Global Drug Target Identification Service — From Phenotypic Hit to Confirmed Target

Six orthogonal chemoproteomics platforms. One integrated answer.

Every drug discovery program reaches a moment when the question shifts from "what does this compound do?" to "what protein does it bind?" Answering that question — definitively, with orthogonal evidence, and across diverse chemical matter — is the core mission of our Global Drug Target Identification service. We combine six orthogonal chemoproteomics and mass spectrometry platforms to map small molecule–protein interactions from phenotypic screening hits, natural products, covalent inhibitors, fragments, and approved drugs, delivering ranked target lists with cross-platform validation and statistical confidence.

At Creative Proteomics MassTarget, our target ID service is built for discovery teams who cannot afford false leads or single-method blind spots. Whether the starting point is a phenotypic hit from an HTS campaign, a bioactive natural product with an unknown mechanism, a covalent fragment series needing proteome-wide selectivity profiling, or a preclinical candidate requiring off-target de-risking, we design a multi-platform engagement matched to the physicochemical properties of your compound and the biological system in which it acts. For integrated multi-omics campaigns that combine target identification with broader pathway analysis, our multi-omics integration service provides a complementary framework for contextualising target findings within the wider cellular response.

Key Advantages:

Six orthogonal chemoproteomics methods under one engagement — no single-method blind spots.
Label-free and label-based options matched to compound type and biological context.
Cross-platform target validation built into every project — targets reported only if confirmed by ≥2 independent methods.
Proteome-wide coverage: 5,000–9,000 proteins quantified per experiment depending on platform and cell type.
Experience across diverse compound classes: covalent inhibitors, non-covalent binders, natural products, fragments, PROTACs, and clinical candidates.

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Global drug target identification service overview: small molecule compound library feeding into six orthogonal chemoproteomics workflows — ABPP, PAL-MS, PISA/TPP, LiP-MS, protein microarray-MS, and affinity pulldown-MS — converging on a validated target list with cross-platform confidence scoring.

What Is Global Target ID Platform Suite Tech Comparison Sample Demo Case Study FAQ

What Is Global Drug Target Identification?

Global drug target identification — also referred to as chemical proteomics-based target deconvolution — is the systematic process of determining which proteins in a complex biological proteome are directly bound by a small molecule of interest. Unlike reductionist biochemical assays that test one protein at a time, chemical proteomics methods query the entire expressed proteome simultaneously, using mass spectrometry to detect and quantify binding events without prior knowledge of the target.

The fundamental logic is consistent across methods: a compound is introduced to a biological system (lysate, intact cell, or tissue), and the proteome is then fractionated or modified based on a property that distinguishes target-bound from unbound proteins — thermal stability, proteolytic susceptibility, affinity capture, or covalent labelling efficiency. Mass spectrometry identifies the enriched or shifted proteins, and statistical analysis distinguishes specific interactors from background.

The "global" descriptor matters: single-method target ID approaches — affinity chromatography alone, for example — can miss targets that lose native conformation upon immobilisation, bind weakly but specifically, or require a cellular context for engagement. By deploying multiple orthogonal methods, we minimise the risk of false negatives and build confidence in each reported target through convergent evidence from independent physicochemical principles. For mechanism-of-action studies requiring integrated proteomics and metabolomics contextualisation, our thermal proteomics for MoA service provides a deeper mechanistic workflow complementing target identification data.

Why Multi-Platform Target Identification Changes the Deconvolution Equation

Eliminates single-method blind spots

Each chemoproteomics platform has inherent biases. Thermal profiling preferentially detects soluble, abundant proteins. ABPP requires an active-site probe. PAL-MS needs compound modification. By deploying ≥2 orthogonal methods, we avoid the false negatives that arise when a compound's target class falls outside any single method's detection envelope.

Builds confidence through convergence

A target identified by thermal shift alone could reflect an indirect downstream effect. A target enriched in PAL-MS could be an abundant background protein. When two independent physicochemical principles identify the same protein, the assignment confidence far exceeds what either method can provide independently. We report only cross-validated targets in our final output.

