bpRNA-align: improved RNA secondary structure global alignment for comparing and clustering RNA structures.

Ribonucleic acid (RNA) is a polymeric molecule that is fundamental to biological processes, with structure being more highly conserved than primary sequence and often key to its function. Advances in RNA structure characterization have resulted in an increase in the number of accurate secondary structures. The task of uncovering common RNA structural motifs with a collective function through structural comparison, providing a level of similarity, remains challenging and could be used to improve RNA secondary structure databases and discover new RNA families. In this work, we present a novel secondary structure alignment method, bpRNA-align. bpRNA-align is a customized global structural alignment method, utilizing an inverted (gap extend costs more than gap open) and context-specific affine gap penalty along with a structural, feature-specific substitution matrix to provide similarity scores. We evaluate our similarity scores in comparison to other methods, using affinity propagation clustering, applied to a benchmarking data set of known structure types. bpRNA-align shows improvement in clustering performance over a broad range of structure types.

LinearTurboFold: Linear-time global prediction of conserved structures for RNA homologs with applications to SARS-CoV-2.

The constant emergence of COVID-19 variants reduces the effectiveness of existing vaccines and test kits. Therefore, it is critical to identify conserved structures in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes as potential targets for variant-proof diagnostics and therapeutics. However, the algorithms to predict these conserved structures, which simultaneously fold and align multiple RNA homologs, scale at best cubically with sequence length and are thus infeasible for coronaviruses, which possess the longest genomes (∼30,000 nt) among RNA viruses. As a result, existing efforts on modeling SARS-CoV-2 structures resort to single-sequence folding as well as local folding methods with short window sizes, which inevitably neglect long-range interactions that are crucial in RNA functions. Here we present LinearTurboFold, an efficient algorithm for folding RNA homologs that scales linearly with sequence length, enabling unprecedented global structural analysis on SARS-CoV-2. Surprisingly, on a group of SARS-CoV-2 and SARS-related genomes, LinearTurboFold's purely in silico prediction not only is close to experimentally guided models for local structures, but also goes far beyond them by capturing the end-to-end pairs between 5' and 3' untranslated regions (UTRs) (∼29,800 nt apart) that match perfectly with a purely experimental work. Furthermore, LinearTurboFold identifies undiscovered conserved structures and conserved accessible regions as potential targets for designing efficient and mutation-insensitive small-molecule drugs, antisense oligonucleotides, small interfering RNAs (siRNAs), CRISPR-Cas13 guide RNAs, and RT-PCR primers. LinearTurboFold is a general technique that can also be applied to other RNA viruses and full-length genome studies and will be a useful tool in fighting the current and future pandemics.

LaRA 2: parallel and vectorized program for sequence-structure alignment of RNA sequences.

BACKGROUND: The function of non-coding RNA sequences is largely determined by their spatial conformation, namely the secondary structure of the molecule, formed by Watson-Crick interactions between nucleotides. Hence, modern RNA alignment algorithms routinely take structural information into account. In order to discover yet unknown RNA families and infer their possible functions, the structural alignment of RNAs is an essential task. This task demands a lot of computational resources, especially for aligning many long sequences, and it therefore requires efficient algorithms that utilize modern hardware when available. A subset of the secondary structures contains overlapping interactions (called pseudoknots), which add additional complexity to the problem and are often ignored in available software. RESULTS: We present the SeqAn-based software LaRA 2 that is significantly faster than comparable software for accurate pairwise and multiple alignments of structured RNA sequences. In contrast to other programs our approach can handle arbitrary pseudoknots. As an improved re-implementation of the LaRA tool for structural alignments, LaRA 2 uses multi-threading and vectorization for parallel execution and a new heuristic for computing a lower boundary of the solution. Our algorithmic improvements yield a program that is up to 130 times faster than the previous version. CONCLUSIONS: With LaRA 2 we provide a tool to analyse large sets of RNA secondary structures in relatively short time, based on structural alignment. The produced alignments can be used to derive structural motifs for the search in genomic databases.

Comparative structural analysis of the human and Schistosoma glutathione transferase dimer interface using selective binding of bromosulfophthalein.

