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\b[Complex composition requires identities, copy numbers and evidence that the components coexist in the same assembly.] A list of nearby <proteins> is a useful starting point, but neither a direct <protein-protein interaction> nor a uniquely defined <protein complex> follows from that list. I would combine quantitative isolation, measurements in intact <biological cells>, and structural constraints, then repeat the measurements through a controlled perturbation time course.
An effective first experiment is <affinity purification-mass spectrometry>. Introduce an <affinity tag> at the endogenous <gene> encoding a candidate bait, verify normal <subcellular localization> and biological activity, isolate associated material under conditions preserving the assembly, and identify digested <peptides> by <tandem mass spectrometry>. Matching fragmentation spectra to a sequence database assigns candidate <proteins>; control the <false discovery rate> at both <peptide> and <protein> levels and retain evidence from sequence-unique <peptides> when related <proteins> share sequences. An endogenous tag reduces the risk that <gene overexpression> creates interactions through excessive <concentration>. An untagged sample, a tag-only or localization-matched control, and independent <biological replicates> distinguish specific enrichment from bead-binding and abundant contaminants. Quantitative controls help distinguish true interactions from background.<SAINT: probabilistic scoring of affinity purification-mass spectrometry data by Hyungwon Choi and others>[pp. 70–73, statistical distinction between true interactions and background using quantitative purification data]{ref}
Isolation has selective losses. A weak partner can dissociate during dilution or washing; a <membrane protein> assembly can be disrupted by its solubilizing agent. Compare extraction conditions, check recovery in the soluble and insoluble fractions, and use reciprocal <co-immunoprecipitation> from an independent subunit. Treatment with a <nuclease> tests whether the association depends on <DNA> or <RNA>, but removal after extraction can itself destabilize a real nucleic-acid-dependent assembly. Purification from mixed tagged and untagged cell populations, with distinguishable <stable isotope labeling>, can test whether partners exchange after cell disruption. A reciprocal enrichment supports coassociation; demonstrating a direct contact still requires additional evidence.
For <stoichiometry>, raw <mass spectrometry> intensity is insufficient because different <peptides> have different digestion and <ionization> efficiencies. Use calibrated <peptide> standards or carefully normalized quantitative estimates, ideally with several reference subunits. <native mass spectrometry> measures intact assembly masses and can distinguish candidate subunit counts, while controlled dissociation helps assign subcomplexes. Bound <lipids>, <post-translational modifications>, gas-phase losses and heterogeneous occupancy complicate assignment, so check the mass against the summed masses of candidate subunits. <Size-exclusion chromatography> coupled to <multi-angle light scattering> independently constrains assembly mass, although an unresolved mixture produces an average and the hydrodynamic size alone is not a molecular mass. Quantitative <mass spectrometry> of <protein complexes> can combine interaction strengths, abundance and stoichiometry.<A human interactome in three quantitative dimensions organized by stoichiometries and abundances by Marco Y. Hein and others>[pp. 712–723, integration of interaction strengths, abundance and stoichiometry]{ref}
To avoid requiring a bait, fractionate native extracts by <size-exclusion chromatography> or another separation and quantify <proteins> in every fraction. Correlated elution profiles suggest membership of the same <protein complex>; comparison across an independent separation helps distinguish coincidence. This <co-fractionation mass spectrometry> can also reveal shifts from a large assembly into smaller forms after treatment. It may merge different assemblies sharing a subunit, and a failure to coelute does not exclude a transient interaction. <chemical cross-linking mass spectrometry> adds spatial information: identify pairs of linked residues, translate them into distance restraints appropriate to the reagent and residue accessibility, and ask whether a proposed assembly can satisfy them. A cross-link is a proximity constraint, not automatically a permanent interface; intermolecular assignment is especially difficult for identical subunits.
