Extraction of site-site bridge functions and effective pair potentials from simulations of polar molecular liquids.

We develop an efficient method to extract site-site bridge functions from molecular simulations. The method is based on the inverse solution of the reference site interaction model. Using the exact long-range asymptotics of site-site direct correlation functions defined by the site-site Ornstein-Zernike equations, we regularize the ill-posed inverse problem, and then calculate site-site bridge functions and effective pair potentials for ambient water, methanol, and ethanol. We have tested the proposed algorithm and checked its performance. Our study has revealed various peculiarities of the site-site bridge functions, such as long-range behavior, strong dependence on the electrostatic interactions. Using the obtained data, we have calculated thermodynamic properties of the solvents, namely, isothermal compressibility, internal energy, and Kirkwood-Buff integrals. The obtained values are in excellent agreement not only with molecular simulations but also with available experimental data. Further extensions of the method are discussed.

Inversion of population distribution of felodipine conformations at increased concentration in dimethyl sulfoxide is a prerequisite to crystal nucleation.

Knowledge of the preferred conformations of biologically active compounds is of the utmost importance for a better understanding of the structure-activity relationships underlying their biological activity, as well as their mechanism of action. Moreover, investigating the mechanism of nucleation from a saturated solution can facilitate the discovery and preparation of new polymorphic forms. To search regularities in the crystal nucleation of biologically active compounds (drugs) from a saturated solution, we studied the conformational preference of felodipine in dilute and saturated solution in dimethyl sulfoxide. The inversion of conformation distribution at increased concentration occurs: conformers that dominate in a dilute solution become the least abundant in the saturated one. Conformers that dominate in the saturated solution are of the same type as revealed in crystalline state by X-ray.

On the characterization of inhomogeneity of the density distribution in supercritical fluids via molecular dynamics simulation and data mining analysis.

We combined molecular dynamics simulation and DBSCAN algorithm (Density Based Spatial Clustering of Application with Noise) in order to characterize the local density inhomogeneity distribution in supercritical fluids. The DBSCAN is an algorithm that is capable of finding arbitrarily shaped density domains, where domains are defined as dense regions separated by low-density regions. The inhomogeneity of density domain distributions of Ar system in sub- and supercritical conditions along the 50 bar isobar is associated with the occurrence of a maximum in the fluctuation of number of particles of the density domains. This maximum coincides with the temperature, Tα, at which the thermal expansion occurs. Furthermore, using Voronoi polyhedral analysis, we characterized the structure of the density domains. The results show that with increasing temperature below Tα, the increase of the inhomogeneity is mainly associated with the density fluctuation of the border particles of the density domains, while with increasing temperature above Tα, the decrease of the inhomogeneity is associated with the core particles.

Capturing, sharing and analysing biophysical data from protein engineering and protein characterization studies.

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.

Titration_DB: storage and analysis of NMR-monitored protein pH titration curves.

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.