Accommodates any compound type

Covalent inhibitors, reversible non-covalent binders, natural product extracts, fragments, PROTACs, and clinical candidates each demand different detection strategies. Our six-platform suite covers the full spectrum: label-free thermal shift for unmodified compounds, ABPP for covalent probes, PAL-MS for non-covalent reversible binders, LiP-MS for conformational changes, protein microarray for complex mixtures, and affinity pulldown for high-affinity immobilised compounds.

Delivers binding site evidence alongside target identity

LiP-MS identifies peptide-level accessibility changes that localise the binding footprint. PAL-MS with MS2 sequencing can identify crosslinked residues. ABPP-MS maps reactivity at individual cysteine sites. The result is not just a target list but a structural hypothesis for where and how your compound engages each identified protein.

Our Chemoproteomics Platform Suite — Six Orthogonal Approaches

We deploy six complementary target identification platforms, selected and combined based on the chemical properties of the compound, the biological system, and the project objectives. Each platform operates on a distinct physicochemical principle, providing orthogonal evidence that collectively builds confidence in target assignment. Our scientists consult on platform selection during project design, matching method choice to compound solubility, available SAR, target class hypothesis, and project timeline.

PLATFORM 1

Activity-Based Protein Profiling (ABPP-MS)

Chemical probes that react selectively with enzyme active sites — serine hydrolases, cysteine proteases, kinases, metalloproteases — measure active-site engagement globally. In competitive format, the test compound displaces probe labelling at target proteins in a concentration-dependent manner, yielding IC₅₀ estimates and selectivity rankings.

Proteome coverage: up to ~60,000 reactive cysteine sites on ~13,000 proteins (HT-ABPP)
Best for: covalent inhibitors, enzyme-directed compounds, fragment-based covalent screens
Output: target engagement profile with IC₅₀ estimates; selectivity ranking across enzyme families
Complementary service: high-throughput ABPP for large-scale covalent fragment screening

PLATFORM 2

Photoaffinity Labelling MS (PAL-MS)

Compounds modified with a photoreactive group (diazirine, benzophenone) and a clickable alkyne handle are incubated with cells or lysate, UV-crosslinked to binding proteins, enriched via click chemistry–biotin conjugation and streptavidin pulldown, and identified by LC-MS/MS. Competitive profiling with excess free compound distinguishes specific from non-specific interactors.

Proteome coverage: 2,000–6,000 proteins depending on probe properties
Best for: non-covalent compounds, natural products, compounds amenable to probe synthesis
Output: ranked target list with enrichment ratios; crosslinked peptide identification for binding site mapping
Dedicated service: photoaffinity labelling MS with probe design consultation

PLATFORM 3

Thermal Stabilisation Assay — PISA/TPP (Label-Free)

Drug binding alters target protein thermal stability. In the Proteome Integral Solubility Alteration (PISA) format, cells or lysates are heated across a temperature gradient, soluble fractions are combined, and DIA-MS quantifies which proteins are stabilised or destabilised by compound treatment — no compound modification required.

Proteome coverage: 5,000–8,000 proteins per experiment (DIA acquisition)
Best for: any soluble compound; works in live cells and tissue lysates; completely label-free
Output: ranked stabilised/destabilised proteins; thermal shift magnitude; K_d estimates from concentration-response series
Platform details: thermal shift proteomics — PISA workflow in 96-well plate format

PLATFORM 4

Limited Proteolysis–MS (LiP-MS)

Drug binding induces conformational changes that alter proteolytic susceptibility. Limited proteolysis with a broad-specificity protease, followed by quantitative MS, identifies peptides that become more or less accessible upon compound binding — providing both target identification and binding site localisation in a single experiment.

Proteome coverage: up to 9,000 proteins in human cell lines
Best for: allosteric modulators, conformational binders, natural products, compounds without known targets
Output: peptide-level accessibility changes; target identification with structural footprint
Dedicated service: LiP-MS service for peptide-resolution target ID

PLATFORM 5

Protein Microarray–MS

Purified human proteins (up to 20,000+) immobilised on a microarray are incubated with the compound, washed stringently, and bound proteins are identified by on-chip tryptic digestion and LC-MS/MS. No compound modification or prior target hypothesis required.

