Structure Prediction

A new complex of coumarin-3-carboxylic acid (HCCA) with Pr(III) is synthesized and its structure and molecular properties are investigated by elemental analysis, IR, Raman, NMR measurements and quantum chemical calculations. The elemental analysis suggested the general ...

Elementary to sophisticated approaches allow for crystal structure prediction from empirical and theoretical principles since a few decades. Some recent efforts in inorganic chemistry for the production and classification of structure-candidates are described. The discussion is focused on an incredibly huge list of hypothetical zeolites, more modest series of aluminum fluoride polymorphs, or titanosilicates (etc) build up from the exploration of a special topological class, the N-connected 3D frameworks.

In this paper a novel design procedure for lumped element Marchand baluns is proposed. An analysis is performed on the balun structure in order to determine the conditions for ideal balun performance in terms of the lumped element values. The analysis is verified by two broadband designs centered around 22.75 GHz and differing only in terms of their impedance transformation

One of the fundamental challenges in designing drug molecule against a disease target or protein is to predict binding affinity between target and drug or small molecule. In this review, our focus will be on advancement in the field of protein-small molecule interaction. This review has been divided into four major sections. In the first section, we will cover software developed for protein structure prediction. This will include prediction of binding pockets and post-translation modifications in proteins. In the second section, we will discuss software packages developed for predicting small-molecule interacting residues in a protein. Advances in the field of docking particularly advancement in the knowledge-based force fields will be discussed in the third part of the review. This section will also cover the method developed for predicting affinity between protein and drug molecules. The fourth section of the review will describe miscellaneous techniques used for designing drug molecules, like pharmacophore modelling. Our major emphasis in this review will be on computational tools that are available free for academic use.

The energy landscape theory of protein folding is a statistical description of a protein's potential surface. It assumes that folding occurs through organizing an ensemble of structures rather than through only a few uniquely defined structural intermediates. It suggests that the most realistic model of a protein is a minimally frustrated heteropolymer with a rugged funnel-like landscape biased toward the native structure. This statistical description has been developed using tools from the statistical mechanics of disordered systems, polymers, and phase transitions of finite systems. We review here its analytical background and contrast the phenomena in homopolymers, random heteropolymers, and protein-like heteropolymers that are kinetically and thermodynamically capable of folding. The connection between these statistical concepts and the results of minimalist models used in computer simulations is discussed. The review concludes with a brief discussion of how the theory helps in the interpretation of results from fast folding experiments and in the practical task of protein structure prediction.

Zusammenfassung. Dieser Übersichtsartikel behandelt die Grundlagen und klinische Anwendungen der Protein-Strukturvorhersage mit der Homology Modelling-Methode. Der Artikel richtet sich vornehmlich an Mediziner und medizinische Chemiker. Da viele Proteine unmittelbar von klinischer Bedeutung sind, ist die Strukturbestimmung entscheidend für das Verständnis der molekularen Grundlage von Stoffwechsel, Signalübertragung, Immunreaktionen und damit assoziierten Krankheiten. Homology Modelling beruht auf dem Prinzip, dass Proteine mit ähnlichen Sequenzen hohe Strukturübereinstimmungen aufweisen. Dabei werden Informationen, wie Atomabstände, Bindungslängen, Bindungswinkel etc., über bereits bekannten Strukturen verwendet, um die unbekannte Proteinstruktur anhand ihrer Sequenz vorauszusagen. Da in Zukunft für jede Proteinfamilie mindestens ein Mitglied mit bekannter Struktur verfügbar sein wird, ist zu erwarten, dass Bedeutung und Anwendbarkeit der Homology Modelling-Methode weiter zunehmen werden. In dieser Arbeit werden die Grundlagen der Methode anhand der Strukturvorhersage des Enzyms Glutamate Decarboxylase (GAD 65) erläutert. GAD 65 spielt als Antigen bei Diabetes Mellitus Typ 1 eine entscheidende Rolle. Schlüsselwörter: Bioinformatik, Homology Modelling, GAD 65, Epitope, Aktiv-Zentrum

Here, we describe two freely available web servers for molecular docking. The PatchDock method performs structure prediction of protein-protein and proteinsmall molecule complexes. The SymmDock method predicts the structure of a homomultimer with cyclic symmetry given the structure of the monomeric unit. The inputs to the servers are either protein PDB codes or uploaded protein structures. The services are available at http://bioinfo3d.cs.tau.ac.il. The methods behind the servers are very efficient, allowing large-scale docking experiments.

