Subcellular positioning: unstable filaments on the move

A key question in cell biology is how proteins and entire protein complexes localize to defined subcellular positions in non-compartmentalized cells or within cell compartments. A recent report involving computational modeling and live-cell imaging suggests that dynamically unstable protein filaments provide an adaptable and versatile positioning system.     

Dead‐end elimination with perturbations (DEEPer): A provable protein design algorithm with continuous sidechain and backbone flexibility

Computational protein and drug design generally require accurate modeling of protein conformations. This modeling typically starts with an experimentally determined protein structure and considers possible conformational changes due to mutations or new ligands. The DEE/A* algorithm provably finds the global minimum‐energy conformation (GMEC) of a protein assuming that the backbone does not move and the sidechains take on conformations from a set of discrete, experimentally observed conformations called rotamers. DEE/A* can efficiently find the overall GMEC for exponentially many mutant sequences. Previous improvements to DEE/A* include modeling ensembles of sidechain conformations and either continuous sidechain or backbone flexibility. We present a new algorithm, DEEPer (D ead‐E nd E limination with Per turbations), that combines these advantages and can also handle much more extensive backbone flexibility and backbone ensembles. DEEPer provably finds the GMEC or, if desired by the user, all conformations and sequences within a specified energy window of the GMEC. It includes the new abilities to handle arbitrarily large backbone perturbations and to generate ensembles of backbone conformations. It also incorporates the shear, an experimentally observed local backbone motion never before used in design. Additionally, we derive a new method to accelerate DEE/A*‐based calculations, indirect pruning, that is particularly useful for DEEPer. In 67 benchmark tests on 64 proteins, DEEPer consistently identified lower‐energy conformations than previous methods did, indicating more accurate modeling. Additional tests demonstrated its ability to incorporate larger, experimentally observed backbone conformational changes and to model realistic conformational ensembles. These capabilities provide significant advantages for modeling protein mutations and protein–ligand interactions. Proteins 2013. © 2012 Wiley Periodicals, Inc.     

基于结构比较的蛋白质模建系统及其评估

对蛋白质三维结构的同源模建的所有关键模块作了详细研究后,现已整合成蛋白质结构模建的软件系统ＰＭＯＤＥＬＬＩＮＧ。它分主链模建和侧链安装两部分,共七个模块组成。经１１族同源蛋白的模建实验的回顾性检表明,与目前流行的商品软件相比,具有操作自动化,模建精度高和运行时间少的优点。文中还对蝮蛇蛇毒磷酯酶,细胞因子受体ＣＤ４０的配体ｇｐ３９,小鼠巨细胞蛋白酶和人白介素１３等至今未知结构的蛋白作了预测性模建。     

PCDRA: PC interactive molecular representation and modeling system

PCDRA was designed to provide the average biologist with a user-friendly molecular display on a low-cost personal computer. The package is menu driven and is built so that a biologist, with tittle or no computing knowledge, finds it easy to use. The system gives a color representation with depth cueing of a protein whose atomic coordinates are stored as a PDB file. Moreover, the system presents several features similar to HYDRA¹ and therefore is a good introduction to molecular graphics, especially for beginners in protein modeling.     

蓖麻毒素A链突变体的设计、表达与活性研究

利用蛋白质结构同源模建并结合表观静电势分析,设计了拟具有生物学活性的蓖麻毒素A链的突变体.将PCR扩增的突变体基因,导入pKK223-3载体中,于大肠杆菌(E.coli)中获得高效、可溶性表达,而且,确证了表达产物具有预期的生物学活性.     

Hybridized distance- and contact-based hierarchical structure modeling for folding soluble and membrane proteins

