>Magister Scientiae - MSc / Background: Despite the effort of scientific research on HIV therapies and to reduce the rate of HIV infection, AIDS still remains one of the major causes of death in the world and mostly in Sub-Saharan Africa. To date, neither a cure, nor an HIV vaccine had been found and the disease can only be managed by using High Active Antiretroviral Therapy (HAART) if detected early. The need for an effective early diagnostic and non-toxic therapeutic treatment has brought about the necessity for the discovery of additional HIV diagnostic methods and treatment regimens to lower mortality rates. Antimicrobial Peptides (AMPs) are components of the first line of defence of prokaryotes and eukaryotes and have been proven to be promising therapeutic agents against HIV. Methods: With the utility of computational biology, this work proposes the use of profile search methods combined with structural modelling to identify putative AMPs with diagnostic and anti-HIV activity. Firstly, experimentally validated anti-HIV AMPs were retrieved from various publicly available AMP databases, APD, CAMP, Bactibase and UniprotKB and classified according to super-families. Hidden Markov Model (HMMER) and Gap Local Alignment of Motifs (GLAM2) profiles were built for each super-family of anti- HIV AMPs. Putative anti-HIV AMPs were identified after scanning genome sequence databases using the trained models, retrieved AMPs and ranked based on their E-values. The 3-D structures of the 10 peptides that were ranked highest were predicted using 1-TASSER. These peptides were docked against various HIV proteins using PatchDock and putative AMPs showing highest affinity and having the correct orientation to the HIV -1 proteins gp 120 and p24 were selected for future work so as to establish their function in HIV therapy and diagnosis. Results: The results of the in silica analysis showed that the constructed models using the HMMER algorithm had better performances compare to that of the models built by the GLAM2 algorithm. Furthermore, the former tool has better statistical and probability explanation compared to the latter tool. Thus only the HMMER scanning results were considered for further study. Out of 1059 species scanned by the HMMER models, 30 putative anti-HIV AMPs were identified from genome scans with the family specific profile models after elimination of duplicate peptides. Docking analysis of putative AMPs against HIV proteins showed that from the 10 best performing anti-HIV AMPs with the highest Escores, molecules 1,3, 8 and 10 firmly binds the gp120 binding pocket at the VIN2 domain and at the point of interaction between gp120 and T cells, with the 1st and 3rd highest scoring anti-HIV AMPs having the highest binding affinities. However, all 10 putative anti-HIV AMPs bind to the N-terminal domain of p24 with large surface interaction, rather than the C-terminal. Conclusion: The in silica approach has made it possible to construct computational models having high performances, and which enabled the identification of putative anti-HIV peptides from genome sequence scans. The in silica validation of these putative peptides through docking studies has shown that some of these AMPs may be involved in HIV/AIDS therapeutics and diagnostics. The molecular validation of these findings will be the way forward for the development of an early diagnostic tool and as a consequence initiate early treatment. This will prevent the invasion of the immune system by blocking the VIN2 domain and thus designing of a successful vaccine with broad neutralizing activity against this domain.

L’analyse microbiologique pour confirmer l’absence de bactéries dans des échantillons biologiques normalement sains, comme le sang, est une routine dans de nombreux laboratoires. En effet, la présence de bactéries dans le sang, appelée bactériémie, peut avoir des conséquences très graves, voire mortelles pour le patient. Le protocole standard pour la détection des bactériémies repose jusqu’ici sur l’enrichissement des échantillons sanguins prélevés sur les patients lors de l’hémoculture, afin d’obtenir une population suffisante pour analyse. La lenteur de ce procédé retarde ainsi de parfois plusieurs jours le diagnostic et donc l’adaptation du traitement antibiotique administré au patient. Ces dernières décennies, des techniques comme l’identification par spectrométrie de masse ou les analyses moléculaires, ont permis de diminuer le délai requis pour identifier les pathogènes en cause. Dans ce contexte, l’emploi de biocapteurs est également une alternative. Ce travail propose d’inclure des sondes à large spectre dans un capteur optique par imagerie SPR (résonance de plasmons de surface). Ce système est déjà développé pour la reconnaissance spécifique de pathogènes au cours de leur croissance dans le sang. Les nouveaux ligands proposés et évalués sont les peptides antimicrobiens (PAM). Ces courts peptides cationiques et amphiphiles, présentent l’avantage d’un large spectre d’interaction couplé à une haute stabilité (chimique, thermique et séchage) comparativement aux anticorps employés jusqu’ici. Leur immobilisation sur des prismes SPRI permet d’évaluer simultanément l’affinité de plusieurs PAM à la même souche bactérienne. Les biocapteurs ainsi préparés ont permis de détecter des souches pathogènes d’Escherichia coli et Staphylococcus aureus en milieu de culture simple, comme en plasma et en sang dilué au milieu d’hémoculture. Le système obtenu permet la détection des pathogènes présents à une concentration initiale de l’ordre de 1 UFC.ml-1, en moins de 24 heures et quel que soit le milieu. Enfin, la mise en place d’analyses statistiques multidimensionnelles a abouti à une classification cohérente des espèces ciblées en milieu simple, comme en sang. Ces résultats montrent le potentiel de ce système pour parvenir à développer un biocapteur à large spectre capable à la fois de détecter mais aussi d’identifier par affinité croisée des pathogènes bactériens. / Microbiological analysis to confirm the absence of bacteria in normally sterile biological samples, such as blood, is routine in many laboratories. The presence of bacteria in blood, called bacteremia, can have very serious, and even fatal consequences for the patient. So far, the standard protocol for their detection has been based on the enrichment of blood samples collected from patients, thanks to blood culture, in order to obtain a sufficient population for analysis. These procedures are time consuming which sometimes lead to delays in diagnosis and subsequent adaptation of antibiotic treatments by several days. In recent decades, techniques such as mass spectrometry identification or molecular analyses have reduced the time required to identify the pathogens involved. In this context, the use of biosensors is another promising alternative. This work proposes to include wide spectrum probes in an optical sensor using SPR imaging (surface plasmon resonance). This system is already developed for the specific recognition of pathogens during their growth in the blood. The new ligands we propose to evaluate are antimicrobial peptides (AMP). These short, cationic and amphiphilic peptides have the advantage of having a broad spectrum of interaction with bacteria, coupled with high stability (chemical, thermal and drying), especially compared to the antibodies used so far in this technique. Their immobilization on SPRI prisms allows the simultaneous evaluation of the affinity of several AMP to the same bacterial strain. The biosensors based on AMP were able to detect pathogenic strains of Escherichia coli and Staphylococcus aureus in simple culture medium, such as plasma and diluted blood in blood culture medium. The system obtained allows the detection of pathogens present at an initial concentration of about 1 CFU.ml-1, in less than 24 hours and in all assayed media. Finally, the implementation of multidimensional statistical analyses has resulted in a consistent classification of targeted species, in simple culture medium, such as blood. These results show the potential of this system to develop a wide-spectrum biosensor capable of both detecting and cross-referencing bacterial pathogens.