The binding channel of Schistosoma glutathione transferase (SGST) has been identified to possess a non-substrate site implicated in enzyme inhibition. This binding channel is formed by the interface of the GST dimer. We produced a comparative characterization of the SGST dimer interface with respect to that of human GST (hGST) analogues using the selective binding of bromosulfophthalein (BSP). First, two SGST and three hGST structures were used as search queries to assemble a data set of 48 empirical GST structures. Sequence alignment to generate a universal residue indexing scheme was then performed, followed by local superposition of the dimer interface. Principal component analysis revealed appreciable variation of the dimer interface, suggesting the potential for selective inhibition of SGST. BSP was found to dock invariably in the dimer interface core pocket, placing it in proximity to the GST catalytic domains, through which it may exert its inhibitory behavior. Binding poses across the GST forms were distinguished with ligand interaction profiling, where SGST complexes showed stabilization of ligand aromatic- and sulfonate moieties, which altogether anchor the ligand and produce a tight association. In comparison, missing aromatic stabilization in the hGST complexes impart large bonding distances, causing mobile poses likely to dissociate. Altogether, this study illustrates the potential for selective inhibition of SGST, rationalizes the selective behavior of the BSP inhibitor, and produces a reliable metric for construction and validation of pharmacophore models of the SGST binding channel.

Effects of semantic reinforcement, semantic discrimination, and affix frequency on new word learning in skilled and less skilled readers in Grades 6 to 12.

Two approaches to word learning were investigated in 1214 6th- to 12th-grade students. Definitions were provided, followed either by two sentences that were semantically correct exemplars, called semantic reinforcement learning, or by one correct sentence and a contrasting incorrect sentence (i.e., example followed by a structurally aligned non-example), called semantic discrimination learning. Type of learning was blocked, and examples and non-examples were explained. Effects of affix frequency were also assessed. Students were taught words, followed by assessments of abilities to recall the meanings of the words immediately after learning them, to choose the correct words among distractors to match given definitions after all words had been instructed, and to judge the semantic veracity of new sentences containing taught words 1-3 days later. Explanatory item response models were used to predict word learning using student and item characteristics along with their interactions. Few grade-related differences emerged. Higher-frequency affixes were generally beneficial for learning and retention across comprehension skill levels and measures. Immediate recall of word meanings was facilitated by semantic reinforcement learning. In contrast, performance after all the words had been instructed was facilitated by semantic discrimination learning, but only for more highly skilled comprehenders. The ability to learn the meanings of new words accounted for unique variance on one measure of reading comprehension, controlling for decoding, previously acquired vocabulary knowledge, and working memory. Results are discussed with reference to models of vocabulary learning and implications for vocabulary instruction for adolescents.

The effect of object similarity and alignment of examples on children's learning and transfer from picture books.

Story picture books with examples can be used to teach young children science concepts. Learners can abstract relational information by comparing the analogical examples in the books, leading to a more abstract transferrable understanding of the concept. The purpose of this study was to determine whether manipulating the content or arrangement of the examples included in a picture book would support children's generalization and transfer of a relational concept, namely color camouflage. In total, 81 3-year-olds and 80 4-year-olds were read one of four books at two visits spaced approximately 1 week apart. Examples were manipulated in a 2 (Object Similarity: high or low) × 2 (Arrangement: interleaved or blocked) design. At each visit, children were asked forced-choice questions with photographs (generalization) and real animals (transfer) and needed to explain their choices. At the first visit, only 3-year-olds who had been read a book with high object similarity displayed generalization and transfer. After they were read the same book again at the second visit, 3-year-olds in all conditions performed above chance on generalization questions but made more correct selections if they had been read the books with blocked examples. The 4-year-olds showed no book-related differences on forced-choice questions at either visit but gave better explanations at the second visit if they had been read interleaved books. Our study provides evidence that picture books with analogical examples can be used to teach children about science but that different types and arrangements of examples may better support children at different ages and with different amounts of prior experience.

Molecular and Structural Evolution of Cytochrome P450 Aromatase.

