the fatty acid kinase, FakA, and incorporated into the bacterial lipids. In this study, we determined the substrate specificity of S. aureus secreted lipases, the effect of human serum albumin (HSA) on eFA incorporation, and the effect of FASII inhibitor, AFN-1252, on eFA incorporation using comprehensive lipidomics. When grown with major donors of fatty acids, cholesteryl esters (CEs) and triglycerides (TGs), Geh was found to be the primary lipase responsible for hydrolyzing CEs, but other lipases could compensate for the function of Geh in hydrolyzing TGs. Lipidomics showed that eFAs were incorporated into all major S. aureus lipid classes and that fatty acid-containing HSA can serve as a source of eFAs. Furthermore, S. aureus grown with UFAs displayed decreased membrane fluidity and increased production of reactive oxygen species (ROS). Exposure to AFN-1252 enhanced UFAs in the bacterial membrane, even without a source of eFAs, indicating a FASII pathway modification. Thus, the incorporation of eFAs alters the S. aureus lipidome, membrane fluidity, and ROS formation, which could affect host-pathogen interactions and susceptibility to membrane-targeting antimicrobials. IMPORTANCEIncorporation of host-derived exogenous fatty acids (eFAs), particularly unsaturated fatty acids (UFAs), by Staphylococcus aureus could affect the bacterial membrane fluidity and susceptibility to antimicrobials. In this work, we found that Geh is the primary lipase hydrolyzing cholesteryl esters and, to a less extent, triglycerides (TGs) and that human serum albumin (HSA) could serve as a buffer of eFAs, where low levels of HSA facilitate the utilization of eFAs, but high levels of HSA inhibit it. The fact that the FASII inhibitor, AFN-1252, leads to an increase in UFA content even in the absence of eFA suggests that membrane property modulation is part of its mechanism of action. Thus, Geh and/or the FASII system look to be promising targets to enhance S. aureus killing in a host environment by restricting eFA utilization or modulating membrane property, respectively.

microbiology↗

bioRxiv2021

High-Throughput Measurement and Machine Learning-Based Prediction of Collision Cross Sections for Drugs and Drug Metabolites

Drug metabolite identification is a bottleneck of drug metabolism studies. Ion mobility-mass spectrometry (IM-MS) enables the measurement of collision cross section (CCS), a unique physical property related to an ions gas-phase size and shape, which can be used to increase the confidence in the identification of unknowns. A current limitation to the application of IM-MS to the identification of drug metabolites is the lack of reference CCS values. In this work, we present the production of a large-scale database of drug and drug metabolite CCS values, assembled using high-throughput in vitro drug metabolite generation and a rapid IM-MS analysis with automated data processing. Subsequently, we used this database to train a machine learning-based CCS prediction model, employing a combination of conventional 2D molecular descriptors and novel 3D descriptors. This novel prediction model enables the prediction of different CCS values for different protomers, conformers, and positional isomers for the first time.

bioinformatics↗