The prediction of loop conformations is one of the challenging problems of homology modeling, due to the large sequence variability associated with these parts of protein structures. In the present study, we introduce a search procedure that evolves in a structural alphabet space deduced from a hidden Markov model to simplify the structural information. It uses a Bayesian criterion to predict, from the amino acid sequence of a loop region, its corresponding word in the structural alphabet space.

Cortical areas are linked through pathways which originate and terminate in specific layers. The factors underlying which layers are involved in specific connections are not well understood. Here we tested whether cortical structure can predict the pattern as well as the relative distribution of projection neurons and axonal terminals in cortical layers, studied with retrograde and anterograde tracers. We used the prefrontal cortices in the rhesus monkey as a model system because their laminar organization varies systematically, ranging from areas that have only three identifiable layers, to those that have six layers. We rated each prefrontal area based on the number and definition of its cortical layers (level 1, lowest; level 5, highest). The structural model accurately predicted the laminar pattern of connections in ∼80% of the cases. Thus, projection neurons from a higher-level cortex originated mostly in the upper layers and their axons terminated predominantly in the deep layers (4-6) of a lower-level cortex. Conversely, most projection neurons from a lower-level area originated in the deep layers and their axons terminated predominantly in the upper layers (1-3) of a higher-level area. In addition, the structural model accurately predicted that the proportion of projection neurons or axonal terminals in the upper to the deep layers would vary as a function of the number of levels between the connected cortices. The power of this structural model lies in its potential to predict patterns of connections in the human cortex, where invasive procedures are precluded.

Because of the relatively large gap of knowledge between number of protein sequences and protein structures, the ability to construct a computational model predicting structure from sequence information has become an important area of research. The knowledge of a protein's structure is crucial in understanding its biological role. In this work, we present a support vector machine based method for recognising a protein's fold from sequence information alone, where this sequence has less similarity with sequences of known structures. We have focused on improving multi-class classification, parameter tuning, descriptor design, and feature selection. The current implementation demonstrates better prediction accuracy than previous similar approaches, and has similar performance when compared with straightforward threading.

The Ohio State leader behaviour description questionnaire XII (LBDQ XII) was used to assess the leadership preference opinions of business managers in England and Germany. Signi®cant differences were noted between the two national groups. The most dramatic difference was on the factor production emphasis, de®ned as ªmeasuring to what degree the manager applies pressure for productive outputº, with the English sample indicating a preference for a leader to demonstrate a signi®cantly higher level of production emphasis than the German sample. Large, signi®cant differences were also observed for demand reconciliation, persuasiveness, tolerance of uncertainty, initiation of structure, predictive accuracy, and superior orientation. For English leaders, the followers seem to prefer a more interventionist approach. For German leaders, the imposition of Ordnung (order) is critical.

In this work, genetic algorithms concepts along with a rotamer library for proteins side chains and implicit solvation potential are used to optimize the tertiary structure of peptides. We starting from the known PDB structure of its backbone which is kept fixed while the side chains allowed adopting the conformations present in the rotamer library. It was used rotamer library independent of backbone and a implicit solvation potential. The structure of Mastoporan-X was predicted using several force fields with a growing complexity; we started it with a field where the only present interaction was Lennard-Jones. We added the Coulombian term and we considered the solvation effects through a term proportional to the solvent accessible area. This paper present good and interesting results obtained using the potential with solvation term and rotamer library. Hence, the algorithm (called YODA) presented here can be a good tool to the prediction problem.