Aromatase is the cytochrome P450 enzyme converting androgens into estrogen in the last phase of steroidogenesis. As estrogens are crucial in reproductive biology, aromatase is found in vertebrates and the invertebrates of the genus Branchiostoma, where it carries out the aromatization reaction of the A-ring of androgens that produces estrogens. Here, we investigate the molecular evolution of this unique and highly substrate-selective enzyme by means of structural, sequence alignment, and homology modeling, shedding light on its key role in species conservation. The alignments led to the identification of a core structure that, together with key and unique amino acids located in the active site and the substrate recognition sites, has been well conserved during evolution. Structural analysis shows what their roles are and the reason why they have been preserved. Moreover, the residues involved in the interaction with the redox partner and some phosphorylation sites appeared late during evolution. These data reveal how highly substrate-selective cytochrome P450 has evolved, indicating that the driving forces for evolution have been the optimization of the interaction with the redox partner and the introduction of phosphorylation sites that give the possibility of modulating its activity in a rapid way.

Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood-Brain Barrier Passage.

A successful passage of the blood-brain barrier (BBB) is an essential prerequisite for the drug molecules designed to act on the central nervous system. The logarithm of blood-brain partitioning (LogBB) has served as an effective index of molecular BBB permeability. Using the three-dimensional (3D) distribution of the molecular electrostatic potential (ESP) as the numerical descriptor, a quantitative structure-activity relationship (QSAR) model termed AlphaQ was derived to predict the molecular LogBB values. To obtain the optimal atomic coordinates of the molecules under investigation, the pairwise 3D structural alignments were conducted in such a way to maximize the quantum mechanical cross correlation between the template and a target molecule. This alignment method has the advantage over the conventional atom-by-atom matching protocol in that the structurally diverse molecules can be analyzed as rigorously as the chemical derivatives with the same scaffold. The inaccuracy problem in the 3D structural alignment was alleviated in a large part by categorizing the molecules into the eight subsets according to the molecular weight. By applying the artificial neural network algorithm to associate the fully quantum mechanical ESP descriptors with the extensive experimental LogBB data, a highly predictive 3D-QSAR model was derived for each molecular subset with a squared correlation coefficient larger than 0.8. Due to the simplicity in model building and the high predictability, AlphaQ is anticipated to serve as an effective computational screening tool for molecular BBB permeability.

A Structure Based Study of Selective Inhibition of Factor IXa over Factor Xa.

Blood coagulation is an essential physiological process for hemostasis; however, abnormal coagulation can lead to various potentially fatal disorders, generally known as thromboembolic disorders, which are a major cause of mortality in the modern world. Recently, the FDA has approved several anticoagulant drugs for Factor Xa (FXa) which work via the common pathway of the coagulation cascade. A main side effect of these drugs is the potential risk for bleeding in patients. Coagulation Factor IXa (FIXa) has recently emerged as the strategic target to ease these risks as it selectively regulates the intrinsic pathway. These aforementioned coagulation factors are highly similar in structure, functional architecture, and inhibitor binding mode. Therefore, it remains a challenge to design a selective inhibitor which may affect only FIXa. With the availability of a number of X-ray co-crystal structures of these two coagulation factors as protein-ligand complexes, structural alignment, molecular docking, and pharmacophore modeling were employed to derive the relevant criteria for selective inhibition of FIXa over FXa. In this study, six ligands (three potent, two selective, and one inactive) were selected for FIXa inhibition and six potent ligands (four FDA approved drugs) were considered for FXa. The pharmacophore hypotheses provide the distribution patterns for the principal interactions that take place in the binding site. None of the pharmacophoric patterns of the FXa inhibitors matched with any of the patterns of FIXa inhibitors. Based on pharmacophore analysis, a selectivity of a ligand for FIXa over FXa may be defined quantitatively as a docking score of lower than -8.0 kcal/mol in the FIXa-grids and higher than -7.5 kcal/mol in the FXa-grids.

Accurate Representation of Protein-Ligand Structural Diversity in the Protein Data Bank (PDB).

The number of available protein structures in the Protein Data Bank (PDB) has considerably increased in recent years. Thanks to the growth of structures and complexes, numerous large-scale studies have been done in various research areas, e.g., protein-protein, protein-DNA, or in drug discovery. While protein redundancy was only simply managed using simple protein sequence identity threshold, the similarity of protein-ligand complexes should also be considered from a structural perspective. Hence, the protein-ligand duplicates in the PDB are widely known, but were never quantitatively assessed, as they are quite complex to analyze and compare. Here, we present a specific clustering of protein-ligand structures to avoid bias found in different studies. The methodology is based on binding site superposition, and a combination of weighted Root Mean Square Deviation (RMSD) assessment and hierarchical clustering. Repeated structures of proteins of interest are highlighted and only representative conformations were conserved for a non-biased view of protein distribution. Three types of cases are described based on the number of distinct conformations identified for each complex. Defining these categories decreases by 3.84-fold the number of complexes, and offers more refined results compared to a protein sequence-based method. Widely distinct conformations were analyzed using normalized B-factors. Furthermore, a non-redundant dataset was generated for future molecular interactions analysis or virtual screening studies.

