Three-dimensional Structure Prediction Research Articles

Homology Modeling and Analysis of Structure Predictions of the Bovine Rhinitis B Virus RNA Dependent RNA Polymerase (RdRp)

Bovine Rhinitis B Virus (BRBV) is a picornavirus responsible for mild respiratory infection of cattle. It is probably the least characterized among the aphthoviruses. BRBV is the closest relative known to Foot and Mouth Disease virus (FMDV) with a ~43% identical polyprotein sequence and as much as 67% identical sequence for the RNA dependent RNA polymerase (RdRp), which is also known as 3D polymerase (3Dpol). In the present study we carried out phylogenetic analysis, structure based sequence alignment and prediction of three-dimensional structure of BRBV 3Dpol using a combination of different computational tools. Model structures of BRBV 3Dpol were verified for their stereochemical quality and accuracy. The BRBV 3Dpol structure predicted by SWISS-MODEL exhibited highest scores in terms of stereochemical quality and accuracy, which were in the range of 2Å resolution crystal structures. The active site, nucleic acid binding site and overall structure were observed to be in agreement with the crystal structure of unliganded as well as template/primer (T/P), nucleotide tri-phosphate (NTP) and pyrophosphate (PPi) bound FMDV 3Dpol (PDB, 1U09 and 2E9Z). The closest proximity of BRBV and FMDV 3Dpol as compared to human rhinovirus type 16 (HRV-16) and rabbit hemorrhagic disease virus (RHDV) 3Dpols is also substantiated by phylogeny analysis and root-mean square deviation (RMSD) between C-α traces of the polymerase structures. The absence of positively charged α-helix at C terminal, significant differences in non-covalent interactions especially salt bridges and CH-pi interactions around T/P channel of BRBV 3Dpol compared to FMDV 3Dpol, indicate that despite a very high homology to FMDV 3Dpol, BRBV 3Dpol may adopt a different mechanism for handling its substrates and adapting to physiological requirements. Our findings will be valuable in the design of structure-function interventions and identification of molecular targets for drug design applicable to Aphthovirus RdRps.

Identification of novel glutathione transferases in Echinococcus granulosus. An evolutionary perspective

Glutathione transferase enzymes (GSTs) constitute a major detoxification system in helminth parasites and have been related to the modulation of host immune response mechanisms. At least three different GSTs classes have been described in Platyhelminthes: Mu, Sigma and Omega. Mining the genome of Echinococcus multilocularis and the ESTs databases of Taenia solium and E. granulosus identified two new GSTs from the cestode E. granulosus, named EgGST2 and EgGST3. It also revealed that the Omega class of GSTs was absent from the Taenidae family. EgGST2 and EgGST3 are actively expressed in the parasite. In order to know the origin of these new GSTs, in silico analyses were performed. While EgGST2 is classified as belonging to the Sigma class, the data obtained for EgGST3 allowed a less clear interpretation. The study of the evolutionary relatedness based on the C-terminal domain sequence, gene structure conservation and three-dimensional structure predictions, suggests that EgGST3 is derived from the Platyhelminthes’ Sigma-class cluster. Interestingly, the N-terminal domain displays some characteristic Omega-class residues, including a Cys residue that is likely to be involved in the catalytic mechanism. We discuss different evolutionary scenarios that could explain the observed patterns.

Rendering RNA in 3D

Integrating biochemical footprinting data with molecular dynamics yields more accurate RNA three-dimensional structure predictions.

A Novel Bat Herpesvirus Encodes Homologues of Major Histocompatibility Complex Classes I and II, C-Type Lectin, and a Unique Family of Immune-Related Genes