Crystallography and NMR system (CNS) is currently a widely used method for fragment-free ab initio protein folding from inter-residue distance or contact maps. Despite its widespread use in protein structure prediction, CNS is a decade-old macromolecular structure determination system that was originally developed for solving macromolecular geometry from experimental restraints as opposed to predictive modeling driven by interaction map data. As such, the adaptation of the CNS experimental structure determination protocol for ab initio protein folding is intrinsically anomalous that may undermine the folding accuracy of computational protein structure prediction. In this paper, we propose a new CNS-free hierarchical structure modeling method called DConStruct for folding both soluble and membrane proteins driven by distance and contact information. Rigorous experimental validation shows that DConStruct attains much better reconstruction accuracy than CNS when tested with the same input contact map at varying contact thresholds. The hierarchical modeling with iterative self-correction employed in DConStruct scales at a much higher degree of folding accuracy than CNS with the increase in contact thresholds, ultimately approaching near-optimal reconstruction accuracy at higher-thresholded contact maps. The folding accuracy of DConStruct can be further improved by exploiting distance-based hybrid interaction maps at tri-level thresholding, as demonstrated by the better performance of our method in folding free modeling targets from the 12th and 13th rounds of the Critical Assessment of techniques for protein Structure Prediction (CASP) experiments compared to popular CNS- and fragment-based approaches and energy-minimization protocols, some of which even using much finer-grained distance maps than ours. Additional large-scale benchmarking shows that DConStruct can significantly improve the folding accuracy of membrane proteins compared to a CNS-based approach. These results collectively demonstrate the feasibility of greatly improving the accuracy of ab initio protein folding by optimally exploiting the information encoded in inter-residue interaction maps beyond what is possible by CNS.     

Recursive protein modeling: a divide and conquer strategy for Protein Structure Prediction and its case study in CASP9

After decades of research, protein structure prediction remains a very challenging problem. In order to address the different levels of complexity of structural modeling, two types of modeling techniques--template-based modeling and template-free modeling--have been developed. Template-based modeling can often generate a moderate- to high-resolution model when a similar, homologous template structure is found for a query protein but fails if no template or only incorrect templates are found. Template-free modeling, such as fragment-based assembly, may generate models of moderate resolution for small proteins of low topological complexity. Seldom have the two techniques been integrated together to improve protein modeling. Here we develop a recursive protein modeling approach to selectively and collaboratively apply template-based and template-free modeling methods to model template-covered (i.e. certain) and template-free (i.e. uncertain) regions of a protein. A preliminary implementation of the approach was tested on a number of hard modeling cases during the 9th Critical Assessment of Techniques for Protein Structure Prediction (CASP9) and successfully improved the quality of modeling in most of these cases. Recursive modeling can significantly reduce the complexity of protein structure modeling and integrate template-based and template-free modeling to improve the quality and efficiency of protein structure prediction.     

The use of soluble protein structures in modeling helical proteins in a layered membrane

Major advances have been made in the prediction of soluble protein structures, led by the knowledge-based modeling methods that extract useful structural trends from known protein structures and incorporate them into scoring functions. The same cannot be reported for the class of transmembrane proteins, primarily due to the lack of high-resolution structural data for transmembrane proteins, which render many of the knowledge-based method unreliable or invalid. We have developed a method that harnesses the vast structural knowledge available in soluble protein data for use in the modeling of transmembrane proteins. At the core of the method, a set of transmembrane protein decoy sets that allow us to filter and train features recognized from soluble proteins for transmembrane protein modeling into a set of scoring functions. We have demonstrated that structures of soluble proteins can provide significant insight into transmembrane protein structures. A complementary novel two-stage modeling/selection process that mimics the two-stage helical membrane protein folding was developed. Combined with the scoring function, the method was successfully applied to model 5 transmembrane proteins. The root mean square deviations of the predicted models ranged from 5.0 to 8.8?Å to the native structures.     

Derivation of rules for comparative protein modeling from a database of protein structure alignments

We describe a database of protein structure alignments as well as methods and tools that use this database to improve comparative protein modeling. The current version of the database contains 105 alignments of similar proteins or protein segments. The database comprises 416 entries, 78,495 residues, 1,233 equivalent entry pairs, and 230,396 pairs of equivalent alignment positions. At present, the main application of the database is to improve comparative modeling by satisfaction of spatial restraints implemented in the program MODELLER (?ali A, Blundell TL, 1993, J Mol Biol 234:779–815). To illustrate the usefulness of the database, the restraints on the conformation of a disulfide bridge provided by an equivalent disulfide bridge in a related structure are derived from the alignments; the prediction success of the disulfide dihedral angle classes is increased to approximately 80%, compared to approximately 55% for modeling that relies on the stereochemistry of disulfide bridges alone. The second example of the use of the database is the derivation of the probability density function for comparative modeling of the cis/trans isomerism of the proline residues; the prediction success is increased from 0% to 82.9% for cis-proline and from 93.3% to 96.2% for trans-proline. The database is available via electronic mail.     

