Structure Prediction Tools Research Articles

Predicting crystal structures with data mining of quantum calculations.

Predicting and characterizing the crystal structure of materials is a key problem in materials research and development. It is typically addressed with highly accurate quantum mechanical computations on a small set of candidate structures, or with empirical rules that have been extracted from a large amount of experimental information, but have limited predictive power. In this Letter, we transfer the concept of heuristic rule extraction to a large library of ab initio calculated information, and we demonstrate that this can be developed into a tool for crystal structure prediction.

Structural genomics: computational methods for structure analysis.

The success of structural genomics initiatives requires the development and application of tools for structure analysis, prediction, and annotation. In this paper we review recent developments in these areas; specifically structure alignment, the detection of remote homologs and analogs, homology modeling and the use of structures to predict function. We also discuss various rationales for structural genomics initiatives. These include the structure-based clustering of sequence space and genome-wide function assignment. It is also argued that structural genomics can be integrated into more traditional biological research if specific biological questions are included in target selection strategies.

Multiple sequence alignments as tools for protein structure and function prediction

Multiple sequence alignments have much to offer to the understanding of protein structure, evolution and function. We are developing approaches to use this information in predicting protein-binding specificity, intra-protein and protein-protein interactions, and in reconstructing protein interaction networks.

RNAsoft: A suite of RNA secondary structure prediction and design software tools.

DNA and RNA strands are employed in novel ways in the construction of nanostructures, as molecular tags in libraries of polymers and in therapeutics. New software tools for prediction and design of molecular structure will be needed in these applications. The RNAsoft suite of programs provides tools for predicting the secondary structure of a pair of DNA or RNA molecules, testing that combinatorial tag sets of DNA and RNA molecules have no unwanted secondary structure and designing RNA strands that fold to a given input secondary structure. The tools are based on standard thermodynamic models of RNA secondary structure formation. RNAsoft can be found online at http://www.RNAsoft.ca.

GeneSeqer@PlantGDB: Gene structure prediction in plant genomes.

The GeneSeqer@PlantGDB Web server (http://www.plantgdb.org/cgi-bin/GeneSeqer.cgi) provides a gene structure prediction tool tailored for applications to plant genomic sequences. Predictions are based on spliced alignment with source-native ESTs and full-length cDNAs or non-native probes derived from putative homologous genes. The tool is illustrated with applications to refinement of current gene structure annotation and de novo annotation of draft genomic sequences. The service should facilitate expert annotation as a community effort by providing convenient access to all public plant sequences via the PlantGDB database, a simple four-step protocol for spliced alignment and visually appealing displays of the predicted gene structures in addition to detailed sequence alignments.

A novel pyridine containing self-assembling system: synthesis, characterization, X-ray crystal structure, 13C solid phase NMR and solution studies

The synthesis of a novel pyridine containing self-assembling system, L, is reported. This system is prepared from the reaction between 2,6-pyridinedicarboxylic acid, [pydc.H2], and 2,6-pyridinediamine, [pyda], in the presence of Et3N in THF as solvent in 88% yield. Double proton transfer from diacid to diamine results in the formation of a self-assembling system consisting of dicationic and dianionic units connecting through H-bonding and ion-pairing simultaneously as shown in the X-ray crystal structure. L is recrystallized from H2O. The crystal system is monoclinic with space group P21/c. One of the carboxylate anions of dianion unit is connected to and directed toward two ammonium cations of dication unit while the other one interacts with neighboring pyridine CH. The characterization was performed using EI, CI, ES mass spectroscopy as well as 1H and 13C solution NMR and X-ray crystal. The solid phase NMR, as a tool for conformation and structure prediction, reveals that the two –CO2− functional groups are not equally involved in the self-assembling system. The 13C solid phase NMR data is in agreement with X-ray crystal structure.

A conserved helix-unfolding motif in the naturally unfolded proteins.

Among the naturally unfolded proteins there are many polypeptides that retain an extended conformation in the absence of any apparent signal. Using sequence alignment and secondary structure prediction tools, a conserved (LS/SL)(D/E)(D/E)(D/E)X(E/D) motif is uncovered in the vicinity of the N-terminus of their unfolded helices. A comparison of these data with published observations allows one to propose that the (LS/SL)(D/E)(D/E)(D/E)X(E/D) motif is a helix-unfolding signal. Furthermore, the strong similarity between this motif and the STXXDE casein kinase II phosphorylation site suggests a regulatory mechanism for the naturally unfolded proteins within the cell.

LiveBench-2: large-scale automated evaluation of protein structure prediction servers.

