PELSA is designed to map ligand-target proteins and pinpoint their binding regions with proteome-wide coverage, pushing beyond the limitations of traditional methodologies. Unlike conventional assays that rely on chemical modification of ligands—often a cumbersome process introducing artifacts or reducing target diversity—PELSA bypasses this step altogether. Instead, it exploits differential proteolytic digestion to reveal where ligands stabilize proteins upon binding.

At the heart of PELSA lies a clever biochemical logic: proteins engaged by ligands display enhanced local stability against enzymatic cleavage. Researchers introduce ligands directly into cell lysates, followed by a precisely timed, partial digestion using trypsin at a concentration of 0.5 mg/ml. This short, controlled digestion selectively trims exposed, unprotected regions, while ligand-bound domains exhibit resistance. When analyzed en masse through advanced mass spectrometry, protected peptide fragments serve as fingerprints of the ligand-binding sites, providing rich spatial and quantitative data.

This localized protection approach enables researchers to achieve dual objectives in a single, streamlined workflow: identifying the target proteins and resolving the fine-scale binding interfaces without any ligand derivatization. Such a stimulus to proteomics is profound because it opens the door to studying an incredibly broad range of ligands—from synthetic drugs and antibodies to native metabolites and metal ions—without prior chemical tailoring or labeling.

Performing and analyzing PELSA experiments, however, demands thoughtful orchestration. Trialing multiple ligand concentrations is essential to tease out dose-dependent binding kinetics and affinities. Timing of the trypsinization step must be precisely calibrated to balance sufficient digestion against over-cleavage that would obscure stabilization signals. Moreover, rigorous quality control across replicates is imperative to distinguish true biological interactions from experimental noise.

To address these challenges and democratize the technology, the authors present PELSA-Decipher, an open-source software suite designed to streamline raw data processing, peptide quantification, and comprehensive visualization of binding events. This computational backbone radically simplifies handling the data complexity inherent to proteome-wide ligand-binding studies, enabling users to extract meaningful insights rapidly.

PELSA’s potential applications are wide-ranging. Among the initial demonstrations were analyses of staurosporine, an ATP-competitive kinase inhibitor, and 5-methyltetrahydrofolate, a critical metabolite in one-carbon metabolism. These case studies validated the assay’s sensitivity and spatial resolution by successfully mapping well-known protein targets and their ligand-bound motifs, underscoring the protocol’s reliability.

Further pushing the envelope, the authors employed PELSA for dose-dependent studies of small-molecule inhibitors of the HSP90 chaperone family, a group of proteins with major roles in cellular stress responses and cancer biology. This analysis not only corroborated known targets but also illuminated subtle dose-responsive binding dynamics previously difficult to observe at scale.

The elegance of PELSA lies not only in its biochemical design but also in its user-centric workflow. The entire protocol can be completed within a remarkably short timeframe—two days in total—encompassing one day for sample preparation and a second day devoted to liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis and computational processing. This efficiency positions PELSA as an attractive tool for rapid, high-throughput target identification in diverse research and pharmaceutical settings.

Beyond its technical strengths, PELSA addresses a persistent bottleneck in ligand interaction studies by overcoming the dependency on ligand modification. This property unlocks an essential level of biological realism by preserving the native state of ligands, which is critical for faithfully capturing physiologically relevant interactions. Consequently, researchers can now profile complex biological systems with minimal perturbation while achieving exquisite molecular detail.

Moreover, the assay’s peptide-centric resolution offers a unique advantage over protein-level approaches. By localizing binding-induced proteolytic protection down to specific protein regions or domains, scientists gain a granular view of interaction landscapes. Such insights are invaluable for rational drug design, allowing medicinal chemists to understand precisely which molecular contacts drive efficacy or selectivity.

From a computational standpoint, PELSA-Decipher’s integration of data processing and visualization tools ensures researchers can handle PELSA datasets with fewer barriers. Its user-friendly interface supports seamless analysis pipelines, facilitating dose response curve fitting, statistical validation, and graphical display of binding sites. This holistic platform empowers both experimentalists and computational biologists to collaborate more effectively.

