[show abstract][hide abstract] ABSTRACT: Large amounts of data are being generated annually on the connection between the sequence, structure and function of proteins using site-directed mutagenesis, protein design and directed evolution techniques. These data provide the fundamental building blocks for our understanding of protein function, molecular biology and living organisms in general. However, much experimental data are never deposited in databases and is thus 'lost' in journal publications or in PhD theses. At the same time theoretical scientists are in need of large amounts of experimental data for benchmarking and calibrating novel predictive algorithms, and theoretical progress is therefore often hampered by the lack of suitable data to validate or disprove a theoretical assumption. We present PEAT (Protein Engineering Analysis Tool), an application that integrates data deposition, storage and analysis for researchers carrying out protein engineering projects or biophysical characterization of proteins. PEAT contains modules for DNA sequence manipulation, primer design, fitting of biophysical characterization data (enzyme kinetics, circular dichroism spectroscopy, NMR titration data, etc.), and facilitates sharing of experimental data and analyses for a typical university-based research group. PEAT is freely available to academic researchers at http://enzyme.ucd.ie/PEAT.
Nucleic Acids Research 11/2010; 38(20):e186. · 8.28 Impact Factor
[show abstract][hide abstract] ABSTRACT: NMR-monitored pH titration experiments are routinely used to measure site-specific protein pKa values. Accurate experimental pKa values are essential in dissecting enzyme catalysis, in studying the pH-dependence of protein stability and ligand binding, in benchmarking pKa prediction algorithms, and ultimately in understanding electrostatic effects in proteins. However, due to the complex ways in which pH-dependent electrostatic and structural changes manifest themselves in NMR spectra, reported apparent pKa values are often dependent on the way that NMR pH-titration curves are analyzed. It is therefore important to retain the raw NMR spectroscopic data to allow for documentation and possible re-interpretation. We have constructed a database of primary NMR pH-titration data, which is accessible via a web interface. Here, we report statistics of the database contents and analyze the data with a global perspective to provide guidelines on best practice for fitting NMR titration curves. Titration_DB is available at http://enzyme.ucd.ie/Titration_DB. Proteins 2010. (c) 2009 Wiley-Liss, Inc.
Proteins Structure Function and Bioinformatics 09/2009; 78(4):843-57. · 3.34 Impact Factor