Improving the accuracy of template-based predictions by mixing and matching between initial models

Background  

Comparative modeling is a technique to predict the three dimensional structure of a given protein sequence based primarily on its alignment to one or more proteins with experimentally determined structures. A major bottleneck of current comparative modeling methods is the lack of methods to accurately refine a starting initial model so that it approaches the resolution of the corresponding experimental structure. We investigate the effectiveness of a graph-theoretic clique finding approach to solve this problem.     

Identification and utilization of arbitrary correlations in models of recombination signal sequences

Background  

A significant challenge in bioinformatics is to develop methods for detecting and modeling patterns in variable DNA sequence sites, such as protein-binding sites in regulatory DNA. Current approaches sometimes perform poorly when positions in the site do not independently affect protein binding. We developed a statistical technique for modeling the correlation structure in variable DNA sequence sites. The method places no restrictions on the number of correlated positions or on their spatial relationship within the site. No prior empirical evidence for the correlation structure is necessary.     

基于结构比较的蛋白质模建系统及其评估：Ⅲ．灵敏的蛋白质结构评估方法

本文按二级结构、疏水性和侧链氢键把蛋白质的局部结构环境划分为６４类,按环境依赖的残基替代频数表构成残基与环境的兼容性分数表,可用来评估一个蛋白质的整体折叠正确与否和检测蛋白质折叠的局部错误。与著名的商品软件Ｐｒｏｆｉｌｅ－３Ｄ相比,此方法不仅能对整体折叠作出合理判断,而且对局部错误的检测更为灵敏。它已成为我们的蛋白质结构模建系统ＰＭＯＤＥＬＩＮＧ的重要模块。     

Systems biology of bacterial chemotaxis

Motile bacteria regulate chemotaxis through a highly conserved chemosensory signal-transduction system. System-wide analyses and mathematical modeling are facilitated by extensive experimental observations regarding bacterial chemotaxis proteins, including biochemical parameters, protein structures and protein-protein interaction maps. Thousands of signaling and regulatory chemotaxis proteins within a bacteria cell form a highly interconnected network through distinct protein-protein interactions. A bacterial cell is able to respond to multiple stimuli through a collection of chemoreceptors with different sensory modalities, which interact to affect the cooperativity and sensitivity of the chemotaxis response. The robustness or insensitivity of the chemotaxis system to perturbations in biochemical parameters is a product of the system's hierarchical network architecture.     

A Dynamic Analysis of IRS-PKR Signaling in Liver Cells: A Discrete Modeling Approach

A major challenge in systems biology is to develop a detailed dynamic understanding of the functions and behaviors in a particular cellular system, which depends on the elements and their inter-relationships in a specific network. Computational modeling plays an integral part in the study of network dynamics and uncovering the underlying mechanisms. Here we proposed a systematic approach that incorporates discrete dynamic modeling and experimental data to reconstruct a phenotype-specific network of cell signaling. A dynamic analysis of the insulin signaling system in liver cells provides a proof-of-concept application of the proposed methodology. Our group recently identified that double-stranded RNA-dependent protein kinase (PKR) plays an important role in the insulin signaling network. The dynamic behavior of the insulin signaling network is tuned by a variety of feedback pathways, many of which have the potential to cross talk with PKR. Given the complexity of insulin signaling, it is inefficient to experimentally test all possible interactions in the network to determine which pathways are functioning in our cell system. Our discrete dynamic model provides an in silico model framework that integrates potential interactions and assesses the contributions of the various interactions on the dynamic behavior of the signaling network. Simulations with the model generated testable hypothesis on the response of the network upon perturbation, which were experimentally evaluated to identify the pathways that function in our particular liver cell system. The modeling in combination with the experimental results enhanced our understanding of the insulin signaling dynamics and aided in generating a context-specific signaling network.     

Building proteins from C alpha coordinates using the dihedral probability grid Monte Carlo method.

