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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 chains and a combined new protein is formed.[1]

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ExamplesEdit

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.[2].

Binding sites also exist on antibodies as specifically coded regions that bind antigens based upon their structure.[3]

Prediction of Binding SitesEdit

Prediction of protein binding sites on DNA, especially for transcription factors, has recently become an area of active research. Various tools have been produced to aid the prediction of DNA binding sites.[4] With the advent of deep learning, newer and more accurate methods have been produced;[5] these methods often benefit from the large volume of available data which is generated from high-throughput technologies, such as the protein binding microarrays[6] and use deep learning modules such as the convolutional neural networks (CNNs) and the recurrent neural nets (RNNs). Several supervised machine learning models and applications have been suggested to identify the binding sites of antibodies,[7] including techniques involving 3D convolutional neural networks.[8]

See alsoEdit

ReferencesEdit

  1. ^ Alberts B, Bray D, Hopkin K, Johnson AD, Lewis J, Raff M, Roberts K, Walter P. (2010) Essential Cell Biology third edition.
  2. ^ D'haeseleer, Patrik. "What are DNA sequence motifs?". Nature Biotechnology. Nature. Retrieved 29 April 2017.
  3. ^ Binding Site - definition from Biology-Online.org
  4. ^ Hassanzadeh, Hamid Reza, et al. "MotifMark: Finding regulatory motifs in DNA sequences." Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE. IEEE, 2017.
  5. ^ Hassanzadeh, Hamid Reza, and May D. Wang. "DeeperBind: enhancing prediction of sequence specificities of DNA binding proteins." Bioinformatics and Biomedicine (BIBM), 2016 IEEE International Conference on. IEEE, 2016.
  6. ^ Newburger, Daniel E., and Martha L. Bulyk. "UniPROBE: an online database of protein binding microarray data on protein–DNA interactions." Nucleic acids research 37.suppl_1 (2008): D77-D82.
  7. ^ Grau J.; Ben-Gal I.; Posch S.; Grosse I. (2006). "VOMBAT: Prediction of Transcription Factor Binding Sites using Variable Order Bayesian Trees" (PDF). Nucleic Acids Research. 34 (W529–W533). doi:10.1093/nar/gkl212.
  8. ^ Jiménez, J; Doerr, S; Martínez-Rosell, G; Rose, AS; De Fabritiis, G (2017). "DeepSite: Protein binding site predictor using 3D-convolutional neural networks". Bioinformatics. 33: 3036–3042. doi:10.1093/bioinformatics/btx350. PMID 28575181.

External linksEdit