Mapping the Hydropathy of Amino Acids Based on Their Local Solvation Structure
Online Publication Date2014-06-05
Print Publication Date2014-06-19
Permanent link to this recordhttp://hdl.handle.net/10754/598757
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AbstractIn spite of its relevant biological role, no general consensus exists on the quantitative characterization of amino acid's hydropathy. In particular, many hydrophobicity scales exist, often producing quite different rankings for the amino acids. To make progress toward a systematic classification, we analyze amino acids' hydropathy based on the orientation of water molecules at a given distance from them as computed from molecular dynamics simulations. In contrast with what is usually done, we argue that assigning a single number is not enough to characterize the properties of an amino acid, in particular when both hydrophobic and hydrophilic regions are present in a residue. Instead we show that appropriately defined conditional probability densities can be used to map the hydrophilic and hydrophobic groups on the amino acids with greater detail than possible with other available methods. Three indicators are then defined based on the features of these probabilities to quantify the specific hydrophobicity and hydrophilicity of each amino acid. The characterization that we propose can be used to understand some of the ambiguities in the ranking of amino acids in the current scales. The quantitative indicators can also be used in combination with standard bioinformatics tools to predict the location of transmembrane regions of proteins. The method is sensitive to the specific environment of the amino acids and can be applied to unnatural and modified amino acids, as well as to other small organic molecules. © 2014 American Chemical Society.
CitationBonella S, Raimondo D, Milanetti E, Tramontano A, Ciccotti G (2014) Mapping the Hydropathy of Amino Acids Based on Their Local Solvation Structure. J Phys Chem B 118: 6604–6613. Available: http://dx.doi.org/10.1021/jp500980x.
SponsorsThe authors are grateful to W. Babiaczyk and A. Gebreissilassie for running the MD simulations preliminary to the Gaussian Mixture reconstruction and analysis performed in this work and to G. Csanyi for pointing us in the direction of Gaussian Mixtures. Funding from the IIT SEED project No. 259 SIMBEDD and from award number KUK-I1-012-43 made by King Abdullah University of Science and Technology (KAUST), is acknowledged.
PublisherAmerican Chemical Society (ACS)
CollectionsPublications Acknowledging KAUST Support
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