Binding affinity is the strength of the binding interaction between a single biomolecule (e.g. protein or DNA) to its ligand/binding partner (e.g. drug or inhibitor).
Binding affinity is influenced by non-covalent intermolecular interactions such as hydrogen bonding, electrostatic interactions, hydrophobic and Van der Waals forces between the two molecules. In addition, binding affinity between a ligand and its target molecule may be affected by the presence of other molecules.
Binding interfaces of protein-protein complexes serve a dual role in protein function, since they can exist both as surfaces of monomeric proteins and as the area buried upon complex formation. They hence should differ in chemical properties from both protein surfaces and protein cores.
Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods. Most computational prediction techniques require protein structures which limit their applicability to protein complexes with known structures.
Determining protein-protein interactions and their binding affinity are important in understanding cellular biological processes, discovery and design of novel therapeutics, protein engineering and mutagenesis studies. Due to the time and effort required in wet lab experiments, computational prediction of binding affinity from sequence or structure is an important area of research. Structure-based methods, though more accurate than sequence-based techniques, are limited in their applicability due to limited availability of protein structure data.
Many computational methods for binding affinity prediction have been proposed based on free energy perturbation, empirical scoring, and force-field potentials. Among computational binding affinity prediction methods, machine learning is preferred because of its implicit treatment of all factors involved in protein-protein interactions (PPIs) and the flexibility of using empirical data instead of a fixed or predetermined function form.
As a result, sequence-based prediction of binding affinity is an important research problem. Sequence-based binding affinity prediction is challenging because proteins interaction and binding affinity are dependent upon protein structures and functions.
For drug discovery, binding affinity is also measured as to rank order hits binding to the target and help design drugs that bind their targets selectively and specifically.
There are many ways to measure binding affinity and dissociation constants, such as ELISAs, gel-shift assays, pull-down assays, equilibrium dialysis, analytical ultracentrifugation, surface plasmon resonance, and spectroscopic assays.
Molecular recognition phenomena involving the association, usually by non-covalent interactions, of ligands to macromolecules with high affinity and specificity play a key role in biology. Non-covalent interactions are a number of relatively weak chemical interactions that stabilize the conformations and the interactions between molecules. Non-covalent interactions are abundant in nature and are very important in many research areas such as chemistry, biology, biochemistry, molecular recognition, drug design, materials science and beyond.
Low-affinity binding (high Ki level) implies that a relatively high concentration of a ligand is required before the binding site is maximally occupied and the maximum physiological response to the ligand is achieved. From the physical point of view, the ligand binding affinity is defined by the binding energy of the ligand to the receptor. The binding energy can be estimated either by the docking method or molecular dynamics (MD) simulations.