Local species diversity may be determined by processes operating locally, such as disturbance, predation and competition, or by regional processes, such as environmental structuring or history. Classical theory focusing on competition predicts that the species combining to form communities will be less similar to each other than they would be if they were assembled at random from a regional species pool. Theory focusing on environmental structuring predicts that species will be more similar to each other than expected by chance. A randomisation test that determines the extent to which local species lists represent random selections from a regional list, based on the average relatedness between species, was applied to data held in the MacroBen database. Little or no evidence was found for species lists of whole faunas at any scale being random subsets of species lists at larger scales. Species tend to be more closely related to each other than would be expected if they were assembled at random. Thus marine soft-sediment macrofauna are not locally assembled at random from regional species pools and it is likely that regional processes determine the assembly of communities. Focusing on the most abundant class within the macrofauna, a different pattern emerges, in that there is a much stronger tendency for local polychaete composition to be a random subset from regional pools at all scales. Thus it is not possible to determine whether local polychaete diversity is independent of both local and regional processes, or determined by a combination of both acting antagonistically.

Spinach is an important dietary vegetable associated with beneficial health effects. Flavonoids have various biological activities such as antioxidant, antibacterial, and anticancer effect Flavonoid including anthocyanin provides brilliant and colored pigments in different plant tissues. Anthocyanidin synthase and dihydroflavonol 4-reductase are responsible for anthocyanin biosynthesis. They contributed in plant protection against UV-B radiation, microbial and herbivore pathogens. A 3D structures of anthocyanidin synthase and dihydroflavonol 4-reductase from spinach are constructed in this study through homology modeling. The homology modeling is done by using the MODELLER 9v7 software. The energy of models was minimized by applying molecular mechanics method. The root mean square deviation (RMSD) for C atoms between the template and the homology-modeled structures was estimated by CE program. The final models were assessed by PROCHECK and WHATCHECK which showed that the final refin...

Secondary structure models of mitochondrial and nuclear (r)RNA sequences are frequently applied to aid the alignment of these molecules in phylogenetic analyses. Additionally, it is often speculated that structure variation of (r)RNA sequences might profitably be used as phylogenetic markers. The benefit of these approaches depends on the reliability of structure models. We used a recently developed approach to show that reliable inference of large (r)RNA secondary structures as a prerequisite of simultaneous sequence and structure alignment is feasible. The approach iteratively establishes local structure constraints of each sequence and infers fully folded individual structures by constrained MFE optimization. A comparison of structure edit distances of individual constraints and fully folded structures showed pronounced phylogenetic signal in fully folded structures. As model sequences we characterized secondary structures of 28S rRNA sequences of selected insects and examined their phylogenetic signal according to established phylogenetic hypotheses.

We report the development of new software, OligoDesign, which provides optimal design of LNA (locked nucleic acid) substituted oligonucleotides for functional genomics applications. LNAs constitute a novel class of bicyclic RNA analogs having an exceptionally high affinity and specificity toward their complementary DNA and RNA target molecules. The OligoDesign software features recognition and filtering of the target sequence by genome-wide BLAST analysis in order to minimize crosshybridization with non-target sequences. Furthermore it includes routines for prediction of melting temperature, self-annealing and secondary structure for LNA substituted oligonucleotides, as well as secondary structure prediction of the target nucleotide sequence. Individual scores for all these properties are calculated for each possible LNA oligonucleotide in the query gene and the OligoDesign program ranks the LNA capture probes according to a combined fuzzy logic score and finally returns the top scoring probes to the user in the output. We have successfully used the OligoDesign tool to design a Caenorhabditis elegans LNA oligonucleotide microarray, which allows monitoring of the expression of a set of 120 potential marker genes for a variety of stress and toxicological processes and toxicologically relevant pathways. The OligoDesign program is freely accessible at http://lnatools.com/.

