Influence of DNA structures on the conversion of sanguinarine alkanolamine form to iminium form

Sanguinarine exhibits pH dependent structural equilibrium between iminium form (structure I) and alkanolamine form (structure II) with a pKa of 7.4 as revealed from spectrophotometric titration. The titration data show that the compound exists almost exclusively as structure I and structure II in the pH range 1 to 6 and 8.5 to 11, respectively. The interaction of structure I and structure II to several B-form natural and synthetic double and single stranded DNAs has been studied by spectrophotometric, spectrofluorimetric and circular dichroic measurements in buffers of pH 5.2 and pH 10.4 where the physicochemical properties of DNA remain in B-form structure. The results show that structure I bind strongly to all B-form DNA structures showing typical hypochromism and bathochromism of the alkaloid's absorption maximum, quenching of steady-state fluorescence intensity and perturbations in circular dichroic spectrum. The structure II does not bind to DNA, but in presence of large amount of DNA significant population of structure I is generated, which binds to DNA and forms a structure I-DNA intercalated complex. The nature and magnitude of the spectral pattern are very much dependent on the structure as well as base composition of each DNA. The generation of the structure I from structure II is significantly affected by increasing ionic strength of the medium. The conversion of structure II to structure I in presence of high concentration of DNA in solution is explained through formation of a binding equilibrium process between structure II and structure I-DNA intercalated complex.     

Efficient parameterized algorithms for biopolymer structure-sequence alignment

Computational alignment of a biopolymer sequence (e.g., an RNA or a protein) to a structure is an effective approach to predict and search for the structure of new sequences. To identify the structure of remote homologs, the structure-sequence alignment has to consider not only sequence similarity, but also spatially conserved conformations caused by residue interactions and, consequently, is computationally intractable. It is difficult to cope with the inefficiency without compromising alignment accuracy, especially for structure search in genomes or large databases. This paper introduces a novel method and a parameterized algorithm for structure-sequence alignment. Both the structure and the sequence are represented as graphs, where, in general, the graph for a biopolymer structure has a naturally small tree width. The algorithm constructs an optimal alignment by finding in the sequence graph the maximum valued subgraph isomorphic to the structure graph. It has the computational time complexity O(k³N²) for the structure of N residues and its tree decomposition of width t. Parameter k, small in nature, is determined by a statistical cutoff for the correspondence between the structure and the sequence. This paper demonstrates a successful application of the algorithm to RNA structure search used for noncoding RNA identification. An application to protein threading is also discussed     

Fully automated ab initio protein structure prediction using I-SITES,HMMSTR and ROSETTA

MOTIVATION: The Monte Carlo fragment insertion method for protein tertiary structure prediction (ROSETTA) of Baker and others, has been merged with the I-SITES library of sequence structure motifs and the HMMSTR model for local structure in proteins, to form a new public server for the ab initio prediction of protein structure. The server performs several tasks in addition to tertiary structure prediction, including a database search, amino acid profile generation, fragment structure prediction, and backbone angle and secondary structure prediction. Meeting reasonable service goals required improvements in the efficiency, in particular for the ROSETTA algorithm. RESULTS: The new server was used for blind predictions of 40 protein sequences as part of the CASP4 blind structure prediction experiment. The results for 31 of those predictions are presented here. 61% of the residues overall were found in topologically correct predictions, which are defined as fragments of 30 residues or more with a root-mean-square deviation in superimposed alpha carbons of less than 6A. HMMSTR 3-state secondary structure predictions were 73% correct overall. Tertiary structure predictions did not improve the accuracy of secondary structure prediction.     

MMDB: Entrez's 3D-structure database

Three-dimensional structures are now known within most protein families and it is likely, when searching a sequence database, that one will identify a homolog of known structure. The goal of Entrez's 3D-structure database is to make structure information and the functional annotation it can provide easily accessible to molecular biologists. To this end, Entrez's search engine provides several powerful features: (i) links between databases, for example between a protein's sequence and structure; (ii) pre-computed sequence and structure neighbors; and (iii) structure and sequence/structure alignment visualization. Here, we focus on a new feature of Entrez's Molecular Modeling Database (MMDB): Graphical summaries of the biological annotation available for each 3D structure, based on the results of automated comparative analysis. MMDB is available at: http://www.ncbi.nlm.nih.gov/Entrez/structure.html.     

