Conserved residues in protein are crucial for maintaining structure and function, either by direct involvement in chemistry or indirectly, by being essential for folding, stability and oligomerisation and are mostly clustered near active sites. The variability of sequences of the same protein from diverse organisms is a reflection of the selective pressures of evolution. Sequence conservation analysis with 3397 bacterial triosephosphate isomerase (TIM) sequences using Plasmodium falciparum (Pf) TIM as template, showed full conservation of ten residues, K12, T75, H95, E97, C126, E165, P166, G209, G210 and G228. The integrity of the enzyme active site, which lies near the dimer interface, makes TIM an obligatory dimer. Attempts to engineer active monomeric TIM have not been successful. The present study assesses the effects of mutations at fully conserved position 75 (Thr) and the highly conserved position 64 (Q: 3011, E: 383) near the dimer interface, using the recombinant Plasmodial enzyme. Residue 64, Gln in Pf, and T75 interact with the catalytic E97 and K12, respectively. Preliminary analysis of available crystal structures showed that Gln 64 takes part in a single intersubunit interaction and maintains the obligatory strained backbone angles of the catalytic K12 residue, while Thr 75 is involved in four intersusunit hydrogen bond interactions. This led to the hypothesis that mostly, Gln at position 64 is crucial for enzyme activity and Thr at position 75 for the integrity of the dimer. Biophysical and kinetic data are reported for four T75 (T75S/V/C/N) and two Q64 (Q64N/E) mutants. The major findings revealed that the mutations at position 64 have a significant effect on dimer integrity with a 1000 fold increase in the dimer dissociation constant compared to the wild type enzyme, while dimer stability was unimpaired for the T75 mutants. Concentration dependence of activity yielded an estimate of dimer dissociation constant (Kd) values (Q64N 73.7±9.2 nM and Q64E 44.6±8.4 nM). Enzyme activity values of the T75 mutants are comparable to the wild type, except for T75N which shows a 4-fold drop in activity. All four T75 mutants show a dramatic fall in activity between 35 °-45 °C. Crystal structure determination of the T75S/V/N mutant offers insights into the variation in local interactions with T75N showing the largest changes. These results were unanticipated emphasising the uncertainties involved in inferring functional and structural role for individual residues based only on analysis of interactions observed in crystal structures. Nanospray ionisation mass spectrometric studies has also been used to probe the oligomeric properties of the three mutant proteins Q64N, Q64E and T75S and the wild type enzyme in the gas phase. The gas phase distributions of dimeric and monomeric species have been examined under a wild range of collision energies (40 – 160 eV). The order in the gas phase, PfTIM wild type > T75S > Q64E ~ Q64N, together with the solution phase experiments described above establish the importance of Q64 and T75 in influencing stability and activity. Inhibition studies with a 27 residue synthetic dimer interface peptide and the Q64 mutants establish that the interaction between the protein and the peptide was facilitated in the case of monomeric species.

Dans ces travaux, nous explorons comment les techniques de Générations Automatiques de Langue Naturelle (GLN) peuvent être utilisées pour aborder la tâche de génération (semi-)automatique de matériel et d'activités dans le contexte de l'apprentissage de langues assisté par ordinateur. En particulier, nous montrons comment un Réalisateur de Surface (RS) basé sur une grammaire peut être exploité pour la création automatique d'exercices de grammaire. Notre réalisateur de surface utilise une grammaire réversible étendue, à savoir SemTAG, qui est une Grammaire d'Arbre Adjoints à Structure de Traits (FB-TAG) couplée avec une sémantique compositionnelle basée sur l'unification. Plus précisément, la grammaire FB-TAG intègre une représentation plate et sous-spécifiée des formules de Logique de Premier Ordre (FOL). Dans la première partie de la thèse, nous étudions la tâche de réalisation de surface à partir de formules sémantiques plates et nous proposons un algorithme de réalisation de surface basé sur la grammaire FB-TAG optimisé, qui supporte la génération de phrases longues étant donné une grammaire et un lexique à large couverture. L'approche suivie pour l'optimisation de la réalisation de surface basée sur FB-TAG à partir de sémantiques plates repose sur le fait qu'une grammaire FB-TAG peut être traduite en une Grammaire d'Arbres Réguliers à Structure de Traits (FB-RTG) décrivant ses arbres de dérivation. Le langage d'arbres de dérivation de la grammaire TAG constitue un langage plus simple que le langage d'arbres dérivés, c'est pourquoi des approches de génération basées sur les arbres de dérivation ont déjà été proposées. Notre approche se distingue des précédentes par le fait que notre encodage FB-RTG prend en compte les structures de traits présentes dans la grammaire FB-TAG originelle, ayant de ce fait des conséquences importantes par rapport à la sur-génération et la préservation de l'interface syntaxe-sémantique. L'algorithme de génération d'arbres de dérivation que nous proposons est un algorithme de type Earley intégrant un ensemble de techniques d'optimisation bien connues: tabulation, partage-compression (sharing-packing) et indexation basée sur la sémantique. Dans la seconde partie de la thèse, nous explorons comment notre réalisateur de surface basé sur SemTAG peut être utilisé pour la génération (semi-)automatique d'exercices de grammaire. Habituellement, les enseignants éditent manuellement les exercices et leurs solutions et les classent au regard de leur degré de difficulté ou du niveau attendu de l'apprenant. Un courant de recherche dans le Traitement Automatique des Langues (TAL) pour l'apprentissage des langues assisté par ordinateur traite de la génération (semi-)automatique d'exercices. Principalement, ces travaux s'appuient