Proteome coverage: up to 20,000+ human proteins depending on array content
Best for: compounds that resist chemical modification; natural product mixtures; fragments; initial binding surveys
Output: ranked list of directly binding proteins with protein-level identification
Service page: protein microarray MS

PLATFORM 6

Affinity Pulldown–MS (Compound Immobilisation)

The compound is immobilised on a solid support via a suitable linker position, incubated with cell lysate, and bound proteins are eluted and identified by LC-MS/MS. Competitive elution with free compound distinguishes specific binders from non-specific matrix adhesives.

Proteome coverage: 1,000–4,000 proteins depending on bead type and wash stringency
Best for: high-affinity binders (K_d < 1 µM) with known SAR permitting linker attachment
Output: competitive displacement–confirmed target list; enrichment ratios
Note: requires SAR knowledge to select immobilisation position without activity loss

Integrated Target ID Workflow

Five stages from compound receipt to cross-validated target report:

Compound characterisation and platform selection

We assess compound solubility, stability, available SAR, and biological context. Based on these parameters, we select the optimal primary and orthogonal secondary platforms. For compounds with no prior SAR or probe chemistry, label-free methods (PISA/TPP, LiP-MS) are recommended as the primary approach. This stage takes 1–2 weeks and defines the project plan and acceptance criteria.

Primary chemoproteomics screen

The compound is tested at a defined concentration (typically 1–10 µM) in the selected biological system. For live-cell methods (PISA/TPP, LiP-MS, PAL-MS), cells are treated with compound or vehicle control in biological triplicate. For lysate-based methods (ABPP, affinity pulldown), lysate is incubated with compound and processed according to the platform-specific protocol. LC-MS/MS is performed on Orbitrap Exploris 480 or Q Exactive HF-X platforms with DIA or TMT acquisition.

Candidate target prioritisation

Raw MS data are processed through the relevant search and quantification pipeline (Spectronaut, MaxQuant, or Proteome Discoverer). Statistical analysis identifies proteins significantly enriched, stabilised, or protected by compound treatment. Candidates are ranked by effect magnitude, statistical significance, and convergence across replicates. Primary and secondary method results are cross-compared — targets appearing in both datasets advance to the highest priority tier.

Orthogonal validation

Priority targets are assessed by the second (or third) orthogonal method. A target identified by PISA/TPP is validated by LiP-MS or PAL-MS; a target from ABPP is confirmed by thermal shift in live cells. Only targets confirmed by ≥2 independent methods are included in the final report. Plate-level QC metrics (Z-factor, CV%) are applied throughout; plates with Z-factor < 0.5 are flagged for repeat measurement.

Reporting and data delivery

Deliverables include a ranked target list with statistical metrics, cross-platform convergence matrix, raw MS spectra files, processed quantification tables, method-specific QC report, and a written interpretation report with hit rationale and recommended follow-up strategy — including biochemical validation, genetic perturbation, or medicinal chemistry coordination priorities.

Global drug target identification workflow: compound characterisation and platform selection, primary chemoproteomics screen (PISA/TPP, ABPP, or PAL-MS), candidate target prioritisation by statistical analysis, orthogonal cross-platform validation, and final reporting with cross-validated target list.

Applications Across Drug Discovery

Global target identification is most impactful where the cost of a wrong target assignment — or a missed target entirely — is highest.

Phenotypic Hit Deconvolution

The most common entry point: a compound or compound series from a phenotypic or cell-based assay shows activity, but the molecular target is unknown. Our integrated workflow — typically starting with PISA/TPP (label-free, no modification required) and PAL-MS (if the compound is amenable to probe synthesis) — generates a ranked candidate target list within 4–8 weeks.

Output: Ranked target list with cross-platform convergence; confirmed targets with orthogonal validation.

Natural Product Mode-of-Action Elucidation

Natural products and their derivatives often have complex, multi-target mechanisms that resist single-method deconvolution. LiP-MS detects conformational changes induced by binding regardless of affinity, while PAL-MS using a clickable natural product probe captures direct interactors. Together, these platforms reveal both primary targets and secondary interactors contributing to the compound's phenotypic profile.

Output: Multi-target engagement map; primary and secondary interactor identification; binding site evidence from LiP-MS accessibility changes.

