Sequence-structure Alignment Research Articles

The Dual-Targeted Fusion Inhibitor Clofazimine Binds to the S2 Segment of the SARS-CoV-2 Spike Protein.

Clofazimine and Arbidol have both been reported to be effective in vitro SARS-CoV-2 fusion inhibitors. Both are promising drugs that have been repurposed for the treatment of COVID-19 and have been used in several previous and ongoing clinical trials. Small-molecule bindings to expressed constructs of the trimeric S2 segment of Spike and the full-length SARS-CoV-2 Spike protein were measured using a Surface Plasmon Resonance (SPR) binding assay. We demonstrate that Clofazimine, Toremifene, Arbidol and its derivatives bind to the S2 segment of the Spike protein. Clofazimine provided the most reliable and highest-quality SPR data for binding with S2 over the conditions explored. A molecular docking approach was used to identify the most favorable binding sites on the S2 segment in the prefusion conformation, highlighting two possible small-molecule binding sites for fusion inhibitors. Results related to molecular docking and modeling of the structure-activity relationship (SAR) of a newly reported series of Clofazimine derivatives support the proposed Clofazimine binding site on the S2 segment. When the proposed Clofazimine binding site is superimposed with other experimentally determined coronavirus structures in structure-sequence alignments, the changes in sequence and structure may rationalize the broad-spectrum antiviral activity of Clofazimine in closely related coronaviruses such as SARS-CoV, MERS, hCoV-229E, and hCoV-OC43.

Infrared: a declarative tree decomposition-powered framework for bioinformatics.

Many bioinformatics problems can be approached as optimization or controlled sampling tasks, and solved exactly and efficiently using Dynamic Programming (DP). However, such exact methods are typically tailored towards specific settings, complex to develop, and hard to implement and adapt to problem variations. We introduce the Infrared framework to overcome such hindrances for a large class of problems. Its underlying paradigm is tailored toward problems that can be declaratively formalized as sparse feature networks, a generalization of constraint networks. Classic Boolean constraints specify a search space, consisting of putative solutions whose evaluation is performed through a combination of features. Problems are then solved using generic cluster tree elimination algorithms over a tree decomposition of the feature network. Their overall complexities are linear on the number of variables, and only exponential in the treewidth of the feature network. For sparse feature networks, associated with low to moderate treewidths, these algorithms allow to find optimal solutions, or generate controlled samples, with practical empirical efficiency. Implementing these methods, the Infrared software allows Python programmers to rapidly develop exact optimization and sampling applications based on a tree decomposition-based efficient processing. Instead of directly coding specialized algorithms, problems are declaratively modeled as sets of variables over finite domains, whose dependencies are captured by constraints and functions. Such models are then automatically solved by generic DP algorithms. To illustrate the applicability of Infrared in bioinformatics and guide new users, we model and discuss variants of bioinformatics applications. We provide reimplementations and extensions of methods for RNA design, RNA sequence-structure alignment, parsimony-driven inference of ancestral traits in phylogenetic trees/networks, and design of coding sequences. Moreover, we demonstrate multidimensional Boltzmann sampling. These applications of the framework-together with our novel results-underline the practical relevance of Infrared. Remarkably, the achieved complexities are typically equivalent to the ones of specialized algorithms and implementations. Infrared is available at https://amibio.gitlabpages.inria.fr/Infrared with extensive documentation, including various usage examples and API reference; it can be installed using Conda or from source.

Efficient synthesis of malonyl-CoA by an acyl-CoA synthetase from Streptomyces sp.

