cis-Dichloridobis[tris­(2-methyl­phen­oxy)phosphane-κP]palladium(II)

In the title compound, [PdCl2(C21H21O3P)2], the Pd atom adopts a slightly distorted square-planar coordination geometry, with pairs of the equivalent ligands in cis positions. Adjacent mol­ecules are linked by weak C—H⋯Cl hydrogen bonds. The crystal structure is additionally stabilized by π–π stacking inter­actions between the aromatic rings [shortest centroid–centroid distance = 3.758 (4) Å]

trans-Dichloridobis(3,5-dimethyl­pyridine-κN)(ethano­lato-κO)oxido­rhenium(V)

The title compound, [Re(C2H5O)Cl2O(C7H9N)2], was crystallized from ethanol. The crystal structure of this complex contains a Re(V) atom in a slightly distorted octahedral coordination geometry with pairs of equivalent ligands in trans positions. Adjacent complex mol­ecules are linked by weak C—H⋯Cl hydrogen bonds. The crystal structure is additionally stabilized by π–π stacking inter­actions between the aromatic rings with centroid–centroid distances of 3.546 (4) Å

How noise in force fields can affect the structural refinement of protein models

Structural refinement of predicted models of biological macromolecules using atomistic or coarse-grained molecular force fields having various degree of error are investigated. The goal of this analysis is to estimate what is the probability for designing an effective structural refinement based on computations of conformational energies using force field, and starting from a structure predicted from the sequence (using template-based, or template-free modeling), and refining it to bring the structure into closer proximity to the native state. It is widely believed that it should be possible to develop such a successful structure refinement algorithm by applying an iterative procedure with stochastic sampling and appropriate energy function, which assesses the quality (correctness) of protein decoys. Here an analysis of noise in an artificially introduced scoring function is investigated for a model of an ideal sampling scheme, where the underlying distribution of RMSDs is assumed to be Gaussian. Sampling of the conformational space is performed by random generation of RMSD values. We demonstrate that whenever the random noise in a force field exceeds some level, it is impossible to obtain reliable structural refinement. The magnitude of the noise, above which a structural refinement, on average is impossible, depends strongly on the quality of sampling scheme and a size of the protein. Finally, possible strategies to overcome the intrinsic limitations in the force fields for impacting the development of successful refinement algorithms are discussed

MultiBody Coarse-Grained Potentials for Native Structure Recognition and Quality Assessment of Protein Models

Multi-body potentials have been of much interest recently because they take into account three dimensional interactions related to residue packing and capture the cooperativity of these interactions in protein structures. Our goal was to combine long range multi-body potentials and short range potentials to improve recognition of native structure among misfolded decoys. We optimized the weights for four-body non-sequential, four-body sequential and short range potentials in order to obtain optimal model ranking results for threading and have compared these data against results obtained with other potentials. (Twenty six different coarse-grained potentials from the Potentials ‘R’Us web server have been used.) Our optimized multi-body potentials outperform all other contact potentials in the recognition of the native structure among decoys, both for models from homology template-based modeling and from template-free modeling in CASP8 decoy sets. We have compared the results obtained for this optimized coarse-grained potentials, where each residue is represented by a single point, with results obtained by using the DFIRE potential, which takes into account atomic level information of proteins. We found that for all proteins larger than 80 amino acids our optimized coarse-grained potentials yield results comparable to those obtained with the atomic DFIRE potential

Free energies for coarse-grained proteins by integrating multibody statistical contact potentials with entropies from elastic network models

We propose a novel method of calculation of free energy for coarse grained models of proteins by combining our newly developed multibody potentials with entropies computed from elastic network models of proteins. Multi-body potentials have been of much interest recently because they take into account three dimensional interactions related to residue packing and capture the cooperativity of these interactions in protein structures. Combining four-body non-sequential, four-body sequential and pairwise short range potentials with optimized weights for each term, our coarse-grained potential improved recognition of native structure among misfolded decoys, outperforming all other contact potentials for CASP8 decoy sets and performance comparable to the fully atomic empirical DFIRE potentials. By combing statistical contact potentials with entropies from elastic network models of the same structures we can compute free energy changes and improve coarse-grained modeling of protein structure and dynamics. The consideration of protein flexibility and dynamics should improve protein structure prediction and refinement of computational models. This work is the first to combine coarse-grained multibody potentials with an entropic model that takes into account contributions of the entire structure, investigating native-like decoy selection

cis-Dichlorido[2,3-dimethyl-3-(4,4,5,5-tetra­methyl-1,3,2λ5-dioxaphospho­lan-2-yl­oxy)butan-2-olato-κ2 O,P]oxido(triphenyl­phosphane-κP)rhenium(V)

