About

Nuno Bandeira, Ph.D., is an Associate Professor at the Skaggs School of Pharmacy and Pharmaceutical Sciences and the Executive Director of the Center for Computational Mass Spectrometry. His research focuses on developing novel mass spectrometry algorithms for the discovery and characterization of biomarker proteins, post-translational modifications, and protein-protein interactions. His laboratory's work encompasses de novo sequencing of complete proteins, detailed analysis of post-translational modifications in signaling pathways and health/disease, sequencing and characterization of non-linear peptidic natural products, high-throughput analysis of structural proteomics and protein interactions, and identification of neuropeptides and endogenous peptides. These efforts aim to enhance proteomics discovery pipelines and facilitate the development of new drug therapeutics. Dr. Bandeira holds a B.S. in Computer Science, an M.Sc. in Applied Artificial Intelligence from the New University of Lisbon, Portugal, and a Ph.D. in Computer Science and Bioinformatics from the University of California, San Diego. His academic achievements include the Human Proteome Organization Young Investigator Award and the Ph.D. Dissertation Award from UCSD. He has served as the Program Committee chair for the 2010 RECOMB Satellite conference on Computational Proteomics and is involved in leadership roles such as Executive Director of the NIH/NCRR Center for Computational Mass Spectrometry. His contributions include the development of the Shotgun Protein Sequencing paradigm, which has become a central tool for de novo sequencing of monoclonal antibodies and toxins, as well as algorithms for sequencing non-ribosomal cyclic peptides. His work has significant implications for disease biomarker discovery, pathogen infection analysis, and drug response characterization.

Research topics

• Computer Science
• Biology
• Computational biology
• Bioinformatics
• Genetics
• Data science
• Chemistry
• Chromatography
• Engineering
• Ecology
• Nanotechnology
• Biochemistry
• Materials science

Selected publications

• Benchmarking Peptide Spectral Library Search

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-01-09

  articleOpen accessSenior authorCorresponding

  ABSTRACT Spectral library search (SLS) is a major approach for peptide identification from tandem mass spectrometry data, with performance depending substantially on the accuracy of the underlying Spectrum-Spectrum Matching (SSM) scoring functions. However, detailed comparative studies remain limited by the absence of comprehensive benchmark datasets. We propose new methods to build SSM scoring functions benchmarks and construct a benchmark dataset with (i) eight query spectrum sets with varying noise level for 476,063 precursors, and (ii) three spectral libraries with experimental, de-noised and predicted spectra for 3,065,819 precursors. We evaluate common spectrum preprocessing scenarios and SSM scoring functions, including SpectraST and EntropyScore. Results revealed this remains an important open problem, with the best recall for still assessed to be poor at just ∼70%, with SpectraST performing best for spectra with little-to-no noise, while JS-divergence showed superior noise resistance. Conversely, Cosine and Entropy score performed substantially worse, with Projected-Cosine performing especially poorly in most cases, with overall performance and relative ranking depending quite significantly on the minimum number of matching peaks. The benchmark dataset (MSV000095946/PXD056205) supports testing and development of new SSM scoring functions and the proposed benchmark construction approach provides an extensible foundation for additional types of SSM evaluation.

  PublisherOA PDFDOI

• The 2025 Report on the Human Proteome from the HUPO Human Proteome Project

  Journal of Proteome Research · 2026-01-05 · 1 citations

  article

  The HUPO Human Proteome Project (HPP) aims to complete the human protein parts list by detecting evidence of expression and of function for all proteins in the human proteome, and make proteomics an integral part of multiomics studies of health and disease. Here we describe the state of the 2025 HPP reference proteome of 19,435 proteins, based on GENCODE v48, UniProtKB 2025_03, Human Protein Atlas 24, MassIVE-KB 2023, and PeptideAtlas 2025-01. We evaluate the progress in the past year, with 93.6% of the proteome detected, and examine the proteins that have not yet been detected to determine where further progress can be made. We also evaluate the progress in determining at least one function for every protein in the HPP target list, finding an increase of 288 proteins in the highest category (FE1) to 5562. Finally, we provide highlights from 12 Biology/Disease-based HPP initiatives, HPP resource pillars, and π-HuB.

