Resources

Proteomics Databases



Metabolomics Databases

• • Co-IP-MS vs XL-MS: Which Protein Interaction Workflow Should You Choose?

  The most useful way to compare Co-IP-MS vs XL-MS is to start with the desired readout. Co-IP-MS is usually stronger for bait-centered partner discovery and complex profiling. XL-MS is usually stronger for mapping spatial proximity within or between proteins. Both workflows can be valuable, but each workflow has different sample requirements, controls, failure modes, and interpretation limits.

• • Hybridoma Monoclonal Antibody Sequencing for Cell Line Transfer and Recombinant Reformatting

  Technical guide for Hybridoma Monoclonal Antibody Sequencing for Cell Line Transfer and Recombinant Reformatting.

• • Monoclonal Antibody Sequencing for Recombinant Recovery: When Should You Sequence an Existing Antibody Asset?

  Technical guide for Monoclonal Antibody Sequencing for Recombinant Recovery: When Should You Sequence an Existing Antibody Asset?.

• • Monoclonal Antibody Sequencing vs De Novo Protein Sequencing: Which Route Fits an Antibody Recovery Project?

  Technical guide for Monoclonal Antibody Sequencing vs De Novo Protein Sequencing: Which Route Fits an Antibody Recovery Project?.

• • Protein Sequencing: Principles, Workflows, and Research Applications

  Protein sequencing determines the amino acid order of a protein or peptide. This information supports cloning, expression design, antibody engineering, biopharmaceutical characterization, and publication of primary structure evidence.

• • Hybridoma Monoclonal Antibody Sequencing: How to Recover VH and VL Sequences from Unstable or Lost-Producing Clones

  Technical guide for Hybridoma Monoclonal Antibody Sequencing: How to Recover VH and VL Sequences from Unstable or Lost-Producing Clones.

• • Studying Membrane Protein Interactions in Native Cells: Why Overexpression Can Mislead and How In-Cell Crosslinking MS Helps

  Membrane protein interactions are difficult to study because the interaction signal depends on more than protein sequence. Local abundance, lipid composition, membrane curvature, compartment identity, trafficking status, and stimulation timing can all shape whether two proteins meet. Many researchers use overexpression to increase signal, simplify antibody capture, or make a weak interaction easier to detect.

• • PhIP-Seq: Methods, Applications and Challenges

  PhIP-Seq, also called phage immunoprecipitation sequencing, was developed for this type of discovery question. The method combines phage display peptide libraries, antibody capture, next- generation sequencing, and enrichment analysis. PhIP-Seq can help researchers identify antibody-reactive peptides, compare serological profiles across groups, and nominate candidate epitope regions.

• • Analyzing Data from PhIP-Seq Experiments

  PhIP-Seq data analysis can decide whether a broad antibody screening experiment becomes a clear candidate list or an ambiguous table of peptide read counts. A serum or plasma cohort may contain biologically meaningful antibody signals, but sequencing output alone does not identify disease-associated epitopes, exposure signatures, or biomarker candidates.

• • Applications of PhIP-Seq in Genomics

  PhIP-Seq, also called phage immunoprecipitation sequencing, can provide that bridge. The method uses DNA-encoded phage display peptide libraries to represent genomic, proteomic, pathogen-derived, or custom antigen spaces. Antibody-containing samples enrich the displayed peptides that antibodies recognize. Sequencing then identifies the peptide-encoding sequences. In genomics-oriented studies, the value comes from connecting sequence-defined libraries with antibody reactivity patterns.