Herpesviruses or herpesviral sequences have been identified in various bat species. Here, we report the isolation, cell tropism, and complete genome sequence of a novel betaherpesvirus from the bat Miniopterus schreibersii (MsHV). In primary cell culture, MsHV causes cytopathic effects (CPE) and reaches peak virus production 2 weeks after infection. MsHV was found to infect and replicate less efficiently in a feline kidney cell, CRFK, and failed to replicate in 13 other cell lines tested. Sequencing of the MsHV genome using the 454 system, with a 224-fold coverage, revealed a genome size of 222,870 bp. The genome was extensively analyzed in comparison to those of related viruses. Of the 190 predicted open reading frames (ORFs), 40 were identified as herpesvirus core genes. Among 93 proteins with identifiable homologues in tree shrew herpesvirus (THV), human cytomegalovirus (HCMV), or rat cytomegalovirus (RCMV), most had highest sequence identities with THV counterparts. However, the MsHV genome organization is colinear with that of RCMV rather than that of THV. The following unique features were discovered in the MsHV genome. One predicted protein, B125, is similar to human herpesvirus 6 (HHV-6) U94, a homologue of the parvovirus Rep protein. For the unique ORFs, 7 are predicted to encode major histocompatibility complex (MHC)-related proteins, 2 to encode MHC class I homologues, and 3 to encode MHC class II homologues; 4 encode the homologues of C-type lectin- or natural killer cell lectin-like receptors;, and the products of a unique gene family, the b149 family, of 16 members, have no significant sequence identity with known proteins but exhibit immunoglobulin-like beta-sandwich domains revealed by three-dimensional (3D) structural prediction. To our knowledge, MsHV is the first virus genome known to encode MHC class II homologues.

Purification, characterization of a CkChn134 protein fromCynanchum komaroviiseeds and synergistic effect with CkTLP againstVerticillium dahliae

Cynanchum komarovii Al Iljinski is a desert plant that has been used as analgesic, anthelminthic, and antidiarrheal, but also as herbal medicine to treat cholecystitis in people. In this work, an antifungal protein with sequence homology to chitinase was isolated from C. komarovii seeds and named CkChn134. The three-dimensional structure prediction of CkChn134 indicated that the protein has a loop domain formed a thin cleft, which is able to bind molecules and substrates. The protein and CkTLP synergistically inhibited the fungal growth of Verticillium dahliae, Fusarium oxysporum, Rhizoctonia solani, Botrytis cinerea, and Valsa mali in vitro. The full-length cDNA was cloned by RT-PCR and RACE-PCR according to the partial protein sequences obtained by nanoESI-MS/MS. The real-time PCR showed that the transcription level of CkChn134 had a significant increase under the stress of ethylene, NaCl, low temperature, drought, and pathogen infection, which indicates that CkChn134 may play an important role in response to abiotic and biotic stresses. The CkChn134 protein was located in the extracellular space/cell wall by CkChn134::GFP fusion protein in transgenic Arabidopsis. Furthermore, overexpression of CkChn134 significantly enhanced the resistance of transgenic Arabidopsis against V. dahliae. Interestingly, the coexpression of CkChn134 and CkTLP showed substantially greater protection against the fungal pathogen V. dahliae than either transgene alone. The results suggest that the CkChn134 is a good candidate protein or gene, and it had a potential synergistic effect with CkTLP for contributing to the development of disease-resistant crops.

Protein structure prediction using distributed parallel particle swarm optimization

Particle swarm optimization is a powerful technique for computer aided prediction of proteins' three-dimensional structure. In this work, employing an all-atom force field and the standard algorithm, as implemented in the ArFlock library in previous work, the low-energy conformations of several peptides of different sizes in vacuum starting from completely extended conformations are investigated. The computed structures are in good overall agreement with experimental data and results from other computer simulations. Periodic boundary conditions applied to the search space improve the performance of the method dramatically, especially when the linear velocity update rule is used. It is also shown that asynchronous parallelization speeds up the simulation better than the synchronous one and reduces the effective time for predictions significantly.

RNA-Puzzles: A CASP-like evaluation of RNA three-dimensional structure prediction

We report the results of a first, collective, blind experiment in RNA three-dimensional (3D) structure prediction, encompassing three prediction puzzles. The goals are to assess the leading edge of RNA structure prediction techniques; compare existing methods and tools; and evaluate their relative strengths, weaknesses, and limitations in terms of sequence length and structural complexity. The results should give potential users insight into the suitability of available methods for different applications and facilitate efforts in the RNA structure prediction community in ongoing efforts to improve prediction tools. We also report the creation of an automated evaluation pipeline to facilitate the analysis of future RNA structure prediction exercises.