Covalent Inhibitor Selectivity Profiling

Covalent inhibitors engage their target irreversibly, raising the stakes for off-target reactivity. ABPP-MS maps cysteine reactivity across the proteome, identifying both on-target engagement and off-target hits. PISA/TPP confirms that the compound stabilises the intended target in live cells, while LiP-MS identifies the specific modified cysteine when paired with MS2 sequencing.

Output: Proteome-wide cysteine engagement map; on-target vs off-target selectivity ratio; covalent adduct confirmation by accurate mass.

PROTAC and Degrader Target Identification

PROTACs form ternary complexes between an E3 ligase and a target protein, inducing ubiquitination and degradation. PAL-MS with a photoaffinity probe captures the ternary complex and identifies both E3 ligase engagement and recruited neo-substrates. PISA/TPP detects the thermal destabilisation signature of proteins undergoing active degradation. For ubiquitin remnant profiling, our ubiquitinomics service provides complementary data.

Output: Ternary complex constituents; degradation signature profile; E3 ligase–target pair identification.

Off-Target Profiling for Clinical Candidate De-Risking

Before a candidate enters preclinical development, a proteome-wide off-target assessment is essential. PISA/TPP in live cells at the therapeutic concentration identifies proteins whose stability changes upon compound treatment. LiP-MS provides orthogonal detection of engagement-independent conformational effects. Targets confirmed by both methods are prioritised for downstream toxicity risk assessment.

Output: Off-target landscape map; cross-platform confirmed off-targets with engagement magnitude; prioritised list for follow-up validation.

Fragment-to-Hit Target Confirmation

Fragment screening by NMR, SPR, or MS identifies weak binders whose cellular targets must be confirmed. Protein microarray-MS with the fragment directly identifies binding proteins from immobilised arrays, while PISA/TPP at high fragment concentrations captures thermal shifts in live cells that confirm target engagement in a native cellular context.

Output: Direct target binding confirmation; cellular engagement evidence; fragment-to-hit progression recommendations.

Technology Comparison: Chemoproteomics vs Alternative Target ID Approaches

Approach	Principle	Direct Target or Modifier?	Live Cell?	Modification Required?	Proteome Coverage	Typical Timeline
Chemical Proteomics (this service)	Multiple orthogonal MS-based methods	Direct target	Yes — multiple platforms	None for PISA/LiP-MS; probe for PAL/ABPP	5,000–13,000+ proteins	1–6 weeks
CRISPR/Cas9 KO Screen	Gene knockout → fitness change	Genetic modifier	Yes	No	~19,000 genes	4–8 weeks
RNAi Knockdown Screen	Gene silencing → phenotypic change	Genetic modifier	Yes	No	~18,000 genes	3–6 weeks
Affinity Chromatography (single-method)	Immobilised compound pulldown	Direct target	No	Compound immobilisation	500–3,000 proteins	2–4 weeks
DARTS	Proteolytic protection	Direct target	No	No	3,000–6,000 proteins	2–4 weeks
SPR / Biophysical	Direct binding measurement	Direct target	No	May require immobilisation	One protein per experiment	Days per protein

For projects where a focused ABPP campaign targeting a specific enzyme family is preferred over global proteome screening, our dedicated ABPP-MS service provides enzyme-family-optimised probe panels. For protein interaction network mapping applications, see our interactomics (AP-MS / proximity) service. For thermal shift–focused target engagement without orthogonal methods, our thermal shift proteomics service provides standalone PISA/TPP workflows.