Looking ahead, PELSA’s versatility raises exciting prospects for its use in mapping endogenous metabolite interactions at systems biology scale. Identifying metabolite-protein interplay with high fidelity could unlock new paradigms in cellular regulation studies, metabolic engineering, and biomarker discovery, thereby broadening the assay’s impact beyond drug development.

In summary, the peptide-centric local stability assay represents a powerful, modification-free strategy for unraveling the complex web of protein-ligand interactions. By cleverly leveraging partial proteolysis coupled with mass spectrometry and sophisticated computational tools, PELSA allows scientists to identify targets with unprecedented depth and speed. Its broad applicability and robust workflow herald a new era for proteomics-driven molecular discovery.

The science community eagerly anticipates further adoption of PELSA and continued enhancements to its accompanying software. As ligand-protein interaction landscapes become ever clearer, opportunities abound for accelerated therapeutic innovation and deeper mechanistic understanding of cellular machinery. PELSA stands poised as a transformative platform in this venture, bridging molecular precision with proteome-scale breadth.

For researchers interested in deploying this technique, the full protocol and the PELSA-Decipher software suite are publicly accessible, fostering transparency and reproducibility. Interested users can download PELSA-Decipher directly from GitHub, ensuring a straightforward avenue to integrate this cutting-edge assay into their experimental arsenal.

Subject of Research: Development and application of a peptide-centric assay (PELSA) for proteome-wide identification of protein targets and ligand binding sites.

Article Title: Peptide-centric local stability assay (PELSA) for sensitive identification of ligand-targeting proteins and binding sites at proteome scale.

Article References:
Wang, K., Li, K., Yan, J. et al. Peptide-centric local stability assay (PELSA) for sensitive identification of ligand-targeting proteins and binding sites at proteome scale. Nat Protoc (2026). https://doi.org/10.1038/s41596-026-01354-w

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41596-026-01354-w

Led by Senior Research Fellow Mohieddin Jafari at Tampere University, this study marks a paradigm shift in understanding therapeutic synergy at the proteome-wide scale. Unlike prior methods that merely detect whether drugs act synergistically, CoPISA delivers a mechanistic panorama by capturing dynamic alterations in protein solubility and stability across thousands of proteins simultaneously. This approach discerns both direct drug targets and collateral downstream effectors, enabling researchers to decode the cellular network perturbations engendered exclusively by combined drug actions, which are invisible when drugs are evaluated as single agents.

The research team applied CoPISA to two promising AML drug pairs: the combination of LY3009120, a pan-RAF inhibitor, with sapanisertib, an mTORC1/2 inhibitor (denoted LS), and ruxolitinib, a JAK1/2 inhibitor, paired with ulixertinib, an ERK1/2 inhibitor (denoted RU). These combinations had previously demonstrated robust anti-leukemic efficacy with minimal toxicity in multiple AML cell lines, patient-derived samples, and in vivo zebrafish xenograft models, but their synergistic molecular underpinnings remained speculative. By deploying CoPISA, Jafari and colleagues provided a high-resolution mechanistic map that reveals how each combination dismantles the leukemia cellular machinery through distinct yet complementary pathways.

CoPISA’s innovation lies in its ability to simultaneously profile thousands of proteins’ solubility and thermal stability changes following treatment, a physicochemical signature indicative of altered protein conformations, interactions, and functional states. This technique transcends traditional binding assays by identifying proteins indirectly affected by treatment, including those in complex signaling cascades or post-translational modification networks. Importantly, CoPISA identifies emergent “AND-gate” or conjunctional targeting effects, wherein proteins exhibit significant alterations only when exposed to both drugs in combination—an effect utterly absent in single-agent treatments.

A striking finding was the identification of conjunctional targeting of critical AML-related proteins such as DNMT3A, NPM1, and TP53. These proteins, central to leukemogenesis and disease progression, remained refractory to single drugs but were selectively destabilized or solubilized by the drug combinations. This revelation unveils previously hidden vulnerabilities in AML cells, providing a molecular rationale for combination therapy’s superior efficacy. Such conjunctional targeting implies that the drug pairs enforce specific molecular conditions simultaneously to overcome resistance mechanisms and disrupt oncogenic signaling dependencies.

