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Hydration is essential for the structural and functional integrity of globular proteins. How much hydration water is required for that integrity? A number of techniques such as X-ray diffraction, nuclear magnetic resonance (NMR) spectroscopy, calorimetry, infrared spectroscopy, and molecular dynamics (MD) simulations indicate that the hydration level is 0.3-0.5 g of water per gram of protein for medium sized proteins. Hydrodynamic properties, when accounted for by modeling proteins as ellipsoids, appear to give a wide range of hydration levels. In this paper we describe an alternative numerical technique for hydrodynamic calculations that takes account of the detailed protein structures. This is made possible by relating hydrodynamic properties (translational and rotational diffusion constants and intrinsic viscosity) to electrostatic properties (capacitance and polarizability). We show that the use of detailed protein structures in predicting hydrodynamic properties leads to hydration levels in agreement with other techniques. A unified picture of protein hydration emerges. There are preferred hydration sites around a protein surface. These sites are occupied nearly all the time, but by different water molecules at different times. Thus, though a given water molecule may have a very short residence time (approximately 100-500 ps from NMR spectroscopy and MD simulations) in a particular site, the site appears fully occupied in experiments in which time-averaged properties are measured.

Electrostatic interactions have been observed to play important roles in the kinetics of protein-protein association. Ionic strength, by its ability to modulate the magnitude of electrostatic interactions, has often been conveniently used to test their presence. From experiments on a wide range of associating proteins, a common feature has emerged: the on rates show strong dependence on ionic strength whereas the off rates are relatively insensitive. Here this feature is explained by an explicit description of a transition state for the association process and the suggestion that this transition is near the final bound state of two proteins. The molecular basis of the transition state in the bimolecular process lies in the fact that the bound state is characterized by local specific (e.g., van der Waals, hydrophobic, and electrostatic) interactions, whereas the unbound state is characterized by translational and rotational freedom. In the transition state the protein-protein pair encounters a free-energy maximum since its translational-rotational entropy is reduced while the specific interactions are not yet attained. In this formalism of the protein-protein association process, the enhancement of on rates by long-range electrostatic interactions can be written (analogous to an ordinary transition-state theory) in the form k(on) = k(0)(on)exp(-G(el)/k(B)T), where G(el) is the electrostatic free energy of the transition state., (Copyright 2001 John Wiley & Sons, Inc.)

We present theory showing that confining a protein to a small inert space (a "cage") should stabilize the protein against reversible unfolding. Examples of such spaces might include the pores within chromatography columns, the Anfinsen cage in chaperonins, the interiors of ribosomes, or regions of steric occlusion inside cells. Confinement eliminates some expanded configurations of the unfolded chain, shifting the equilibrium from the unfolded state toward the native state. The partition coefficient for a protein in a confined space is predicted to decrease significantly when the solvent is changed from native to denaturing conditions. Small cages are predicted to increase the stability of the native state by as much as 15 kcal/mol. Confinement may also increase the rates of protein or RNA folding.

Protein-protein interaction sites are predicted from a neural network with sequence profiles of neighboring residues and solvent exposure as input. The network was trained on 615 pairs of nonhomologous complex-forming proteins. Tested on a different set of 129 pairs of nonhomologous complex-forming proteins, 70% of the 11,004 predicted interface residues are actually located in the interfaces. These 7732 correctly predicted residues account for 65% of the 11,805 residues making up the 129 interfaces. The main strength of the network predictor lies in the fact that neighbor lists and solvent exposure are relatively insensitive to structural changes accompanying complex formation. As such, it performs equally well with bound or unbound structures of the proteins. For a set of 35 test proteins, when the input was calculated from the bound and unbound structures, the correct fractions of the predicted interface residues were 69 and 70%, respectively.