Malonyl-CoA is a precursor of fatty acids, polyketides, and bio-based chemicals with potential applications in medicine, antibiotics, and fuels. However, its low intracellular concentration and high cost have led to difficulties in research and production. To develop an efficient method for producing malonyl-CoA, we screened the acyl-CoA synthetase (ACS) gene from Streptomyces sp. using sequence-structure alignment. This protein contains conserved sequences and active sites for malonyl-CoA synthetases. The purified recombinant enzyme ACS was heterologously expressed in Escherichia coli BL21 and characterised. The results showed that it converted the substrates malonate and CoA into malonyl-CoA. Under the optimal conditions, the specific activity of the purified ACS was 32.3 U·mg−1 and the conversion rate reached 98.8%. In addition, when the cell-free extracts were used as catalysts, the highest yield of malonyl-CoA was obtained after 4 h, yielding 24.2 g·L−1 with a conversion rate of 90.3%. After the product was purified and vacuum freeze-dried, a solid powder of malonyl-CoA was obtained. This study characterised and identified a new ACS and optimised the reaction conditions to efficiently synthesise pure malonyl-CoA in vitro in high yield using enzyme-mediated methods.

Heterogeneity in winged helix-turn-helix and substrate DNA interactions: Insights from theory and experiments.

Specific interactions between transcription factors (TFs) and substrate DNA constitute the fundamental basis of gene expression. Unlike in TFs like basic helix-loop-helix or basic leucine zippers, prediction of substrate DNA is extremely challenging for helix-turn-helix (HTH). Experimental techniques like chromatin immunoprecipitation combined with massively parallel DNA sequencing remains a viable option. We characterize the molecular basis of heterogeneity in HTH-DNA interaction using in silico tools and thence validate them experimentally. Given the profound functional diversity in HTH, we focus primarily on winged-HTH (wHTH). We consider 180 wHTH TFs, whose experimental three-dimensional structures are available in DNA bound/unbound conformations. Starting with PDB-wide scanning and curation of data, we construct a phylogenetic tree, which distributes 180 wHTH sequences under multiple sub-groups. Structure-sequence alignment followed by detailed intra/intergroup analysis, covariation studies and extensive network theory analysis help us to gain deep insight into heterogeneous wHTH-substrate DNA interactions. A central aim of this study is to find a consensus to predict the substrate DNA sequence for wHTH, amidst heterogeneity. The strength of our exhaustive theoretical investigations including molecular docking are successfully tested through experimental characterization of wHTH TF from Sulfurimonas denitrificans.

Protein remote homology recognition using local and global structural sequence alignment

Protein Remote Homology and fold Recognition (PRHR) is the most crucial task to predict the protein patterns. To achieve this task, Sequence-Order Frequency Matrix-Sampling and Deep learning with Smith-Waterman (SOFM-SDSW) were designed using large-scale Protein Sequences (PSs), which take more time to determine the high-dimensional attributes. Also, it was ineffective since the SW was only applied for local alignment, which cannot find the most matches between the PSs. Hence, in this manuscript, a rapid semi-global alignment algorithm called SOFM-SD-GlobalSW (SOFM-SDGSW) is proposed that facilitates the affine-gap scoring and uses sequence similarity to align the PSs. The major aim of this paper is to enhance the alignment of SW algorithm in both locally and globally for PRHR. In this algorithm, the Maximal Exact Matches (MEMs) are initially obtained by the bit-level parallelism rather than to align the individual characters. After that, a subgroup of MEMs is obtained to determine the global Alignment Score (AS) using the new adaptive programming scheme. Also, the SW local alignment scheme is used to determine the local AS. Then, both local and global ASs are combined to produce a final AS. Further, this resultant AS is considered to train the Support Vector Machine (SVM) classifier to recognize the PRH and folds. Finally, the test results reveal the SOFM-SDGSW algorithm on SCOP 1.53, SCOP 1.67 and Superfamily databases attains an ROC of 0.97, 0.941 and 0.938, respectively, as well as, an ROC50 of 0.819, 0.846 and 0.86, respectively compared to the conventional PRHR algorithms.

CRISPRtracrRNA: robust approach for CRISPR tracrRNA detection.

