Revvity Signals integration

Shaping biologics data for Signals.

In short

Bionamic analyses your data, shapes it to match your libraries, and submits records directly to Signals.

Three columns. Messy, incomplete data: overlapping Excel, FASTA and instrument files. The Bionamic workbook, where the data is analysed, enriched and structured. Complete records in a Revvity Signals material library, each with a developability plot, linked chains and a status

Three steps, run from the workbook.

Annotate, validate, register. All three read the library definition from your tenant as it is right now, so the workbook and Signals cannot hold different ideas of what a record needs.

01

Annotate: turn the data you have into the data you need.

Interact with your data inside a spreadsheet-based workbook, powered by a growing list of tools, and analyse it in interactive plots and tables. The example below demonstrates deep developability profiling of antibody sequences.

The antibody example

From pasted sequences to a profiled table.

VH and VL sequences pasted straight from Excel are numbered ANARCI and folded into an Fv model ABodyBuilder2 , then run through developability Aggrescan3D SASA SURFMAP , immunogenicity MixMHC2pred and humanness BioPhi OASis Sapiens predictions, and benchmarked against clinical-stage antibodies SAbDab . The molecules are annotated with CDR and liability features, and the computed properties land back in the same table, with column names matching the Signals fields they will fill.

The example above uses a handful of these, and the list keeps growing:

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.

02

Validate: check the table against the live library, before anything is written.

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.

03

Register: chains first, then the record that links them.

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 ends up on the record.

From the antibody example above. The field names are your library's, and the scripts match on what the tenant reports, so renaming a field in Signals changes what is filled, with no mapping table to keep in step and nothing to redeploy.

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

See it with your own library.

We are glad to walk through the whole round trip against your own Signals library and your own molecules, and to answer whatever comes up along the way.

Contact Bionamic