Asymmetric stability among the transmembrane helices of lactose permease

Combining structure determinations from nuclear magnetic resonance (NMR) data and molecular dynamics simulations (MD) under the same environmental conditions revealed a startling asymmetry in the intrinsic conformational stability of secondary structure in the transmembrane domain of lactose permease (LacY). Eleven fragments, corresponding to transmembrane segments (TMs) of LacY, were synthesized, and their secondary structure in solution was determined by NMR. Eight of the TMs contained significant regions of helical structure. MD simulations, both in DMSO and in a DMPC bilayer, showed sites of local stability of helical structure in these TMs, punctuated by regions of conformational instability, in substantial agreement with the NMR data. Mapping the stable regions onto the crystal structure of LacY reveals a marked asymmetry, contrasting with the pseudosymmetry in the static structure: the secondary structure in the C-terminal half is more stable than in the N-terminal half. The relative stability of secondary structure is likely exploited in the transport mechanism of LacY. Residues supporting proton conduction are in more stable regions of secondary structure, while residues key to substrate binding are found in considerably unstable regions of secondary structure.     

ncRNA consensus secondary structure derivation using grammar strings

Many noncoding RNAs (ncRNAs) function through both their sequences and secondary structures. Thus, secondary structure derivation is an important issue in today's RNA research. The state-of-the-art structure annotation tools are based on comparative analysis, which derives consensus structure of homologous ncRNAs. Despite promising results from existing ncRNA aligning and consensus structure derivation tools, there is a need for more efficient and accurate ncRNA secondary structure modeling and alignment methods. In this work, we introduce a consensus structure derivation approach based on grammar string, a novel ncRNA secondary structure representation that encodes an ncRNA's sequence and secondary structure in the parameter space of a context-free grammar (CFG) and a full RNA grammar including pseudoknots. Being a string defined on a special alphabet constructed from a grammar, grammar string converts ncRNA alignment into sequence alignment. We derive consensus secondary structures from hundreds of ncRNA families from BraliBase 2.1 and 25 families containing pseudoknots using grammar string alignment. Our experiments have shown that grammar string-based structure derivation competes favorably in consensus structure quality with Murlet and RNASampler. Source code and experimental data are available at http://www.cse.msu.edu/~yannisun/grammar-string.     

Prediction of homologous protein structures based on conformational searches and energetics

A "knowledge-based" method of predicting the unknown structure of a protein from a homologous known structure using energetics to determine a sidechain conformation is proposed. The method consists of exchanging the residues in the known structure for the sequence of the unknown protein. Then a conformational search with molecular mechanics energy minimization is done on the exchanged residues. The lowest energy conformer is the one picked to be the predicted structure. In the structure of bovine trypsin, the importance of including a solvation energy term in the search is demonstrated for solvent accessible residues, while molecular mechanics alone is enough to correctly predict the conformation of internal residues. The correctness of the model is assessed by a volume error overlap of the predicted structure compared to the crystal structure. Finally, the structure of rat trypsin is predicted from the crystal structure of bovine trypsin. The sequences of these two proteins are 74% identical and all of the significant changes between them are on external residues. Thus, the inclusion of solvation energy in the conformational search is necessary to accurately predict the structure of the exchanged residues.     

When to believe what you see

The recent X-ray crystal structure of a hammerhead ribozyme derived from Schistosoma mansoni containing the rate-enhancing peripheral domain has a catalytic core that is very different from the catalytic core present in the structure of the "minimal" hammerhead, which lacks a peripheral domain (Martick and Scott, 2006). The new structure reconciles many of the disagreements between the minimal hammerhead structure and the biochemical data on the cleavage properties of chemically modified hammerheads. The new structure also emphasizes the dynamic nature of small RNA domains and provides a cautionary tale for everyone who tries to use structure to understand function.     

Information theory provides a comprehensive framework for the evaluation of protein structure predictions

Protein structure prediction has a number of important ad hoc similarity measures for evaluating predictions, but would benefit from a measure that is able to provide a common framework for a broad range of comparisons. Here we show that a mutual information-like measure can provide a comprehensive framework for evaluating protein structure prediction of all types. We discuss the concept of information, its application to secondary structure, and the obstacle to applying it to 3D structure. On the basis of the insights from the secondary structure case, we present an approach to work around the 3D difficulties, and develop a method to measure the mutual information provided by a 3D structure prediction. We integrate the evaluation of all types of protein structure prediction into a single framework, and compare the amount of information provided by various prediction methods, including secondary structure prediction. Within this broadened framework, the idea that structure is better preserved than sequence during evolution is evaluated quantitatively for the globin family. A nearly perfect sequence match in the globin family corresponds to about 300 bits of information, whereas a nearly perfect structural match for the same two proteins corresponds to about 2500 bits of information, where bits of information describes the probability of obtaining a match of similar closeness by chance. Mutual information provides both a theoretical basis for evaluating structure similarity and an explanatory surround for existing similarity measures.     