RNA fragment modeling with a nucleobase discrete-state model

In this work we develop an approach for predicting the tertiary structures of RNA fragments by combining an RNA nucleobase discrete state (RNAnbds) model, a sequential Monte Carlo method, and a statistical potential. The RNAnbds model is designed for optimizing the configuration of nucleobases with respect to their preceding ones along the sequence and their spatial neighbors, in contrast to previous works that focus on RNA backbones. The tests of our approach with the fragments taken from a small RNA pseudoknot and a 23S ribosome RNA show that for short fragments (<10 nucleotides), the root mean square deviations (RMSDs) between the predicted and the experimental ones are generally smaller than 3 Å; for slightly longer fragments (10-15 nucleotides), most RMSDs are smaller than 4 Å. The comparison of our method with another physics-based predictor with a testing set containing nine loops shows that ours is superior in both accuracy and efficiency. Our approach is useful in facilitating RNA three-dimensional structure prediction as well as loop modeling. It also holds the promise of providing insight into the structural ensembles of RNA loops.

Structural Bioinformatics and Molecular Dynamics Simulations Studies of Cathepsins as a Potential Target for Drug Discovery

Prediction of three-dimensional structure of cathepsins, and molecular dynamics simulations of cathepsin S were studied by interaction with the drug molecule with virtual screening 681,158 compounds from ZINC database. The result of study showed top 1 ranked was obtained with drug molecule ZINC 23215439 reaction with cathepsin S. This demonstrates that the active site of cathepsin S Cys25, His164 and binding site Gln19 and Gly 20 are essential for interactions of cathepsin S-ZINC 23215439 inhibitor complex. Coulomb-SR and Lennard-Jones-SR interactions energy of amino acids and drug molecule ZINC code 23215439 which consisted in active site of cathepsin S have been evaluated.

RNA-Puzzles: A CASP-like evaluation of RNA three-dimensional structure prediction

RNA-Puzzles: A CASP-like evaluation of RNA three-dimensional structure prediction

Bacterial toxin-antitoxin systems

Toxin-antitoxin (TA) systems are composed of two elements: a toxic protein and an antitoxin which is either an RNA (type I and III) or a protein (type II). Type II systems are abundant in bacterial genomes in which they move via horizontal gene transfer. They are generally composed of two genes organized in an operon, encoding a toxin and a labile antitoxin. When carried by mobile genetic elements, these small modules contribute to their stability by a phenomenon denoted as addiction. Recently, we developed a bioinformatics procedure that, along with experimental validation, allowed the identification of nine novel toxin super-families. Here, considering that some toxin super-families exhibit dramatic sequence diversity but similar structure, bioinformatics tools were used to predict tertiary structures of novel toxins. Seven of the nine novel super-families did not show any structural homology with known toxins, indicating that combination of sequence similarity and three-dimensional structure prediction allows a consistent classification. Interestingly, the novel super-families are translation inhibitors similar to the majority of known toxins indicating that this activity might have been selected rather than more detrimental traits such as DNA-gyrase inhibitors, which are very toxic for cells.

Evolution of prokaryotic homologues of the eukaryotic SEFIR protein domain

SEF/IL17 receptor (SEFIR) domains are mainly found in IL17 receptors (IL17Rs) and their adaptor proteins CIKS (connection to IKK and SAPK/JNK), which exert a host defense role in numbers of infectious diseases and promote inflammatory pathology in autoimmunity. Exploring the evolutionary pathway of SEFIR domains will provide further insight into their functions. Here, we have identified 84 SEFIR domain-containing proteins from more than 1400 prokaryotic genomes. As most SEFIR domain-containing bacterial genomes possess a single SEFIR encoding gene and the SEFIR protein domain forms homodimeric complexes like the Toll/IL1 receptor (TIR) domain, the single bacterial SEFIR proteins may receive binding partners from other organisms. Through comparative and phylogenetic sequence analyses, we show that bacterial SEFIR domain is more similar to that of vertebrate CIKS than IL17R, and it possibly emerges via a lateral gene transfer (LGT) from animals. In addition, our secondary and three-dimensional structural predictions of SEFIR domains reveal that human and pathogenic bacterial SEFIR domains share similar structural and electrostatic features. Our findings provide important clues for further experimental researches on determining the functions of SEFIR proteins in pathogenic prokaryotes.