MotivationThe CRISPR-Cas9 system is a Type II CRISPR system that has rapidly become the most versatile and widespread tool for genome engineering. It consists of two components, the Cas9 effector protein, and a single guide RNA that combines the spacer (for identifying the target) with the tracrRNA, a trans-activating small RNA required for both crRNA maturation and interference. While there are well-established methods for screening Cas effector proteins and CRISPR arrays, the detection of tracrRNA remains the bottleneck in detecting Class 2 CRISPR systems.ResultsWe introduce a new pipeline CRISPRtracrRNA for screening and evaluation of tracrRNA candidates in genomes. This pipeline combines evidence from different components of the Cas9-sgRNA complex. The core is a newly developed structural model via covariance models from a sequence-structure alignment of experimentally validated tracrRNAs. As additional evidence, we determine the terminator signal (required for the tracrRNA transcription) and the RNA–RNA interaction between the CRISPR array repeat and the 5′-part of the tracrRNA. Repeats are detected via an ML-based approach (CRISPRidenify). Providing further evidence, we detect the cassette containing the Cas9 (Type II CRISPR systems) and Cas12 (Type V CRISPR systems) effector protein. Our tool is the first for detecting tracrRNA for Type V systems.Availability and implementationThe implementation of the CRISPRtracrRNA is available on GitHub upon requesting the access permission, (https://github.com/BackofenLab/CRISPRtracrRNA). Data generated in this study can be obtained upon request to the corresponding person: Rolf Backofen (backofen@informatik.uni-freiburg.de).Supplementary information Supplementary data are available at Bioinformatics online.

CRFalign: A Sequence-Structure Alignment of Proteins Based on a Combination of HMM-HMM Comparison and Conditional Random Fields.

Sequence–structure alignment for protein sequences is an important task for the template-based modeling of 3D structures of proteins. Building a reliable sequence–structure alignment is a challenging problem, especially for remote homologue target proteins. We built a method of sequence–structure alignment called CRFalign, which improves upon a base alignment model based on HMM-HMM comparison by employing pairwise conditional random fields in combination with nonlinear scoring functions of structural and sequence features. Nonlinear scoring part is implemented by a set of gradient boosted regression trees. In addition to sequence profile features, various position-dependent structural features are employed including secondary structures and solvent accessibilities. Training is performed on reference alignments at superfamily levels or twilight zone chosen from the SABmark benchmark set. We found that CRFalign method produces relative improvement in terms of average alignment accuracies for validation sets of SABmark benchmark. We also tested CRFalign on 51 sequence–structure pairs involving 15 FM target domains of CASP14, where we could see that CRFalign leads to an improvement in average modeling accuracies in these hard targets (TM-CRFalign ) compared with that of HHalign (TM-HHalign ) and also that of MRFalign (TM-MRFalign ). CRFalign was incorporated to our template search framework called CRFpred and was tested for a random target set of 300 target proteins consisting of Easy, Medium and Hard sets which showed a reasonable template search performance.

Tree diet: reducing the treewidth to unlock FPT algorithms in RNA bioinformatics

Hard graph problems are ubiquitous in Bioinformatics, inspiring the design of specialized Fixed-Parameter Tractable algorithms, many of which rely on a combination of tree-decomposition and dynamic programming. The time/space complexities of such approaches hinge critically on low values for the treewidth tw of the input graph. In order to extend their scope of applicability, we introduce the Tree-Diet problem, i.e. the removal of a minimal set of edges such that a given tree-decomposition can be slimmed down to a prescribed treewidth tw'. Our rationale is that the time gained thanks to a smaller treewidth in a parameterized algorithm compensates the extra post-processing needed to take deleted edges into account. Our core result is an FPT dynamic programming algorithm for Tree-Diet, using 2^{O(tw)}n time and space. We complement this result with parameterized complexity lower-bounds for stronger variants (e.g., NP-hardness when tw' or tw-tw' is constant). We propose a prototype implementation for our approach which we apply on difficult instances of selected RNA-based problems: RNA design, sequence-structure alignment, and search of pseudoknotted RNAs in genomes, revealing very encouraging results. This work paves the way for a wider adoption of tree-decomposition-based algorithms in Bioinformatics.

