BIND database

Chen, X., Liu, M., Gilson, M.K. The binding database Overview and user s guide. Biopoly-mers/Nucleic Acid Sci. 2002,61,127—il. 

Using PubMed abstracts, the PreBIND system can identify protein-protein interactions with an SVM system. The protein-protein interactions identified by the automated PreBIND system are then combined and scrutinized manually to produce the BIND database (http //bind.ca). Based on a L10%O cross-validation of a dataset of 1094 abstracts, the SVM approach had a precision and recall of 92%, whereas a naive Bayes classifier had a precision and recall of 87%. 

H-J 1998. Prediction of Binding Constants of Protein Ligands A Fast Method for the aritisation of Hits Obtained from De Novo Design or 3D Database Search Programs. Journal of nputer-Aided Molecular Design 12 309-323. 

If it is known that a drug must bind to a particular spot on a particular protein or nucleotide, then a drug can be tailor-made to bind at that site. This is often modeled computationally using any of several different techniques. Traditionally, the primary way of determining what compounds would be tested computationally was provided by the researcher s understanding of molecular interactions. A second method is the brute force testing of large numbers of compounds from a database of available structures. 

The basic structural unit of these two-sheet p helix structures contains 18 amino acids, three in each p strand and six in each loop. A specific amino acid sequence pattern identifies this unit namely a double repeat of a nine-residue consensus sequence Gly-Gly-X-Gly-X-Asp-X-U-X where X is any amino acid and U is large, hydrophobic and frequently leucine. The first six residues form the loop and the last three form a p strand with the side chain of U involved in the hydrophobic packing of the two p sheets. The loops are stabilized by calcium ions which bind to the Asp residue (Figure S.28). This sequence pattern can be used to search for possible two-sheet p structures in databases of amino acid sequences of proteins of unknown structure. 

From these preliminary results, we can assume that such enantiophore queries could be used to search in a database of compounds with unknown enantioselectiv-ities. With respect to the percentage of resolved samples that are retrieved in CHIRBASE, the resulting list should contain a similar yield of compounds providing favorable specific interactions with the CSP receptor binding sites. 

These first-created enantiophores are rudimentary, but may serve as useful guidelines for a further design of more sophisticated and efficient search queries in consideration of possible alternative modes of binding and conformational changes in the CSP receptor structure. Undoubtedly, this query optimization will soon take advantage of the backgrounds of our new 3D-database project called CHIR-SOURCE. 

These structural key descriptors incorporate a remarkable amount of pertinent molecular arrangements covering each type of interaction involved in ligand-receptor bindings [26]. Since every structure in a database is represented by one or more of the 960 key codes available in ISIS, suppose that two molecules include respectively A and B key codes, then the Tanimoto coefficient is given by ... 

Virtual screening uses computational docking methods to assess which of a large database of compounds will fit into the unliganded structure of the target protein. Current protocols and methods can, with up to 80% success, predict the binding position and orientation of ligands that are known to bind... 

Start with a database of randomly chosen N ligands of length M grown in the binding site of protein P. 

This approach can be generalized to all possible types of experimental data that may be generated. All chemical structures available in public databases or internal to a company typically feature at least the in vitro binding assay data and additionally, the three-dimensional structure of the protein and/or bound ligand. A chemical compound C will therefore be ... 

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