DOCKER, an interactive program for simulating protein receptor and substrate interactions

The use of predictive models to develop chromatography-based purification processes

Chromatography is the workhorse of biopharmaceutical downstream processing because it can selectively enrich a target product while removing impurities from complex feed streams. This is achieved by exploiting differences in molecular properties, such as size, charge and hydrophobicity (alone or in different combinations). Accordingly, many parameters must be tested during process development in order to maximize product purity and recovery, including resin and ligand types, conductivity, pH, gradient profiles, and the sequence of separation operations. The number of possible experimental conditions quickly becomes unmanageable. Although the range of suitable conditions can be narrowed based on experience, the time and cost of the work remain high even when using high-throughput laboratory automation. In contrast, chromatography modeling using inexpensive, parallelized computer hardware can provide expert knowledge, predicting conditions that achieve high purity and efficient recovery. The prediction of suitable conditions in silico reduces the number of empirical tests required and provides in-depth process understanding, which is recommended by regulatory authorities. In this article, we discuss the benefits and specific challenges of chromatography modeling. We describe the experimental characterization of chromatography devices and settings prior to modeling, such as the determination of column porosity. We also consider the challenges that must be overcome when models are set up and calibrated, including the cross-validation and verification of data-driven and hybrid (combined data-driven and mechanistic) models. This review will therefore support researchers intending to establish a chromatography modeling workflow in their laboratory.

Molecular recognition and docking algorithms

Molecular docking is an invaluable tool in modern drug discovery. This review focuses on methodological developments relevant to the field of molecular docking. The forces important in molecular recognition are reviewed and followed by a discussion of how different scoring functions account for these forces. More recent applications of computational chemistry tools involve library design and database screening. Last, we summarize several critical methodological issues that must be addressed in future developments.

Substrate binding and catalysis by glutathione reductase as derived from refined enzyme: substrate crystal structures at 2 A resolution

The X-ray structure analyses of four glutathione reductase complexes and derivatives have been extended to 2 A resolution and refined. The results are discussed in conjunction with the structure of the oxidized native enzyme known at 1.54 A resolution. While the residual co-ordinate errors are around 0.2 A, some significant shifts even in this range could be established. Points of particular interest are the 3.2 A approach of C4N of nicotinamide to N5F of flavin in hydride transfer geometry, the hydrogen bond geometries of the 2'-phosphate of NADPH as compared to inferior geometries for an inorganic phosphate binding together with NADH, the differential mobilities of parts of the substrates as derived from refined atomic temperature factors, and the stabilization of the thiolate of the proximal Cys63 by conformational changes of neighboring residues as well as by flavin. In addition, catalytically competent His467' is seen to interact more optimally with the sulfur of glutathione-I than with the distal sulfur of Cys58. The observed participation of water molecules for both NADPH and glutathione binding is so extensive that a prediction of the binding mode merely from the polypeptide structure would be very difficult. The accurately known geometries allowed us to draw some conclusions on the enzyme mechanism and suggest a possible scenario of the catalysis.

A method for fast energy estimation and visualization of protein-ligand interaction

A new computational and graphical method for facilitating ligand-protein docking studies is developed on a three-dimensional computer graphics display. Various physical and chemical properties inside the ligand binding pocket of a receptor protein, whose structure is elucidated by X-ray crystal analysis, are calculated on three-dimensional grid points and are stored in advance. By utilizing those tabulated data, it is possible to estimate the non-bonded and electrostatic interaction energy and the number of possible hydrogen bonds between protein and ligand molecules in real time during an interactive docking operation. The method also provides a comprehensive visualization of the local environment inside the binding pocket. With this method, it becomes easier to find a roughly stable geometry of ligand molecules, and one can therefore make a rapid survey of the binding capability of many drug candidates. The method will be useful for drug design as well as for the examination of protein-ligand interactions.

Solvent accessible surface area and excluded volume in proteins

An analytical formula has been derived for the calculation of the solvent accessible surface area of a protein molecule or equivalently the surface area exterior to an arbitrary number of overlapping spheres. The directional derivative of this function with respect to atomic co-ordinates is provided to facilitate minimization procedures used with molecular docking algorithms and energy calculations. An analytical formula for the calculation of the volume enclosed within the accessible surface, the excluded volume, is also derived. Although the area function is not specific to the structures of proteins, the derivation was motivated by the need for a computationally feasible simulation of the hydrophobic effect in proteins. A computer program using the equations for area has been tested and has had limited application to the docking of protein alpha-helices. Possible relationships of the solvent excluded volume to hydrophobic interaction free energy and transfer free energy of solute molecules are derived from the statistical mechanics of solution.