How characterisation techniques enable scientists to study the invisible world, from protein interactions to material properties
In the quest to understand the world around us, from the inner workings of a living cell to the materials that build our modern world, scientists are faced with a fundamental challenge: how do you study what you cannot directly see?
The answer lies in characterisation—a powerful set of techniques that act as science's eyes and ears, allowing researchers to infer the properties, structure, and composition of the invisible.
Whether it's identifying a new protein interaction that could lead to a life-saving drug or developing a stronger, lighter metal for aerospace, characterisation is the crucial first step that turns unknown samples into understood components. This process is the bedrock of discovery, enabling innovations across medicine, technology, and environmental science by providing a detailed portrait of the microscopic and molecular world 2 5 .
Revealing structures beyond the limits of human vision
Understanding interactions at the atomic and molecular level
Determining characteristics that define material behavior
At its core, characterisation is the process of systematically identifying and describing the key features of a substance or system. The specific approach varies dramatically by field, but the underlying goal remains the same: to create a definitive profile of the subject under study.
In the realm of data science, characterisation involves summarizing the general characteristics of a target dataset. This process helps in understanding data quality, structure, and underlying patterns, which is essential for everything from business intelligence to machine learning 2 .
In the physical sciences, such as materials science and chemistry, characterisation techniques are used to analyze a material's structure, composition, and properties. Methods like X-ray diffraction, scanning electron microscopy, and nuclear magnetic resonance are indispensable for this task 4 9 .
In the life sciences, characterisation is used to understand the intricate machinery of biology. A central area of focus is the study of protein-protein interactions (PPIs). It is estimated that over 80% of proteins do not work in isolation but rather interact with others to form complex networks 5 .
To illustrate the power of characterisation, let's examine a real-world experiment designed to study the interaction between two proteins: the tumor suppressor p53 and its regulatory protein, MDM2. This interaction is a critical cancer drug target because inhibiting it can reactivate p53's ability to fight tumors 5 .
The experiment uses a technique called Fluorescence Polarisation (FP). The principle is elegant: a small peptide from p53 is tagged with a fluorescent dye. When this tagged peptide is excited by polarised light, it tumbles rapidly in solution, emitting light in a relatively depolarised state.
However, if the peptide binds to the much larger MDM2 protein, the resulting complex tumbles much more slowly. This slower rotation results in a higher degree of polarised light being emitted. The change in polarisation is measured in millipolarisation units (mP) and is directly proportional to the amount of binding 5 .
Molecular rotation affects polarization of emitted light
Fast rotation
Low polarization
Slow rotation
High polarization
A fixed, low concentration of the fluorescently tagged p53 peptide is prepared in a buffer solution.
A solution of the MDM2 protein is prepared and added to the p53 peptide in increasing concentrations across a multi-well plate.
The plate is incubated to allow the binding reaction between p53 and MDM2 to reach equilibrium.
A microplate reader equipped with polarising filters is used to excite the samples with polarised light and measure the polarisation of the emitted light.
The mP values are plotted against the concentration of MDM2 to generate a binding curve, from which the dissociation constant (Kd)—a precise measure of binding affinity—can be calculated 5 .
The results from the FP assay provide a clear, quantitative measure of the p53-MDM2 interaction. As the concentration of MDM2 increases, the FP signal (mP) also increases, following a sigmoidal curve that plateaus once all the p53 peptide is bound.
Table 1: The increase in Fluorescence Polarisation (mP) with increasing concentrations of MDM2 protein indicates the formation of a complex with the fluorescent p53 peptide. The data begins to plateau at higher concentrations as binding sites become saturated.
The calculated Kd value tells researchers how tightly the p53 peptide binds to MDM2. A low Kd indicates strong binding. This information is crucial for:
While FP is excellent for studying interactions in solution, the full spectrum of characterisation requires a diverse toolkit. Each technique has unique strengths, making it suitable for different kinds of questions. The following table compares some of the most prevalent biophysical methods used in PPI studies, illustrating the trade-offs between throughput, information gained, and practical constraints 5 .
| Method | Principle | Advantages | Disadvantages | Affinity Range |
|---|---|---|---|---|
| Fluorescence Polarisation (FP) | Measures change in molecular rotation upon binding | High-throughput, low cost, simple "mix-and-read" format | Requires a size change; susceptible to fluorescence interference | nM to mM |
| Surface Plasmon Resonance (SPR) | Measures mass change on a sensor surface | Label-free, provides real-time kinetic data | Requires immobilization, which can affect binding | sub-nM to low mM |
| Isothermal Titration Calorimetry (ITC) | Measures heat released or absorbed during binding | Label-free, provides full thermodynamic profile | Low throughput, high protein consumption | nM to sub-mM |
| Nuclear Magnetic Resonance (NMR) | Measures changes in magnetic properties of atomic nuclei | Provides high-resolution structural information | Requires isotopic labeling, high sample consumption | µM to mM |
Table 2: This comparison highlights how scientists select characterisation techniques based on the specific information they need, whether it's binding strength, reaction speed, or structural detail.
Techniques like FP allow for rapid screening of thousands of compounds, making them ideal for drug discovery pipelines where speed and efficiency are critical.
Methods like SPR provide real-time data on binding kinetics, revealing not just if molecules interact, but how fast they associate and dissociate.
Behind every successful characterisation experiment is a suite of reliable reagents and materials. These essential tools form the basic building blocks of discovery in the molecular biology lab.
Enzymes essential for amplifying DNA sequences via Polymerase Chain Reaction (PCR) for further analysis .
Solutions that maintain a stable pH and chemical environment, ensuring biological reactions occur correctly and reproducibly 3 .
Molecules like fluorescein or Cy5 used to tag proteins or DNA, enabling detection and measurement in techniques like FP and microscopy 5 .
Molecular "scissors" that cut DNA at specific sequences, fundamental to techniques like molecular cloning .
Used to separate and purify biological molecules like DNA or proteins from a complex mixture 3 .
Consumables certified to be free of contaminating RNases, which is critical when working with easily degraded RNA 3 .
Table 3: From enzymes that power reactions to consumables that prevent contamination, these reagents are the unsung heroes of reliable and reproducible scientific characterisation.
The field of characterisation is constantly evolving, pushing the boundaries of what we can observe. Current challenges include handling the immense complexity and high dimensionality of data from modern instruments and developing methods to study biological processes in real-time within living cells 2 5 .
Future directions point toward more integrated and automated platforms that can handle complex workflows with minimal human intervention, increasing reproducibility and throughput.
The increased use of in-situ techniques that analyze materials under real-world conditions, providing more relevant data about how systems behave in their natural environments.
As these tools become more powerful and accessible, they will continue to illuminate the darkest corners of the natural world, fueling the innovations of tomorrow and reminding us that the key to answering the biggest scientific questions often lies in perfectly characterising the smallest of details.