Ferritin-like molecules are unique to cellular iron homeostasis because they can store iron at concentrations much higher than those dictated by the solubility of Fe 3+ . Very little is known about the protein interactions that deliver iron for storage or promote the mobilization of stored iron from ferritinlike molecules. Here, we report the X-ray crystal structure of Pseudomonas aeruginosa bacterioferritin (Pa-BfrB) in complex with bacterioferritin-associated ferredoxin (Pa-Bfd) at 2.0 Å resolution. As the first example of a ferritin-like molecule in complex with a cognate partner, the structure provides unprecedented insight into the complementary interface that enables the [2Fe-2S] cluster of Pa-Bfd to promote hememediated electron transfer through the BfrB protein dielectric (∼18 Å), a process that is necessary to reduce the core ferric mineral and facilitate mobilization of Fe 2+ . The Pa-BfrB−Bfd complex also revealed the first structure of a Bfd, thus providing a first view to what appears to be a versatile metal binding domain ubiquitous to the large Fer2_BFD family of proteins and enzymes with diverse functions. Residues at the Pa-BfrB−Bfd interface are highly conserved in Bfr and Bfd sequences from a number of pathogenic bacteria, suggesting that the specific recognition between Pa-BfrB and Pa-Bfd is of widespread significance to the understanding of bacterial iron homeostasis.

In recent years, molecular dynamics simulations of proteins in explicit mixed solvents have been applied to various problems in protein biophysics and drug discovery, including protein folding, protein surface characterization, fragment screening, allostery, and druggability assessment. In this study, we perform a systematic study on how mixtures of organic solvent probes in water can reveal cryptic ligand binding pockets that are not evident in crystal structures of apo proteins. We examine a diverse set of eight PDB proteins that show pocket opening induced by ligand binding and investigate whether solvent MD simulations on the apo structures can induce the binding site observed in the holo structures. The cosolvent simulations were found to induce conformational changes on the protein surface, which were characterized and compared with the holo structures. Analyses of the biological systems, choice of probes and concentrations, druggability of the resulting induced pockets, and application to drug discovery are discussed here.

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We present a novel method to estimate the contributions of translational and rotational entropy to protein-ligand binding affinity. The method is based on estimates of the configurational integral through the sizes of clusters obtained from multiple docking positions. Cluster sizes are defined as the intervals of variation of center of ligand mass and Euler angles in the cluster. Then we suggest a method to consider the entropy of torsional motions. We validate the suggested methods on a set of 135 PDB protein-ligand complexes by comparing the averaged root-mean square deviations (RMSD) of the top-scored ligand docked positions, accounting and not accounting for entropy contributions, relative to the experimentally determined positions. We demonstrate that the method increases docking accuracy by 10-21% when used in conjunction with the AutoDock docking program, thus reducing the percent of incorrectly docked ligands by 1.4-fold to four-fold, so that in some cases the percent of ligands correctly docked to within an RMSD of 2 A is above 90%. We show that the suggested method to account for entropy of relative motions is identical to the method based on the Monte Carlo integration over intervals of variation of center of ligand mass and Euler angles in the cluster.

We present results of testing of the ability of eleven popular scoring functions to predict native docked positions using a recently developed method [1] We analyze how the choice of a RMSD-tolerance and a low bound of dense clusters impacts on docking accuracy of the scoring methods. We derive optimal intervals of the RMSD-tolerance for 11 scoring functions.

Conformational changes upon protein-protein association are the key element of the binding mechanism. The study presents a systematic large-scale analysis of such conformational changes in the side chains. The results indicate that short and long side chains have different propensities for the conformational changes. Long side chains with three or more dihedral angles are often subject to large conformational transition. Shorter residues with one or two dihedral angles typically undergo local conformational changes not leading to a conformational transition. The relationship between the local readjustments and the equilibrium fluctuations of a side chain around its unbound conformation is suggested. Most of the side chains undergo larger changes in the dihedral angle most distant from the backbone. The frequencies of the core-to-surface interface transitions of six nonpolar residues and Tyr are larger than the frequencies of the opposite, surface-to-core transitions. The binding increases both polar and nonpolar interface areas. However, the increase of the nonpolar area is larger for all considered classes of protein complexes, suggesting that the protein association perturbs the unbound interfaces to increase the hydrophobic contribution to the binding free energy. To test modeling approaches to side-chain flexibility in protein docking, conformational changes in the X-ray set were compared with those in the docking decoys sets. The results lead to a better understanding of the conformational changes in proteins and suggest directions for efficient conformational sampling in docking protocols.

Structure fluctuations in proteins affect a broad range of cell phenomena, including stability of proteins and their fragments, allosteric transitions, and energy transfer. This study presents a statistical-thermodynamic analysis of relationship between the sequence composition and the distribution of residue fluctuations in protein-protein complexes. A one-node-per-residue elastic network model accounting for the nonhomogeneous protein mass distribution and the interatomic interactions through the renormalized inter-residue potential is developed. Two factors, a protein mass distribution and a residue environment, were found to determine the scale of residue fluctuations. Surface residues undergo larger fluctuations than core residues in agreement with experimental observations. Ranking residues over the normalized scale of fluctuations yields a distinct classification of amino acids into three groups: ͑i͒ highly fluctuating-Gly, Ala, Ser, Pro, and Asp, ͑ii͒ moderately fluctuating-Thr, Asn, Gln, Lys, Glu, Arg, Val, and Cys, and ͑iii͒ weakly fluctuating-Ile, Leu, Met, Phe, Tyr, Trp, and His. The structural instability in proteins possibly relates to the high content of the highly fluctuating residues and a deficiency of the weakly fluctuating residues in irregular secondary structure elements ͑loops͒, chameleon sequences, and disordered proteins. Strong correlation between residue fluctuations and the sequence composition of protein loops supports this hypothesis. Comparing fluctuations of binding site residues ͑interface residues͒ with other surface residues shows that, on average, the interface is more rigid than the rest of the protein surface and Gly, Ala, Ser, Cys, Leu, and Trp have a propensity to form more stable docking patches on the interface. The findings have broad implications for understanding mechanisms of protein association and stability of protein structures.

In the context of virtual database screening, calculations of protein-ligand binding entropy of relative and overall molecular motions are challenging, owing to the inherent structural complexity of the ligand binding well in the energy landscape of protein-ligand interactions and computing time limitations. We describe a fast statistical thermodynamic method for estimation the binding entropy to address the challenges. The method is based on the integration of the configurational integral over clusters obtained from multiple docked positions. We apply the method in conjunction with 11 popular scoring functions (AutoDock, ChemScore, DrugScore, D-Score, F-Score, G-Score, LigScore, LUDI, PLP, PMF, X-Score) to evaluate the binding entropy of 100 protein-ligand complexes. The averaged values of binding entropy contribution vary from 6.2 to 9.1 kcal/mol, showing good agreement with literature. We calculate positional sizes and the angular volume of the native ligand wells. The averaged geometric mean of positional sizes in principal directions varies from 0.8 to 1.4 A. The calculated range of angular volumes is 3.3-11.8 rad(2). Then we demonstrate that the averaged six-dimensional volume of the native well is larger than the volume of the most populated non-native well in energy landscapes described by all of 11 scoring functions.