Identification of Protease Inhibition Mechanism by Iturin A against Agriculture Cutworm (Spodoptera litura) by Homology Modeling and Molecular Dynamics

Objective: Spodoptera litura, otherwise known as cutworm, belongs to the Noctuidae tribe, which is a severe scourge for numerous crop systems and is considered one of Asian tropical agriculture's most important insects. The world's leading environmental threats are plant pests, and the already commercialized pesticides are extremely poisonous and non-biodegradable and maybe additional residues harmful to the ecosystem. The increased resistance in pests often demands the need for advanced, active pesticides that are environmentally friendly and biodegradable. Methods: In the current work, the significance of proteases for the Spodoptera litura digestive system has been determined by the use of microbial metabolite protease inhibitor (Iturin A) in silico models. In the present study, we developed a model based on sequence structural alignment of known crystal structure 2D1I protease from Homo sapiens. The model's reliability evaluation was performed using programs such as PROCHECK, WHAT IF, PROSA, Validate 3D, ERRAT, etc. Results: In an attempt to find new inhibitors for Protease docking, the study was carried out with Iturin A. PMDB ID for the produced protease model was submitted to identify new inhibitors for Protease docking, and its accession number is PM0082285. The detailed study of enzyme-inhibitor interactions identified similar core residues; GLU215, LEU216, LYS217, and GLU237 have demonstrated their role in the binding efficacy of ligands. Conclusion: The latest homology modeling and docking experiments on the protease model will provide useful insight knowledge for the logical approach of constructing a wide spectrum of novel insecticide against Spodoptera.

Deep forest ensemble learning for classification of alignments of non-coding RNA sequences based on multi-view structure representations.

Non-coding RNAs (ncRNAs) play crucial roles in multiple biological processes. However, only a few ncRNAs’ functions have been well studied. Given the significance of ncRNAs classification for understanding ncRNAs’ functions, more and more computational methods have been introduced to improve the classification automatically and accurately. In this paper, based on a convolutional neural network and a deep forest algorithm, multi-grained cascade forest (GcForest), we propose a novel deep fusion learning framework, GcForest fusion method (GCFM), to classify alignments of ncRNA sequences for accurate clustering of ncRNAs. GCFM integrates a multi-view structure feature representation including sequence-structure alignment encoding, structure image representation and shape alignment encoding of structural subunits, enabling us to capture the potential specificity between ncRNAs. For the classification of pairwise alignment of two ncRNA sequences, the F-value of GCFM improves 6% than an existing alignment-based method. Furthermore, the clustering of ncRNA families is carried out based on the classification matrix generated from GCFM. Results suggest better performance (with 20% accuracy improved) than existing ncRNA clustering methods (RNAclust, Ensembleclust and CNNclust). Additionally, we apply GCFM to construct a phylogenetic tree of ncRNA and predict the probability of interactions between RNAs. Most ncRNAs are located correctly in the phylogenetic tree, and the prediction accuracy of RNA interaction is 90.63%. A web server (http://bmbl.sdstate.edu/gcfm/) is developed to maximize its availability, and the source code and related data are available at the same URL.

Structural characterization of ITS2 and CBC species concept applications in the tribe Coluteocarpeae (Brassicaceae)

The taxonomic utility of internal transcribed spacer 2 ITS2 secondary structures in different plant groups, as well as in Brassicaceae, has been addressed by many studies. Although characterization and applications of ITS2 secondary structures for the members of main Brassicaceae lineages Lineages I, II, III, and expanded Lineage II have been studied, the utility of compensatory base change CBC species concept has not been the subject of any studies thus far. In the current study, the ITS2 secondary structures of 49 Coluteocarpeae expanded Lineage II specimens were investigated to determine relationships among the species. In addition to the utility of the CBC species concept, the availability of hemi-CBC and nonstructural substitutions NSTs , which are also used for generic and species delimitation, were tested and discussed. A maximum likelihood tree, based on the sequence-structural alignment of the 49 specimens, was constructed to test the different generic assumptions reported by different researchers in the literature. The structural analysis showed that the ITS2 secondary structures of all of the Coluteocarpeae members exhibited a 4-fingered hand model, which was common in the majority of the family members. No CBCs were observed, whereas hemi-CBCs and NSTs were common among the tribe members. Although hemi-CBCs and NSTs were useful for distinguishing most of the Coluteocarpeae species Noccaea aptera Velen. F.K. Mey., Noccaea aghrica P.H. Davis & Kit Tan M. Firat & Ozudogru, Noccaea fendleri A.Gray Holub subsp. glauca A. Nelson Al-Shehbaz & M. Koch, Noccaea griffithiana Boiss. F.K. Mey., Noccaea rubescens Schott & Kotschy ex Boiss. F.K. Mey., etc. , they were not effective for delimitating some problematic species, such as members of Thlaspiceras F.K. Mey. A phylogenetic tree based on the sequence-structural dataset of the ITS2 showed that generic delimitation of Al-Shehbaz was more acceptable due to the fact that Noccaea sensu Al-Shehbaz is monophyletic.