Sample Requirements

Component	Format Options	Recommended Input	Minimum Input	Key Notes
Compound (test article)	Powder or DMSO stock	5–10 mg (or 10 mM stock, 100 µL)	1 mg (or 5 mM stock, 50 µL)	Provide MW, purity, known solubility; note any light/oxygen/moisture sensitivity; avoid TFA >0.05%
Cell line (live-cell methods)	Adherent or suspension culture; frozen pellet	2 × 10⁷ cells per condition (triplicate)	5 × 10⁶ cells per condition	Provide cell type, passage number, culture conditions; confirm compound permeability; coordinate treatment timeline
Tissue (ex vivo PISA)	Snap-frozen; fresh on wet ice	50–100 mg per condition	20 mg per condition	Specify species, organ, storage conditions; coordinate cold-chain logistics for organ-specific studies
Lysate (lysate-based methods)	Clarified lysate in MS-compatible buffer	2–5 mg total protein per condition	0.5 mg per condition	Specify lysis buffer; provide protease/phosphatase inhibitor info; avoid glycerol >5% or detergents above CMC
Probe synthesis information (PAL-MS only)	SAR data recommended	Activity data for probe-bearing analogue	Known inactive analogue for control	PAL probe design consultation included; provide IC₅₀ or K_d of parent compound if available
Reference compound (positive control)	10 mM DMSO stock	≥ 50 µL	10 µL	Known target binder recommended as positive control for method validation; provide target identity and IC₅₀ data if available

All samples should be shipped on dry ice with completed sample submission forms. Biological triplicates are recommended for all quantitative comparisons; minimum two independent biological replicates for publication-grade data. For compounds that degrade on thawing or are light-sensitive, discuss cold-chain and handling requirements with our team before shipment.

Deliverables

Ranked target list: per-protein enrichment or thermal shift magnitude, p-value or q-value, fold-change, and confidence score across all methods applied
Cross-platform convergence matrix: visual comparison showing which targets were identified by each orthogonal method, with concordance scoring for cross-validated hits
Raw MS spectra files: full .raw or .mzML files for independent re-analysis or regulatory submission
Processed quantification tables: protein-level and peptide-level quantification with statistical metrics per method
Method-specific QC report: protein coverage depth, CV distribution, Z-factor per plate (PISA/TPP), enrichment efficiency (PAL-MS/ABPP), cross-correlation of replicates
Binding site evidence (when method supports it): peptide-level accessibility changes (LiP-MS), crosslinked residue identification (PAL-MS), or reactive cysteine mapping (ABPP-MS)
Written interpretation report: scientific synthesis of findings — which targets are most confidently assigned, why, and recommended follow-up strategy including biochemical validation, genetic perturbation, or medicinal chemistry coordination

For studies requiring integration of target ID data with cellular metabolomics or functional endpoint assays, our team can design a multi-platform campaign incorporating pharmaco-proteomics and pharmaco-metabolomics profiling alongside the target identification workflow.

Representative Results

Cross-platform target convergence volcano plot from a PISA/TPP screen of a kinase inhibitor in live HCT116 cells, with on-target (kinase domain) and off-target (non-kinase) hits highlighted in red and blue respectively.

Multi-platform target convergence: PISA/TPP + LiP-MS cross-validation

PISA/TPP screen of a clinical-stage kinase inhibitor in live HCT116 cells identified 42 significantly stabilised proteins (FDR < 0.05, fold-change > 1.5). Parallel LiP-MS in the same cell line identified 18 proteins with significant peptide-level accessibility changes. Nine proteins appeared in both datasets — a convergence rate of 50% on the LiP-MS side and 21% on the PISA side — all nine confirmed by SPR with purified protein. The convergence matrix (inset) shows the nine validated targets, including the intended kinase target and eight additional interactors.

Competitive ABPP-MS selectivity profiling heatmap showing a covalent inhibitor tested across 48 cysteine residues in 32 proteins, with colour intensity representing percent probe displacement at three compound concentrations.

ABPP-MS covalent inhibitor selectivity profile: 48 cysteines, three concentrations

Competitive ABPP-MS profiling of a cysteine-targeting covalent inhibitor at 0.1, 1, and 10 µM across 48 functional cysteine sites in 34 proteins. The probe displacement heatmap identifies the intended target at sub-micromolar concentrations (IC₅₀ = 120 nM) and six off-target sites engaged only at the highest concentration (≥ 10 µM). Three of six off-target sites were confirmed by orthogonal PISA/TPP in live cells.

PAL-MS target identification bar chart showing enrichment ratios for the top 20 proteins captured by a clickable photoaffinity probe of a natural product, with ACAT1 as the top hit at >10-fold enrichment over control.

