Protein descriptors

Below, some common strategies for description of peptide and DNA sequences are briefly reviewed starting from amino add descriptors, which are of fundamental importance for deriving most of the protein descriptors. The last section deals with some approaches for proteomics map characterization. 

A simple approach to protein description consists of representing a protein by a sequence of properties of its constituent amino acids. Each amino acid is described by one ore more properties and therefore the total number of protein descriptors is given by the product of the number of amino acids in the protein and the number of selected amino acid properties. As this number of descriptors increases very fast with the size of proteins, this approach is usually applied to small- and medium-size peptides. Moreover, in QSAR studies that require uniform-length descriptors, it can be used only to describe a series of peptide analogues, vhich are peptide sequences with the same length. To enable QSAR studies of peptide sequences with different length, some method is required that is able to translate the peptide sequences into vectorial descriptors with the same number of variables. For example, ACC transforms were applied to compress information about principal properties of amino acids into peptide sequences with different length. 

A general approach to derive protein descriptors is based on representing a protein by a macromolecular graph in which vertices represent the a-carbon of the amino acid residues and edges represent the covalent peptidic bonds. Loops on vertices can be added to account for noncovalent interactions within a chain or between chains. 

For instance, several protein descriptors both topological and geometric were calculated by weighting amino acids with the WHIM descriptors related to size (Am), shape (Km), and atom distribution density (Dm) of the single amino acids [Mauri, Ballabio et al, 2008]. These amino acid properties were calculated on the isolated 3D structure of amino acids and are... 

To construct the protein descriptors, z-scales were used, z-scales, originally developed as a descriptor of amino acids, contain three variables designated Zj, z, and Zj [14]. The z parameters were determined by principal component analysis of 29 physicochemical parameters characterizing 20 natural amino acids. The first, second, and third principal components, corresponding to z, z, and z, respectively, could be tentatively interpreted as hydrophobicity, steric, and electronic properties. 

GAs or other methods from evolutionary computation are applied in various fields of chemistry Its tasks include the geometry optimization of conformations of small molecules, the elaboration of models for the prediction of properties or biological activities, the design of molecules de novo, the analysis of the interaction of proteins and their ligands, or the selection of descriptors [18]. The last application is explained briefly in Section 9.7.6. 

B and W J Howe 1991. Computer Design of Bioactive Molecules - A Method for Receptor-Based Novo Ligand Design. Proteins Structure, Function and Genetics 11 314-328. i H L 1965. The Generation of a Unique Machine Description for Chemical Structures - A hnique Developed at Chemical Abstracts Service. Journal of Chemical Documentation 5 107-113. J 1995. Computer-aided Estimation of Symthetic Accessibility. PhD thesis. University of Leeds, itan R, N Bauman, J S Dixon and R Venkataraghavan 1987. Topological Torsion A New )lecular Descriptor for SAR Applications. Comparison with Other Descriptors. Journal of emical Information and Computer Science 27 82-85. 

Pet food purchases are based on the satisfaction of the owner, and pet food proliferation is enormous with accompanying advertising descriptors including natural, Hte, low calorie, high calorie, low protein, and high protein. New therapeutic series, sizes, densities, colors, and attractive packaging have also added to the proliferation. 

Hansch and Leo [13] described the impact of Hpophihdty on pharmacodynamic events in detailed chapters on QSAR studies of proteins and enzymes, of antitumor drugs, of central nervous system agents as well as microbial and pesticide QSAR studies. Furthermore, many reviews document the prime importance of log P as descriptors of absorption, distribution, metabolism, excretion and toxicity (ADMET) properties [5-18]. Increased lipophilicity was shown to correlate with poorer aqueous solubility, increased plasma protein binding, increased storage in tissues, and more rapid metabolism and elimination. Lipophilicity is also a highly important descriptor of blood-brain barrier (BBB) permeability [19, 20]. Last, but not least, lipophilicity plays a dominant role in toxicity prediction [21]. 

It is also important to remember that, in contrast to other methods, VolSurf can calculate descriptors for small, medium and large molecules, as well as for biopolymers such as DNA fragments, peptides and proteins. 

---