Dihedral probability grid Monte Carlo (DPG-MC) is a general-purpose method of conformational sampling that can be applied to many problems in peptide and protein modeling. Here we present the DPG-MC method and apply it to predicting complete protein structures from C alpha coordinates. This is useful in such endeavors as homology modeling, protein structure prediction from lattice simulations, or fitting protein structures to X-ray crystallographic data. It also serves as an example of how DPG-MC can be applied to systems with geometric constraints. The conformational propensities for individual residues are used to guide conformational searches as the protein is built from the amino-terminus to the carboxyl-terminus. Results for a number of proteins show that both the backbone and side chain can be accurately modeled using DPG-MC. Backbone atoms are generally predicted with RMS errors of about 0.5 A (compared to X-ray crystal structure coordinates) and all atoms are predicted to an RMS error of 1.7 A or better.     

Hepatitis C Virus RNA Replication and Virus Particle Assembly Require Specific Dimerization of the NS4A Protein Transmembrane Domain

Hepatitis C virus (HCV) NS4A is a single-pass transmembrane (TM) protein essential for viral replication and particle assembly. The sequence of the NS4A TM domain is highly conserved, suggesting that it may be important for protein-protein interactions. To test this hypothesis, we measured the potential dimerization of the NS4A TM domain in a well-characterized two-hybrid TM protein interaction system. The NS4A TM domain exhibited a strong homotypic interaction that was comparable in affinity to glycophorin A, a well-studied human blood group antigen that forms TM homodimers. Several mutations predicted to cluster on a common surface of the NS4A TM helix caused significant reductions in dimerization, suggesting that these residues form an interface for NS4A dimerization. Mutations in the NS4A TM domain were further examined in the JFH-1 genotype 2a replicon system; importantly, all mutations that destabilized NS4A dimers also caused defects in RNA replication and/or virus assembly. Computational modeling of NS4A TM interactions suggests a right-handed dimeric interaction of helices with an interface that is consistent with the mutational effects. Furthermore, defects in NS4A oligomerization and virus particle assembly of two mutants were rescued by NS4A A15S, a TM mutation recently identified through forward genetics as a cell culture-adaptive mutation. Together, these data provide the first example of a functionally important TM dimer interface within an HCV nonstructural protein and reveal a fundamental role of the NS4A TM domain in coordinating HCV RNA replication and virus particle assembly.     

Anharmonic Normal Mode Analysis of Elastic Network Model Improves the Modeling of Atomic Fluctuations in Protein Crystal Structures

Protein conformational dynamics, despite its significant anharmonicity, has been widely explored by normal mode analysis (NMA) based on atomic or coarse-grained potential functions. To account for the anharmonic aspects of protein dynamics, this study proposes, and has performed, an anharmonic NMA (ANMA) based on the C_α-only elastic network models, which assume elastic interactions between pairs of residues whose C_α atoms or heavy atoms are within a cutoff distance. The key step of ANMA is to sample an anharmonic potential function along the directions of eigenvectors of the lowest normal modes to determine the mean-squared fluctuations along these directions. ANMA was evaluated based on the modeling of anisotropic displacement parameters (ADPs) from a list of 83 high-resolution protein crystal structures. Significant improvement was found in the modeling of ADPs by ANMA compared with standard NMA. Further improvement in the modeling of ADPs is attained if the interactions between a protein and its crystalline environment are taken into account. In addition, this study has determined the optimal cutoff distances for ADP modeling based on elastic network models, and these agree well with the peaks of the statistical distributions of distances between C_α atoms or heavy atoms derived from a large set of protein crystal structures.     

Crystal structures of mutant ribosomal proteins L1

Nine mutant ribosomal proteins L1 from the bacterium Thermus thermophilus and archaeon Methanococcus jannaschii were obtained and their crystal structures were determined and analyzed. The structure of the S179C TthL1 mutant, determined earlier, was also analyzed. In half of the proteins studied, point mutations of the amino acid residues exposed on the protein surface essentially changed the spatial structure of the protein. This proves that a correct study of biological processes with the help of site-directed mutagenesis requires a preliminary determination or, at least, modeling of the structures of mutant proteins. A detailed comparison of the structures of the L1 mutants and the corresponding wild-type L1 proteins demonstrated that the side chain of a mutated amino acid residue tends to adopt a location similar to that of the side chain of the corresponding residue in the wild-type protein. This observation assists in modeling the structure of mutant proteins.