Complex macrocycle exploration: parallel, heuristic, and constraint-based conformer generation using ForceGen.

ForceGen is a template-free, non-stochastic approach for 2D to 3D structure generation and conformational elaboration for small molecules, including both non-macrocycles and macrocycles. For conformational search of non-macrocycles, ForceGen is both faster and more accurate than the best of all tested methods on a very large, independently curated benchmark of 2859 PDB ligands. In this study, the primary results are on macrocycles, including results for 431 unique examples from four separate benchmarks. These include complex peptide and peptide-like cases that can form networks of internal hydrogen bonds. By making use of new physical movements ("flips" of near-linear sub-cycles and explicit formation of hydrogen bonds), ForceGen exhibited statistically significantly better performance for overall RMS deviation from experimental coordinates than all other approaches. The algorithmic approach offers natural parallelization across multiple computing-cores. On a modest multi-core workstation, for all but the most complex macrocycles, median wall-clock times were generally under a minute in fast search mode and under 2 min using thorough search. On the most complex cases (roughly cyclic decapeptides and larger) explicit exploration of likely hydrogen bonding networks yielded marked improvements, but with calculation times increasing to several minutes and in some cases to roughly an hour for fast search. In complex cases, utilization of NMR data to constrain conformational search produces accurate conformational ensembles representative of solution state macrocycle behavior. On macrocycles of typical complexity (up to 21 rotatable macrocyclic and exocyclic bonds), design-focused macrocycle optimization can be practically supported by computational chemistry at interactive time-scales, with conformational ensemble accuracy equaling what is seen with non-macrocyclic ligands. For more complex macrocycles, inclusion of sparse biophysical data is a helpful adjunct to computation

Kinetics of protein-DNA interaction: facilitated target location in sequence-dependent potential

Recognition and binding of specific sites on DNA by proteins is central for many cellular functions such as transcription, replication, and recombination. In the process of recognition, a protein rapidly searches for its specific site on a long DNA molecule and then strongly binds this site. Here we aim to find a mechanism that can provide both a fast search (1-10 sec) and high stability of the specific protein-DNA complex ($K_d=10^{-15}-10^{-8}$ M). Earlier studies have suggested that rapid search involves the sliding of a protein along the DNA. Here we consider sliding as a one-dimensional (1D) diffusion in a sequence-dependent rough energy landscape. We demonstrate that, in spite of the landscape's roughness, rapid search can be achieved if 1D sliding is accompanied by 3D diffusion. We estimate the range of the specific and non-specific DNA-binding energy required for rapid search and suggest experiments that can test our mechanism. We show that optimal search requires a protein to spend half of time sliding along the DNA and half diffusing in 3D. We also establish that, paradoxically, realistic energy functions cannot provide both rapid search and strong binding of a rigid protein. To reconcile these two fundamental requirements we propose a search-and-fold mechanism that involves the coupling of protein binding and partial protein folding. Proposed mechanism has several important biological implications for search in the presence of other proteins and nucleosomes, simultaneous search by several proteins etc. Proposed mechanism also provides a new framework for interpretation of experimental and structural data on protein-DNA interactions

Variational approach to protein design and extraction of interaction potentials

We present and discuss a novel approach to the direct and inverse protein folding problem. The proposed strategy is based on a variational approach that allows the simultaneous extraction of amino acid interactions and the low-temperature free energy of sequences of amino acids. The knowledge-based technique is simple and straightforward to implement even for realistic off-lattice proteins because it does not entail threading-like procedures. Its validity is assessed in the context of a lattice model by means of a variety of stringent checks.Comment: 5 pages, 3 figure

Portal protein functions akin to a DNA-sensor that couples genome-packaging to icosahedral capsid maturation.

Tailed bacteriophages and herpesviruses assemble infectious particles via an empty precursor capsid (or \u27procapsid\u27) built by multiple copies of coat and scaffolding protein and by one dodecameric portal protein. Genome packaging triggers rearrangement of the coat protein and release of scaffolding protein, resulting in dramatic procapsid lattice expansion. Here, we provide structural evidence that the portal protein of the bacteriophage P22 exists in two distinct dodecameric conformations: an asymmetric assembly in the procapsid (PC-portal) that is competent for high affinity binding to the large terminase packaging protein, and a symmetric ring in the mature virion (MV-portal) that has negligible affinity for the packaging motor. Modelling studies indicate the structure of PC-portal is incompatible with DNA coaxially spooled around the portal vertex, suggesting that newly packaged DNA triggers the switch from PC- to MV-conformation. Thus, we propose the signal for termination of \u27Headful Packaging\u27 is a DNA-dependent symmetrization of portal protein

Molecular modeling of an antigenic complex between a viral peptide and a class I major histocompatibility glycoprotein

Computer simulation of the conformations of short antigenic peptides (&lo residues) either free or bound to their receptor, the major histocompatibility complex (MHC)- encoded glycoprotein H-2 Ld, was employed to explain experimentally determined differences in the antigenic activities within a set of related peptides. Starting for each sequence from the most probable conformations disclosed by a pattern-recognition technique, several energyminimized structures were subjected to molecular dynamics simulations (MD) either in vacuo or solvated by water molecules. Notably, antigenic potencies were found to correlate to the peptides propensity to form and maintain an overall a-helical conformation through regular i,i + 4 hydrogen bonds. Accordingly, less active or inactive peptides showed a strong tendency to form i,i+3 hydrogen bonds at their Nterminal end. Experimental data documented that the C-terminal residue is critical for interaction of the peptide with H-2 Ld. This finding could be satisfactorily explained by a 3-D Q.S.A.R. analysis postulating interactions between ligand and receptor by hydrophobic forces. A 3-D model is proposed for the complex between a high-affinity nonapeptide and the H- 2 Ld receptor. First, the H-2 Ld molecule was built from X-ray coordinates of two homologous proteins: HLA-A2 and HLA-Aw68, energyminimized and studied by MD simulations. With HLA-A2 as template, the only realistic simulation was achieved for a solvated model with minor deviations of the MD mean structure from the X-ray conformation. Water simulation of the H-2 Ld protein in complex with the antigenic nonapeptide was then achieved with the template- derived optimal parameters. The bound peptide retains mainly its a-helical conformation and binds to hydrophobic residues of H-2 Ld that correspond to highly polymorphic positions of MHC proteins. The orientation of the nonapeptide in the binding cleft is in accordance with the experimentally determined distribution of its MHC receptor-binding residues (agretope residues). Thus, computer simulation was successfully employed to explain functional data and predicts a-helical conformation for the bound peptid

Euclidean distance geometry and applications

Euclidean distance geometry is the study of Euclidean geometry based on the concept of distance. This is useful in several applications where the input data consists of an incomplete set of distances, and the output is a set of points in Euclidean space that realizes the given distances. We survey some of the theory of Euclidean distance geometry and some of the most important applications: molecular conformation, localization of sensor networks and statics.Comment: 64 pages, 21 figure