A homology-based structure prediction method ideally gives both a correct fold assignment and an accurate query-template alignment. In this article we show that the combination of two existing methods, PSI-BLAST and threading, leads to significant enhancement in the success rate of fold recognition. The combined approach, termed COBLATH, also yields much higher alignment accuracy than found in previous studies. It consists of two-way searches both by PSI-BLAST and by threading. In the PSI-BLAST portion, a query is used to search for hits in a library of potential templates and, conversely, each potential template is used to search for hits in a library of queries. In the threading portion, the scoring function is the sum of a sequence profile and a 6x6 substitution matrix between predicted query and known template secondary structure and solvent exposure. "Two-way" in threading means that the query's sequence profile is used to match the sequences of all potential templates and the sequence profiles of all potential templates are used to match the query's sequence. When tested on a set of 533 nonhomologous proteins, COBLATH was able to assign folds for 390 (73%). Among these 390 queries, 265 (68%) had root-mean-square deviations (RMSDs) of less than 8 A between predicted and actual structures. Such high success rate and accuracy make COBLATH an ideal tool for structural genomics.

Structures of 79 proteins involved in human diseases were predicted by sequence alignments with structural templates. The predicted structures for ALDP and CSA, proteins responsible for adrenoleukodystrophy and the Cockayne syndrome, respectively, were analyzed to elucidate the molecular basis of disease mutations. In particular we positioned residue P484 of ALDP in the homodimer interface. This positioning is consistent with a recent experimental finding that the mutation P484R significantly decreases the self-interaction of ALDP and suggests that the disease mechanism of this mutation lies in the impaired ALDP dimerization. We identified two new WD repeats in CSA and suggest that one of these forms part of the interaction surface with other proteins.

Electrostatic interactions are known experimentally to enhance the rate of protein-protein association by three to four orders of magnitude. However, theoretical efforts to quantitatively account for such rate enhancement have been hampered by the need to consider a large number of relative configurations of two associating proteins sampled during their diffusional encounter. Our recent work indicates that a good estimate of the rate enhancement is given by the average Boltzmann factor in the region of configurational space where association can effectively take place. This estimate is tested on a model system consisting of two spherical proteins, each with a "reactive patch." Three different forms of interaction potential are considered. Comparison with exact results for the association rate constant demonstrates that predictions based on the local Boltzmann factor are accurate to within approximately 50% for realistic sizes of the reactive region and amplitudes of the interaction potential.

The boundary element technique is implemented to solve for the electrostatic potential of macromolecules in an ionic solution. This technique entails solving surface integral equations that are equivalent to the Poisson and the Poisson-Boltzmann equations governing the electrostatic potential inside the macromolecules and and in the solvent. A simple but robust method is described for discretizing the macromolecular surfaces in order to approximate the integral equations by linear algebraic equations. Particular attention is paid to the interaction energy between two macromolecules, and an iterative procedure is devised to make the calculation more efficient. This iterative procedure is illustrated in the electron transfer system of cytochrome c and cytochrome c peroxidase.

A unified model is presented for protein-protein association processes that are under the influences of electrostatic interaction and diffusion (e.g., protein oligomerization, enzyme catalysis, electron and energy transfer). The proteins are modeled as spheres that bear point charges and undergo translational and rotational Brownian motion. Before association can occur the two spheres have to be aligned properly to form a reaction complex via diffusion. The reaction complex can either go on to form the product or it can dissociate into the separate reactants through diffusion. The electrostatic interaction, like diffusion, influences every step except the one that brings the reaction complex into the product. The interaction potential is obtained by extending the Kirkwood-Tanford protein model (Tanford, C., and J. G. Kirkwood. 1957. J. Am. Chem. Soc. 79:5333-5339) to two charge-embedded spheres and solving the consequent equations under a particular basis set. The time-dependent association rate coefficient is then obtained through Brownian dynamics simulations according an algorithm developed earlier (Zhou, H.-X. 1990. J. Phys. Chem. 94:8794-8800). This method is applied to a model system of the cytochrome c and cytochrome c peroxidase association process and the results confirm the experimental dependence of the association rate constant on the solution ionic strength. An important conclusion drawn from this study is that when the product is formed by very specific alignment of the reactants, as is often the case, the effect of the interaction potential is simply to scale the association rate constant by a Boltzmann factor. This explains why mutations in the interface of the reaction complex have strong influences on the association rate constant whereas those away from the interface have minimal effects. It comes about because the former mutations change the interaction potential of the reaction complex significantly and the latter ones do not.