Protein Threading Based on Multiple Protein Structure Alignment.

Protein threading, a method employed in protein three-dimensional (3D) structure prediction was only proposed in the early 1990's although predicting protein 3D structure from its given amino acid sequence has been around since 1970's. Here we describe a protein threading method/system that we have developed based on multiple protein structure alignment. In order to compute multiple structure alignments, we developed a similar structure search program on massive parallel computers and a program for constructing a multiple structure alignment from pairwise structure alignments, where the latter is based on the center star method for sequence alignment. A simple dynamic-programming based algorithm which uses a profile matrix obtained from the result of multiple structure alignment was also developed to compute a threading (i.e., an alignment between a target sequence and a known structure). Using this system, we participated in the threading category (category AL) of CASP3 (Third Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction). The results are discussed.

Four-and-a-half LIM domains protein 2 (FHL2) is associated with the development of craniofacial musculature in the teleost fish Sparus aurata.

Four-and-a-half LIM domains protein 2 (FHL2) is involved in major cellular mechanisms such as regulation of gene transcription and cytoskeleton modulation, participating in physiological control of cardiogenesis and osteogenesis. Knowledge on underlying mechanisms is, however, limited. We present here new data on FHL2 protein and its role during vertebrate development using a marine teleost fish, the gilthead seabream (Sparus aurata L.). In silico comparison of vertebrate protein sequences and prediction of LIM domain three-dimensional structure revealed a high degree of conservation, suggesting a conserved function throughout evolution. Determination of sites and levels of FHL2 gene expression in seabream indicated a central role for FHL2 in the development of heart and craniofacial musculature, and a potential role in tissue calcification. Our data confirmed the key role of FHL2 protein during vertebrate development and gave new insights into its particular involvement in craniofacial muscle development and specificity for slow fibers.

ASTRO‐FOLD 2.0: An enhanced framework for protein structure prediction

The three-dimensional (3-D) structure prediction of proteins, given their amino acid sequence, is addressed using the first principles-based approach ASTRO-FOLD 2.0. The key features presented are: (1) Secondary structure prediction using a novel optimization-based consensus approach, (2) β-sheet topology prediction using mixed-integer linear optimization (MILP), (3) Residue-to-residue contact prediction using a high-resolution distance-dependent force field and MILP formulation, (4) Tight dihedral angle and distance bound generation for loop residues using dihedral angle clustering and non-linear optimization (NLP), (5) 3-D structure prediction using deterministic global optimization, stochastic conformational space annealing, and the full-atomistic ECEPP/3 potential, (6) Near-native structure selection using a traveling salesman problem-based clustering approach, ICON, and (7) Improved bound generation using chemical shifts of subsets of heavy atoms, generated by SPARTA and CS23D. Computational results of ASTRO-FOLD 2.0 on 47 blind targets of the recently concluded CASP9 experiment are presented.

The Energy Computation Paradox and ab initio Protein Folding

The routine prediction of three-dimensional protein structure from sequence remains a challenge in computational biochemistry. It has been intuited that calculated energies from physics-based scoring functions are able to distinguish native from nonnative folds based on previous performance with small proteins and that conformational sampling is the fundamental bottleneck to successful folding. We demonstrate that as protein size increases, errors in the computed energies become a significant problem. We show, by using error probability density functions, that physics-based scores contain significant systematic and random errors relative to accurate reference energies. These errors propagate throughout an entire protein and distort its energy landscape to such an extent that modern scoring functions should have little chance of success in finding the free energy minima of large proteins. Nonetheless, by understanding errors in physics-based score functions, they can be reduced in a post-hoc manner, improving accuracy in energy computation and fold discrimination.

Physics-based de novo prediction of RNA 3D structures.

