DDA / DIA protein ID & quant
Peptide and protein identification and quantification from data-dependent or data-independent acquisition, with FDR control.
Core Bioinformatics
Proteomics & Structural Analysis
From raw mass spectra to proteins, modifications, and 3D structure. We analyse DDA and DIA proteomics—identifying and quantifying proteins and PTMs—and run structural work from AlphaFold prediction to docking and dynamics, handing back tables, models, and figures documented for publication and review.
Raw MS · mzML in
Proteins · PTMs · structures out
Typical turnaround 3–7 days
Individual pricing from $149
Illustrative sample output
b ions
y ions
unmatched
Overview
Rigorous proteomics & structure work, without the in-house pipeline overhead
Proteomics turns raw mass spectra into a quantified picture of the proteins in a sample—which are present, in what amounts, and how they are modified. Structural bioinformatics takes it further, predicting and analysing the 3D shapes those proteins fold into and how they bind. The hard part is rarely running a single tool; it is choosing search and quantification strategies matched to your acquisition, handling missing values and PTM localisation correctly, and documenting every decision so the result survives peer review.
We build and run that workflow for you. Whether you have a DIA proteomics cohort to quantify, a phosphoproteomics experiment to localise, or a protein whose structure and binding you want to model, we apply established, peer-reviewed tools—never opaque in-house black boxes—and return results with the tool versions and parameters recorded for full reproducibility.
Capabilities
What we analyse
One service spanning proteins end to end—from raw mass spectra and quantification to modifications, predicted structures, and binding.
Label-free & isobaric quant
LFQ, TMT/iTRAQ, and SILAC quantification with normalization, batch handling, and match-between-runs where appropriate.
PTMs & phosphoproteomics
Modification-focused searches with site localisation for phospho, ubiquitin, acetyl, and glyco, plus differential-modification testing.
Differential & statistics
Rigorous differential-abundance testing across conditions with imputation, mixed models, and multiple-testing control.
Structure prediction
Monomer and complex structure prediction with confidence metrics (pLDDT, PAE) for proteins with or without known templates.
Molecular docking
Ligand and protein-protein docking to predict binding poses and rank interactions for mechanism and lead exploration.
Molecular dynamics
MD simulations to assess stability, conformational change, and interactions, with free-energy estimates by MM/PBSA.
Structure analysis & comparison
Structure superposition, similarity search, binding-site and interface analysis, and comparison against known folds.
The Pipeline
How a proteomics & structure analysis runs
A transparent, best-practice sequence—each step chosen for your data and question and documented in the final report. Nothing is a black box.
Steps are adapted to your study: DDA vs. DIA, label-free vs. TMT, quantification only vs. structure and docking. We confirm the plan with you before any compute begins.
Raw MS QC & conversion
Instrument-file QC, conversion to open formats where needed, and run-level quality checks before searching.
Tools: ThermoRawFileParser · mzML · RawTools
Peptide & protein identification
Database search against your proteome with FDR control—DDA, DIA (library-based or library-free), or both combined.
Tools: DIA-NN · MSFragger · MaxQuant · Percolator
Quantification & normalization
Label-free, isobaric (TMT), or DIA quantification with normalization, roll-up, and missing-value handling.
Tools: IonQuant · TMT · DIA-NN
Differential & statistical analysis
Rigorous testing across conditions, PTM-site analysis where relevant, and enrichment to interpret the hits.
Tools: MSstats · limma · Perseus
Structure prediction & modelling
Predict monomer or complex structures, assess confidence, and prepare models for downstream analysis.
Tools: AlphaFold3 · ColabFold · ESMFold
Docking, dynamics & interaction
Dock ligands or partners, run molecular-dynamics simulations, and analyse binding sites and interfaces.
Tools: AutoDock Vina · GROMACS · Foldseek
Reporting & delivery
Quant tables, structure models, publication-quality figures, a QC report, and reproducible methods with every tool version.
Tools: PyMOL · ggplot2 · versioned methods manifest
Tools & Technologies
Established, peer-reviewed tools—matched to your data
We select from the field's standard toolkit rather than forcing every dataset through one pipeline. A representative set of what we work with:
Raw MS & conversion
ThermoRawFileParser
msconvert
mzML
OpenMS
RawTools
Search engines (DDA / DIA)
DIA-NN
MSFragger
MaxQuant
Comet
Skyline
Percolator
Quantification
IonQuant
FlashLFQ
TMT
SILAC
Philosopher
Statistics & PTM
MSstats
limma
Perseus
PTM-Shepherd
DEP
Structure prediction
AlphaFold2
AlphaFold3
ColabFold
ESMFold
RoseTTAFold
Docking
AutoDock Vina
DiffDock
HADDOCK
ClusPro
Glide
Molecular dynamics
GROMACS
OpenMM
AMBER
NAMD
MM/PBSA
Structure analysis & viz
PyMOL
ChimeraX
Foldseek
TM-align
fpocket
Reference Resources
Public knowledge behind every result
Identifications and models are only as good as the references behind them. We build on the community's authoritative, versioned resources.
