| Literature DB >> 35887019 |
Romain Magnez1, Christian Bailly2, Xavier Thuru1.
Abstract
The review highlights how protein-protein interactions (PPIs) have determining roles in most life processes and how interactions between protein partners are involved in various human diseases. The study of PPIs and binding interactions as well as their understanding, quantification and pharmacological regulation are crucial for therapeutic purposes. Diverse computational and analytical methods, combined with high-throughput screening (HTS), have been extensively used to characterize multiple types of PPIs, but these procedures are generally laborious, long and expensive. Rapid, robust and efficient alternative methods are proposed, including the use of Microscale Thermophoresis (MST), which has emerged as the technology of choice in drug discovery programs in recent years. This review summarizes selected case studies pertaining to the use of MST to detect therapeutically pertinent proteins and highlights the biological importance of binding interactions, implicated in various human diseases. The benefits and limitations of MST to study PPIs and to identify regulators are discussed.Entities:
Keywords: biophysical methods; immune checkpoint; microscale thermophoresis; protein antibodies; protein functions; protein–protein interactions; small molecules
Mesh:
Substances:
Year: 2022 PMID: 35887019 PMCID: PMC9315744 DOI: 10.3390/ijms23147672
Source DB: PubMed Journal: Int J Mol Sci ISSN: 1422-0067 Impact factor: 6.208
Most commonly used biochemical and biophysical methods to investigate PPIs.
| Methods | Description | Ref. |
|---|---|---|
| Co-immunoprecipitation | Gold standard with endogenous proteins | [ |
| Affinity electrophoresis | For binding constants | [ |
| Phage display | HTS | [ |
| Proximity ligation assay (PLA) | Immuno-histochemical method | [ |
| Tandem affinity purification (TAP) | High-throughput identification | [ |
| Surface plasmon resonance (SPR) | Label-free/immobilization required | [ |
| Dynamic light scattering (DLS) | Screening/No immobilization or labeling | [ |
| Bio-layer interferometry (BLI) | HTS/Label-free | [ |
| Isothermal titration calorimetry (ITC) | Quantitative/Thermodynamics/No label or immobilization | [ |
| Microscale thermophoresis (MST) | HTS/No immobilization/Can work in complex medium | [ |
Figure 1Comparison between antibodies, peptides and small molecules with main advantages and disadvantages as PPI modulators.
Figure 2(A) Schematic representation of the optical system. Fluorescent molecules in the 16 capillaries are excited and the fluorescence detected by the same objective. An IR laser heats up locally, and thermophoresis of the fluorescent molecules across the temperature gradient is detected. (B) The intensity of fluorescence changes due to the movement of molecules away from the heated area differs when the ligand is bound. A binding curve can be established from difference of thermophoresis between the fluorescent molecules of both unbound and bound states against the ligand concentration. Binding constants Kd can be derived from binding curves. Graphs are represented as fraction bound against ligand concentration. Data represent three independent experiments and were fitted to a Kd binding model assuming a 1:1 binding stoichiometry.
PD-L1 binding of selected small molecules evaluated by MST compared to SPR and ITC.
| Compounds | MST Kd (nM) | SPR Kd (nM) | ITC Kd (nM) |
|---|---|---|---|
| BMSpep-57 * | 19 ± 2 | 20 ± 2 | / |
| BMS-103 * | 44 ± 13 | 16 ± 2 |
|
| BMS-142 * | 13.2 ± 1.5 | 12 ± 2 |
|
| Pyrazolone 11 ** | 83 ± 12 |
| 120 |
| Pyrazolone 17 ** | 1.19 ± 0.4 |
| / |
| Pyrazolone 32 ** | 19 ± 3 |
| / |
* Compounds from BMS, as described in [66]. ** Pyrazolone derivatives, recently designed and characterized [72,73].
Figure 3Binding of BMS-142, BMS-103, Pyrazolone 17, Pyrazolone 32 against human PD-L1 protein using MST. Binding of all four compounds to labeled PD-L1 resulted in a clear response in fluorescence signal, dependent on the concentration of the compound. Graphs are represented as Fnorm [‰] against ligand concentration. Data represent three independent experiments and were fitted to a Kd binding model assuming a 1:1 binding stoichiometry.