Further mechanistic dissection revealed that the LS combination predominantly reconfigures processes associated with SUMOylation, chromatin condensation, mitotic regulation, and VEGF-mediated cell adhesion. These alterations signify a coordinated disruption of genomic stability and cell division, effectively halting leukemia cell proliferation. On the other hand, the RU combination perturbs a distinct set of pathways, prominently impairing DNA damage checkpoint controls, mitochondrial energy metabolism, and RNA splicing machinery. This mechanistic divergence highlights the specificity of proteome remodeling by different drug pairs, underscoring the adaptability of AML cells to unique stressors imposed by varied therapeutic regimens.

Such detailed proteome-level signatures permit an unprecedented understanding of how combination therapies function as integrated systems, rather than merely additive effects of two drugs. By revealing non-linear biological responses and emergent molecular phenotypes, CoPISA provides a platform to rationally design drug combinations that maximize synergy, minimize toxicity, and circumvent resistance mutations. This approach could be transformative in guiding precision oncology, where patient-specific molecular contexts demand carefully tailored combination regimens.

Jafari emphasizes that the implications of CoPISA extend beyond AML. Because its readout is based on general physicochemical changes in protein solubility and stability, it is broadly applicable across cancer types and therapeutic classes, including targeted agents, chemotherapeutics, and immunomodulators. The capacity to delineate mechanistic signatures from drug combinations enables a systems biology approach to uncovering new drug targets and resistance pathways, potentially accelerating drug discovery and repurposing efforts.

The research team is currently expanding the application of CoPISA into acute lymphoblastic leukemia (ALL), reporting preliminary results that mirror the mechanistic insights obtained in AML. These early findings signal a promising future for CoPISA-enabled investigations into diverse hematological malignancies and solid tumors. Moreover, by refining and scaling this workflow, it may soon become a routine component of preclinical drug development pipelines and clinical trial biomarker discovery.

Given the genetic heterogeneity and adaptive plasticity of cancers like AML, tools such as CoPISA play an essential role in advancing precision medicine. By illuminating the molecular interplay of drug pairs, this method empowers clinicians and researchers to predict effective combinations, monitor treatment responses at a mechanistic level, and ultimately improve patient outcomes. The molecular granularity afforded by CoPISA could redefine how combination therapies are conceptualized, offering hope against cancers that have long resisted curative treatment.

As drug combinations continue to dominate the therapeutic landscape in oncology, the ability to mechanistically profile their effects at the proteome level represents a monumental step forward. CoPISA bridges a critical knowledge gap by transforming proteomics into a dynamic readout of drug synergy, enabling a deeper appreciation of how targeted combinations can exploit cancer cell dependencies. This pioneering work not only unveils novel biological principles like conjunctional targeting but also charts a course toward more rational, effective, and safer cancer treatments.

The publication of these findings in Nature Communications marks a milestone for proteomics-driven therapy design and signals exciting new directions for cancer research. The scientific community eagerly anticipates further developments from Tampere University and collaborators harnessing CoPISA to unlock the full potential of combinatorial drug therapies in oncology and beyond.

Subject of Research:
Mechanistic profiling of combinational drug therapies in acute myeloid leukemia through proteome-wide solubility and stability alterations.

Article Title:
Solubility based mechanistic profiling of combinatorial drug therapy

News Publication Date:
25-Mar-2026

Web References:
https://doi.org/10.1038/s41467-026-70394-3

References:
Jafari M. et al. (2026). Solubility based mechanistic profiling of combinatorial drug therapy. Nature Communications.

Keywords:
Acute myeloid leukemia, AML, combinational therapy, proteomics, CoPISA, protein solubility, protein stability, drug synergy, conjunctional targeting, LY3009120, sapanisertib, ruxolitinib, ulixertinib, precision medicine, drug resistance mechanisms.