Adequate initial configurations for molecular dynamics simulations consist of arrangements of molecules distributed in space in such a way to approximately represent the system's overall structure. In order that the simulations are not disrupted by large van der Waals repulsive interactions, atoms from different molecules must keep safe pairwise distances. Obtaining such a molecular arrangement can be considered a packing problem: Each type molecule must satisfy spatial constraints related to the geometry of the system, and the distance between atoms of different molecules must be greater than some specified tolerance. We have developed a code able to pack millions of atoms, grouped in arbitrarily complex molecules, inside a variety of three‐dimensional regions. The regions may be intersections of spheres, ellipses, cylinders, planes, or boxes. The user must provide only the structure of one molecule of each type and the geometrical constraints that each type of molecule must satisfy. Building complex mixtures, interfaces, solvating biomolecules in water, other solvents, or mixtures of solvents, is straightforward. In addition, different atoms belonging to the same molecule may also be restricted to different spatial regions, in such a way that more ordered molecular arrangements can be built, as micelles, lipid double‐layers, etc. The packing time for state‐of‐the‐art molecular dynamics systems varies from a few seconds to a few minutes in a personal computer. The input files are simple and currently compatible with PDB, Tinker, Molden, or Moldy coordinate files. The package is distributed as free software and can be downloaded from http://www.ime.unicamp.br/∼martinez/packmol/. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2009

The analysis of structural mobility in molecular dynamics plays a key role in data interpretation, particularly in the simulation of biomolecules. The most common mobility measures computed from simulations are the Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuations (RMSF) of the structures. These are computed after the alignment of atomic coordinates in each trajectory step to a reference structure. This rigid-body alignment is not robust, in the sense that if a small portion of the structure is highly mobile, the RMSD and RMSF increase for all atoms, resulting possibly in poor quantification of the structural fluctuations and, often, to overlooking important fluctuations associated to biological function. The motivation of this work is to provide a robust measure of structural mobility that is practical, and easy to interpret. We propose a Low-Order-Value-Optimization (LOVO) strategy for the robust alignment of the least mobile substructures in a simulation. These substructures are automatically identified by the method. The algorithm consists of the iterative superposition of the fraction of structure displaying the smallest displacements. Therefore, the least mobile substructures are identified, providing a clearer picture of the overall structural fluctuations. Examples are given to illustrate the interpretative advantages of this strategy. The software for performing the alignments was named MDLovoFit and it is available as free-software at: http://leandro.iqm.unicamp.br/mdlovofit

Thiazolidinediones (TZDs) act through peroxisome proliferator activated receptor (PPAR) γ to increase insulin sensitivity in type 2 diabetes (T2DM), but deleterious effects of these ligands mean that selective modulators with improved clinical profiles are needed. We obtained a crystal structure of PPARγ ligand binding domain (LBD) and found that the ligand binding pocket (LBP) is occupied by bacterial medium chain fatty acids (MCFAs). We verified that MCFAs (C8–C10) bind the PPARγ LBD in vitro and showed that they are low-potency partial agonists that display assay-specific actions relative to TZDs; they act as very weak partial agonists in transfections with PPARγ LBD, stronger partial agonists with full length PPARγ and exhibit full blockade of PPARγ phosphorylation by cyclin-dependent kinase 5 (cdk5), linked to reversal of adipose tissue insulin resistance. MCFAs that bind PPARγ also antagonize TZD-dependent adipogenesis in vitro. X-ray structure B-factor analysis and molecular dynamics (MD) simulations suggest that MCFAs weakly stabilize C-terminal activation helix (H) 12 relative to TZDs and this effect is highly dependent on chain length. By contrast, MCFAs preferentially stabilize the H2-H3/β-sheet region and the helix (H) 11-H12 loop relative to TZDs and we propose that MCFA assay-specific actions are linked to their unique binding mode and suggest that it may be possible to identify selective PPARγ modulators with useful clinical profiles among natural products.

Nuclear receptors are important targets for pharmaceuticals, but similarities between family members cause difficulties in obtaining highly selective compounds. Synthetic ligands that are selective for thyroid hormone (TH) receptor β (TRβ) vs. TRα reduce cholesterol and fat without effects on heart rate; thus, it is important to understand TRβ-selective binding. Binding of 3 selective ligands (GC-1, KB141, and GC-24) is characterized at the atomic level; preferential binding depends on a nonconserved residue (Asn-331β) in the TRβ ligand-binding cavity (LBC), and GC-24 gains extra selectivity from insertion of a bulky side group into an extension of the LBC that only opens up with this ligand. Here we report that the natural TH 3,5,3′-triodothyroacetic acid (Triac) exhibits a previously unrecognized mechanism of TRβ selectivity. TR x-ray structures reveal better fit of ligand with the TRα LBC. The TRβ LBC, however, expands relative to TRα in the presence of Triac (549 Å ³ vs. 461 Å ³ ), and molecular dynamics simulations reveal that water occupies the extra space. Increased solvation compensates for weaker interactions of ligand with TRβ and permits greater flexibility of the Triac carboxylate group in TRβ than in TRα. We propose that this effect results in lower entropic restraint and decreases free energy of interactions between Triac and TRβ, explaining subtype-selective binding. Similar effects could potentially be exploited in nuclear receptor drug design.

Many algorithms exist for protein structural alignment, based on internal protein coordinates or on explicit superposition of the structures. These methods are usually successful for detecting structural similarities. However, current practical methods are seldom supported by convergence theories. In particular, although the goal of each algorithm is to maximize some scoring function, there is no practical method that theoretically guarantees score maximization. A practical algorithm with solid convergence properties would be useful for the refinement of protein folding maps, and for the development of new scores designed to be correlated with functional similarity.