Here we extend the ability to predict hydrodynamic coefficients and other solution properties of rigid macromolecular structures from atomic-level structures, implemented in the computer program HYDROPRO, to models with lower, residue-level resolution. Whereas in the former case there is one bead per nonhydrogen atom, the latter contains one bead per amino acid (or nucleotide) residue, thus allowing calculations when atomic resolution is not available or coarse-grained models are preferred. We parameterized the effective hydrodynamic radius of the elements in the atomic- and residue-level models using a very large set of experimental data for translational and rotational coefficients (intrinsic viscosity and radius of gyration) for >50 proteins. We also extended the calculations to very large proteins and macromolecular complexes, such as the whole 70S ribosome. We show that with proper parameterization, the two levels of resolution yield similar and rather good agreement with experimental data. The new version of HYDROPRO, in addition to considering various computational and modeling schemes, is far more efficient computationally and can be handled with the use of a graphical interface.

The hydrodynamic properties of cylindrical ͑rodlike and discoidal͒ particles in dilute solution have been computed using the bead-shell model treatment. Previous results ͓Tirado and García de la Torre, J. Chem. Phys. 71, 2581 ͑1979͒; 73, 1993 ͑1980͔͒ for rods with length-to-diameter ratio p Ͼ2 are now extended to short cylinders and disks down to pϭ0.1. The intrinsic viscosity is obtained for rods and disks, and results are presented for the three rotational relaxation times of a cylindrical particle. The hydrodynamic properties are expressed in forms that have a weak variation with p, and are therefore useful for the analysis of experimental values. We present examples of the determination of the length and diameter of the cylindrical particles, for DNA oligonucleotides and tobacco mosaic virus.

Chemoreceptors in bacteria detect a variety of signals and feed this information into chemosensory pathways that represent a major mode of signal transduction. The five chemoreceptors from have served as traditional models in the study of this protein family. Genome analyses revealed that many bacteria contain much larger numbers of chemoreceptors with broader sensory capabilities. Chemoreceptors differ in topology, sensing mode, cellular location, and, above all, the type of ligand binding domain (LBD). Here, we highlight LBD diversity using well-established and emerging model organisms as well as genomic surveys. Nearly a hundred different types of protein domains that are found in chemoreceptor sequences are known or predicted LBDs, but only a few of them are ubiquitous. LBDs of the same class recognize different ligands, and conversely, the same ligand can be recognized by structurally different LBDs; however, recent studies began to reveal common characteristics in signal-LBD relationships. Although signals can stimulate chemoreceptors in a variety of different ways, diverse LBDs appear to employ a universal transmembrane signaling mechanism. Current and future studies aim to establish relationships between LBD types, the nature of signals that they recognize, and the mechanisms of signal recognition and transduction.

The conventional Kirkwood-Riseman calculation of the hydrodynamic properties of bead models gives abnormal results for rotational quantities and the intrinsic viscosities for models with a few beads or when one bead is dominant. The reason is that beads are treated as point sources of friction. This can be remedied by introducing terms that are neglected in the conventional treatment of orders 0 and -3 in interbead distances. An alternative strategy is the cubic substitution in which each bead is replaced by a cubic array of minibeads. These procedures require a computational overload that, in the case of the intrinsic viscosity, can be avoided using an estimate of the correction due to the nonzero volume of the beads. We have found how such a correction can be estimated from the geometry of the model, and its application yields results that are within the range of typical experimental errors.