  PublisherDOI

• Mo_xSe_y clusters

  ioChem-BD Computational Chemistry Datasets · 2026-05-05

  datasetOpen access1st authorCorresponding
  PublisherDOI

• Alkamines reveal a hidden layer of steroid and drug metabolism

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-05-15

  articleOpen access

  Abstract Biomedical research overlooks most genes in favor of a well-studied minority, yet whether analogous blind spots exist in metabolomics remains unknown. We show that reductive amination, forming secondary amines from aldehydes or ketones and amines, generates a previously hidden class of metabolites we term alkamines. Multiplexed synthesis of 8,475 alkamines combined with MS/MS searches across 1.7 billion spectra identified 1,626 candidates across multiple species and organs. Of these, 56 were confirmed in biological samples, including 27 steroid- and 12 drug-derived alkamines matching prescription patterns. Notably, 77% of synthesized alkamines are absent from PubChem. This combinatorial logic likely explains why alkamines have evaded detection and suggests drug metabolism frameworks substantially underestimate drug-derived metabolite diversity. Reductive amination is an overlooked route modifying steroids, bile acids, and xenobiotics.

  PublisherDOI

• Uranyl_+_glyxGT

  ioChem-BD Computational Chemistry Datasets · 2026-02-06

  datasetOpen access1st authorCorresponding
  PublisherDOI

• The ProteomeXchange consortium in 2026: making proteomics data FAIR

  Nucleic Acids Research · 2025-11-06 · 20 citations

  articleOpen access

  The ProteomeXchange consortium of proteomics resources (http://www.proteomexchange.org) was established to standardize open data practices in the mass spectrometry (MS)-based proteomics field. Here, we describe the main developments in ProteomeXchange in the last 3 years. The six member databases of ProteomeXchange, spread out in three different continents, are the PRIDE database, PeptideAtlas, MassIVE, jPOST, iProX, and Panorama Public. We provide updated data submission statistics, showcasing that the number of datasets submitted to ProteomeXchange resources has continued to accelerate every year. Through June 2025, 64 330 datasets had been submitted to ProteomeXchange resources, and from those, 30 097 (47%) just in the last 3 years. We also report on the improvements in the support for the standards developed by the Proteomics Standards Initiative, e.g. for Universal Spectrum Identifiers and for SDRF (Sample and Data Relationship Format)-Proteomics. Additionally, we highlight the increase in data reuse activities of public datasets, including targeted reanalyses of datasets of different proteomics data types, and the development of novel machine learning approaches. Finally, we summarize our plans for the near future, covering the development of resources for controlled-access human proteomics data, and for the support of non-MS proteomics approaches.

  PublisherDOI

• Pep2Prob Benchmark: Predicting Fragment Ion Probability for MS$^2$-based Proteomics

  ArXiv.org · 2025-08-12

  preprintOpen accessSenior author

  Proteins perform nearly all cellular functions and constitute most drug targets, making their analysis fundamental to understanding human biology in health and disease. Tandem mass spectrometry (MS$^2$) is the major analytical technique in proteomics that identifies peptides by ionizing them, fragmenting them, and using the resulting mass spectra to identify and quantify proteins in biological samples. In MS$^2$ analysis, peptide fragment ion probability prediction plays a critical role, enhancing the accuracy of peptide identification from mass spectra as a complement to the intensity information. Current approaches rely on global statistics of fragmentation, which assumes that a fragment's probability is uniform across all peptides. Nevertheless, this assumption is oversimplified from a biochemical principle point of view and limits accurate prediction. To address this gap, we present Pep2Prob, the first comprehensive dataset and benchmark designed for peptide-specific fragment ion probability prediction. The proposed dataset contains fragment ion probability statistics for 608,780 unique precursors (each precursor is a pair of peptide sequence and charge state), summarized from more than 183 million high-quality, high-resolution, HCD MS$^2$ spectra with validated peptide assignments and fragmentation annotations. We establish baseline performance using simple statistical rules and learning-based methods, and find that models leveraging peptide-specific information significantly outperform previous methods using only global fragmentation statistics. Furthermore, performance across benchmark models with increasing capacities suggests that the peptide-fragmentation relationship exhibits complex nonlinearities requiring sophisticated machine learning approaches.