RMalign: an RNA structural alignment tool based on a novel scoring function RMscore.

BACKGROUND: RNA-protein 3D complex structure prediction is still challenging. Recently, a template-based approach PRIME is proposed in our team to build RNA-protein 3D complex structure models with a higher success rate than computational docking software. However, scoring function of RNA alignment algorithm SARA in PRIME is size-dependent, which limits its ability to detect templates in some cases. RESULTS: Herein, we developed a novel RNA 3D structural alignment approach RMalign, which is based on a size-independent scoring function RMscore. The parameter in RMscore is then optimized in randomly selected RNA pairs and phase transition points (from dissimilar to similar) are determined in another randomly selected RNA pairs. In tRNA benchmarking, the precision of RMscore is higher than that of SARAscore (0.88 and 0.78, respectively) with phase transition points. In balance-FSCOR benchmarking, RMalign performed as good as ESA-RNA with a non-normalized score measuring RNA structural similarity. In balance-x-FSCOR benchmarking, RMalign achieves much better than a state-of-the-art RNA 3D structural alignment approach SARA due to a size-independent scoring function. Take the advantage of RMalign, we update our RNA-protein modeling approach PRIME to version 2.0. The PRIME2.0 significantly improves about 10% success rate than PRIME. CONCLUSION: Based on a size-independent scoring function RMscore, a novel RNA 3D structural alignment approach RMalign is developed and integrated into PRIME2.0, which could be useful for the biological community in modeling protein-RNA interaction.

The BRaliBase dent-a tale of benchmark design and interpretation.

BRaliBase is a widely used benchmark for assessing the accuracy of RNA secondary structure alignment methods. In most case studies based on the BRaliBase benchmark, one can observe a puzzling drop in accuracy in the 40-60% sequence identity range, the so-called 'BRaliBase Dent'. In this article, we show this dent is owing to a bias in the composition of the BRaliBase benchmark, namely the inclusion of a disproportionate number of transfer RNAs, which exhibit a conserved secondary structure. Our analysis, aside of its interest regarding the specific case of the BRaliBase benchmark, also raises important questions regarding the design and use of benchmarks in computational biology.

Relational Scaffolding Enhances Children's Understanding of Scientific Models.

Models are central to the practice and teaching of science. Yet people often fail to grasp how scientific models explain their observations of the world. Realizing the explanatory power of a model may require aligning its relational structure to that of the observable phenomena. In the present study, we tested whether relational scaffolding-guided comparisons between observable and modeled events-enhances children's understanding of scientific models. We tested relational scaffolding during instruction of third graders about the day/night cycle, a topic that involves relating Earth-based observations to a space-based model of Earth's rotation. Experiment 1 found that participants (N = 108) learned more from instruction that incorporated relational scaffolding. Experiment 2 (N = 99) found that guided comparison-not merely viewing observable and modeled events-is a critical component of relational scaffolding, especially for children with low initial knowledge. Relational scaffolding could be applied broadly to assist the many students who struggle with science.

Structural modulation of a periplasmic sugar-binding protein probes into its evolutionary ancestry.