The aim of LiveBench is to provide a continuous evaluation of structure prediction servers to inform developers and users about the current state-of-the-art structure prediction tools. LiveBench differs from other evaluation experiments because it is a large-scale and a fully automated procedure. Since LiveBench-1, which finished in April 2000, and related but independent CASP3 and CAFASP1 experiments, significant progress in the field has occurred. Some of the new developments have already been assessed at the recent CASP4 and CAFASP2 experiments (both independently of LiveBench), but others have not been observed yet because they entail developments carried out only recently. These include the availability of new servers (Pcons, FUGUE, and Coblath) and the enhancement of previously existing tools (mGenThreader, Sam-T, and 3D-PSSM), which illustrate the fast rate at which the field is advancing. Consequently, to keep in pace with the development, we present the results of the second large-scale evaluation of protein structure prediction servers. Of the 11 fold recognition servers evaluated, two servers appear to be most sensitive. One of these is 3D-PSSM, a server significantly improved after LiveBench-1. The other top performer is the new consensus server Pcons, which significantly outperformed other servers in the specificity of predictions. LiveBench-2 shows that the top performing servers are able to accurately recognize a fold for about one third of the "difficult" targets, a clear improvement over LiveBench-1 results. Given that automated structure prediction is increasingly becoming a biologists companion, the guidelines drawn from the LiveBench experiments are likely to provide users with valuable and timely information for their prediction needs.

Perspectives in inorganic structural biology: solution structures of metalloproteins.

The achievements in the structural characterization in solution, through NMR spectroscopy, of proteins containing metal ions are reviewed and discussed. We call this branch "inorganic structural biology". The results of this approach are presented here for cytochrome b5, used in this paper as a case system. These results are discussed particularly in the light of their relevance for understanding the biological function of the proteins. Furthermore, the extension of the characterization to the internal motions and to the folding/unfolding processes, as well as the development of tools for structure prediction, are critically presented. The message is that the complete characterization of a biological molecule cannot be limited to a static description of the structure but it should go beyond, analyzing the internal motions occurring at various time scales as well as the behavior in different conditions, such as in the presence of denaturing agents.

Ab initio prediction of the solution structures and populations of a cyclic pentapeptide in DMSO based on an implicit solvation model.

Using a recently developed statistical mechanics methodology, the solution structures and populations of the cyclic pentapeptide cyclo(D-Pro(1)-Ala(2)-Ala(3)-Ala(4)-Ala(5)) in DMSO are obtained ab initio, i.e., without using experimental restraints. An important ingredient of this methodology is a novel optimization of implicit solvation parameters, which in our previous publication [Baysal, C.; Meirovitch, H. J Am Chem Soc 1998, 120, 800-812] has been applied to a cyclic hexapeptide in DMSO. The molecule has been described by the simplified energy function E(tot) = E(GRO) + summation operator(k) sigma(k)A(k), where E(GRO) is the GROMOS force-field energy, sigma(k) and A(k) are the atomic solvation parameter (ASP) and the solvent accessible surface area of atom k. This methodology, which relies on an extensive conformational search, Monte Carlo simulations, and free energy calculations, is applied here with E(tot) based on the ASPs derived in our previous work, and for comparison also with E(GRO) alone. For both models, entropy effects are found to be significant. For E(tot), the theoretical values of proton-proton distances and (3)J coupling constants agree very well with the NMR results [Mierke, D. F.; Kurz, M.; Kessler, H. J Am Chem Soc 1994, 116, 1042-1049], while the results for E(GRO) are significantly worse. This suggests that our ASPs might be transferrable to other cyclic peptides in DMSO as well, making our methodology a reliable tool for an ab initio structure prediction; obviously, if necessary, parts of this methodology can also be incorporated in a best-fit analysis where experimental restraints are used.

Metacomputing in practice: a distributed compute server for pharmaceutical industry

We describe a distributed high-performance compute server that has been implemented for running compute-intensive applications on a mixture of HPC systems interconnected by Inter-and Intranet. With a practical industrial background, our work focusses on high availability, efficient job load balancing, security, and the easy integration of HPC computing into the daily work-flow at pharmaceutical companies. The work was done in the course of the ESPRIT project P hase A Distributed Pharmaceutical Application Server The client software is implemented in Java. All results are displayed in a web browser and can be forwarded to the next stage of applications used in the drug design cycle. The server software handles the job load balancing between the participating HPC nodes and is capable of managing multi-site applications. Our environment currently supports four key applications that are used in rational drug design and drug target identification. They range from the automatic functional annotation of protein sequences to three-dimensional protein structure prediction tools and protein comparison applications.

