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A better understanding of protein aggregation is bound to translate into critical advances in several areas, including the treatment of misfolded protein disorders and the development of self-assembling biomaterials for novel commercial applications. Because of its ubiquity and clinical potential, albumin is one of the best-characterized models in protein aggregation research; but its properties in different conditions are not completely understood. Here, we carried out all-atom molecular dynamics simulations of albumin to understand how electrostatics can affect the conformation of a single albumin molecule just prior to self-assembly. We then analyzed the tertiary structure and solvent accessible surface area of albumin after electrostatically triggered partial denaturation. The data obtained from these single protein simulations allowed us to investigate the effect of electrostatic interactions between two proteins. The results of these simulations suggested that hydrophobic attractions and counterion binding may be strong enough to effectively overcome the electrostatic repulsions between the highly charged monomers. This work contributes to our general understanding of protein aggregation mechanisms, the importance of explicit consideration of free ions in protein solutions, provides critical new insights about the equilibrium conformation of albumin in its partially denatured state at low pH, and may spur significant progress in our efforts to develop biocompatible protein hydrogels driven by electrostatic partial denaturation.

A server (CheShift) has been developed to predict 13 C ␣ chemical shifts of protein structures. It is based on the generation of 696,916 conformations as a function of the , , , 1 and 2 torsional angles for all 20 naturally occurring amino acids. Their 13 C ␣ chemical shifts were computed at the DFT level of theory with a small basis set and extrapolated, with an empirically-determined linear regression formula, to reproduce the values obtained with a larger basis set. Analysis of the accuracy and sensitivity of the CheShift predictions, in terms of both the correlation coefficient R and the conformational-averaged rmsd between the observed and predicted 13 C ␣ chemical shifts, was carried out for 3 sets of conformations: (i) 36 x-ray-derived protein structures solved at 2.3 Å or better resolution, for which sets of 13 C ␣ chemical shifts were available; (ii) 15 pairs of x-ray and NMR-derived sets of protein conformations; and (iii) a set of decoys for 3 proteins showing an rmsd with respect to the x-ray structure from which they were derived of up to 3 Å. Comparative analysis carried out with 4 popular servers, namely SHIFTS, SHIFTX, SPARTA, and PROSHIFT, for these 3 sets of conformations demonstrated that CheShift is the most sensitive server with which to detect subtle differences between protein models and, hence, to validate protein structures determined by either x-ray or NMR methods, if the observed 13 C ␣ chemical shifts are available. CheShift is available as a web server. chemical shifts prediction ͉ DFT calculations ͉ validation server A ccurate and fast validation of protein structures constitutes a long-standing problem in NMR spectroscopy (1-3). Investigators have proposed a plethora of methods to determine the accuracy and reliability of protein structures in recent years (4-8). Despite this progress, there is a growing need for more sophisticated, physics-based and fast structure-validation methods (1, 2, 7). With these goals in mind, we recently proposed a new, physics-based solution of this important problem (9), viz., a methodology that makes use of observed and computed 13 C ␣ chemical shifts (at the DFT level of theory) for an accurate validation of protein structures in solution (9) and in a crystal (10). Assessment of the ability of computed 13 C ␣ chemical shifts to reproduce observed values for a single or an ensemble of structures in solution and in a crystal was accomplished by using the conformationally-averaged root-mean-square-deviation (ca-rmsd) as a scoring function (9). While computationally intensive, this methodology has several advantages: (i) it makes use of the 13 C ␣ chemical shifts, not shielding, that are ubiquitous to proteins; (ii) it can be computed accurately from the , , and torsional angles; (iii) there is no need for a priori knowledge of the oligomeric state of the protein; and (iv) no knowledgebased information or additional NMR data are required.However, the primary and the most serious limitation of the method is the computational cost of such calculations, whi...