Substrate-binding proteins (SBPs) are periplasmic proteins consisting of two α/ß domains joined by a hinge region with specificity towards cognate ligands. Based on three-dimensional fold, sugar-specific SBPs have been classified into cluster B and cluster D-I. The analysis of sequences and structures of sugar-binding pocket of cluster D-I SBPs revealed the presence of extra residues on two loops (L1, L2) and a helix (H1) in few members of this family, that binds specifically to monosaccharides. Presence of conserved histidine in L2 and tryptophan in H1 can be considered as the identity marks for the cluster D-I monosaccharide-binding SBPs. A glucose binding protein (ppGBP) from Pseudomonas putida CSV86 was found to contain a structural fold similar to oligosaccharide-binding cluster D-I SBPs, but functionally binds to only glucose due to constriction of its binding pocket mainly by L2 (375-382). ppGBP with partial deletion of L2 (ppGBPΔL2) was created, crystallized and biochemical characterization was performed. Compared to wild type ppGBP, the ppGBPΔL2 structure showed widening of the glucose-binding pocket with ∼80% lower glucose binding. Our results show that the substrate specificity of SBPs can be altered by modulating the size of the binding pocket. Based on this, we propose a sub classification of cluster D-I SBPs into (i) cluster D-I(a)-monosaccharide-binding SBPs and (ii) cluster D-I(b)-oligosaccharide-binding SBPs. This study also provides the direct structural and functional correlation indicating that divergence of proteins may occur through insertions or deletions of sequences in the already existing SBPs leading to evolution at the functional level.

Bayesian comparison of protein structures using partial Procrustes distance.

An important topic in bioinformatics is the protein structure alignment. Some statistical methods have been proposed for this problem, but most of them align two protein structures based on the global geometric information without considering the effect of neighbourhood in the structures. In this paper, we provide a Bayesian model to align protein structures, by considering the effect of both local and global geometric information of protein structures. Local geometric information is incorporated to the model through the partial Procrustes distance of small substructures. These substructures are composed of ß-carbon atoms from the side chains. Parameters are estimated using a Markov chain Monte Carlo (MCMC) approach. We evaluate the performance of our model through some simulation studies. Furthermore, we apply our model to a real dataset and assess the accuracy and convergence rate. Results show that our model is much more efficient than previous approaches.

Structure alignment-based classification of RNA-binding pockets reveals regional RNA recognition motifs on protein surfaces.

BACKGROUND: Many critical biological processes are strongly related to protein-RNA interactions. Revealing the protein structure motifs for RNA-binding will provide valuable information for deciphering protein-RNA recognition mechanisms and benefit complementary structural design in bioengineering. RNA-binding events often take place at pockets on protein surfaces. The structural classification of local binding pockets determines the major patterns of RNA recognition. RESULTS: In this work, we provide a novel framework for systematically identifying the structure motifs of protein-RNA binding sites in the form of pockets on regional protein surfaces via a structure alignment-based method. We first construct a similarity network of RNA-binding pockets based on a non-sequential-order structure alignment method for local structure alignment. By using network community decomposition, the RNA-binding pockets on protein surfaces are clustered into groups with structural similarity. With a multiple structure alignment strategy, the consensus RNA-binding pockets in each group are identified. The crucial recognition patterns, as well as the protein-RNA binding motifs, are then identified and analyzed. CONCLUSIONS: Large-scale RNA-binding pockets on protein surfaces are grouped by measuring their structural similarities. This similarity network-based framework provides a convenient method for modeling the structural relationships of functional pockets. The local structural patterns identified serve as structure motifs for the recognition with RNA on protein surfaces.

The evolution of function within the Nudix homology clan.

The Nudix homology clan encompasses over 80,000 protein domains from all three domains of life, defined by homology to each other. Proteins with a domain from this clan fall into four general functional classes: pyrophosphohydrolases, isopentenyl diphosphate isomerases (IDIs), adenine/guanine mismatch-specific adenine glycosylases (A/G-specific adenine glycosylases), and nonenzymatic activities such as protein/protein interaction and transcriptional regulation. The largest group, pyrophosphohydrolases, encompasses more than 100 distinct hydrolase specificities. To understand the evolution of this vast number of activities, we assembled and analyzed experimental and structural data for 205 Nudix proteins collected from the literature. We corrected erroneous functions or provided more appropriate descriptions for 53 annotations described in the Gene Ontology Annotation database in this family, and propose 275 new experimentally-based annotations. We manually constructed a structure-guided sequence alignment of 78 Nudix proteins. Using the structural alignment as a seed, we then made an alignment of 347 "select" Nudix homology domains, curated from structurally determined, functionally characterized, or phylogenetically important Nudix domains. Based on our review of Nudix pyrophosphohydrolase structures and specificities, we further analyzed a loop region downstream of the Nudix hydrolase motif previously shown to contact the substrate molecule and possess known functional motifs. This loop region provides a potential structural basis for the functional radiation and evolution of substrate specificity within the hydrolase family. Finally, phylogenetic analyses of the 347 select protein domains and of the complete Nudix homology clan revealed general monophyly with regard to function and a few instances of probable homoplasy. Proteins 2017; 85:775-811. © 2016 Wiley Periodicals, Inc.