The title compound, cis-[Re(C12H24O4P)Cl2O(C18H15P)], was prepared from the analogous trans isomer [Głowiak et al. (2000 ▶). Polyhedron, 19, 2667–2672] by a trans–cis isomerization reaction. The ReV atom adopts a distorted octa­hedral coordination geometry. Besides being coordinated by the oxide and the butano­late O atoms, the ReV atom is coordinated by a pair of chloride ligands and two P atoms in cis positions with respect to each other. In the crystal, adjacent mol­ecules are linked by weak C—H⋯Cl inter­actions, forming a three-dimensional network

Elastic network normal modes provide a basis for protein structure refinement

It is well recognized that thermal motions of atoms in the protein native state, the fluctuations about the minimum of the global free energy, are well reproduced by the simple elastic network models (ENMs) such as the anisotropic network model (ANM). Elasticnetwork models represent protein dynamics as vibrations of a network of nodes (usually represented by positions of the heavy atoms or by the Cα atoms only for coarse-grained representations) in which the spatially close nodes are connected by harmonic springs. These models provide a reliable representation of the fluctuational dynamics of proteins and RNA, and explain various conformational changes in protein structures including those important for ligand binding. In the present paper, we study the problem of protein structure refinement by analyzing thermal motions of proteins in non-native states. We represent the conformational space close to the native state by a set of decoys generated by the I-TASSER protein structure prediction server utilizing template-free modeling. The protein substates are selected by hierarchical structure clustering. The main finding is that thermal motions for some substates, overlap significantly with the deformations necessary to reach the native state. Additionally, more mobile residues yield higher overlaps with the required deformations than do the less mobile ones. These findings suggest that structural refinement of poorly resolved protein models can be significantly enhanced by reduction of the conformational space to the motions imposed by the dominant normal modes.This article is published as Gniewek, Pawel, Andrzej Kolinski, Robert L. Jernigan, and Andrzej Kloczkowski. "Elastic network normal modes provide a basis for protein structure refinement." The Journal of chemical physics 136:195101 (2012). doi: 10.1063/1.4710986. Posted with permission.</p

How noise in force fields can affect the structural refinement of protein models

Structural refinement of predicted models of biological macromolecules using atomistic or coarse-grained molecular force fields having various degree of error are investigated. The goal of this analysis is to estimate what is the probability for designing an effective structural refinement based on computations of conformational energies using force field, and starting from a structure predicted from the sequence (using template-based, or template-free modeling), and refining it to bring the structure into closer proximity to the native state. It is widely believed that it should be possible to develop such a successful structure refinement algorithm by applying an iterative procedure with stochastic sampling and appropriate energy function, which assesses the quality (correctness) of protein decoys. Here an analysis of noise in an artificially introduced scoring function is investigated for a model of an ideal sampling scheme, where the underlying distribution of RMSDs is assumed to be Gaussian. Sampling of the conformational space is performed by random generation of RMSD values. We demonstrate that whenever the random noise in a force field exceeds some level, it is impossible to obtain reliable structural refinement. The magnitude of the noise, above which a structural refinement, on average is impossible, depends strongly on the quality of sampling scheme and a size of the protein. Finally, possible strategies to overcome the intrinsic limitations in the force fields for impacting the development of successful refinement algorithms are discussed.This is the peer reviewed version of the following article: Gniewek, Pawel, Andrzej Kolinski, Robert L. Jernigan, and Andrzej Kloczkowski. "How noise in force fields can affect the structural refinement of protein models?" Proteins: Structure, Function, and Bioinformatics 80, no. 2 (2012): 335-341, which has been published in final form at DOI: 10.1002/prot.23240. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.</p