Resolution-adapted recombination of structural features significantly improves sampling in restraint-guided structure calculation

Recent work has shown that NMR structures can be determined by integrating sparse NMR data with structure prediction methods such as Rosetta. The experimental data serve to guide the search for the lowest energy state towards the deep minimum at the native state which is frequently missed in Rosetta de novo structure calculations. However, as the protein size increases, sampling again becomes limiting; for example, the standard Rosetta protocol involving Monte Carlo fragment insertion starting from an extended chain fails to converge for proteins over 150 amino acids even with guidance from chemical shifts (CS-Rosetta) and other NMR data. The primary limitation of this protocol--that every folding trajectory is completely independent of every other--was recently overcome with the development of a new approach involving resolution-adapted structural recombination (RASREC). Here we describe the RASREC approach in detail and compare it to standard CS-Rosetta. We show that the improved sampling of RASREC is essential in obtaining accurate structures over a benchmark set of 11 proteins in the 15-25 kDa size range using chemical shifts, backbone RDCs and HN-HN NOE data; in a number of cases the improved sampling methodology makes a larger contribution than incorporation of additional experimental data. Experimental data are invaluable for guiding sampling to the vicinity of the global energy minimum, but for larger proteins, the standard Rosetta fold-from-extended-chain protocol does not converge on the native minimum even with experimental data and the more powerful RASREC approach is necessary to converge to accurate solutions.     

Hierarchical clustering of the correlation patterns: new method of domain identification in proteins

New method of identification of dynamical domains in proteins - Hierarchical Clustering of the Correlation Patterns (HCCP) is proposed. HCCP allows to identify the domains using single three-dimensional structure of the studied proteins and does not require any adjustable parameters that can influence the results. The method is based on hierarchical clustering performed on the matrices of correlation patterns, which are obtained by the transformation of ordinary pairwise correlation matrices. This approach allows to extract additional information from the correlation matrices, which increases reliability of domain identification. It is shown that HCCP is insensitive to small variations of the pairwise correlation matrices. Particularly it produces identical results if the data obtained for the same protein crystallized with different spatial positions of domains are used for analysis. HCCP can utilize correlation matrices obtained by any method such as normal mode or essential dynamics analysis, Gaussian network or anisotropic network models, etc. These features make HCCP an attractive method for domain identification in proteins.     

The C-terminal region of mitochondrial glycerol-3-phosphate acyltransferase-1 interacts with the active site region and is required for activity

Glycerol phosphate acyltransferase (GPAT) catalyzes the formation of 1-acyl-sn-glycerol-3-phosphate from glycerol-3-phosphate and long chain fatty acyl-CoA substrates. We previously determined the topography of the mitochondrial GPAT1 isoform (mtGPAT1, 828 amino acids). mtGPAT1 has two transmembrane domains (TMDs) (aa 472-493 and aa 576-592) with both the N- and C-termini facing the cytosol and a loop (aa 494-575) facing the intermembrane space. Alignment of amino acid sequences from mtGPAT1 and other acyltransferases and site directed mutagenesis studies have demonstrated that the active site of the enzyme resides in the N-terminal domain of the protein. In this study, we sequentially truncated the C-terminal domain and characterized the properties of the resulting mutants expressed in CHO cells. Although the mutants were overexpressed, none of them conferred GPAT activity. The loss of activity was not due to the miss-targeting of the proteins since immunofluorescence experiments demonstrated their mitochondrial localization. Instead, chemical crosslinking and protein cleavage studies demonstrated that the N- and C-termini of the protein interact. These results suggest that the C-terminal domain is necessary for mtGPAT1 activity, and probably contributes to catalysis or substrate binding.     

Automated phylogenetic detection of recombination using a genetic algorithm

The evolution of homologous sequences affected by recombination or gene conversion cannot be adequately explained by a single phylogenetic tree. Many tree-based methods for sequence analysis, for example, those used for detecting sites evolving nonneutrally, have been shown to fail if such phylogenetic incongruity is ignored. However, it may be possible to propose several phylogenies that can correctly model the evolution of nonrecombinant fragments. We propose a model-based framework that uses a genetic algorithm to search a multiple-sequence alignment for putative recombination break points, quantifies the level of support for their locations, and identifies sequences or clades involved in putative recombination events. The software implementation can be run quickly and efficiently in a distributed computing environment, and various components of the methods can be chosen for computational expediency or statistical rigor. We evaluate the performance of the new method on simulated alignments and on an array of published benchmark data sets. Finally, we demonstrate that prescreening alignments with our method allows one to analyze recombinant sequences for positive selection.     