Common Structural Core of Three-Dozen Residues Reveals Intersuperfamily Relationships.

Identification of relationships among protein families or superfamilies is a challenge. However, functionally essential protein regions typically retain structural integrity, even when the corresponding protein sequences evolve. Consequently, comparison of protein structures enables deeper phylogenetic analyses than achievable through the use of sequence information only. Here, we focus on a group of distantly related viral and cellular enzymes involved in nucleic acid or nucleotide processing and synthesis. All these enzymes share an apparently similar protein fold at their active site, which resembles the palm subdomain of the right-hand-shaped polymerases. Using a structure-based hierarchical clustering method, we identified a common structural core of 36 equivalent residues for this functionally diverse group of enzymes, representing five protein superfamilies. Based on the properties of these 36 residues, we deduced a structural distance-based tree in which the proteins were accurately clustered according to the established family classification. Within this tree, the enzymes catalyzing genomic nucleic acid replication or transcription were separated from those performing supplementary nucleic acid or nucleotide processing functions. In addition, we found that the family Y DNA polymerases are structurally more closely related to the nucleotide cyclase superfamily members than to the other members of the DNA/RNA polymerase superfamily, and these enzymes share 88 equivalent residues comprising a Β: 1- Α: 1- Α: 2- Β: 2- Β: 3- Α: 3- Β: 4- Α: 4- Β: 5 fold. The results highlight the power of structure-based hierarchical clustering in identifying remote evolutionary relationships. Furthermore, our study implies that a protein substructure of only three-dozen residues can contain a substantial amount of information on the evolutionary history of proteins.

Ligand-based virtual screening under partial shape constraints.

Ligand-based virtual screening has proven to be a viable technology during the search for new lead structures in drug discovery. Despite the rapidly increasing number of published methods, meaningful shape matching as well as ligand and target flexibility still remain open challenges. In this work, we analyze the influence of knowledge-based sterical constraints on the performance of the recently published ligand-based virtual screening method mRAISE. We introduce the concept of partial shape matching enabling a more differentiated view on chemical structure. The new method is integrated into the LBVS tool mRAISE providing multiple options for such constraints. The applied constraints can either be derived automatically from a protein-ligand complex structure or by manual selection of ligand atoms. In this way, the descriptor directly encodes the fit of a ligand into the binding site. Furthermore, the conservation of close contacts between the binding site surface and the query ligand can be enforced. We validated our new method on the DUD and DUD-E datasets. Although the statistical performance remains on the same level, detailed analysis reveal that for certain and especially very flexible targets a significant improvement can be achieved. This is further highlighted looking at the quality of calculated molecular alignments using the recently introduced mRAISE dataset. The new partial shape constraints improved the overall quality of molecular alignments especially for difficult targets with highly flexible or different sized molecules. The software tool mRAISE is freely available on Linux operating systems for evaluation purposes and academic use (see http://www.zbh.uni-hamburg.de/raise ).

What's so funny? Modelling incongruity in humour production.

Finding something humorous is intrinsically rewarding and may facilitate emotion regulation, but what creates humour has been underexplored. The present experimental study examined humour generated under controlled conditions with varying social, affective, and cognitive factors. Participants listed five ways in which a set of concept pairs (e.g. MONEY and CHOCOLATE) were similar or different in either a funny way (intentional humour elicitation) or a "catchy" way (incidental humour elicitation). Results showed that more funny responses were produced under the incidental condition, and particularly more for affectively charged than neutral concepts, for semantically unrelated than related concepts, and for responses highlighting differences rather than similarities between concepts. Further analyses revealed that funny responses showed a relative divergence in output dominance of the properties typically associated with each concept in the pair (that is, funny responses frequently highlighted a property high in output dominance for one concept but simultaneously low in output dominance for the other concept); by contrast, responses judged not funny did not show this pattern. These findings reinforce the centrality of incongruity resolution as a key cognitive ingredient for some pleasurable emotional elements arising from humour and demonstrate how it may operate within the context of humour generation.