Gaussian process functional regression modeling for batch data

A Gaussian process functional regression model is proposed for the analysis of batch data. Covariance structure and mean structure are considered simultaneously, with the covariance structure modeled by a Gaussian process regression model and the mean structure modeled by a functional regression model. The model allows the inclusion of covariates in both the covariance structure and the mean structure. It models the nonlinear relationship between a functional output variable and a set of functional and nonfunctional covariates. Several applications and simulation studies are reported and show that the method provides very good results for curve fitting and prediction.     

Adaptability of restrained molecular dynamics for tertiary structure prediction: application to Crotalus atrox venom phospholipase A2

In order to assess the adaptability and/or applicability of the restrained molecular dynamics (RMD) simulation for building a possible tertiary structure of a protein from the X-ray crystal structure of a family reference protein, the tertiary structure prediction of Crotalus atrox venom phospholipase A2 (PLA2) was attempted based on the X-ray crystal structure of bovine pancreatic PLA2. For the formation of secondary and tertiary structures from the fully extended starting structure, the RMD simulation with interatomic distance restraints and torsion angle restraints, which were derived from homologous amino acid sequence regions in the reference protein, was carried out until the molecular system was fully equilibrated. The predicted tertiary structure of C. atrox venom PLA2 was compared with its X-ray crystal structure, and furthermore the utility of this method was discussed by reference to the similar tertiary structure prediction of beta-trypsin from the X-ray crystal structure of an elastase.     

RNA-SSPT: RNA Secondary Structure Prediction Tools

The prediction of RNA structure is useful for understanding evolution for both in silico and in vitro studies. Physical methods like NMR studies to predict RNA secondary structure are expensive and difficult. Computational RNA secondary structure prediction is easier. Comparative sequence analysis provides the best solution. But secondary structure prediction of a single RNA sequence is challenging. RNA-SSPT is a tool that computationally predicts secondary structure of a single RNA sequence. Most of the RNA secondary structure prediction tools do not allow pseudoknots in the structure or are unable to locate them. Nussinov dynamic programming algorithm has been implemented in RNA-SSPT. The current studies shows only energetically most favorable secondary structure is required and the algorithm modification is also available that produces base pairs to lower the total free energy of the secondary structure. For visualization of RNA secondary structure, NAVIEW in C language is used and modified in C# for tool requirement. RNA-SSPT is built in C# using Dot Net 2.0 in Microsoft Visual Studio 2005 Professional edition. The accuracy of RNA-SSPT is tested in terms of Sensitivity and Positive Predicted Value. It is a tool which serves both secondary structure prediction and secondary structure visualization purposes.     

Structural stability of DNA in nonaqueous solvents

One of the defining physicochemical features of DNA in aqueous solution is its ability to maintain a double-helical structure and for this structure to undergo a cooperative, heat-induced denaturation (melting). Herein we show that a 21-mer synthetic DNA can form and maintain such a duplex structure not only in water but even in 99% glycerol; moreover, this double-helical structure reversibly and cooperatively melts in that solvent, with a T(m) value of some 30 degrees lower than in water. Two much larger, natural DNAs, from calf thymus and salmon testes, exhibit similar behavior in glycerol. All three DNAs can also sustain a double-helical structure in 99% ethylene glycol, although its thermostability (as reflected by the melting temperature) is some 20 degrees lower than in glycerol. In contrast, no duplex structure of any of the DNAs was detected in 99% formamide, methanol, or DMSO. This solvent trend resembles that previously observed in studies of protein structure and folding and underscores the importance of hydrophobic interactions in both protein and DNA structure and stability. Our findings suggest that water may not be unique as a suitable medium not only for protein structure but also for that of nucleic acids.     

Roles of Remote and Contact Forces in Epithelial Cell Structure Formation

Cancer cells collectively form a large-scale structure for their growth. In this article, we report that HeLa cells, epithelial-like human cervical cancer cells, aggressively migrate on Matrigel and form a large-scale structure in a cell-density-dependent manner. To explain the experimental results, we develop a simple model in which cells interact and migrate using the two fundamentally different types of force, remote and contact forces, and show how cells form a large-scale structure. We demonstrate that the simple model reproduces experimental observations, suggesting that the remote and contact forces considered in this work play a major role in large-scale structure formation of HeLa cells. This article provides important evidence that cancer cells form a large-scale structure and develops an understanding into the poorly understood mechanisms of their structure formation.     