Protein-ligand docking: current status and future challenges

Understanding the ruling principles whereby protein receptors recognize, interact, and associate with molecular substrates and inhibitors is of paramount importance in drug discovery efforts. Protein-ligand docking aims to predict and rank the structure(s) arising from the association between a given ligand and a target protein of known 3D structure. Despite the breathtaking advances in the field over the last decades and the widespread application of docking methods, several downsides still exist. In particular, protein flexibility-a critical aspect for a thorough understanding of the principles that guide ligand binding in proteins-is a major hurdle in current protein-ligand docking efforts that needs to be more efficiently accounted for. In this review the key concepts of protein-ligand docking methods are outlined, with major emphasis being given to the general strengths and weaknesses that presently characterize this methodology. Despite the size of the field, the principal types of search algorithms and scoring functions are reviewed and the most popular docking tools are briefly depicted. Recent advances that aim to address some of the traditional limitations associated with molecular docking are also described. A selection of hand-picked examples is used to illustrate these features.     

Structural refinement of protein segments containing secondary structure elements: Local sampling, knowledge-based potentials, and clustering

In this article, we present an iterative, modular optimization (IMO) protocol for the local structure refinement of protein segments containing secondary structure elements (SSEs). The protocol is based on three modules: a torsion-space local sampling algorithm, a knowledge-based potential, and a conformational clustering algorithm. Alternative methods are tested for each module in the protocol. For each segment, random initial conformations were constructed by perturbing the native dihedral angles of loops (and SSEs) of the segment to be refined while keeping the protein body fixed. Two refinement procedures based on molecular mechanics force fields - using either energy minimization or molecular dynamics - were also tested but were found to be less successful than the IMO protocol. We found that DFIRE is a particularly effective knowledge-based potential and that clustering algorithms that are biased by the DFIRE energies improve the overall results. Results were further improved by adding an energy minimization step to the conformations generated with the IMO procedure, suggesting that hybrid strategies that combine both knowledge-based and physical effective energy functions may prove to be particularly effective in future applications.     

Designing convergent cellular automata

Cellular automata (CA) have been used by biologists to study dynamic non-linear systems where the interaction between cell behaviour and end-pattern is investigated. It is difficult to achieve convergence of a CA towards a specific static pattern and a common solution is to use genetic algorithms and evolve a ruleset that describes cell behaviour. This paper presents an alternative means of designing CA to converge to specific static patterns. A matrix model is introduced and analysed then a design algorithm is demonstrated. The algorithm is significantly less computationally intensive than equivalent evolutionary algorithms, and not limited in scale, complexity or number of dimensions.     

Automatic classification within families of transposable elements: Application to the mariner Family

The higher levels of the classification of transposable elements (TEs) from Classes to Superfamilies or Families, is regularly updated, but the lower levels (below the Family) have received little investigation. In particular, this applies to the Families that include a large number of copies. In this article we propose an automatic classification of DNA sequences. This procedure is based on an aggregation process using a pairwise matrix of distances, allowing us to define several groups characterized by a sphere with a central sequence and a radius. This method was tested on the mariner Family, because this is probably one of the most extensively studied Families. Several Subfamilies had already been defined from phylogenetic analyses based on multiple alignments of complete or partial amino-acid sequences of the transposase. The classification obtained here from DNA sequences of 935 items matches the phylogenies of the transposase. The rate of error from a posteriori re-assignment is relatively low.     

采用RAPD技术探讨花椒的遗传多样性及其与环境的关系

采用RAPD分子标记方法分析11个花椒品种的遗传多样性,以及遗传多样性与环境因素的相关性。从60条随机引物中筛选出10条引物,共扩增出67条带,平均每个引物扩增出6．7条,其中56条具有多态性,多态比例为84％。根据品种间的遗传距离构建的聚类分析树状图,11个花椒品种的相似系数在0．08-0．92之间,可分为2个类群,这种分类与花椒的叶的外形分类结果一致。不同地域种植的同一品种材料遗传距离较大,显示花椒的遗传多样性与地域分布有关系。