Local species diversity may be determined by processes operating locally, such as disturbance, predation and competition, or by regional processes, such as environmental structuring or history. Classical theory focusing on competition predicts that the species combining to form communities will be less similar to each other than they would be if they were assembled at random from a regional species pool. Theory focusing on environmental structuring predicts that species will be more similar to each other than expected by chance. A randomisation test that determines the extent to which local species lists represent random selections from a regional list, based on the average relatedness between species, was applied to data held in the MacroBen database. Little or no evidence was found for species lists of whole faunas at any scale being random subsets of species lists at larger scales. Species tend to be more closely related to each other than would be expected if they were assembled at random. Thus marine soft-sediment macrofauna are not locally assembled at random from regional species pools and it is likely that regional processes determine the assembly of communities. Focusing on the most abundant class within the macrofauna, a different pattern emerges, in that there is a much stronger tendency for local polychaete composition to be a random subset from regional pools at all scales. Thus it is not possible to determine whether local polychaete diversity is independent of both local and regional processes, or determined by a combination of both acting antagonistically.

<p>Protein backbone RMSD of COX-1 model, over a time frame of 15ns (in black), shows stability in the last 5ns time frame, deviating about 0.35nm from the native structure whereas, protein backbone RMSD of COX-2 model, for 10ns (in red), achieved stability after 5ns and maintained till the final 10ns, with an average deviation of about 0.3nm. The plot has been generated using the GRACE plotting tool.</p

Background: Our goal is to develop a state-of-the-art protein secondary structure predictor, with an intuitive and biophysically-motivated energy model. We treat structure prediction as an optimization problem, using parameterizable cost functions representing biological "pseudoenergies". Machine learning methods are applied to estimate the values of the parameters to correctly predict known protein structures. Results: Focusing on the prediction of alpha helices in proteins, we show that a model with 302 parameters can achieve a Q α value of 77.6% and an SOV α value of 73.4%. Such performance numbers are among the best for techniques that do not rely on external databases (such as multiple sequence alignments). Further, it is easier to extract biological significance from a model with so few parameters. Conclusion: The method presented shows promise for the prediction of protein secondary structure. Biophysically-motivated elementary free-energies can be learned using SVM techniques to construct an energy cost function whose predictive performance rivals state-of-the-art. This method is general and can be extended beyond the all-alpha case described here. Background It remains an important and relevant problem to accurately predict the secondary structure of proteins based on their amino acid sequence. The identification of basic secondary structure elements-alpha helices, beta strands, and coils-is a critical prerequisite for many tertiary structure predictors, which consider the complete threedimensional protein structure. To date, there has been a broad array of approaches to secondary structure prediction, including statistical techniques, neural networks, hidden Markov models, support vector machines, nearest neighbor methods and energy minimization. In terms of

Background In recent years, RNA molecules that are not translated into proteins (ncRNAs) have drawn a great deal of attention, as they were shown to be involved in many cellular functions. One of the most important computational problems regarding ncRNA is to predict the secondary structure of a molecule from its sequence. In particular, we attempted to predict the secondary structure for a set of unaligned ncRNA molecules that are taken from the same family, and thus presumably have a similar structure. Results We developed the RNAspa program, which comparatively predicts the secondary structure for a set of ncRNA molecules in linear time in the number of molecules. We observed that in a list of several hundred suboptimal minimal free energy (MFE) predictions, as provided by the RNAsubopt program of the Vienna package, it is likely that at least one suggested structure would be similar to the true, correct one. The suboptimal solutions of each molecule are represented as a layer of...

