Register antibodies in Revvity Signals
Analyze antibody data in a Bionamic workbook, prepare it for your material libraries, and submit annotated records directly to Revvity Signals.
From source data to registered records
The workflow has three steps: annotate, validate, and register. Each reads the library definition from your Signals tenant so that the workbook uses your current registration requirements.
What arrives at the other end is a finished record. Each antibody sits in the material library with its chains referenced by their own registered identifiers, its isotype, molecular weight, isoelectric point and extinction coefficient already computed, a picture of the format it was built to, and an identifier that ties the record back to the work it came from. Nothing is left for somebody to fill in afterwards.
Annotate your antibody data
Interact with your data inside a spreadsheet-based workbook, powered by a growing list of tools, and analyze it in interactive plots and tables. The example below demonstrates deep developability profiling of antibody sequences.
In this example, VH and VL sequences pasted from Excel are numbered with ANARCI and folded into an Fv model with ABodyBuilder2. The workflow then applies developability tools (Aggrescan3D, SASA, and SURFMAP), immunogenicity prediction (MixMHC2pred), and humanness analysis (BioPhi, OASis, and Sapiens), with benchmarking against clinical-stage antibodies using SAbDab. Molecules receive CDR and liability annotations, and computed properties return to the same table with column names matching the Signals fields.
More scientific tools available in Bionamic
Structure prediction and modelling
- Boltz-2
- Chai-1
- ABodyBuilder2
- NanoBodyBuilder2
- TCRBuilder2
Complexes with protein, ligand, DNA and RNA; antibody Fv from VH and VL, nanobodies and TCRs, with per-residue predicted error.
Design and mutation scanning
- RFdiffusion
- RFantibody
- ProteinMPNN
- ProteinMPNN-ddG
- AntiFold
- ESM-IF1
- ThermoMPNN
De novo backbones against a chosen antigen and epitope, sequence design on a fixed backbone, and exhaustive point-mutation scans for fitness and ΔΔG.
Developability and biophysics
- Antibody profiling
- Therapeutic nanobody profiler
- DeepSP
- DeepViscosity
- Aggrescan3D
- NetSolP
- MusiteDeep
- SEMA-3D
Aggregation, viscosity, solubility, phosphorylation sites and conformational B-cell epitopes.
Humanness and immunogenicity
- BioPhi
- OASis
- Sapiens
- MHCflurry
- MHCfovea
- MixMHC2pred
- TLimmuno2
- DeepImmuno
Humanness scoring and humanisation, MHC-I and MHC-II binding, and immunogenicity prediction.
Language models and small molecules
- AMPLIFY
- ADMET-AI
- GNINA
Protein language model likelihoods and embeddings, 49 ADMET endpoints from SMILES, and docking with CNN pose and affinity rescoring.
Sequence search and alignment
- BLAST
- HMMER
- MMseqs2
- DIAMOND
- CD-HIT
- Clustal Omega
- seqkit
Numbering and germlines
- ANARCI
- Immunum
- IgBLAST
- IMGT germline search
IMGT, Kabat, Chothia, Martin and Aho, with heavy, kappa and lambda chains detected automatically.
Repertoires and NGS
- OAS paired and unpaired
- SAbDab
- FASTQ to HMM counting
- Library mapping
- Sanger AB1
Display-library counting with pre and post selection, closest-member matching and profile HMM building.
Docking, structure and surfaces
- AutoDock Vina
- GNINA
- LightDock
- US-align
- Foldseek
- SURFMAP
- PyMOL
- SASA
Including a prebuilt antibody structure database and surface property mapping.
Simulation and cheminformatics
- GROMACS
- OpenMM
- APBS
- PDB2PQR
- PROPKA
- RDKit
- ProtParam
- Codon optimisation
Molecular dynamics on GPU, electrostatics, descriptors and properties from SMILES.
Validate against the Signals library
The Signals library definitions are read in real time from your tenant, and your data tables are checked against them. Mandatory fields are marked and dropdowns are synced with attribute lists.
Register chains and antibody records
The workbook table rows are submitted and become Signals records. Here, each individual chain is defined by sequence, so a chain shared by two constructs is registered once and linked twice. The records include all developability-related fields.
What the records contain
The antibody example includes the following data and attachments. Field names follow your Signals library definitions.
Identity and provenance
- Deterministic ID, derived from the sequences and used for the uniqueness check
- Link to the registered VH and VL chain assets
- Project, target, species
- Date analysed
Developability
- Molecular weight, isoelectric point
- Net charge at pH 7.4 and pH 5.5, Fv charge asymmetry
- CDR-H3 and CDR-L3 length
- Sequence liabilities, and which of them fall in CDRs
- Aggregation propensity, aggregation-prone residues, exposed hydrophobics
Immunogenicity and humanness
- MHC-II strong binders, epitope hotspots, peak epitope coverage
- OASis humanness percentile, per chain
- Proposed humanising mutations, per chain
- Closest human germline V and J, and residues differing from it, on each chain
Attachments
- Developability radar against clinical-stage antibodies
- Aggregation, exposure and immunogenicity profiles
- Hydrophobicity, stickiness and electrostatics surface maps
- The folded Fv structure
- The underlying aggregation data