The functional significance of leaf structure: a search for generalizations

The coupling between leaf structure and function is illustrated with reference to two examples, the C₄ photosynthetic pathway and leaf pubescence. A distinction is made between function and functional significance. The latter is defined as the role, significance or consequence of a structure, whereas the former is more simply the action that a structure is capable of performing. Using the two examples, four generalizations are made concerning the relationships between structure, function and functional significance: the functional significance of leaf structure is environment-dependent; the relationship between functional significance and structure is sometimes non-intuitive; functional equivalency means that there is often more than one 'solution' to the same 'constraint'; and the consequences of leaf structure can exert profound effects at levels of organization beyond those of the individual organism and may play a critical role in determining community structure and function, through interactions with other species and trophic levels. The importance of understanding the consequences in variation in leaf structure at the global scale is illustrated with reference to the issue of global climate change.     

Transformation of an alpha-helix peptide into a beta-hairpin induced by addition of a fragment results in creation of a coexisting state

Intrinsic rules of determining the tertiary structure of a protein have been unknown partly because physicochemical factors that contribute to stabilization of a protein structure cannot be represented as a linear combination of local interactions. To clarify the rules on the nonlinear term caused by nonlocal interaction in a protein, we tried to transform a peptide that has a fully helical structure (Target Peptide or TP) into a peptide that has a beta-hairpin structure (Designed Peptide or DP) by adding seven residues to the C terminus of TP. According to analyses of nuclear magnetic resonance measurements, while the beta-hairpin structure is stabilized in some DPs, it is evident that the helical structure observed in TP is also persistent and even extended throughout the length of the molecule. As a result, we have produced a peptide molecule that contains both the alpha-helix and beta-hairpin conformation at an almost equally populated level. The helical structures contained in these DPs were more stable than the helix in TP, suggesting that stabilizing one conformation does not result in destabilizing the other conformation. These DPs can thus be regarded as an isolated peptide version of the chameleon sequence, which has the capability of changing the secondary structure depending on the context of the surrounding environment in a protein structure. The fact that the transformation of one secondary structure caused stabilization of both the original and the induced structure would shed light on the mechanism of protein folding.     

Bayesian Model of Protein Primary Sequence for Secondary Structure Prediction

Determining the primary structure (i.e., amino acid sequence) of a protein has become cheaper, faster, and more accurate. Higher order protein structure provides insight into a protein’s function in the cell. Understanding a protein’s secondary structure is a first step towards this goal. Therefore, a number of computational prediction methods have been developed to predict secondary structure from just the primary amino acid sequence. The most successful methods use machine learning approaches that are quite accurate, but do not directly incorporate structural information. As a step towards improving secondary structure reduction given the primary structure, we propose a Bayesian model based on the knob-socket model of protein packing in secondary structure. The method considers the packing influence of residues on the secondary structure determination, including those packed close in space but distant in sequence. By performing an assessment of our method on 2 test sets we show how incorporation of multiple sequence alignment data, similarly to PSIPRED, provides balance and improves the accuracy of the predictions. Software implementing the methods is provided as a web application and a stand-alone implementation.     

Molecular dynamics simulations on parallel and antiparallel C.G*G triplexes

Molecular dynamics (MD) studies have been carried out on the Hoogsteen hydrogen bonded parallel and the reverse Hoogsteen hydrogen bonded antiparallel C.G*G triplexes. Earlier, the molecular mechanics studies had shown that the parallel structure was energetically more favourable than the antiparallel structure. To characterize the structural stability of the two triplexes and to investigate whether the antiparallel structure can transit to an energetically more favourable structure, due to the local fluctuations in the structure during the MD simulation, the two structures were subjected to 200ps of constant temperature vacuum MD simulations at 300K. Initially no constraints were applied to the structures and it was observed that for the antiparallel triplex, the structure showed a large root mean square deviation from the starting structure within the first 12ps and the N4-H41--O6 hydrogen bond in the WC duplex got distorted due to a high propeller twist and a moderate increase in the opening angle in the basepairs. Starting from an initial value of 30 degrees , helical twist of the average structure from this simulation had a value of 36 degrees , while the parallel structure stabilized at a twist of 33 degrees. In spite of the hydrogen bond distortions in the antiparallel triplex, it was energetically comparable to the parallel triplex. To examine the structural characteristics of an undistorted structure, another MD simulation was performed on the antiparallel triplex by constraining all the hydrogen bonds. This structure stabilized at an average twist of 33 degrees. In the course of the dynamics though the energy of the molecule - compared to the initial structure - improved, it did not become comparable to the parallel structure. Energy minimization studies performed in the presence of explicit water and counterions also showed the two structures to be equally favourable energetically. Together these results indicate that the parallel C.G*G triplex with Hoogsteen hydrogen bonds also represents a stereochemically and energetically favourable structure for this class of triplexes.