Protein automated methods

It has been recognized that many of the time-consuming interactive tasks carried out by an expert during the process of spectral analysis could be done more efficiently by automated computational systems [6]. Over the past few years, this potential has been realized to some degree. Today automated methods for NMR structure determination are playing a more and more prominent role and can be expected to largely supersede the conventional manual approaches to solving three-dimensional protein structures in solution. 

Day, S.H., Mao, A., White, R., Schulz-Utermoehl, T., Miller, R. and Beconi, M.G. (2005) A semi-automated method for measuring the potential for protein covalent binding in drug discovery. Journal of Pharmacological and Toxicological Methods, 52 (2), 278-285. 

Within the last two years since the Sequencer of Beckman Instruments became generally available, the automated method has been successfully applied predominantly to comparisons of homologous proteins such as the immunoglobulins. Elucidation of the sequence of 20 to 30 N-terminal residues in peptides as long as the L-chains, comprising 214 amino acids or the H-chains with more than 400 residues would have been impossible to achieve by the manual procedure without prior cleavage of the molecules and isolation of the N-terminal regions. 

The SCOP database is curated manually, with the objective of placing proteins in the correct evolutionary framework based on conserved structural features. Two similar enterprises, the CATH (class, architecture, topology, and homologous superfamily) and FSSP (/old classification based on structure-structure alignment of proteins) databases, make use of more automated methods and can provide additional information. 

Scheich C, Sievert V, Biissow K (2003), An automated method for high-throughput protein purification applied to a comparison of His-tag and GST-tag affinity chromatography, BMC Biotechnol. 3 12-19. 

An exponentially growing number of 3D protein structures is available in the public domain. Consequently, the number of projects relying on structural information has increased, and structure-based ligand design is nowadays routinely carried out at all major pharmaceutical companies. The amount of structural knowledge is so large that automated methods are needed to make full use of it. 

Bader, G.D. and Hogue, C.W. (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4, 1-27. 

This chapter has provided a brief review of approaches relevant to the comprehensive determination of subcellular location. These methods are automated, reproducible and more sensitive than visual examination, and their utility has already been demonstrated in pilot projects. However, much work remains to be done, especially to develop automated methods for synthesizing diverse sources information on subcellular location into accurate models of the distribution of all proteins within all cell types. 

Simon and his coworkers have developed (426) a quantitative 3D-QSAR approach, the minimal steric (topologic) difference (MTD) approach. Oprea et al. (452) compared MTD and CoMFA on affinity of steroids for their binding proteins and found similar results. Snyder and colleagues (453) developed an automated method for pharmacophore extraction that can provide a clear-cut distinction between agonist and antagonist pharmacophores. Klopman (404,454) developed a procedure for the automatic detection of common molecular structural features present in a training set of compounds. This has been used to produce candidate pharmacophores for a set of antiulcer compounds (404). Extensions (454)of this approach allow differentiation between substructures responsible for activity and those that modulate the activity. 

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