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4.3:

Conserved Binding Sites

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Molecular Biology
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JoVE Core Molecular Biology
Conserved Binding Sites

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Generally, ligand binding sites are located within specific amino acid clusters or domains, dedicated to a certain type of interaction.   Parts that are crucial to the function of a domain, such as ligand binding, remain unchanged during evolution because, as a rule, mutations that eliminate vital functions are eliminated by natural selection.  For instance, many nuclear proteins, including transcription factors, contain FF domains that bind to RNA polymerase II. These domains get their name from the two phenylalanine amino acids they contain on separate helices.  These phenylalanine amino acids, together with a few other highly-conserved amino acids form the hydrophobic core of the binding site. Replacing these amino acids would disrupt the formation of this specific structure, hence affect its ability to bind to RNA polymerase II.  Scientists use evolutionary tracing to find conserved regions of domains. This is performed by comparing genome and protein sequences of similar domains and identifying the amino acids that remain unchanged. Subsequent analyses of these related sequences allow identification of clusters formed by the conserved amino acids. These data can be used to create 3D models to determine the shapes of proteins as well as the optimal structures of their binding sites. Analyzing conserved sequences and structures helps scientists understand evolutionary relationships between proteins but also allows them to predict binding sites of novel proteins containing comparable clusters.

4.3:

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.

Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the interaction energy of different amino acid residues with the ligand and predicts the ones where binding energy is at a minimum to be potential binding sites. However, examining conserved sequences is often used in conjunction with other methodologies to enhance this prediction further. Structurally conserved residues can be used to distinguish between binding sites and exposed protein surfaces. The amino acids, Trp, Phe, and Met, are highly conserved in binding sites, and no such conservation is observed in the case of exposed protein surfaces.

Various computational tools can predict binding sites using a mix of structural, energetic, and conserved binding site methodologies.  ConCavity is a tool that can be used to predict 3D ligand-binding pockets and individual ligand-binding residues. The algorithm used directly integrates evolutionary sequence conservation estimates with structure-based prediction. Another tool, MONKEY, is used to identify conserved transcription-factor binding sites in multispecies alignments. It employs factor specificity and binding-site evolution models to compute the likelihood that putative sites are conserved and assign statistical significance to each prediction.

Suggested Reading

  1. Ma, B., Elkayam, T., Wolfson, H., & Nussinov, R. (2003). Protein–protein interactions: structurally conserved residues distinguish between binding sites and exposed protein surfaces. Proceedings of the National Academy of Sciences, 100(10), 5772-5777.
  2. Tsujikawa, H., Sato, K., Wei, C., Saad, G., Sumikoshi, K., Nakamura,S., … & Shimizu, K. (2016). Development of a protein–ligand-binding site prediction method based on interaction energy and sequence conservation. Journal of structural and functional genomics, 17(2-3), 39-49.
  3. Capra, J. A., Laskowski, R. A., Thornton, J. M., Singh, M., & Funkhouser, T. A. (2009). Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure. PLoS Comput Biol, 5(12), e1000585.
  4. Moses, A. M., Chiang, D. Y., Pollard, D. A., Iyer, V. N., & Eisen, M. B. (2004). MONKEY: identifying conserved transcription-factor binding sites in multiple alignments using a binding site-specific evolutionary model. Genome biology, 5(12), 1-15.