Local species diversity may be determined by processes operating locally, such as disturbance, predation and competition, or by regional processes, such as environmental structuring or history. Classical theory focusing on competition predicts that the species combining to form communities will be less similar to each other than they would be if they were assembled at random from a regional species pool. Theory focusing on environmental structuring predicts that species will be more similar to each other than expected by chance. A randomisation test that determines the extent to which local species lists represent random selections from a regional list, based on the average relatedness between species, was applied to data held in the MacroBen database. Little or no evidence was found for species lists of whole faunas at any scale being random subsets of species lists at larger scales. Species tend to be more closely related to each other than would be expected if they were assembled at random. Thus marine soft-sediment macrofauna are not locally assembled at random from regional species pools and it is likely that regional processes determine the assembly of communities. Focusing on the most abundant class within the macrofauna, a different pattern emerges, in that there is a much stronger tendency for local polychaete composition to be a random subset from regional pools at all scales. Thus it is not possible to determine whether local polychaete diversity is independent of both local and regional processes, or determined by a combination of both acting antagonistically.

The immensely popular fields of cancer research and bioinformatics overlap in many different areas, e.g. large data repositories that allow for users to analyze data from many experiments (data handling, databases), pattern mining, microarray data analysis, and interpretation of proteomics data. There are many newly available resources in these areas that may be unfamiliar to most cancer researchers wanting to incorporate bioinformatics tools and analyses into their work, and also to bioinformaticians looking for real data to develop and test algorithms. This review reveals the interdependence of cancer research and bioinformatics, and highlight the most appropriate and useful resources available to cancer researchers. These include not only public databases, but general and specific bioinformatics tools which can be useful to the cancer researcher. The primary foci are function and structure prediction tools of protein genes. The result is a useful reference to cancer researchers a...

Genome3D, available at http://www.genome3d.eu, is a new collaborative project that integrates UKbased structural resources to provide a unique perspective on sequence-structure-function relationships. Leading structure prediction resources (DomSerf, FUGUE, Gene3D, pDomTHREADER, Phyre and SUPERFAMILY) provide annotations for UniProt sequences to indicate the locations of structural domains (structural annotations) and their 3D structures (structural models). Structural annotations and 3D model predictions are currently available for three model genomes (Homo sapiens, E. coli and baker's yeast), and the project will extend to other genomes in the near future. As these resources exploit different strategies for predicting structures, the main aim of Genome3D is to enable comparisons between all the resources so that biologists can see where predictions agree and are therefore more trusted. Furthermore, as these methods differ in whether they build their predictions using CATH or SCOP, Genome3D also contains the first official mapping between these two databases. This has identified pairs of similar superfamilies from the two resources at various degrees of consensus (532 bronze pairs, 527 silver pairs and 370 gold pairs).

A three-dimensional cellular automaton (CA) model has been coupled with finite-element (FE) heat flow calculation to predict the solidification grain structures in a Ni-base superalloy. In the present CA–FE model, Gaussian distribution of nucleation sites was adopted and the Kurz–Giovanola–Trivedi model was extended to multi-component alloys to account for the growth kinetics of the dendrite tip. In particular, for describing

Local species diversity may be determined by processes operating locally, such as disturbance, predation and competition, or by regional processes, such as environmental structuring or history. Classical theory focusing on competition predicts that the species combining to form communities will be less similar to each other than they would be if they were assembled at random from a regional species pool. Theory focusing on environmental structuring predicts that species will be more similar to each other than expected by chance. A randomisation test that determines the extent to which local species lists represent random selections from a regional list, based on the average relatedness between species, was applied to data held in the MacroBen database. Little or no evidence was found for species lists of whole faunas at any scale being random subsets of species lists at larger scales. Species tend to be more closely related to each other than would be expected if they were assembled at random. Thus marine soft-sediment macrofauna are not locally assembled at random from regional species pools and it is likely that regional processes determine the assembly of communities. Focusing on the most abundant class within the macrofauna, a different pattern emerges, in that there is a much stronger tendency for local polychaete composition to be a random subset from regional pools at all scales. Thus it is not possible to determine whether local polychaete diversity is independent of both local and regional processes, or determined by a combination of both acting antagonistically.