  PublisherOA PDFDOI

• Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

  ArXiv.org · 2025-02-21

  preprintOpen access

  Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis; improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and ultimately enabling AI-empowered virtual cells.

  PublisherOA PDFDOI

• Charting the undiscovered metabolome with synthetic multiplexing implicates ibuprofen-carnitine in myotoxicity

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-11-19 · 6 citations

  preprint

  In untargeted metabolomics, reference MS/MS libraries are essential for structural annotation, yet currently explain only 6.9% of the more than 1.7 billion MS/MS spectra in public repositories. We hypothesized that many unannotated features arise from simple, biologically plausible transformations of endogenous and exposure-derived compounds. To test this, we created a reference resource by synthesizing over 100,000 compounds using multiplexed reactions that mimic such biochemical transformations. 91% of the compounds synthesized are absent from existing structural databases. Through improvements in the construction of the computational infrastructure that enables pan repository-scale MS/MS comparisons, searching this biologically inspired MS/MS library increased the overall reference-based match rate by 17.4%, yielding over 60 million new matches and raising the global pan-repository MS/MS annotation rate to 8.1%. By facilitating structural hypotheses for previously uncharacterized MS/MS data, this framework expands the accessible detectable biochemical landscape across human, animal, plant, and microbial systems, revealing previously undescribed metabolites such as ibuprofen-carnitine and 5-ASA-phenylpropionic acid conjugates arising from drug-host and host-microbiome co-metabolism.

  PublisherDOI

• microbeMASST: a taxonomically informed mass spectrometry search tool for microbial metabolomics data

  Nature Microbiology · 2024 · 109 citations

  • Computational biology
  • Biology
  • Chemistry

  microbeMASST, a taxonomically informed mass spectrometry (MS) search tool, tackles limited microbial metabolite annotation in untargeted metabolomics experiments. Leveraging a curated database of >60,000 microbial monocultures, users can search known and unknown MS/MS spectra and link them to their respective microbial producers via MS/MS fragmentation patterns. Identification of microbe-derived metabolites and relative producers without a priori knowledge will vastly enhance the understanding of microorganisms' role in ecology and human health.

  PublisherOA PDFDOI

Recent grants

• Technology Research and Development Project 2: Antibiotics sequencing and drug discovery

  NIH · $2.7M · 2019

• Technology Research and Development Project 2: Antibiotics sequencing and drug discovery

  NIH · $2.7M · 2019

• MassIVE.quant: a curated and scalable community resource for quantitative proteomics

  NIH · $1.3M · 2019–2023

• ABI Innovation: ProteoCarta: a community-scale resource for multi-species proteomics big data

  NSF · $899k · 2018–2023

• NIH Grant P41GM103484

  NIH · $9.4M · 2021

Frequent coauthors

• Pieter C. Dorrestein

  University of California, San Diego

  138 shared

• Mingxun Wang

  University of California, Riverside

  70 shared

• William H. Gerwick

  Scripps Institution of Oceanography

  56 shared

• Bradley S. Moore

  University of California, San Diego

  46 shared

• Juan Antonio Vizcaíno

  European Bioinformatics Institute

  43 shared

• Eric W. Deutsch

  InSysBio (Russia)

  42 shared

• Yasset Pérez‐Riverol

  European Bioinformatics Institute

  40 shared

• Benjamin Pullman

  AstraZeneca (United Kingdom)

  38 shared

Awards & honors

• 2006 Human Proteome Organization (HUPO) Young Investigator A…
• 2007 Ph.D. Dissertation Award (CSE/UCSD)