- UniProt
- Reference protein sequences and annotation for database searching.
- PDB
- Experimentally determined 3D structures for templates and validation.
- AlphaFold DB
- Predicted structures for millions of proteins across proteomes.
- PRIDE
- ProteomeXchange repository for depositing and reusing MS data.
- MassIVE & PeptideAtlas
- MS datasets and spectral libraries for DIA and reanalysis.
- InterPro / Pfam
- Protein domain and family signatures for functional annotation.
- CATH / SCOP
- Structural classification of protein folds and superfamilies.
- PROSITE
- Curated motifs and functional-site patterns for proteins.
- STRING
- Protein–protein interaction networks for interaction context.
Choosing an Acquisition
DDA vs. DIA vs. targeted (PRM/SRM)
There is no single best acquisition—only the right one for your question and scale. A quick orientation; we will help you decide.
| Dimension | DDA | DIA | Targeted (PRM/SRM) |
|---|---|---|---|
| Acquisition | Selects top ions for fragmentation | Fragments all ions in m/z windows | Monitors a predefined peptide list |
| Quantification | Good; some run-to-run variability | Consistent across many samples | Most precise for chosen targets |
| Missing values | More, in large cohorts | Fewer; reproducible matrices | Minimal for target set |
| Scope | Discovery; strong for PTMs | Deep, large-scale quantification | A focused panel of proteins |
| Best suited to | Exploratory & modification studies | Cohorts & biomarker discovery | Validation & assays |
What You Receive
A complete, documented deliverable
Not just a protein list dropped in a folder—every output you need to interpret, publish, and reproduce the work.
- Protein & peptide identification tables with FDR
- Quantification matrices (LFQ / TMT / DIA) with normalization
- Differential-abundance results with statistics (TSV / XLSX)
- PTM site tables & localisation scores, where applicable
- Predicted structures (PDB) with confidence metrics
- Docking poses / MD trajectories & binding analysis, where applicable
- Publication-ready figures & methods with tool versions
Built for reproducibility, not just a result
Every pipeline follows documented best practices—controlled false-discovery rates, careful missing-value handling, and honest reporting of quantification confidence—so a differential hit is real rather than an imputation artifact. For predicted structures we report confidence metrics (pLDDT, PAE) and never present a model as if it were an experimental structure. Each run records its tool versions, parameters, and reference builds.
The practical payoff: your methods section writes itself, a reviewer can re-run the analysis, and a result from today can be reproduced a year from now. We will also tell you honestly when a design or sample size won't support the conclusion you're after.
FAQ
Proteomics & structural questions
What researchers and project leads most often ask before starting a proteomics or structural project.
We start from raw MS files from your core facility — vendor formats (.raw, .d, .wiff) or open mzML — for DDA or DIA experiments. If you already have a search-engine output or protein-quantification table, we can pick up from there for statistics and interpretation.
DDA is well established and suits discovery and PTM work; DIA gives more complete, reproducible quantification across many samples with fewer missing values. We help you match the acquisition to your study, and analyze either with modern search engines.
Yes. We run PTM-focused searches for phosphorylation, ubiquitination, acetylation, and glycosylation, localize modification sites, and test for differential modification between conditions.
Yes. We run AlphaFold2, AlphaFold3, ColabFold, and ESMFold for monomers and complexes, assess model confidence (pLDDT, PAE), and compare or search structures with tools like Foldseek and TM-align.
Yes. We perform ligand and protein-protein docking, run molecular-dynamics simulations to assess stability and interactions, and can estimate binding free energies with MM/PBSA — useful for mechanism and early drug-discovery questions.
Protein and peptide identification and quantification tables, differential-abundance results with statistics, PTM site tables, predicted structures with confidence metrics, docking poses or simulation trajectories, publication-quality figures, and reproducible methods with every tool version.
Related
Where proteomics & structure work leads next
Have mass-spec data or a protein to model?
Tell us your organism, data type, and question—we'll scope it honestly, including if a different design would serve you better.