sur des textes extraits du Web, utilisent des techniques d'apprentissage automatique et des techniques d'analyse de textes (par exemple, analyse de phrases, POS tagging, etc.). Ces approches confrontent l'apprenant à des phrases qui ont des syntaxes potentiellement complexes et du vocabulaire varié. En revanche, l'approche que nous proposons dans cette thèse aborde la génération (semi-)automatique d'exercices du type rencontré dans les manuels pour l'apprentissage des langues. Il s'agit, en d'autres termes, d'exercices dont la syntaxe et le vocabulaire sont faits sur mesure pour des objectifs pédagogiques et des sujets donnés. Les approches de génération basées sur des grammaires associent les phrases du langage naturel avec une représentation linguistique fine de leur propriété morpho-syntaxiques et de leur sémantique grâce à quoi il est possible de définir un langage de contraintes syntaxiques et morpho-syntaxiques permettant la sélection de phrases souches en accord avec un objectif pédagogique donné. Cette représentation permet en outre d'opérer un post-traitement des phrases sélectionées pour construire des exercices de grammaire / In this work, we explore how Natural Language Generation (NLG) techniques can be used to address the task of (semi-)automatically generating language learning material and activities in Camputer-Assisted Language Learning (CALL). In particular, we show how a grammar-based Surface Realiser (SR) can be usefully exploited for the automatic creation of grammar exercises. Our surface realiser uses a wide-coverage reversible grammar namely SemTAG, which is a Feature-Based Tree Adjoining Grammar (FB-TAG) equipped with a unification-based compositional semantics. More precisely, the FB-TAG grammar integrates a flat and underspecified representation of First Order Logic (FOL) formulae. In the first part of the thesis, we study the task of surface realisation from flat semantic formulae and we propose an optimised FB-TAG-based realisation algorithm that supports the generation of longer sentences given a large scale grammar and lexicon. The approach followed to optimise TAG-based surface realisation from flat semantics draws on the fact that an FB-TAG can be translated into a Feature-Based Regular Tree Grammar (FB-RTG) describing its derivation trees. The derivation tree language of TAG constitutes a simpler language than the derived tree language, and thus, generation approaches based on derivation trees have been already proposed. Our approach departs from previous ones in that our FB-RTG encoding accounts for feature structures present in the original FB-TAG having thus important consequences regarding over-generation and preservation of the syntax-semantics interface. The concrete derivation tree generation algorithm that we propose is an Earley-style algorithm integrating a set of well-known optimisation techniques: tabulation, sharing-packing, and semantic-based indexing. In the second part of the thesis, we explore how our SemTAG-based surface realiser can be put to work for the (semi-)automatic generation of grammar exercises. Usually, teachers manually edit exercises and their solutions, and classify them according to the degree of dificulty or expected learner level. A strand of research in (Natural Language Processing (NLP) for CALL addresses the (semi-)automatic generation of exercises. Mostly, this work draws on texts extracted from the Web, use machine learning and text analysis techniques (e.g. parsing, POS tagging, etc.). These approaches expose the learner to sentences that have a potentially complex syntax and diverse vocabulary. In contrast, the approach we propose in this thesis addresses the (semi-)automatic generation of grammar exercises of the type found in grammar textbooks. In other words, it deals with the generation of exercises whose syntax and vocabulary are tailored to specific pedagogical goals and topics. Because the grammar-based generation approach associates natural language sentences with a rich linguistic description, it permits defining a syntactic and morpho-syntactic constraints specification language for the selection of stem sentences in compliance with a given pedagogical goal. Further, it allows for the post processing of the generated stem sentences to build grammar exercise items. We show how Fill-in-the-blank, Shuffle and Reformulation grammar exercises can be automatically produced. The approach has been integrated in the Interactive French Learning Game (I-FLEG) serious game for learning French and has been evaluated both based in the interactions with online players and in collaboration with a language teacher

Systematic studies of basal nonsense suppression, orthogonality of tRNAPyl variants, and cross recognition between codons and tRNA anticodons are reported. E. coli displays detectable basal amber and opal suppression but shows a negligible ochre suppression. Although detectable, basal amber suppression is fully inhibited when a pyrrolysyl-tRNA synthetase (PylRS)-tRNAPyl_CUA pair is genetically encoded. trnaPyl_CUA is aminoacylated by an E. coli aminoacyl-tRNA synthetase at a low level, however, this misaminoacylation is fully inhibited when both PylRS and its substrate are present. Besides that it is fully orthogonal in E. coli and can be coupled with PylRS to genetically incorporate a NAA at an ochre codon, tRNAPyl_UUA is not able to recognize an UAG codon to induce amber suppression. This observation is in direct conflict with the wobble base pair hypothesis and enables using an evolved M. jannaschii tyrosyl-tRNA synthetase-tRNAPyl_UUA pair and the wild type or evolved PylRS-tRNAPyl_UUA pair to genetically incorporate two different NAAs at amber and ochre codons. tRNAPyl_UCA is charged by E. coli tryptophanyl-tRNA synthetase, thus not orthogonal in E. coli. Mutagenic studies of trnaPyl_UCA led to the discovery of its G73U form which shows a higher orthogonality. Mutating trnaPyl_CUA to trnaPyl_UCCU not only leads to the loss of the relative orthogonality of tRNAPyl in E. coli but also abolishes its aminoacylation by PylRS.