Computer simulations using density functional theory based ab initio path integral molecular dynamics have been carried out to investigate hydrogen bonding in water under ambient conditions. Structural predictions for both H 2 O and D 2 O, which include the effects of zero-point energy, thermal motion, and many body polarization effects, are contrasted with classical simulations that ignore nuclear quantum effects. The calculated effect of H/D isotope substitution on the water structure is much smaller than the difference between the classical and quantum path integral results, and is in excellent agreement with the measured H/D difference data from both neutron and x-ray scattering.

XAFS structures are solved routinely and hundreds of publications appear per annum. Limitations in theoretical predictions and XAFS analytical frameworks lead to significant uncertainty in results. This impairs structural predictions and prevents ab initio determination. The highest accuracy experimental data have been obtained using the XERT and the most popular technique to analyse the structure. We apply an accurate w 2 fitting procedure to the molybdenum attenuation data including error propagation and improve the XAFS determinations by between 5% and 70%.

Three-prime untranslated regions (3'UTRs) are widely recognized as important post-transcriptional regulatory regions of mRNAs. RNA-binding proteins and small non-coding RNAs such as microRNAs (miRNAs) bind to functional elements within 3'UTRs to influence mRNA stability, translation and localization. These interactions play many important roles in development, metabolism and disease. However, even in the most well-annotated metazoan genomes, 3'UTRs and their functional elements are not well defined. Comprehensive and accurate genome-wide annotation of 3'UTRs and their functional elements is thus critical. We have developed an open-access database, available at http:// www.UTRome.org, to provide a rich and comprehensive resource for 3'UTR biology in the wellcharacterized, experimentally tractable model system Caenorhabditis elegans. UTRome.org combines data from public repositories and a largescale effort we are undertaking to characterize 3'UTRs and their functional elements in C. elegans, including 3'UTR sequences, graphical displays, predicted and validated functional elements, secondary structure predictions and detailed data from our cloning pipeline. UTRome.org will grow substantially over time to encompass individual 3'UTR isoforms for the majority of genes, new and revised functional elements, and in vivo data on 3'UTR function as they become available. The UTRome database thus represents a powerful tool to better understand the biology of 3'UTRs.

We review three general approaches to determining the topology of integral cytoplasmic membrane proteins. (i) Inspection of the amino acid sequence and use of algorithms to predict membrane spanning segments allows the construction of topological models. For many proteins, the mere identification of such segments and an analysis of the distribution of basic amino acids in hydrophilic domains leads to correct structure predictions. For others, additional factors must come into play in determining topology. (ii) Gene fusion analysis of membrane proteins, in many cases, leads to complete topological models. Such analyses have been

This paper reports an intercomparison study of a stratocumulus-topped planetary boundary layer (PBL) generated from ten 3D large eddy simulation (LES) codes and four 2D cloud-resolving models (CRMs). These models vary in the numerics, the parameterizations of the subgrid-scale (SGS) turbulence and condensation processes, and the calculation of longwave radiative cooling. Cloud-top radiative cooling is often the major source of buoyant production of turbulent kinetic energy in the stratocumulus-topped PBL. An idealized nocturnal stratocumulus case was selected for this study. It featured a statistically horizontally homogeneous and nearly solid cloud deck with no drizzle, no solar radiation, little wind shear, and little surface heating. Results of the two-hour simulations showed that the overall cloud structure, including cloud-top height, cloud fraction, and the vertical distributions of many turbulence statistics, compared well among all LESs despite the code variations. However, the entrainment rate was found to differ significantly among the simulations. Among the model uncertainties due to numerics, SGS turbulence, SGS condensation, and radiation, none could be identified to explain such differences. Therefore, a follow-up study will focus on simulating the entrainment process. The liquid water mixing ratio profiles also varied significantly among the simulations; these profiles are sensitive to the algorithm used for computing the saturation mixing ratio. Despite the obvious differences in eddy structure in two-and three-dimensional simulations, the cloud structure predicted by the 2D CRMs was similar to that obtained by the 3D LESs, even though the momentum fluxes, the vertical and horizontal velocity variances, and the turbulence kinetic energy profiles predicted by the 2D CRMs all differ significantly from those of the LESs.