PAL-MS target capture: natural product probe enrichment profile

Photoaffinity probe based on a bioactive polyphenol scaffold was incubated with live HepG2 cells, UV-crosslinked, and enriched by click chemistry–biotin pulldown. Top 20 enriched proteins shown with enrichment ratio relative to UV-only control. Top hit (ACAT1, >10-fold enrichment) was confirmed by thermal shift in live cells (T_agg shift = 2.1°C at 10 µM) and by SPR using purified protein (K_d = 10.3 µM).

Case Study: Multi-Platform Target Deconvolution of Chlorogenic Acid Identifies ACAT1 as a Direct Target

Wang Q., Du T., Zhang Z., et al. "Target fishing and mechanistic insights of the natural anticancer drug candidate chlorogenic acid." Acta Pharmaceutica Sinica B 2024;14(10):4431–4442. https://doi.org/10.1016/j.apsb.2024.07.005 PMCID: PMC11544177.

Background

Chlorogenic acid (CGA), a naturally occurring polyphenol abundant in coffee, fruits, and vegetables, has been reported to exhibit anticancer activity across multiple cancer cell lines. However, like many natural products with broad bioactivity, its direct protein target(s) remained undefined — impeding rational optimisation and mechanism-based development. Conventional affinity chromatography approaches had been attempted but were complicated by CGA's polypharmacology and the non-specific binding inherent to phenolic compound classes.

Methods

The authors applied a multi-platform chemical proteomics strategy. First, a bifunctional photoaffinity probe (PAL-CGA) was designed by installing a minimalist diazirine crosslinker and an alkyne click handle at a position on the CGA scaffold that preserved bioactivity. PAL-CGA was incubated with live cancer cells, UV-irradiated to covalently capture interacting proteins, conjugated to biotin via CuAAC click chemistry, and enriched on streptavidin beads. LC-MS/MS identified captured proteins. Parallel validation employed a thermal stabilization assay to confirm target engagement in live cells without probe modification, followed by surface plasmon resonance (SPR) to measure direct binding affinity and molecular docking to predict the binding mode.

Results

PAL-MS identified acyl-coenzyme A:cholesterol acyltransferase 1 (ACAT1) as the top enriched protein, with >10-fold enrichment over controls. Thermal stabilization assay confirmed that CGA stabilised ACAT1 in live HepG2 and HCT116 cells in a concentration-dependent manner (T_agg shift = 2.1°C at 10 µM). SPR using purified ACAT1 protein measured a direct binding K_d of 10.3 µM. Molecular docking predicted that CGA occupies the ACAT1 active site, forming hydrogen bonds with key catalytic residues H359 and H460. Functional validation showed that CGA inhibits ACAT1 enzymatic activity (IC₅₀ = 8.7 µM) and reduces cholesterol esterification in cancer cells — a mechanistic link between ACAT1 binding and the observed antiproliferative effect.

Significance for Global Drug Target Identification

This study demonstrates three principles central to our target ID service: first, that photoaffinity probe design enables direct capture of natural product–protein interactions even for compounds with modest affinity; second, that orthogonal methods — PAL-MS for capture, thermal stabilization assay for live-cell confirmation, SPR for affinity measurement — provide convergent evidence across independent physicochemical principles; and third, that chemical proteomics can resolve the target of a complex natural product where earlier single-method approaches had been inconclusive.

Multi-platform target deconvolution workflow from Wang et al. 2024 showing PAL-MS enrichment of ACAT1 from live cells, thermal stabilization assay confirmation, SPR binding affinity measurement, and molecular docking of chlorogenic acid in the ACAT1 active site.

Figure 1 from Wang et al. 2024 (Acta Pharm Sin B, DOI: 10.1016/j.apsb.2024.07.005, PMC11544177). Multi-platform target deconvolution of chlorogenic acid using PAL-MS, thermal stabilization assay, SPR, and molecular docking — demonstrating orthogonal cross-validation of ACAT1 as the direct target. CC BY 4.0.

FAQ

Frequently Asked Questions

Q: What types of compounds are compatible with your target ID service?

Most small molecules are compatible with at least one platform in our suite. Soluble, non-covalent compounds with moderate-to-high affinity (K_d < 10 µM) are well-suited to PISA/TPP, LiP-MS, and PAL-MS. Covalent inhibitors are ideal for ABPP-MS. Natural products and fragments are compatible with protein microarray-MS and LiP-MS. We assess compound-specific compatibility during the project design phase and recommend the optimal method combination for each chemical class.