Current experiments on structural determination cannot keep up the pace with the steadily emerging RNA sequences and new functions. This underscores the request for an accurate model for RNA three-dimensional (3D) structural prediction. Although considerable progress has been made in mechanistic studies, accurate prediction for RNA tertiary folding from sequence remains an unsolved problem. The first and most important requirement for the prediction of RNA structure from physical principles is an accurate free energy model. A recently developed three-vector virtual bond-based RNA folding model ("Vfold") has allowed us to compute the chain entropy and predict folding free energies and structures for RNA secondary structures and simple pseudoknots. Here we develop a free energy-based method to predict larger more complex RNA tertiary folds. The approach is based on a multiscaling strategy: from the nucleotide sequence, we predict the two-dimensional (2D) structures (defined by the base pairs and tertiary contacts); based on the 2D structure, we construct a 3D scaffold; with the 3D scaffold as the initial state, we combine AMBER energy minimization and PDB-based fragment search to predict the all-atom structure. A key advantage of the approach is the statistical mechanical calculation for the conformational entropy of RNA structures, including those with cross-linked loops. Benchmark tests show that the model leads to significant improvements in RNA 3D structure prediction.

ModeRNA: a tool for comparative modeling of RNA 3D structure

RNA is a large group of functionally important biomacromolecules. In striking analogy to proteins, the function of RNA depends on its structure and dynamics, which in turn is encoded in the linear sequence. However, while there are numerous methods for computational prediction of protein three-dimensional (3D) structure from sequence, with comparative modeling being the most reliable approach, there are very few such methods for RNA. Here, we present ModeRNA, a software tool for comparative modeling of RNA 3D structures. As an input, ModeRNA requires a 3D structure of a template RNA molecule, and a sequence alignment between the target to be modeled and the template. It must be emphasized that a good alignment is required for successful modeling, and for large and complex RNA molecules the development of a good alignment usually requires manual adjustments of the input data based on previous expertise of the respective RNA family. ModeRNA can model post-transcriptional modifications, a functionally important feature analogous to post-translational modifications in proteins. ModeRNA can also model DNA structures or use them as templates. It is equipped with many functions for merging fragments of different nucleic acid structures into a single model and analyzing their geometry. Windows and UNIX implementations of ModeRNA with comprehensive documentation and a tutorial are freely available.

Short-Range Contact Preferences and Long-Range Indifference: Is Protein Folding Stoichiometry Driven?

Mittal et al. (1) recently advanced an unconventional view on protein folding. By analyzing the spatial neighborhoods of amino acid residues in an extensive set of structures in the Protein Data Bank (PDB), the authors concluded that preferential interactions between amino acid residues do not drive protein folding. In this connection, it should be noted that preferential interactions between amino acids are the basis for introducing knowledge-based potentials, which in turn provide the underpinning for present day three-dimensional protein structure prediction by modeling and simulation (2-5 and references therein). Instead of these preferential interactions, Mittal et al. indicate that “protein folding is a direct consequence of a narrow band of stoichiometric occurrences of amino-acids in the primary sequences” (1). According to the authors, this observation is akin to Chargaff’s discovery that the molar ratios of adenine and thymine and that of guanine and cytosine in DNA were not far from unity (6).

The Fragment-based Consistency Score in Model Quality Assessment for De Novo Prediction of Protein Structures

Prediction of three-dimensional structures of proteins from amino-acid sequences is an important problem of bioinformatics. We still do not have, however, a reliable computational method for de novo prediction, i.e., prediction of structures that do not have homologues of known structures. One way to improve techniques of de novo prediction is to assess quality of the predicted model structures to select candidate models from them before knowing the experimentally determined structure. In this paper, we develop a new method of model quality assessment for de novo prediction, the fragment-based consistency score (FCS) method. In the FCS method, fragments from library proteins are collected for each fragment of the model, and structural similarities between the model fragment and the library fragments are measured for assessing qualities of the model. Three structural indices are employed; secondary structure, local density and local contact order. The optimal performance was obtained when relatively correlated fragments are collected, and structural similarity is measured in fuzzy comparison and averaged by finite width matching. The FCS method can select partially correct models for hard targets of de novo prediction examined in CASP7 and CASP8. We also evaluated the abilities of the FCS method to select structural models of loops or coil regions in targets, and to distinguish proteins which are similar in sequence but have greatly different conformations from each other. The proposed FCS method helps to improve the de novo prediction scheme and is also useful to solve difficult structures in the template-based modeling.