RNA consensus structures for inferring green algal phylogeny: A three-taxon analysis for Golenkinia/Jenufa, Sphaeropleales and Volvocales (Chlorophyta, Chlorophyceae)

Background and Aims: Order please! was a recently published, very nice and apt title for a persistent problem in green algae phylogenetics. The green algal class Chlorophyceae comprises two clades (SV and OCC) made up of five orders (Sphaeropleales, Volvocales, Oedogoniales, Chaetopeltidales and Chaetophorales). A variety of data and methods have shown that one further group (Golenkinia+Jenufa) cannot be unambiguously placed among the five orders. In addition, concerning Sphaeropleaceae and/or Treubarinia the monophyly of Sphaeropleales and Volvocales remain unresolved. Methods: IUPAC (International Union of Pure and Applied Chemistry) encoded consensus sequences for different 18S rDNA data sets as well as corresponding 18S rDNA consensus structures were assembled for Sphaeropleales, Volvocales (with or without Treubarinia) and a Golenkinia+Jenufa assemblage to independently infer phylogenetic relationships in a three-taxon analysis. Using a known template structure, individual 18S rDNA secondary structures were predicted by homology modelling. Sequences and their individual secondary structures, automatically encoded by a 12-letter alphabet (each nucleotide with its three structural states, paired left, paired right, or unpaired), were simultaneously aligned; consensus structures and IUPAC encoded consensus sequences were read out from the different sequence-structure alignments. Key Results: In contrast to previous studies using 18S rDNA data, results of this study corroborate chloroplast data and strongly support a sister group relationship between Golenkinia+Jenufa and the Sphaeropleales. The Golenkinia+Jenufa assemblage shows 330 matches to the Sphaeropleales (sequence-structure consensus) but only 214 matches to the Volvocales (sequence-structure consensus). Phylogenetically informative nucleotides are highlighted and visualized in their structural context taking into account structural domains (I-IV) and hypervariable regions (V2-V9). Rooting the three-taxon scenario remains difficult because the extremely long branches of Golenkinia and Jenufa are attracted to a chosen outgroup, reducing bootstrap support in any conceivable four-taxon tree obtained by parsimony or profile-neighbor-joining analysis. Conclusions: This algal case study of 18S rDNA consensus data (IUPAC encoded sequences and consensus structures) coupled with profile distances between groups of sequences, demonstrated that a phylogenetic problem can be reduced to a three-taxon analysis.

The mesa trail and the interacting heads motif of myosin II

Myosin II molecules in the thick filaments of striated muscle form a structure in which the heads interact with each other and fold back onto the tail. This structure, the “interacting heads motif” (IHM), provides a mechanistic basis for the auto-inhibition of myosin in relaxed thick filaments. Similar IHM interactions occur in single myosin molecules of smooth and nonmuscle cells in the switched-off state. In addition to the interaction between the two heads, which inhibits their activity, the IHM also contains an interaction between the motor domain of one head and the initial part (subfragment 2, S2) of the tail. This is thought to be a crucial anchoring interaction that holds the IHM in place on the thick filament. S2 appears to cross the head at a specific location within a broader region of the motor domain known as the myosin mesa. Here, we show that the positive and negative charge distribution in this part of the mesa is complementary to the charge distribution on S2. We have designated this the “mesa trail” owing to its linear path across the mesa. We studied the structural sequence alignment, the location of charged residues on the surface of myosin head atomic models, and the distribution of surface charge potential along the mesa trail in different types of myosin II and in different species. The charge distribution in both the mesa trail and the adjacent S2 is relatively conserved. This suggests a common basis for IHM formation across different myosin IIs, dependent on attraction between complementary charged patches on S2 and the myosin head. Conservation from mammals to insects suggests that the mesa trail/S2 interaction plays a key role in the inhibitory function of the IHM.

