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In biochemistry, a binding site is a region on a protein or piece of DNA or RNA to which ligands (specific molecules and/or ions) may form a chemical bond. Characteristics of binding sites are chemical specificity, a measure of the types of ligands that will bond, and affinity, which is a measure of the strength of the chemical bond.
An equilibrium exists between unbound ligands and bound ligands. Saturation is the fraction of total binding sites that are occupied at any given time. When more than one type of ligand can bind to a binding site, the ligands can compete with each other.
Binding sites are often an important component of the functional characterization of biomolecules. For example, the characterization of the active site of a substrate to an enzyme is essential to model the reaction mechanism responsible for the chemical change from substrate to product.
Binding sites on proteins can sometimes recognize other proteins. When a binding site of one protein identifies with another protein's surface, a non-covalent bond is formed between the two polypeptide (peptide) chains and a combined new protein is formed.
A more specific type of binding site is the transcription factor binding site present on DNA. Short, recurring patterns in DNA often indicate sequence-specific binding sites for proteins such as nucleases and transcription factors; ribosome binding, mRNA processing, and transcription termination are also signaled by these sequence motifs.. Prediction of protein (esp. transcription factors) binding sites on DNA has recently become an area of active research and different tools have been produced for it. With the advent of deep learning, newer and more accurate methods have been produced; these methods often benefit from the large volume of available data which is generated from high-throughput technologies, such as the protein binding microarrays and use deep learning modules such as the convolutional neural networks (CNNs) and the recurrent neural nets (RNNs).
Binding sites also exist on antibodies as specifically coded regions that bind antigens based upon their structure. Several supervised machine learning models and applications were suggested to identify the binding sites, including techniques involving 3D convolutional neural networks.
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