Lewis Structures Are Models for Predicting Molecular Structure, Not Electronic Structure

This article argues against a close relationship between Lewis dot structures and electron structure obtained from quantum mechanical calculations. Lewis structures are a powerful tool for structure prediction, though they are classical models of bonding and do not predict electronic structure. The "best" Lewis structures are those that, when combined with the VSEPR model, allow the accurate prediction of molecular properties, such as polarity, bond length, bond angle, and bond strength. These structures are achieved by minimizing formal charges within the molecule, even if it requires an expanded octet on atoms beyond the second period. Lewis structures that show an expanded octet do not imply full d-orbital involvement in the bonding. They suggest that the presence of low-lying d-orbitals is important in producing observed molecular structures.Based on this work, the presence of electron density, not a large separation in charge, is responsible for the short bond lengths and large angles in species containing nonmetal atoms from beyond the second period. This result contradicts results obtained from natural population analysis, a method that attempts to derive Lewis structures from molecular orbital calculations.

Geometric recognition as a tool for predicting structures of molecular complexes

The structures of two binary complexes (the TEM-1/BLIP complex and the trypsin/amiloride complex) were predicted prior to their experimental determination and compared to the corresponding experimental structures when these became available. In both predictions the rigid-body geometric docking algorithm ranked the correct solution among the top ones. Additional information concerning the structure and chemical character of the binding site of one of the molecules in the complex was used to single out the correct solution. The results indicate that the combination of geometric surface matching with biochemical information produces a useful tool for structure prediction.

Practical evaluation of comparative modelling and threading methods

Six protein pairs, all with known 3D-structures, were used to evaluate different protein structure prediction tools. Firstly, alignments between a target sequence and a template sequence or structure were obtained by sequence alignment with QUANTA or by threading with THREADER, 123D and PHD Topits. Secondly, protein structure models were generated using MODELLER. The two protein structure assessment tools used were the root mean square deviation (RMSD) compared with the experimental target structure and the total 3D profile score. Also the accuracy of the active sites of models built in the absence and presence of ligands was investigated. Our study confirms that threading methods are able to yield more accurate models than comparative modelling in cases of low sequence identity (<30%). However, a gap of 2 Å(RMSD) exists between the theoretically best model and the models obtained by threading methods. For high sequence identities (>30%) comparative modelling using MODELLER resulted in accurate models. Furthermore, the total 3D profile score was not always able to distinguish correct from incorrect folds when different alignment methods were used. Finally, we found it to be important to include possible ligands in the model-building process in order to prevent unrealistic filling of active site areas.

Update on protein structure prediction: results of the 1995 IRBM workshop

Computational tools for protein structure prediction are of great interest to molecular, structural and theoretical biologists due to a rapidly increasing number of protein sequences with no known structure. In October 1995, a workshop was held at IRBM to predict as much as possible about a number of proteins of biological interest using ab initio prediction of fold recognition methods. 112 protein sequences were collected via an open invitation for target submissions. 17 were selected for prediction during the workshop and for 11 of these a prediction of some reliability could be made. We believe that this was a worthwhile experiment showing that the use of a range of independent prediction methods and thorough use of existing databases can lead to credible and useful ab initio structure predictions.

MacMatch: a tool for pattern-based protein secondary structure prediction.

A program is described for predicting the secondary structure of globular proteins on an Apple Macintosh computer. MacMatch produces turn predictions using augmented regular expression pattern matching. Helix and strand predictions are based on a neural network. Protein structural class (alpha/alpha), beta/beta, alpha/beta can be exploited to improve secondary structure prediction. The program is simple to use, and the package includes sets of tested patterns and trained neural networks.

CGEMA and VGAP: a Colour Graphics Editor for Multiple Alignment using a Variable GAP penalty. Application to the muscarinic acetylcholine receptor

Today, more than 40 protein amino acid (AA) sequences of membrane receptors coupled to guanine nucleotide binding proteins (G-proteins) are available. For those working in the field of medicinal chemistry, these sequences present a new type of information that should be taken into consideration. To make maximal use of sequence data it is essential to be able to compare different protein sequences in a similar way to that used for small molecules. A prerequisite, however, is the availability of a processing environment that enables one to handle sequences in an easy way, both by hand and by computer. In order to meet these ends, the package CGEMA (Colour Graphics Editor for Multiple Alignment) was developed in our laboratory. The programme uses a user-definable colour coding for the different AAs. Sequences can be aligned by hand or by computer, using VGAP, and both approaches can be combined. VGAP is a novel in-house written alignment programme with a variable gap penalty that also handles consecutive alignments using one sequence as a probe. In addition, secondary structure prediction tools are available. From the 20 protein sequences, available for the muscarinic acetylcholine receptor, 13 different sequences were selected, covering the subtypes m1 to m5. By comparing the sequences, two major groups are revealed that correspond to those found by considering the transducing system coupled to the various receptor subtypes. Different parts of the protein sequences are identified as characterizing the subtype and binding the ligands, respectively.