Freiburg RNA tools: a central online resource for RNA-focused research and teaching.

The Freiburg RNA tools webserver is a well established online resource for RNA-focused research. It provides a unified user interface and comprehensive result visualization for efficient command line tools. The webserver includes RNA-RNA interaction prediction (IntaRNA, CopraRNA, metaMIR), sRNA homology search (GLASSgo), sequence-structure alignments (LocARNA, MARNA, CARNA, ExpaRNA), CRISPR repeat classification (CRISPRmap), sequence design (antaRNA, INFO-RNA, SECISDesign), structure aberration evaluation of point mutations (RaSE), and RNA/protein-family models visualization (CMV), and other methods. Open education resources offer interactive visualizations of RNA structure and RNA-RNA interaction prediction as well as basic and advanced sequence alignment algorithms. The services are freely available at http://rna.informatik.uni-freiburg.de.

On the Evolution of Specificity in Members of the Yeast Amino Acid Transporter Family as Parts of Specific Metabolic Pathways.

In the recent years, molecular modeling and substrate docking, coupled with biochemical and genetic analyses have identified the substrate-binding residues of several amino acid transporters of the yeast amino acid transporter (YAT) family. These consist of (a) residues conserved across YATs that interact with the invariable part of amino acid substrates and (b) variable residues that interact with the side chain of the amino acid substrate and thus define specificity. Secondary structure sequence alignments showed that the positions of these residues are conserved across YATs and could thus be used to predict the specificity of YATs. Here, we discuss the potential of combining molecular modeling and structural alignments with intra-species phylogenetic comparisons of transporters, in order to predict the function of uncharacterized members of the family. We additionally define some orphan branches which include transporters with potentially novel, and to be characterized specificities. In addition, we discuss the particular case of the highly specific l-proline transporter, PrnB, of Aspergillus nidulans, whose gene is part of a cluster of genes required for the utilization of proline as a carbon and/or nitrogen source. This clustering correlates with transcriptional regulation of these genes, potentially leading to the efficient coordination of the uptake of externally provided l-Pro via PrnB and its enzymatic degradation in the cell.

CMV: visualization for RNA and protein family models and their comparisons.

SummaryA standard method for the identification of novel RNAs or proteins is homology search via probabilistic models. One approach relies on the definition of families, which can be encoded as covariance models (CMs) or Hidden Markov Models (HMMs). While being powerful tools, their complexity makes it tedious to investigate them in their (default) tabulated form. This specifically applies to the interpretation of comparisons between multiple models as in family clans. The Covariance model visualization tools (CMV) visualize CMs or HMMs to: I) Obtain an easily interpretable representation of HMMs and CMs; II) Put them in context with the structural sequence alignments they have been created from; III) Investigate results of model comparisons and highlight regions of interest.Availability and implementationSource code (http://www.github.com/eggzilla/cmv), web-service (http://rna.informatik.uni-freiburg.de/CMVS).Supplementary information Supplementary data are available at Bioinformatics online.

Protein structure modeling and refinement by global optimization in CASP12.

For protein structure modeling in the CASP12 experiment, we have developed a new protocol based on our previous CASP11 approach. The global optimization method of conformational space annealing (CSA) was applied to 3 stages of modeling: multiple sequence-structure alignment, three-dimensional (3D) chain building, and side-chain re-modeling. For better template selection and model selection, we updated our model quality assessment (QA) method with the newly developed SVMQA (support vector machine for quality assessment). For 3D chain building, we updated our energy function by including restraints generated from predicted residue-residue contacts. New energy terms for the predicted secondary structure and predicted solvent accessible surface area were also introduced. For difficult targets, we proposed a new method, LEEab, where the template term played a less significant role than it did in LEE, complemented by increased contributions from other terms such as the predicted contact term. For TBM (template-based modeling) targets, LEE performed better than LEEab, but for FM targets, LEEab was better. For model refinement, we modified our CASP11 molecular dynamics (MD) based protocol by using explicit solvents and tuning down restraint weights. Refinement results from MD simulations that used a new augmented statistical energy term in the force field were quite promising. Finally, when using inaccurate information (such as the predicted contacts), it was important to use the Lorentzian function for which the maximal penalty arising from wrong information is always bounded.

