A database, AmyProt, was developed that collated details of 32 human amyloid proteins associated with disease and 488 associated mutations and polymorphisms, of which 316 are classified as amyloid. A detailed profile of the mutations was developed in terms of location within domains and secondary structures of the proteins and functional effects of the mutations. The data was used to test the hypothesis that mutations enhance amyloidosis in human amyloid proteins have distinctive characteristics, in terms of specific location within proteins and physico-chemical characteristics, which differentiate them from non-amyloid forming polymorphisms in amyloid proteins and from disease mutations and polymorphisms in non-amyloid disease linked proteins. The aim was to use these characteristics to train a prediction algorithm for amyloid mutations that will provide a more accurate prediction than current general disease prediction tools and amyloid prediction tools that focus on aggregating regions. 66 location specific features and changes upon mutation of 366 amino acids propensities, derived from the amino acid index database AAindex, were analysed. A significant proportion of mutations were located with aggregating regions, however the majority of mutations were not associated with these regions. An analysis of motifs showed that amyloid mutations had a significant association with transmembrane helix motifs such as GxxxG. Statistical analysis of substitutions mutations, using substitution matrices, showed that amyloid mutations have a decrease in α-helix propensity and overall secondary structure propensity compared to the disease mutations and disease and amyloid polymorphisms. Machine learning was used to reduce the large set of features to a set of 18 features. These included location near transmembrane helices, secondary structure features; transmembrane and extracellular domains and 4 amino acid propensities: knowledge-based membrane propensity scale from 3D helix; α-helix propensity; partition coefficient; normalized frequency of coil. The AmyProt mutations and non-amyloid polymorphisms were used to train and test the novel amyloid mutation prediction tool, AmyPred, the first tool developed purely to predict amyloid mutations. AmyPred predicts the amyloidogenicity of mutations as a consensus by majority vote (CMV) and mean probability (CMP) of 5 classifiers. Validation of AmyPred with 27 amyloid mutations and 20 non-amyloid mutations from APP, Tau and TTR proteins, gave classification accuracies of 0.7/0.71 (CMV/CMP) and with an MCC of 0.4 (CMV) and 0.41 (CMP). AmyPred out performed other tools such as SIFT (0.37) and PolyPhen (0.36) and the amyloid consensus prediction tool, MetAmyl (0.13). Finally, AmyPred was used to analyse p53 mutations to characterize amyloid and non-amyloid mutations within this protein.

Notre génome contient des centaines de milliers de variants génétiques, qui pour la plupart, n’ont aucun impact sur notre santé. Après séquençage, il faut les filtrer pour ne conserver que ceux qui sont potentiellement impliquées dans une maladie. On utilise des annotateurs qui prédisent l’impact des variants. Ces prédictions sont faites sans tenir compte des variants en cis dans le même gène. Pourtant, des variants neutres peuvent, lorsqu’ils sont réunis chez un individu, devenir délétères. J’ai donc développé l’outil bioinformatique GEMPROT qui permet de visualiser l’effet des variants génétiques sur la séquence protéique et de mettre en évidence les combinaisons de variants touchant un même domaine fonctionnel.J’ai ensuite étudié l’impact de deux variants associés à la p.Phe508del (508del) sur la protéine CFTR.Le variant p.Val470M est présent sur tous les haplotypes portant la délétion mais pas sur la séquence de référence, qui est généralement utilisée pour la construction de plasmides. Nous avons montré des différences de fonction de la protéine CFTR selon l’acide aminé en position 470. La fonction est augmentée avec une Valine et il convient donc de s’assurer, lors de la construction de plasmides, que le contexte haplotypique des variants étudiés est bien respecté. Le variant p.Ile1027Thr conduit à une dégradation de la fonction de la protéine 508del.Ce variant n’est présent que sur une partie des haplotypes 508del et pourrait donc avoir un effet modificateur de l’expression de la délétion. En conclusion, nous montrons l’importance de la prise en compte des contextes haplotypiques dans l’étude des maladies et proposons un outil bioinformatique pour le faire. / We all carry hundreds of thousands genetic variations in our genome that, for the most of them, have no impact on our health. After sequencing, they must be filtered to only retain those potentially involved in a disease. We use annotators that predict the impact of variants.These predictions are done for each variant taken independently without considering cis variants in the same gene. However, neutral variants can become deleterious when associated together. I have developed the bioinformatics tool GEMPROT, which makes it possible to visualize the effect of genetic variants on the protein sequence and to highlight combinations of variants affecting the same functional domain.I then studied the impact of two variants associated with p.Phe508del (508del) on CFTR protein function.The variant p.Val470M is present on all carrying deletion haplotypes but not on the reference sequence, which is generally used for the construction of plasmids. We have shown differences in the function of the mutated CFTR protein 508del according to the amino acid at position 470. The function is increased with a Valine and it is therefore necessary to ensure, when constructing plasmids, that the haplotype context of the studied variants is well respected.The variant p.Ile1027Thr leads to a degradation of the function of the 508del protein. This variant is present only on a portion of the 508del haplotypes and could therefore have a modifying effect on deletion expression. In conclusion, we show the importance of considering haplotype contexts in the diseases studies and propose a bioinformatics tool to do so.