Bioinformatics computational power

An important factor in the progress of bioinformatics has been the constant increase in computer speed and memory capacity of desktop computers and the increasing sophistication of data processing techniques. The computation power of common personal computers has increased within 12 years approximately 100-fold in processor speed, 250-fold in RAM memory space and 500-fold or more in hard disk space, while the price has nearly halved. This enables acquisition, transformation, visuahsation and interpretation of large amounts of data at a fraction of the cost compared to 12 years ago. Presently, bioanalytical databases are also growing quickly in size and many databases are directly accessible via the Internet One of the first chemical databases to be placed on the Internet was the Brookha-ven protein data bank, which contains very valuable three-dimensional structural data of proteins. The primary resource for proteomics is the ExPASy (Expert Protein Analysis System) database, which is dedicated to the analysis of protein sequences and structures and contains a rapidly growing index of 2D-gel electrophoresis maps. Some primary biomolecular database resources compiled from spectroscopic data are given in Tab. 14.1. 

Artificial neural networks are versatile tools for a number of applications, including bioinformatics. However, they are not thinking machines nor are they black boxes to blindly feed data into with expectations of miraculous results. Neural networks are typically computer software implementations of algorithms, which fortunately may be represented by highly visual, often simple diagrams. Neural networks represent a powerful set of mathematical tools, usually highly nonlinear in nature, that can be used to perform a number of traditional statistical chores such as classification, pattern recognition and feature extraction. 

The MudPlT approach is not without problems. A fully automated procedure as described requires (almost) continuous MS data acquisition for more than 15 hours, resulting in several thousands of mass spectra that put high demands on computers and bioinformatics. The procedure is limited by the ability of the mass spectrometer to rapidly switch between MS and MS-MS analysis under DDA control. With current technology, certainly not all peptides present can be analysed with both modes (Ch. 17.7.2). The power of the approach is greatly enhanced by performing a protein prefractionation, either by RPLC [8, 45] or by means of a protein-specific enrichment technique such as AfC (Ch. 17.4.1). 

Clearly, additional efforts to streamline 3D protein structure determination by MAS NMR are necessary. Novel concepts may make extensive use of the rapidly increasing power of bioinformatics and computational chemistry. Already, general approaches have appeared to predict 3D structure from a limited set of solution-state NMR data. " In parallel, the ability to relate molecular structure to NMR detectable parameters by ab initio quantum chemistry calculations and DFT approaches is dramatically improving.These techniques already have empowered novel material science applications of solid-state NMR and will greatly expand the use of MAS solid-state NMR for the study of polypeptides and proteins. Complementary to crystallographic... 

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