Q: How many targets do you typically identify per project?

The number varies with compound properties, biological system, and stringency thresholds. In a typical PISA/TPP screen against a drug-like compound in a human cell line, we identify 2–15 significantly shifted proteins. Of these, 1–5 are usually confirmed by a second orthogonal method. ABPP campaigns identify a defined family of enzyme targets depending on probe selectivity. Each project report transparently documents the full candidate list with statistical support — not just the confirmed subset — so you retain complete information about the chemical proteomics landscape of your compound.

Q: How do you distinguish true targets from false positives?

Two mechanisms: statistical rigour within each method and cross-platform validation. Within each method, we apply FDR-controlled target detection, triplicate measurements, effect-size thresholds, and competition controls (for PAL-MS and ABPP). Across methods, a target identified by two independent physicochemical principles — for example, thermal stabilisation in PISA/TPP and conformational protection in LiP-MS — carries substantially more confidence than a hit from any single method. Our final report includes only targets that survive both statistical and orthogonal validation filters.

Q: What if no target is identified?

No-target outcomes are rare with our multi-platform approach, but they can occur for compounds with very low affinity (K_d > 50 µM), poor solubility, or targets expressed below the detection limit of the proteomics platform. In these cases, we report the technical outcome transparently, discuss the likely explanations (low abundance target, low affinity, solubility limitations), and recommend alternative approaches — including higher-concentration retest, alternative cell lines, or target-immobilised methods. A pilot feasibility experiment can be conducted before committing to a full campaign for compounds with uncertain compatibility.

Q: Can you work with natural products or complex mixtures?

Yes. Protein microarray-MS is particularly well-suited to complex mixtures, as it detects binding without requiring pre-purification of individual components. LiP-MS detects conformational changes independent of affinity, making it useful for weakly binding natural products. PAL-MS with a clickable probe can be applied to natural products that contain a suitable functional handle for modification. Our scientists assess the specific properties of your natural product or mixture during the feasibility consultation.

Q: How long does a typical target ID project take?

A standard two-platform project (primary screen + orthogonal validation) takes 6–8 weeks from sample receipt to final report. Single-platform feasibility experiments can be completed in 2–3 weeks. Full multi-platform campaigns (3+ methods) typically require 8–12 weeks. Timelines are confirmed during project design and depend on cell culture requirements, probe synthesis needs (PAL-MS), and the number of orthogonal methods deployed.

Q: Do I need to know my compound's target class or mechanism to start?

No — in fact, the absence of a target hypothesis is the most common starting point for target identification projects. Label-free methods (PISA/TPP, LiP-MS) require no target hypothesis and no compound modification, making them ideal first-line approaches for de novo target deconvolution campaigns. If a target hypothesis does exist, we can incorporate it into the platform selection and validation strategy to confirm or challenge the proposed mechanism.

Q: Can you identify binding sites in addition to target proteins?

Yes. LiP-MS identifies peptide-level accessibility changes that correspond to binding sites, providing a structural footprint of the drug–protein interaction. PAL-MS with MS2 sequencing of crosslinked peptides can identify the specific residues engaged by the probe. ABPP-MS maps reactivity at individual cysteine residues, identifying the exact site of covalent modification. Binding site information is included in the deliverable package whenever the applied method supports it.

References

Wang Q., Du T., Zhang Z., et al. Target fishing and mechanistic insights of the natural anticancer drug candidate chlorogenic acid. Acta Pharm Sin B. 2024;14(10):4431–4442.
Tao A.J., Jiang J., Gadbois G.E., et al. A biotin targeting chimera (BioTAC) system to map small molecule interactomes in situ. Nat Commun. 2023;14:8016.
Niphakis M.J., Cravatt B.F. Ligand discovery by activity-based protein profiling. Cell Chem Biol. 2024;31(9):1636–1651.
Scharnow A.M., Solinski A.E., et al. In situ biofilm affinity-based protein profiling identifies the streptococcal hydrolase GbpB as the target of a carolacton-inspired chemical probe. J Am Chem Soc. 2024;146(33):23449–23456.

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