We describe a new method for local protein structure prediction based on a library of short sequence pattern that correlate strongly with protein three-dimensional structural elements. The library was generated using an automated method for ®nding correlations between protein sequence and local structure, and contains most previously described local sequence-structure correlations as well as new relationships, including a diverging type-II b-turn, a frayed helix, and a proline-terminated helix. The query sequence is scanned for segments 7 to 19 residues in length that strongly match one of the 82 patterns in the library. Matching segments are assigned the three-dimensional structure characteristic of the corresponding sequence pattern, and backbone torsion angles for the entire query sequence are then predicted by piecing together mutually compatible segment predictions. In predictions of local structure in a test set of 55 proteins, about 50% of all residues, and 76% of residues covered by high-con®dence predictions, were found in eight-residue segments within 1.4 A Ê of their true structures. The predictions are complementary to traditional secondary structure predictions because they are considerably more speci®c in turn regions, and may contribute to ab initio tertiary structure prediction and fold recognition.

Motivation: Recognizing that many intrinsically disordered regions in proteins play key roles in vital functions and also in some diseases, identification of the disordered regions has became a demanding process for structure prediction and functional characterization of proteins. Therefore, many studies have been motivated on accurate prediction of disorder. Mostly, machine learning techniques have been used for dealing with the prediction problem of disorder due to the capability of extracting the complex relationships and correlations hidden in large data sets. Results: In this study, a novel method, named Border Vector Detection and Extended Adaptation (BVDEA) was developed for predicting disorder as an alternative accurate classifier. The classifier performs the predictions by using three types of structural features belonging to proteins. For attesting the performance of the method, three computational learning techniques and eleven specific tools were used for comparison. Training was executed based on the data by 5-fold cross validation. When compared with the three learning methods of GRNN, LVQ and BVDA, the proposed method gives the best accuracy on classification. The BVDEA also provides faster and more robust learning as compared to the others. The new method provides a significant contribution to predicting disorder and order regions of proteins.

Dihedral angles of amino acids are of considerable importance in protein tertiary structure prediction as they define the backbone of a protein and hence almost define the protein's entire conformation. Most ab initio protein structure prediction methods predict the secondary structure of a protein before predicting the tertiary structure because three-dimensional fold consists of repeating units of secondary structures. Hence, both dihedral angles and secondary structures are important in tertiary structure prediction of proteins. Here we describe a database called DASSD (Dihedral Angle and Secondary Structure Database of Short Amino acid Fragments) that contains dihedral angle values and secondary structure details of short amino acid fragments of lengths 1, 3 and 5. Information stored in this database was extracted from a set of 5,227 nonredundant high resolution (less than 2-angstroms) protein structures. In total, DASSD stores details for about 733,000 fragments. This database finds application in the development of ab initio protein structure prediction methods using fragment libraries and fragment assembly techniques. It is also useful in protein secondary structure prediction.

RNA plays a critical role in mediating every step of cellular information transfer from genes to functional proteins. Pseudoknots are functionally important and widely occurring structural motifs found in all types of RNA. Therefore predicting their structures is an important problem. In this paper, we present a new RNA pseudoknot structure prediction method based on term rewriting. The method is implemented using the Mfold RNA/DNA folding package and the term rewriting language Maude. In our method, RNA structures are treated as terms and rules are discovered for predicting pseudoknots. Our method was tested on 211 pseudoknots in PseudoBase and achieves an average accuracy of 74.085% compared to the experimentally determined structure. In fact, most pseudoknots discovered by our method achieve an accuracy of above 90%. These results indicate that term rewriting has a broad potential in RNA applications ranging from prediction of pseudoknots to discovery of higher level RNA structures involving complex RNA tertiary interactions.