In living <biological cells>, <proximity labeling> captures an interaction neighbourhood before disruptive extraction. A bait fused to <TurboID> marks nearby <proteins> with <biotin>, allowing stringent enrichment and <mass spectrometry>. The original <TurboID> work demonstrated useful labeling over roughly ten minutes, which is much faster than older long-labeling implementations.<Efficient proximity labeling in living cells and organisms with TurboID by Tess C. Branon and others>[Fig. 2 and Supplementary Figs. 7–11, labeling-time and compartment comparisons]{ref} A localization-matched enzyme control, a no-added-<biotin> control and matched fusion abundance are essential. The labeling pulse integrates both spatial proximity and residence time; ten-minute labeling cannot resolve a seconds-long interaction sequence. Changes in <ATP>, <biotin> availability, enzyme activity or access to reactive residues can change the labeling signal independently of complex membership. A pulse followed by a chase does not make the covalent label disappear when the partner leaves.
Structural methods distinguish alternative interpretations of these measurements. <cryo-electron microscopy> can assign ordered subunits, estimate occupancy of resolved features, and separate some compositionally different particle classes. Flexible or small subunits may remain invisible despite being present. <X-ray crystallography> resolves stable interfaces but selects material that crystallizes. <Förster resonance energy transfer> between appropriately positioned labels tests nanometre-scale proximity in intact <biological cells>; <single-particle tracking> and <fluorescence correlation spectroscopy> can test comobility and exchange. Label placement, spectral cross-talk and accidental <colocalization> require controls. None of these techniques by itself reliably inventories every component.
The computational analysis should preserve these distinctions. Construct a <protein interaction network> with separate edge types for copurification, cross-linking and proximity labeling. Cluster reproducibly supported associations to propose modules, distinguish a stable core from variable accessory components, and integrate localization and measured <protein> abundance. Models such as <AlphaFold 3> can propose interfaces for experimentally supported candidate components, but a confident predicted geometry does not demonstrate that an interaction occurs in the tested condition.<Accurate structure prediction of biomolecular interactions with AlphaFold 3 by Josh Abramson and others>[pp. 493–500, joint structural prediction of biomolecular complexes]{ref} Test interfaces by <site-directed mutagenesis>, confirming normal <protein folding>, expression and localization before interpreting a lost interaction. Evaluate a proposed assembly against independently held-out cross-links or other measurements to reduce circular validation.
For perturbations, use vehicle controls, several doses and early-to-late sampling; randomize preparation and instrument order. Quantify input abundance alongside bait-normalized partner recovery. An illustrative relative recruitment statistic for partner $i$ at time $t$ is
$$
R_i(t)=\frac{I_i^{\mathrm{IP}}(t)/I_b^{\mathrm{IP}}(t)}{I_i^{\mathrm{IP}}(0)/I_b^{\mathrm{IP}}(0)}\,\frac{A_i(0)}{A_i(t)},
$$
where $I^{\mathrm{IP}}$ is background-corrected recovered intensity, $b$ is the bait and $A_i$ is input abundance. This is an operational comparison under matched extraction and measurement response, not an absolute occupancy or <dissociation constant>. A doubled recovered partner signal with doubled input abundance and unchanged bait recovery gives $R_i=1$, illustrating why raw enrichment alone would mislead. Values near the detection limit need uncertainty bounds rather than unstable ratios.
A drug-induced loss of several core components could mean disassembly, degradation, changed localization or poor recovery. Distinguish them with input <proteomics>, microscopy and native fractionation, then test reversibility after washout and rescue by a drug-resistant target when feasible. For stress, monitor viability and extraction-resistant material; for disease-associated <mutations>, compare otherwise matched genetic backgrounds and rescue with the normal allele. <post-translational modifications> may recruit an accessory partner without changing the core. <hydrogen-deuterium exchange mass spectrometry> can then test interface exposure, but its altered exchange reports accessibility and structural dynamics rather than a new subunit by itself. \b[A convincing remodeling claim is a reproducible change in assembly membership or occupancy, separated from changes in abundance, localization and assay recovery, and connected to a functional consequence.]
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