Accurate template‐based modeling in CASP12 using the IntFOLD4‐TS, ModFOLD6, and ReFOLD methods

Our aim in CASP12 was to improve our Template-Based Modeling (TBM) methods through better model selection, accuracy self-estimate (ASE) scores and refinement. To meet this aim, we developed two new automated methods, which we used to score, rank, and improve upon the provided server models. Firstly, the ModFOLD6_rank method, for improved global Quality Assessment (QA), model ranking and the detection of local errors. Secondly, the ReFOLD method for fixing errors through iterative QA guided refinement. For our automated predictions we developed the IntFOLD4-TS protocol, which integrates the ModFOLD6_rank method for scoring the multiple-template models that were generated using a number of alternative sequence-structure alignments. Overall, our selection of top models and ASE scores using ModFOLD6_rank was an improvement on our previous approaches. In addition, it was worthwhile attempting to repair the detected errors in the top selected models using ReFOLD, which gave us an overall gain in performance. According to the assessors' formula, the IntFOLD4 server ranked 3rd/5th (average Z-score > 0.0/-2.0) on the server only targets, and our manual predictions (McGuffin group) ranked 1st/2nd (average Z-score > -2.0/0.0) compared to all other groups.

XSuLT: a web server for structural annotation and representation of sequence-structure alignments.

The web server XSuLT, an enhanced version of the protein alignment annotation program JoY, formats a submitted multiple-sequence alignment using three-dimensional (3D) structural information in order to assist in the comparative analysis of protein evolution and in the optimization of alignments for comparative modelling and construct design. In addition to the features analysed by JoY, which include secondary structure, solvent accessibility and sidechain hydrogen bonds, XSuLT annotates each amino acid residue with residue depth, chain and ligand interactions, inter-residue contacts, sequence entropy, root mean square deviation and secondary structure and disorder prediction. It is also now integrated with built-in 3D visualization which interacts with the formatted alignment to facilitate inspection and understanding. Results can be downloaded as stand-alone HTML for the formatted alignment and as XML with the underlying annotation data. XSuLT is freely available at http://structure.bioc.cam.ac.uk/xsult/.

Multi-objective two-level swarm intelligence approach for multiple RNA sequence-structure alignment

Abstract This paper proposes a novel two-level particle swarm optimization algorithm for multi-objective optimization (MO-TLPSO) employed to a challenging problem of bioinformatics i.e. RNA sequence-structure alignment. Level one of the proposed approach optimizes the dimension of each swarm which is sequence length for the addressed problem, whereas level two optimizes the particle positions and then evaluates both the conflicting objectives. The conflicting objectives of the addressed problem are obtaining optimal multiple sequence alignment as well as optimal secondary structure. Optimal secondary structure is obtained by TL-PSOfold, the structure is further used for computing the contribution of base pairing of individual sequence and the co-variation between aligned positions of sequences so as to make the structure closer to the natural one. The results are tested against the popular softwares for pairwise and multiple alignment at BRAlibase benchmark datasets. Proposed work is so far the first multi-objective optimization based approach for structural alignment of multiple RNA sequences without converting the problem into single objective. Also, it is the first swarm intelligence based approach that addresses sequence-structure alignment issue of RNA sequences. Simulation results are compared with the state-of-the-art and competitive approaches. MO-TLPSO is found well competent in producing pairwise as well as multiple sequence-structure alignment of RNA. The claim is supported by performing statistical significance testing using one way ANOVA followed by Bonferroni post-hoc analysis for both kind of alignments.